Child and Adolescent Psychopathology [3 ed.] 111916995X, 9781119169956

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Child and Adolescent Psychopathology

Child and Adolescent Psychopathology Third Edition

Edited by

Theodore P. Beauchaine Stephen P. Hinshaw

Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved. Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in Canada. No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400, fax 978-646-8600, or on the Web at www.copyright.com. Requests to the publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, 201-748-6011, fax 201-748-6008, or online at www.wiley.com/go/permissions. Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages. Readers should be aware that Internet Web sites offered as citations and/or sources for further information may have changed or disappeared between the time this was written and when it is read. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that the publisher is not engaged in rendering professional services. If legal, accounting, medical, psychological or any other expert assistance is required, the services of a competent professional should be sought. For general information on our other products and services, please contact our Customer Care Department within the U.S. at 800-956-7739, outside the U.S. at 317-572-3986, or fax 317-572-4002. Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com. Library of Congress Cataloging-in-Publication Data Names: Beauchaine, Theodore P., editor. | Hinshaw, Stephen P., editor. Title: Child and adolescent psychopathology / edited By Theodore P. Beauchaine, Stephen P. Hinshaw. Description: Third edition. | Hoboken, N.J. : John Wiley & Sons Inc., [2017] | Includes bibliographical references and index. Identifiers: LCCN 2016026246 | ISBN 9781119169956 (cloth) | ISBN 9781119169963 (epdf) | ISBN 9781119169970 (epub) Subjects: LCSH: Child psychopathology. | Adolescent psychopathology. Classification: LCC RJ499 .C48237 2016 | DDC 618.92/89—dc23 LC record available at https://lccn.loc.gov/2016026246

Printed in the United States of America third edition HB Printing 10 9 8 7 6 5 4 3 2 1

Contents

Foreword

ix

Preface

xiii

List of Contributors

xvii

Part I THE DEVELOPMENTAL PSYCHOPATHOLOGY APPROACH TO UNDERSTANDING MENTAL ILLNESS 1

2

3

Developmental Psychopathology as a Scientific Discipline: A 21st-Century Perspective Stephen P. Hinshaw

3

Classifying Psychopathology: The DSM, Empirically Based Taxonomies, and the Research Domain Criteria Theodore P. Beauchaine and Daniel N. Klein

33

Genetic, Environmental, and Epigenetic Influences on Behavior Theodore P. Beauchaine, Lisa Gatzke-Kopp, and Ian R. Gizer

68

Part II VULNERABILITIES AND RISK FACTORS FOR PSYCHOPATHOLOGY 4

Risk and Resilience in Child and Adolescent Psychopathology Bruce E. Compas, Meredith Gruhn, and Alexandra H. Bettis

113

5

Child Maltreatment and Risk for Psychopathology Sara R. Jaffee

144

6

Impulsivity and Vulnerability to Psychopathology Emily Neuhaus and Theodore P. Beauchaine

178

7

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability to Psychopathology Jerome Kagan

213

v

vi Contents 8

9

The Adaptive Calibration Model of Stress Responsivity: Concepts, Findings, and Implications for Developmental Psychopathology Bruce J. Ellis, Marco Del Giudice, and Elizabeth A. Shirtcliff

237

Exposure to Teratogens as a Risk Factor for Psychopathology Lauren R. Doyle, Nicole A. Crocker, Susanna L. Fryer, and Sarah N. Mattson

277

10

Brain Injury and Vulnerability to Psychopathology Peter Arnett, Jessica E. Meyer, Victoria C. Merritt, Lisa Gatzke-Kopp, and Katherine E. Shannon Bowen

316

11

Emotion Dysregulation as a Vulnerability to Psychopathology Pamela M. Cole, Sarah E. Hall, and Nastassia J. Hajal

346

12

Neighborhood Effects on the Development of Delinquency Wesley G. Jennings and Nicholas M. Perez

387

Part III

EXTERNALIZING DISORDERS

13

Attention-Deficit/Hyperactivity Disorder Joel Nigg

14

Oppositional Defiant Disorder, Conduct Disorder, and Juvenile Delinquency Benjamin B. Lahey and Irwin D. Waldman

15

Substance Use Disorders Sandra A. Brown, Kristin L. Tomlinson, and Jennifer Winward

Part IV

407

449 497

INTERNALIZING DISORDERS

16

Anxiety Disorders Carl F. Weems and Wendy K. Silverman

531

17

Obsessive-Compulsive and Related Disorders Emily Ricketts, Deepika Bose, and John Piacentini

560

18

Depressive Disorders Daniel N. Klein, Brandon L. Goldstein, and Megan Finsaas

610

19

The Development of Borderline Personality and Self-Inflicted Injury Erin A. Kaufman, Sheila E. Crowell, and Mark F. Lenzenweger

642

Part V 20

21

OTHER DISORDERS

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents Bruce D. Perry Bipolar Disorder Joseph C. Blader, Donna J. Roybal, Colin L. Sauder, and Gabrielle A. Carlson

683 706

Contents

vii

22

Autism Spectrum Disorder Susan Faja and Geraldine Dawson

745

23

Childhood-Onset Schizophrenia Robert F. Asarnow and Jennifer K. Forsyth

783

24

Eating Disorders Eric Stice and Deanna Linville

818

About the Authors

839

Author Index

841

Subject Index

875

Foreword

T

he remarkable third edition of Child and Adolescent Psychopathology represents an academic tour de force presenting the science of development associated with progressions to mental disorder. These processes are typically multiple and interacting. Indeed, their importance is clear, as neurodevelopmental models of psychopathology are dominant today. Sadly, both stigmatization—primarily from profound misunderstanding of mental disorders— and low economic status remain barriers to research and treatment (Martinez & Hinshaw, 2016; Merikangas et al., 2011). The chapters show remarkable breadth, including the challenge of integrating genetics, brain imaging, brain trauma, and prenatal and physiological as well as environmental variables in a clinically meaningful way. Clinicians have already benefitted from studies detailing patterns of continuity and discontinuity. Indeed, such investigations can help to prevent premature prediction and labeling that in itself may be harmful. These models, as well as the transactional nature of many dysfunctional behaviors, preclude simplistic causal pathways. Brain imaging has yet to contribute to clinical diagnosis and care, even though longitudinal and large-sample cross-sectional studies are starting to indicate subpopulation developmental brain phenotypes that have integrative potential for developmental psychopathology (Giedd et al., 2015; Gur, 2016). For example, it is possible that different developmental trajectories in attention-deficit/hyperactivity disorder reflect alternate clinical forms, as delayed cortical developmental may well relate to greater improvement in adolescence (Shaw et al., 2013). In our sister science of developmental neurobiology, true “clinical breakthroughs” have emerged, such as the use of rapamycin for tuberous sclerosis (Franz et al., 2006), and magnesium infusion for prevention of cerebral palsy (Rouse et al., 2008). These are large-effect-size interventions of interest to child psychiatrists because of associated psychopathologies in these conditions. Both were serendipitous discoveries, which by definition cannot be planned. At the same time, it remains troubling how much risk remains embedded in political arenas of community infrastructure (e.g., support for schools, housing, and law enforcement). We must transcend psychobiology to incorporate multiple levels of analysis, as amply shown in the following chapters. ix

x Foreword The Research Domain Criteria (RDoC; Cuthbert, 2014), highlighted in a number of chapters, do not represent a truly new approach. Dimensional as well as categorical measures have been hallmarks of NIH-funded psychiatric research for decades (Weinberger, Glick, & Klein, 2015), and neurobiologically founded, multiple-levels-of analysis research has contributed to key advances in our understanding of etiology since at least the mid-20th century (Beauchaine & Thayer, 2015). Evidence is mounting for age- and category-related interactions with dimensional brain MRI measures (e.g., Wiggins et al., 2016). In all, the RDoC provides a useful and surprisingly interactive set of measures. Finally, I found inspiration in the several authors who reviewed the predictive and possible treatment implications of regulatory physiological measures for developmental psychopathology. Ultimately, these models will be judged on when and how these regulatory processes can be changed, given the complexity of initial measurements and the potential for highly individualized treatment plans. One might read this entire volume as a basis for future personalized therapies, paralleling the present movement in medicine. In all, the chapters herald considerable promise for the future. Judith L. Rapoport, MD Chief, Child Psychiatry Branch National Institute of Mental Health 10 Center Drive Building 10, Room 3 N202 Bethesda, MD 20892-1600

REFERENCES Beauchaine, T. P., & Thayer, J. F. (2015). Heart rate variability as a transdiagnostic biomarker of psychopathology. International Journal of Psychophysiology, 98, 338–350. Cuthbert, B. N. (2014). The RDoC framework: Facilitating transition from ICD/DSM to dimensional approaches that integrate neuroscience and psychopathology. World Psychiatry, 13, 28–35. Franz, D. N., Leonard, J., Tudor, C., Chuck, G., Care, M., Sethuraman, G., . . . Crone, K. R. (2006). Rapamycin causes regression of astrocytomas in tuberous sclerosis complex. Annals of Neurology, 59, 490–498. Giedd, J. N., Raznahan, A., Alexander-Bloch, A., Schmitt, E., Gogtay, N., & Rapoport, J. L. (2015). Child psychiatry branch of the National Institute of Mental Health longitudinal structural magnetic resonance imaging study of human brain development. Neuropsychopharmacology, 40, 43–49. Gur, R. C. (2016). Prospective community studies linking cognitive deficits to subclinical symptoms and a step toward precision medicine. JAMA Psychiatry, 73, 109–110.

Foreword

xi

Martinez, A., & Hinshaw, S. P. (2016). Mental health stigma: Theory, developmental issues, and research priorities. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 997–1039). Hoboken, NJ: Wiley. Merikangas, K. R., He, J. P., Burstein, M., Swendsen, J., Avenevoli, S., Case, B., . . . Olfson, M. (2011). Service utilization for lifetime mental disorders in U.S. adolescents: Results of the National Comorbidity Survey-Adolescent Supplement (NCS-A). Journal of the American Academy of Child and Adolescent Psychiatry, 50, 32–45. Rouse, D. J., Hirtz, D. G., Thom, E., Varner, M. W., Spong, C. Y., Mercer, B. M., . . . Roberts, J. M. (2008). A randomized, controlled trial of magnesium sulfate for the prevention of cerebral palsy. New England Journal of Medicine, 359, 895–905. Shaw, P., Malek, M., Watson, B., Greenstein, D., de Rossi, P., & Sharp, W. (2013). Trajectories of cerebral cortical development in childhood and adolescence and adult attention-deficit/hyperactivity disorder. Biological Psychiatry, 74, 599–606. Weinberger, D. R., Glick, I. D., & Klein, D. F. (2015). Whither Research Domain Criteria (RDoC)?: The good, the bad, and the ugly. JAMA Psychiatry, 72, 1161–1162. Wiggins, J. L., Brotman, M. A., Adleman, N. E., Kim, P., Oakes, A. H., Reynolds, R. C., . . . Leibenluft, E. (2016). Neural correlates of irritability in disruptive mood dysregulation and bipolar disorders. American Journal of Psychiatry. Epub ahead of print.

Preface

A

s we noted in the preface of the second edition of Child and Adolescent Psychopathology (Beauchaine & Hinshaw, 2013), global costs of mental illness—in terms of morbidity, mortality, and other forms of human suffering—are staggering. In many developed countries including the United States, over one third of individuals suffer from a major psychiatric disorder at some point in their lives (Kessler et al., 2009). In low- and middle-income countries, mental disorders account for 25% and 34%, respectively, of total years lived with disability, yet most of those affected receive no treatment (WHO World Mental Health Survey Consortium, 2004). Although treatment rates are slightly higher in wealthy countries, mental disorders continue to carry significant stigma. As a result, many avoid seeking help, and a lack of treatment parity remains for mental disorders vs. other health-related conditions (Hinshaw, 2007; Martinez & Hinshaw, 2016). When the two of us met nearly 18 years ago, knowledge of the causes of mental illnesses was quite limited compared to today. Although behavioral genetics studies had shown that most psychiatric disorders are at least moderately heritable, little was known about molecular genetic, neural, or hormonal mechanisms of heritability. Moreover, neither epigenetic alterations in gene expression, nor rare structural variants, had been identified as possible mechanisms through which environment might confer vulnerability to psychopathology. Many prevailing models of mental illness still pitted nature and nurture against each other as competing causes of psychopathology. Transactional models, in which biological vulnerabilities are presumed to interact with environmental risk factors to eventuate in mental illness, were few in number and limited in specification of neurobiological mechanisms, as advanced neuroimaging was in still in its infancy. Given limitations in technology, most of what we learned about mental illness has traditionally been obtained through observation and classification of symptoms (see Chapter 2 [Beauchaine & Klein]). Although useful in early stages of identifying different forms of mental illness, symptom classification often tells us little if anything about underlying causal processes—be they biological or environmental—that lead to particular disorders. In editing this book, we therefore sought authors with expertise in the developmental psychopathology perspective, which emerged only about 35 years ago (see Chapter 1 [Hinshaw]). This perspective follows from xiii

xiv Preface the observation that human behavioral traits—including those that predispose to psychopathology—almost always arise from complex transactions between biological vulnerabilities and exposure to environmental risks across development. For example, heritable conditions such as attention-deficit/hyperactivity disorder, depression, schizophrenia, and substance dependence are shaped strongly by environmental influences, and effects of environmentally transmitted risks such as child maltreatment are moderated by genes and other biological predispositions (see e.g., Beauchaine & McNulty, 2013). Furthermore, through epigenetic mechanisms, the expression of several genes that are implicated in behavior regulation can be altered by experience, including exposure to stress and trauma—findings that defy anachronistic distinctions derived from reductionistic models. Thus, we asked all authors to identify both biological and environmental contributors to psychopathology and to discuss how these interact and transact across development to amplify risk. This dynamic view of mental disorders served as the impetus for both the first and second editions of this book, and continues as a driving force behind the current third edition, which includes substantially updated material. Before the first edition was published, most graduate-level psychopathology texts were organized around the symptom-based approach to classifying mental illness, with limited consideration of the genetic and neural underpinnings of behavior or the interplay between biological vulnerabilities and environmental risk factors across development. However, in the nine years since the first edition was published, appreciation for the complexity of such transactions in the development of psychopathology has increased, and many new and exciting findings have emerged (see e.g., Beauchaine & Goodman, 2015). Elucidating causes of mental illness is an international public health concern. The better we understand etiology across all relevant levels of analysis, including genetic, neural, familial, and cultural (to name a few), the better position we are in to devise more effective prevention and intervention programs (Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008). Thus, even though this text does not address treatment, we hope readers will keep in mind while digesting each chapter how important it is to identify causes of mental illness in our efforts to reduce human suffering. This motivation played a central role in the National Institute of Mental Health (2015) establishing the Research Domain Criteria (RDoC) project. RDoC is a collaboration between NIMH and researchers around the world to develop a neuroscience-informed system of characterizing psychopathology that identifies genetic, neural, hormonal, and social determinants of major behavioral systems that contribute to human function, and at the extremes, mental illness (see Chapter 2 [Beauchaine & Klein]). Readers will likely note that some disorders that are often addressed in psychopathology texts are not included in this book. For example, we do not cover developmental disorders or intellectual disability. In omitting these disorders, we are not implying that they are unimportant. Rather, the vast literature on developmental disabilities makes it difficult to cover the topic adequately in a text that already includes 24 chapters. Thus, we were left with a difficult choice, and we

Preface xv decided not to limit coverage of the conditions contained herein. We refer interested readers to other sources (e.g., Burack, Hodapp, Iarocci, & Zigler, 2011) for excellent coverage of this domain. We now invite you to join us in the quest for a deeper understanding of the development of mental disorders, which almost always originate in childhood and adolescence. We hope that our emphases on genetic and other biological vulnerabilities, and how these interact with environmental risk factors and contexts will challenge any preconceived notions you may have about what is “biological” and what is “environmental” in relation to normal and atypical development. We hope as well that our coverage will prompt the next generation of investigators, clinicians, and policymakers to pursue the daunting but essential goal of explaining, treating, and preventing the devastation that so often accompanies psychopathology. Theodore P. Beauchaine Stephen P. Hinshaw

REFERENCES Beauchaine, T. P., & Goodman, S. H. (Eds.). (2015). Ontogenic process models of psychopathology [Special Section]. Journal of Abnormal Psychology, 124, 771–877. Beauchaine, T. P., & Hinshaw, S. P. (2013). Preface. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Child and adolescent psychopathology (2nd ed., pp. xi–xiii). Hoboken, NJ: Wiley. Beauchaine, T. P., & McNulty, T. (2013). Comorbidities and continuities as ontogenic processes: Toward a developmental spectrum model of externalizing behavior. Development and Psychopathology, 25, 1505–1528. Beauchaine, T. P., Neuhaus, E., Brenner, S. L., & Gatzke-Kopp, L. (2008). Ten good reasons to consider biological processes in prevention and intervention research. Development and Psychopathology, 20, 745–774. Burack, J. A., Hodapp, R. M., Iarocci, G., & Zigler, E. (Eds.). (2011). The Oxford handbook of intellectual disability and development. New York, NY: Oxford University Press. Hinshaw, S. P. (2007). The mark of shame: Stigma of mental illness and an agenda for change. New York, NY: Oxford University Press. Kessler, R. C., Aguilar-Gaxiola, S., Alonso, J., Chatterji, S., Lee, S., Ormel, J., . . . Wang, P. S. (2009). The global burden of mental disorders: An update from the WHO World Mental Health (WMH) Surveys. Epidemiologia e Psichiatria Sociale, 18, 23–33. Martinez, A., & Hinshaw, S. P. (2016). Mental health stigma: Theory, developmental issues, and research priorities. In D. Cicchetti (Ed.), Developmental psychopathology. Vol. 4: Risk, resilience, and intervention (3rd ed., pp. 997–1039). Hoboken, NJ: Wiley. National Institute of Mental Health. (2015). NIMH strategic plan for research. Retrieved from http://www.nimh.nih.gov/about/strategic-planning-reports/index.shtml WHO World Mental Health Survey Consortium. (2004). Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. Journal of the American Medical Association, 291, 2581–2590.

List of Contributors

Peter Arnett Pennsylvania State University

Sheila E. Crowell University of Utah

Robert F. Asarnow UCLA School of Medicine

Nicole A. Crocker San Francisco VA Medical Center

Theodore P. Beauchaine The Ohio State University

Geraldine Dawson Duke University School of Medicine

Alexandra H. Bettis Vanderbilt University Joseph C. Blader University of Texas Health Science Center at San Antonio Deepika Bose University of California, Los Angeles Sandra A. Brown University of California, San Diego Gabrielle A. Carlson State University of New York at Stony Brook

Marco Del Giudice University of New Mexico Lauren R. Doyle San Diego State University Bruce J. Ellis University of Utah Susan Faja Harvard Medical School Megan Finsaas State University of New York at Stony Brook Jennifer K. Forsyth UCLA Department of Psychology

Pamela M. Cole Pennsylvania State University

Susanna L. Fryer University of California San Francisco

Bruce E. Compas Vanderbilt University

Lisa Gatzke-Kopp Pennsylvania State University xvii

xviii

List of Contributors

Brandon L. Goldstein State University of New York at Stony Brook Meredith Gruhn Vanderbilt University Sarah E. Hall Wheaton College Nastassia J. Hajal University of California, Los Angeles Stephen P. Hinshaw University of California, Berkeley Sara R. Jaffee University of Pennsylvania Wesley G. Jennings University of South Florida Jerome Kagan Harvard University Erin A. Kaufman University of Utah Daniel N. Klein State University of New York at Stony Brook Benjamin B. Lahey University of Chicago Mark F. Lenzenweger State University of New York at Binghamton and Weill Cornell Medical College

Victoria C. Merritt Pennsylvania State University Jessica E. Meyer Pennsylvania State University Emily Neuhaus Seattle Children’s Research Institute Joel Nigg Oregon Health and Science University Nicholas M. Perez University of South Florida John Piacentini University of California, Los Angeles Bruce D. Perry Child Trauma Academy Houston, TX Emily Ricketts University of California, Los Angeles Donna J. Roybal University of Texas Health Science Center at San Antonio Colin L. Sauder University of Texas Health Science Center at San Antonio Katherine E. Shannon Bowen University of Washington Elizabeth A. Shirtcliff Iowa State University

Deanna Linville University of Oregon

Wendy K. Silverman Yale University School of Medicine

Sarah N. Mattson San Diego State University

Eric Stice Oregon Research Institute

List of Contributors Kristin L. Tomlinson University of California, San Diego

Carl F. Weems Iowa State University

Ian R. Gizer University of Missouri

Jennifer Winward University of California, San Diego

Irwin D. Waldman Emory University

xix

Child and Adolescent Psychopathology

PART I

THE DEVELOPMENTAL PSYCHOPATHOLOGY APPROACH TO UNDERSTANDING MENTAL ILLNESS

CHAPTER 1

Developmental Psychopathology as a Scientific Discipline A 21st-Century Perspective STEPHEN P. HINSHAW

I

nformation continues to accumulate, at an increasingly rapid pace, about the complex processes and mechanisms underlying the genesis and maintenance of child and adolescent forms of mental disorder. Our major goal for this, the third edition of Child and Adolescent Psychopathology—in chapters written by international experts on the topics of interest—is to present current information, particularly surrounding core vulnerabilities and risk factors for major dimensions and categories of behavioral and emotional problems of youth. As in our prior editions (Beauchaine & Hinshaw, 2008, 2013), we emphasize psychobiological vulnerabilities in the active context of environmental forces that shape development. Framed somewhat differently, an important objective for each chapter is to delineate potential ontogenic processes in progressions to mental disorder, signifying mechanisms underlying individual development, with the realization that multiple vulnerabilities and risk factors interact and transact in case-specific yet ultimately predictable ways (Beauchaine & Hinshaw, 2016; Beauchaine & McNulty, 2013; Hinshaw, 2015). Parallel to the first two editions, we do not prioritize assessment or treatment-related information in this book, given that such coverage would necessitate a second or even third volume (e.g., Mash & Barkley, 2006, 2007). Although the book’s title focuses on children and adolescents, I note immediately that psychopathology, in many (if not most) cases, unfolds across the entire lifespan. Most so-called adult manifestations of mental disorder have origins, if not outright symptom presentations, prior to age 18. Moreover, even the earliest-appearing forms of behavioral and emotional disturbance typically portend escalating symptoms and impairments that can persist for decades (e.g., Kessler, Berglund, Demler, Jin, & Walters, 2005). Because resilience is also a possibility (Luthar, 2006), lifespan 3

4 The Developmental Psychopathology Approach approaches to the topics of interest in this book are increasingly mandated for thorough understanding, carrying profound clinical as well as scientific implications. The child is the father of the man—and the mother of the woman—given that adults emerge from a cascading set of processes set in motion years before. Before delving further, I immediately acknowledge the major debt that Ted Beauchaine and I owe to all of our contributors, as each is a major force in the scientific literature. We asked them to integrate state-of-the-art knowledge into the chapters that follow. Indeed, given the fast-escalating sophistication of mechanistic accounts of the development of psychopathology—which are now integrating genetic vulnerability and brain architecture in the presence of contextual forces across development, providing unprecedented levels of synthesis (Hinshaw, 2015)— no current compendium can afford to rest on the laurels of previous editions. The field’s work is emerging at ever-more-detailed levels of analysis, with the promise of accounts that should, in the future, better inform evidence-based practice in the context of validated knowledge structures that can be applied to the clinical phenomena under consideration. In this initial chapter, I delineate the clinical and policy-related importance of the subject matter at hand, explicate core principles of developmental psychopathology (DP), and provide a general overview of the sequence of the chapters and their contents. In so doing I aim to set the stage for the cutting-edge advances and wisdom provided in the remainder of the volume.

RELEVANCE AND IMPORTANCE The subject matter under consideration in this volume is at once clinically compelling and conceptually fascinating. Mental disorders yield substantial impairment, pain, and suffering for individuals, families, communities, and even cultures. The levels of personal and family tragedy involved are often devastating (Hinshaw, 2008a). At the same time, multifactorial vulnerabilities and risk factors— along with the complex, transactional developmental progressions that produce symptoms and impairments—challenge investigators from disciplines as diverse as neuroscience, genomics, public health, psychology, psychiatry, and public policy to emerge with new insights and syntheses. Overall, the clinical need is urgent and the scientific motivation compelling. I begin with the concept of impairment. As elaborated in nearly every working guide to psychopathology (e.g., American Psychiatric Association, 2013; Wakefield, 1992), a designation of mental illness mandates, beyond behavior patterns or symptoms, that the individual in question display impairment or “harm” before a diagnosis is made. Clinically, then, attention must be paid to the often-excruciating pain and suffering attending to conditions as diverse as autism-spectrum disorders, various sequelae of maltreatment, severe attention deficits and impulsivity, interpersonal aggression, significant anxiety and mood disorders, thought disorders (including schizophrenia), eating-related conditions, self-destructive behavior patterns and personality configurations, and substance use disorders. Each is

Developmental Psychopathology as a Scientific Discipline

5

linked to setback and suffering, societal reverberations, and significant costs, the latter measurable in terms of huge expenditures borne by society, not related just to treatment per se but to the long-range outcomes of interpersonal, educational, and vocational failure that often attend to mental disorders (for an example of the huge costs linked to attention-deficit/hyperactivity disorder [ADHD], see Hinshaw & Scheffler, 2014). Of course, impairment and harm—whether personal or experienced by others— are not sufficient for designating individuals as suffering from a mental disorder. In the view of Wakefield (1992), both harm (which involves a value-laden component) and dysfunction (a scientific construct) are required before mental illness should be diagnosed. Per Wakefield, dysfunction is “the failure of a mental mechanism to perform a natural function for which it was designed by evolution” (p. 373). Although mental health fields lack the objective markers and pathognomonic signs1 as those found in medicine and neurology (see Chapter 2 [Beauchaine & Klein]), our aim for the accumulated work in the present volume is to propel knowledge of dysfunctional mechanisms related to child and adolescent psychopathology. At the same time, findings from each chapter remind us that the origins of mental health conditions are reciprocal, dynamic, multilevel, and fully linked with processes linked to environmental context. Not every aspect of psychopathology is necessarily impairing. At the level of evolution, it cannot be the case that mental disorder is inevitably or inexorably linked to personal failure or reduced fecundity; otherwise, how would conditions such as severe thought and mood disorders have perpetuated across human history (for evolutionary psychological explanations of mental disorder, see Neese, 2005)? Partial genetic loadings or vulnerabilities in biological relatives may well carry adaptive advantage; at least some aspects of symptoms could yield inspiration or thriving. Still, clinical and population-level facts regarding impairment linked to mental illness are stark. Emotional and behavioral problems among children and adolescents are distressingly prevalent and often lead to serious impairments in such crucial life domains as academic achievement, interpersonal competencies, and independent living skills (for thorough accounts, see Mash & Barkley, 2014). These conditions incur intensive pain for individuals, families, and communities at large, delimiting life opportunities and triggering major burdens for caregivers, school districts, and health care systems. In short, far too many young lives are compromised by mental illness. Moreover, child and adolescent conditions and mental-health-related issues are growing in impact. As just one harrowing example, recent data from the World Health Organization reveal that, worldwide, the number-one cause of death for girls aged 15–19 years is now suicide (World Health Organization, 2014). 1. A pathognomonic sign is an indicator, usually biological, that at once (1) proves that a person suffers from a disease of known etiology, and (2) eliminates all other disease processes as potential causes. For a detailed discussion of the role of pathognomonic signs in medicine vs. psychiatry/psychology, see Beauchaine and Thayer (2015).

6 The Developmental Psychopathology Approach Rates of self-injury have escalated rapidly over the past decades, and conditions like autism and ADHD are undergoing huge increases in diagnosed prevalence (e.g., Visser et al., 2014). The age of onset of serious mood disorders appears to be dropping, signaling the importance of contextual “push” in unearthing vulnerability (Hinshaw, 2009). In both the developing and developed world, serious mental disorder in youth portends major life consequences and even tragedy (see, for example, Sawyer et al., 2002). Moving beyond childhood and adolescence per se, each year the Global Burden of Disease findings convey that a number of mental health conditions (along with neurological and substance use disorders) are among the world’s most impairing illnesses (Whiteford, Ferrari, Degenhardt, Feigin, & Vos, 2015). Indeed, the variable called “years lived with disability” is dominated by individuals with mental disorders in our current era, on par with and often surpassing so-called physical diseases. By the time of adulthood, economic costs related to mental illness escalate with respect to employment-related impairments, yielding huge public-entitlement expenditures and lack of productivity. In short, from a number of important lenses, mental disorders are tragically impairing, robbing individuals of opportunities to thrive and be productive, often in the prime of their lives. If readers sense a call to action in these words, they have read my intentions precisely. Crucially, mental health and physical health are inexplicably intertwined. It is now well known that serious mental disorder is associated with reductions in life expectancy averaging from 10 to 25 years (e.g., Chang et al., 2011). The reasons here are plentiful: high-risk lifestyles, lack of access to medical care, suicide, homicide, co-occurring chronic (e.g., cardiovascular disease; diabetes), and infectious (e.g., HIV) illnesses, and related unhealthy practices such as smoking and substance abuse. Even nonpsychotic disorders (e.g., ADHD; many forms of depression) are linked to long-term health risks (e.g., Barkley, Murphy, & Fischer, 2008). Recent findings reveal links between a range of mental disorders and a startling list of chronic physical illnesses (Scott et al., 2016). Given this set of enormously costly, persistent, and deeply human consequences and needs, why not rely on traditional clinical efforts in psychology and psychiatry for solutions, given their long, venerable histories? As detailed in earlier accounts, however, these efforts have led to static views of psychopathology, with priority given to categorical diagnoses that inevitably lump together individuals with substantially different etiologic pathways into the same “condition” (e.g., Cicchetti, 1984, 1990). Moreover, the reciprocally deterministic nature of development, both typical and atypical, is not well captured by such static diagnostic systems (or nosologies, see Chapter 2 [Beauchaine & Klein]). Because of the huge expansion of knowledge in a host of related fields and subfields, the complex yet compelling perspectives offered by DP have taken hold with increasing rapidity, providing a call to investigators from a host of seemingly disparate disciplines regarding the promise of uncovering relevant mechanisms. Absent the multifaceted nature of DP models and paradigms, traditional perspectives are too often sterile

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and impoverished, carrying huge potential for treatments and prevention efforts to be directed at the wrong targets. Despite scientific and clinical urgency surrounding this entire topic, barriers stand in the way of increased scientific understanding and access to evidence-based treatment. Perhaps the primary issue is that mental disturbance, at any age, remains highly stigmatized (e.g., Hinshaw, 2007; Hinshaw & Stier, 2008; Martinez & Hinshaw, 2016). Intensive stigma and shame—related to the unpredictability of the behavior patterns in question, the threat they convey to perceivers’ well-being, and their media-propelled linkages to violence and incompetence—too often preclude help seeking, prevent empathic responses, and serve to render mental health a lower priority than physical health, despite inextricable linkages between the two. Depressingly, although public knowledge of mental illness has grown considerably since the 1950s, the U.S. public is far more likely to link mental illness with dangerousness than in the past (see Phelan, Link, Stueve, & Pescosolido, 2000). Moreover, rates of stigma and social distance related to mental illness have not changed appreciably in recent decades (Pescosolido et al., 2010). Reasons are complex but may relate to (a) increased numbers of seriously impaired individuals on the streets, without needed community services and resources; (b) enhanced public awareness that “dangerousness” is one of the few mandates for involuntary commitment to hospitals—along with frequent media attention linking mental illness to mass shootings, oftentimes inaccurately; and (c) the tenuousness of evidence that biogenetic ascriptions to mental illness (i.e., that it is a “brain disease” or a “disease like any other”) can eliminate stigmatization (see Haslam & Kvaale, 2015; Martinez, Piff, Mendoza-Denton, & Hinshaw, 2011; Pescosolido et al., 2010). Indeed, although biological perspectives are a necessary antidote to the “blaming the family” and “castigating the individual” perspectives that dominated psychology and psychiatry for much of the 20th century, their reductionistic promotion is neither accurate nor aiding the cause of stigma reduction, in part because they appear to promote pessimism and dehumanization. Instead, DP perspectives offer complex as opposed to simplistic or reductionistic conceptions of mental disorder, potentially leading to appreciation of the multidetermined biological and contextual factors related to psychopathology instead of personal or family weakness or blame, or notions of genetic flaw (e.g., Haslam & Kvaale, 2015; Martinez & Hinshaw, 2016). In all, despite major advances in both basic science and clinical applications in recent years, as highlighted in the following chapters, the field’s knowledge of developing brains and minds in multiple, interacting contexts is still rudimentary. It is hard to imagine otherwise, given the sheer complexity of the subject matter under consideration. As noted in introductory chapters to the earlier editions of this volume (Hinshaw, 2008b, 2013), the trajectory of human prenatal neural development is nothing short of staggering, with literally thousands of new neurons proliferating during each second of development after the first few weeks following conception, as well as massive pruning and synaptogenesis in the first several years of life. Still, for those who enjoy a challenge and are excited by questions that will take both many

8 The Developmental Psychopathology Approach decades and many great minds and scientific teams to answer—with the potential payoff of bettering the human condition—the hope is that this volume will serve as a call to join the major scientific and clinical efforts so urgently needed. Indeed, if the field is to continue to make headway toward understanding, treating, and preventing the serious clinical conditions that emerge during childhood and adolescence, the best minds of the current and forthcoming generations of scholars and clinicians need to join the effort. At this point, I provide a review of core axioms and principles of DP. These points reflect the multidisciplinarity and transactional nature of the field, signifying that static models and unidimensional conceptions are simply not able to explain the fascinating and troubling development of maladaptive behavior patterns comprising the domain of psychopathology.

PRINCIPLES OF DP Many of the conceptual bases for integrating developmental principles and models into the study of child and adolescent psychopathology have been present for several centuries, spanning diverse fields and disciplines (e.g., Cicchetti, 1990). Yet it is only in the past 40 years that DP has taken formal shape as a perspective on behavioral and emotional disturbance throughout the lifespan, and as a major conceptual guidepost for the study of both normal and atypical development. During this period, DP has exerted a major force on clinical child psychology, child psychiatry, developmental psychology, developmental neuroscience, and a number of other disciplines in both behavioral and neurological sciences. Not only have new courses been formed at major universities, but journals have been created and new paradigms of conceptualizing mental disorder have gained traction (Insel et al., 2010; see Chapter 2 [Beauchaine & Klein]). It is remarkable how pervasive the DP perspective has become, galvanizing a host of clinical and scientific efforts and in the process becoming mainstream. DP simultaneously comprises a theoretical model regarding the origins of mental disorders, a multidisciplinary approach linking principles of normative development to the genesis and maintenance of psychopathology, and a scientific discipline closely tied to clinical child and adolescent psychology and psychiatry but transcending the usual diagnosis-based emphases of these fields (Cicchetti, 2016; Lewis & Randolph, 2014). Through its focus on the dynamic interplay of biology and context, genes and environments, and transactional processes linking multilevel influences to the development of healthy and atypical functioning, DP has come to dominate current conceptual models of psychopathology. Many of its core ideas emerge from disciplines such as philosophy, systems theory, and embryology (see Gottlieb & Willoughby, 2006, for elaboration). The syntheses represented in this volume, reflecting DP’s continuing growth into the first two decades of the 21st century, are cutting-edge, given the major knowledge explosion in recent years, related largely to greater understanding of psychobiological influences as they transact with contextual forces.

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What characterizes a truly developmental view of psychopathology, versus descriptive, symptom-focused presentations dominating most classification systems? DP’s originators contended with this core question (e.g., Achenbach, 1974; Cicchetti, 1990; Rutter & Sroufe, 1984; Sroufe & Rutter, 2000), and current syntheses still grapple with the fundamental issues involved (Cicchetti, 2016; Lewis & Rudolph, 2014). From my perspective the key issues constitute multidisciplinarity; acknowledgment of dynamic, multilevel processes; and appreciation of systems-level change in producing developmental transitions (whether the systems are biological or social). Despite the many gains that have been made, it is important to realize at the same time how far we must still travel to comprehend the development and maintenance of psychopathology via the tools and models of DP. The trail ahead is long and steep. I list several core points that are commonly viewed as central to the DP perspective. These include the necessity of (a) interweaving studies of normal development and pathological functioning into a true synthesis; (b) examining developmental continuities and discontinuities of traits, behavior patterns, emotional responses, and disorders; (c) exploring both risk and protective factors and their interplay, so that competence, strength, and resilience as well as pathology and impairment can be understood; (d) involving reciprocal, transactional models of influence in the field’s causal models through which linear patterns of association and causation are replaced by probabilistic, dynamic, nonlinear, and complex conceptual models; and (e) capturing the importance of both psychobiological vulnerabilities and social/cultural context in understanding the function of behavioral and emotional patterns. Three related principles bear emphasis: 1. Multiple pathways to pathology exist. Indeed, disparate routes may lead to behaviorally indistinguishable conditions or outcomes, exemplifying the construct of equifinality. For example, aggressive behavior can result from physical abuse, from a heritable tendency toward disinhibition, from injury to the frontal lobes, from coercive parenting interchanges with the developing child, from prenatal and perinatal risk factors acting in concert with early experiences of insecure attachment or parental rejection, or—as is probably most often the case—from different combinations of these vulnerabilities and risk factors. A key problem with static nosologies is their assumption that everyone receiving a similar psychiatric diagnosis has the “same” underlying patterns and processes of psychopathology. Similarly, multifinality pertains when a given vulnerability, risk factor, or initial state fans out into disparate outcomes across different individuals (Cicchetti & Rogosch, 1996). Maltreatment may or may not lead to severe maladaptation, depending on a host of intervening factors. As another example, extremes of inhibited temperament may induce intense shyness and social withdrawal; but other, healthier outcomes are also possible, depending on the presence or absence of additional risk or protective factors.

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The Developmental Psychopathology Approach 2. DP models often place emphasis on person-centered research designs, in which the typical practice of examining global effects of one or more risk/protective variables across an entire sample or population is supplemented by consideration of unique subgroups—whether defined by genotypes, personality variables, socialization practices, neighborhoods, or other key factors—and their unique developmental journeys across the lifespan (see Bergman, von Eye, & Magnusson, 2006). From a slightly different perspective, developmental continuities and discontinuities may well differ across homogeneous subgroups of participants. Even in variable-centered research, key moderator variables and mediator processes must always be considered (e.g., Hinshaw, 2002; Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001), to ensure that (a) results are applicable to subsets of participants grouped on moderator variable of interest (males versus females, those from different ethnic groups, or those with different patterns of comorbidity) and (b) underlying mechanisms of change, gleaned from mediator variables, are taken into account. 3. Given the rapid growth in recent years of genomic models as well as brain imaging methods, DP researchers in the 21st century must pay increasing attention to the role of the brain, and neuroscientific principles in general, toward accounting for the wide range of extant pathologies and their devastating effects. The field has come a long way from the middle of the 20th century, when biological and temperamental factors were virtually ignored in accounts of child development and psychopathology. Again, however, progress will be stalled if the psychosocial reductionism of prior generations is replaced by biological and genetic reductionism in the current era. A key antidote is for students and investigators to embrace a multiple-levels-of-analysis approach, integrating across genes and gene products, neural systems, and temperamental traits and core behavioral patterns, in contexts of families, schools, and neighborhoods, including the general culture (Cicchetti, 2008; Insel et al., 2010). Isolated, single-factor or single-level models and paradigms are inadequate to the task.

In other words, the greatest potential for progress in the DP field is made when investigators travel back and forth between “micro” and “macro” levels—including intermediate steps or pathways—to understand mechanisms that underlie development of adjustment and maladjustment. The essential task is to link events at the level of genes (e.g., genetic polymorphisms; transcription and translation), neurotransmitters, and neuroanatomical development, into individual differences in temperament, social cognition, and emotional response patterns. At the same time, such bottom-up conceptions must be supplemented by top-down understanding of ways in which family interaction patterns, peer relations, school factors, and neighborhood/community variables influence the developing, plastic brain, even at the level of gene expression (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Overall, progress toward understanding pathological behavior will require

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multidisciplinary efforts in which investigators ranging from geneticists and biochemists, scientists focusing on basic psychological processes and individual psychopathology, experts on family and neighborhood processes, examiners of clinical service systems, and public health officials as well as policy experts must work collaboratively and in increasingly diversified ways. The phenomena under consideration are too complex, too dynamic, and too multifaceted to be understood by an exclusive focus on psychobiological processes, family factors, peer processes, or cultural factors in isolation. Performing the necessary kinds of investigations often mandates large-scale, complex, and interdisciplinary work, necessitating collaborations across traditional disciplinary boundaries. Note that key concepts and principles of DP have been stated and restated across a large number of articles, chapters, and books. Indeed, detailed discussion could easily fill a volume unto itself. The challenge for the current chapter is to encapsulate several core tenets, in the service of foreshadowing and illuminating content on specific processes and specific mental dimensions and disorders.

Normal and Atypical Development Are Mutually Informative As opposed to the study of discrete, mutually exclusive categories of disorder, DP models emphasize that nearly all relevant phenomena represent aberrations in continua of normal developmental pathways and processes—and, accordingly, that without understanding typical development, the study of pathology will remain incomplete and decontextualized. As just one example, related to a research area within my own expertise, illuminating the nature of ADHD requires thorough understanding of normative development of attention, impulse control, and self-regulation (e.g., Barkley, 2015; Hinshaw & Scheffler, 2014; Nigg, Hinshaw, & Huang-Pollack, 2006; Sonuga-Barke, Bitsakou, & Thompson, 2010; see also Chapter 13 [Nigg]). Similarly, investigations of autism must account for the development of interpersonal awareness and empathy, as well as social motivation—which typically takes place over the first several years of life—to gain understanding of the devastating consequences of failure to attain such development (Dawson & Toth, 2006; see also Chapter 22 [Faja & Dawson]). Additional examples exist across all forms of disordered emotion and behavior. Although considered set breaking at the outset of modern DP conceptions, this point is now taken for granted: Few would doubt the wisdom of understanding developmental sequences and processes associated with healthy outcomes as extremely relevant to elucidation of pathology. Intriguingly, however, the process is conceptualized as bidirectional, as investigations of pathological conditions—sometimes referred to as adaptational failures in the language of DP (Sroufe, 1997)—can and should provide a unique perspective on normative development. Thus, the study of disrupted developmental progressions can and should facilitate our understanding of what is normative. This core tenet of DP, of mutual interplay between normality and pathology, is now

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espoused widely. Examples abound in neurology, where the study of disrupted neural systems enhances understanding of healthy brain functioning (Gazzaniga, Ivry, & Mangun, 2014). But just how appropriate is this perspective for DP? Outside of neurological formulations, where single lesions or single genes are investigated quite specifically, can studies of psychopathology inform normal development? It is commonly accepted that greater knowledge of basic emotion, cognition, attention, memory, social awareness, self-regulation, and so forth feeds into understanding of pathology. Indeed, almost no forms of mental disorder constitute clearly demarcated, qualitatively distinct categories or taxa, so processes applying to individuals near the peak of the bell curve are likely to apply to those further out on the continuum. Yet regarding the other direction—the informing of normal-range processes from study of the abnormal—we can legitimately ask what has been learned from far more complex developmental processes linked to mental disorder as regards application to normative development. In other words, in the absence of surgical lesions in certain brain tracts or single-gene forms of pathology such as phenylketonuria, can the far messier domain of psychopathology cycle back to inform developmental science? Examples are becoming more apparent. The horrific experiments of nature that occurred when infants and toddlers in Eastern Europe were subjected to harsh, sterile institutionalization in large orphanages several decades ago, which included a bare minimum of human contact, provide important data (see O’Connor, 2006, for review). From accumulated research evidence, it is now clear that the more months—during infancy, toddlerhood, and the preschool years—a child is exposed to such conditions, the worse his or her developmental outcomes, both cognitively and socially. In short, the longer the periods of deprivation, the lower the chances for recovery. Intriguingly, the most common behavioral outcomes related to such early deprivation include inattention and overactivity, rather than conduct problems per se—a clear example of equifinality, given that heritable risk is the strongest contributor to such problems in more normative samples (see Kennedy et al., 2016; Kreppner et al., 2001). Moreover, assignment to foster care can mitigate such developmental risk, if performed during the second or third year of life (Nelson et al., 2008). Indeed, for previously institutionalized girls, random assignment to foster care, compared to continued institutionalization, led to improvements in internalizing behavior patterns, mediated by the gaining of attachment security via change from institutional care to family placements (McLaughlin, Zeanah, Fox, & Nelson, 2012). Thus, even in a harshly abandoned and deprived sample, attachment processes were implicated in reductions of anxiety and depression. Whereas mediators of competence in more normative samples are still open to exploration, the extent of social and cognitive “catch-up” following removal from harsh institutional care is potentially informative about normal-range development of secure relationships and cognitive performance.

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As reviewed in introductory chapters of previous editions of this volume (Hinshaw, 2008b, 2013), further examples exist from the domains of ADHD and autism-spectrum disorders. For the former, information about disruptions to inhibitory control and reward-related mechanisms from individuals with clinical levels of the relevant symptoms informs developmental science about normative development of self-regulation and intrinsic motivation. Regarding autism, intensive investigation of social deficits has relevance to understanding normative development of “theory of mind” during the toddler and preschool years. Other examples abound outside the realm of neurodevelopmental disorders, in the areas of depression, anxiety, and response to trauma. Certainly, symptoms and systems at play in all such domains are more complex than in classic cases from neurology, but two-way communication between the atypical and typical is possible. If our text had a “post-chapter quiz”—or suggestions for extra credit for readers and students—I would suggest there be mandated exploration, when examining relevant literature and pertinent clinical cases, of specific ways in which knowledge of pathological patterns can inform normative development. My guess is that this task could be an eye-opener for everyone involved.

Developmental Continuities and Discontinuities With this principle, it is commonly asserted that DP models must emphasize both continuous and discontinuous processes at work in the development of pathology. Taking the specific example of externalizing and antisocial behavior, it is well known from a number of longitudinal investigations that antisocial behaviors show strong stability across time, meaning that correlations are substantial between early measures of aggressive and antisocial tendencies and those made at later times. In other words, rank order remains relatively preserved, such that the most aggressive individuals at early points in development remain highly aggressive, compared to others, across development. But does this well-replicated finding mean that the precise forms of externalizing, antisocial behavior remain constant? Clearly not, given that children who exhibit extreme temper tantrums and defiance during toddlerhood and preschool years are not especially likely to exhibit high rates of tantrums during adolescence. Rather, they have a strong likelihood of displaying early verbal aggression and then beyond-normative physical aggression in grade school, excessive covert antisocial behaviors in preadolescence, and high rates of delinquency by their teen years, followed by adult manifestations of antisocial behavior after adolescence, including partner abuse (e.g., Moffitt, 2006). In short, continuities exist, but these are heterotypic in nature, as the actual form of the underlying antisocial trait changes form with development. The implications are profound. That is, investigators of continuity of psychopathology must take into account developmental progressions. Continuity may not be linear or static: During development, new life opportunities and brain maturation portend ascension of new

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forms of pathological behavior. Predictability may well exist, but in complex and nonlinear fashion (see Hinshaw et al., 2012; Meza, Owens, & Hinshaw, 2016; and Swanson, Owens, & Hinshaw, 2014, for the example of emerging self-harm as girls with ADHD grow into their adult years). Another important consideration is that patterns of continuity may differ considerably across separable subgroups with different developmental patterns or trajectories. Not all highly aggressive or antisocial children remain so, as some are prone to desist with the transition to adolescence. Others, however—the so-called early starter or life-course-persistent subgroup—maintain high rates through at least early adulthood, although, as noted in the paragraph above, the specific forms of the antisocial actions change across development. Yet not all early starters persist. In addition, a large subset of youth do not display major externalizing problems in childhood but instead shows a sharp increase with adolescence (Moffitt, 2006). Understanding such continuities and discontinuities via relatively homogeneous subgroups is likely to yield greater understanding than plots of overall curves or “growth.” Sophisticated statistical strategies (for example, growth mixture modeling) are increasingly used to aid and abet this search for separable trajectories or classes defined on patterns of change of the relevant dependent variable (Muthén et al., 2002). In all, continuities abound across the course of development, but developmental associations of interest are not often simple or simplistic. The kinds of developmental perspectives emphasized in DP, and in this text, mandate examination of life trajectories, interactive and transactional processes, and multiple-levels-of-analysis perspectives. Without their consideration, relevant models are once again destined to oversimplification and a loss of relevant clinical information.

Risk and Protective Factors A key focus of a discipline such as DP—with the term psychopathology embedded in its title—is to discover the nature of behavioral and emotional problems, syndromes, and disorders. Many different definitional schemes have been invoked to define and explain psychopathological functioning, with none able to provide a complete picture. Indeed, it is clear that biological vulnerabilities, psychological processes, environmental potentiators, and cultural-level norms and expectations all play major roles in defining and understanding behavioral manifestations that are considered abnormal and pathological in a particular social context. Both biological vulnerabilities and environmental risk factors are antecedent variables that predict such dysfunction, and the ultimate goal is to discover which variables are both malleable and potentially causal of the disorder in question (Kraemer et al., 1997; see also Kraemer et al., 2001). Yet disordered behavior is not uniform, so vulnerabilities and risk factors are not inevitable predictors. Indeed, for most individuals with diagnosable forms of psychopathology, symptoms

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and impairments wax and wane over time. It is often difficult to know when dysfunction precisely begins; it is also quite normative for periods of serious problems to be followed by healthier adjustment. In fact, the myth that mental disturbance is uniformly debilitating, handicapping, and permanent is a key reason for the continuing stigmatization of mental illness (Hinshaw, 2007; Hinshaw & Cicchetti, 2000). Crucially, not all individuals who experience vulnerabilities and risk factors for disorder develop subsequent pathology. Resilience is the term used to define unexpectedly good outcomes, or competence, despite the presence of adversity or risk (Luthar, 2006; Masten & Cicchetti, 2016). Indeed, the concept of multifinality, noted previously, directly implies that, depending on a host of biological, environmental, and contextual factors, variegated outcomes may well emanate from common risk factors, with the distinct possibility of resilience and positive adaptation in some cases. DP is therefore involved centrally in the search for what have been called protective factors: variables and processes that mitigate vulnerability/risk and promote more successful outcomes than would be expected in their presence. Controversy surrounds the construct of resilience, the nature of protective factors, and the definitions of competent functioning (see Burt, Coatsworth, & Masten, 2016). Some have claimed that there is no need to invoke a set of special, mysterious processes that are involved in resilience, given that a certain percentage of any sample exposed to a risk factor will show better-than-expected outcomes and that protective factors are all too often simply the opposite poles of what we typically think of as risk variables or vulnerabilities (e.g., higher rather than lower IQ; easier rather than more difficult temperament; warm and structured rather than cold and lax parenting). Still, it is crucial to examine processes that may be involved in promoting competence and strength rather than disability and despair, given that such processes may be harnessed for prevention efforts and may provide key conceptual leads toward the understanding of both pathology and competence. In short, gaining understanding of why some children who are born into poverty fare well in adolescence and adulthood (see, for example, Wadsworth, Evans, Grant, Carter, & Duffy, 2016), why some individuals with alleles that tend to confer risk for pathological outcomes do not evidence psychopathology, why some youth with difficult temperamental features develop into highly competent adults, and why some people who lack secure attachments or enriching environments during their early years nonetheless show academic and social competence is essential for knowledge of both health and maladjustment. It is not just a luxury but a necessity to investigate positive developmental outcomes, given the inseparability of health and pathology. Competence can shed light on the pathways that deflect from pathology and, in so doing, may provide otherwise hidden insights into necessary developmental components of adjustment versus maladjustment (Luthar, 2006; Masten & Cicchetti, 2016).

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Reciprocal, Transactional, Ontogenic Process Models Linear models of causation, for which static psychological or psychobiological variables are assumed to respond in invariant ways to the influence of vulnerabilities and risk factors, are not adequate to the task of explaining psychopathology and its development. Richters (1997) provided detailed explication, highlighting that unique explanatory systems are needed to deal with “open systems” such as human beings. Pathways to adolescent and adult functioning are marked by reciprocal patterns or chains, in which children influence parents, teachers, and peers, who in turn shape the further development of the child (for an early, influential model, see Bell, 1968). Such mutually interactive processes propel themselves over time, leading to what are termed transactional models. Some developmental processes appear to operate via cascading, escalating chains (Masten et al., 2006) or even “symphonic” effects (Boyce, 2006). Indeed, nonlinear, dynamic systems models are needed to explicate core developmental phenomena (Granic & Hollenstein, 2006). Sensitive data-analytic strategies and innovative research designs are crucial tools for fostering greater understanding of such phenomena. These kinds of models can be used to elucidate equifinal and multifinal processes, as described above. They also exemplify, once again, problems inherent in static, categorical models of pathology (e.g., American Psychiatric Association, 2013; see Chapter 2 [Beauchaine & Klein]). Recognition of such problems led the leadership of the National Institute of Mental Health to develop, several years ago, an alternative to categorical diagnosis, via an endeavor called the Research Domain Criteria (RDoC; see Insel et al., 2010). This dimensional means of accounting for psychopathology specifically embodies a multiple-levels-of-analysis approach by positing a number of core, dimensional behavioral systems, with clear biological substrates, shaped by context. At the same time, ontogenic process models of psychopathology have witnessed a resurgence (see Beauchaine & Hinshaw, 2016; Beauchaine & McNulty, 2013), whereby heritable vulnerabilities transact with toxic contextual forces (e.g., coercive family interactions; violent neighborhoods) to yield psychopathology, particularly of the externalizing variety. Self-injury appears to fall in the same domain of relevant processes (see Chapter 19 [Crowell]). In all instances, static and/or linear models of influence must give way to reciprocal and transactional chains of influence.

Psychobiological Discoveries Intersect and Interact With Context The genomic era has been upon us for some time, and advances in brain imaging research—despite criticisms of its methods and false-positive rates (Vul, Harris, Winkielman, & Pashler, 2009)—have made the developing brain far more accessible to scientific view than ever before. Although it is mistaken, as emphasized throughout, to give primacy to any single level of analysis (brain, context, or other), we have asked contributors to pay particular attention to psychobiological

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factors and processes. Part of the reason is historical: Family systemic and environmental views dominated the field for much of the 20th century. Also, we now know that without understanding potential effects of genes, physiological processes, and biological vulnerabilities to psychopathology, there is little hope of understanding the most severe forms of disorder. Yet the brain is remarkably plastic and contexts influence biological unfolding. Thus, Ted Beauchaine and I have asked authors to emphasize contextualization of the psychobiological perspectives they present. In fact, reductionistic accounts of (a) the primacy of single genes, (b) the inevitable predictability of later functioning from early temperament, or (c) the placement of psychopathology completely inside brightly colored brain images are as short-sighted as the exclusively environmental accounts of psychopathology that dominated much of the 20th century. Indeed, a key tenet of DP is that family, school-related, neighborhood, and wider cultural contexts are central for the unfolding of aberrant as well as adaptive behavior. This point cannot be overemphasized. What may have been adaptive, genetically mediated benefits at one point in human evolutionary history may be maladaptive in current times, given major environmental and cultural changes that render certain traits far less advantageous than previously (e.g., storage of fat in times of uncertain meals and sudden need for survival-related activity; presence of undue anxiety in relation to certain feared stimuli when conditions have markedly changed with respect to sedentary lifestyles). There are few absolutes in terms of behavior patterns that are inherently maladaptive or risk factors that inevitably yield dysfunction; cultural setting and context are all-important for understanding and creating healthy versus unhealthy adaptation. Similarly, key environmental factors (such as parenting styles) are not always uniformly positive or uniformly negative in terms of their developmental effects. Deater-Deckard and Dodge (1997) showed that authoritarian parenting predicts antisocial behavior among White, middle-class children but not necessarily among African-American families. At the same time, many forms of mental disorder are present at roughly equivalent rates across multiple cultures, revealing key evidence for universality. Yet effects of risk or protective factors often differ markedly depending on developmental timing, family and social contexts, and niches that exist in given cultures for their expression and resolution (Serafica & Vargas, 2006). In short, the DP perspective tells us clearly that setting and context are all-important (see also Rutter et al., 1997). The area of gene × environment interactions in DP provides an important, if contentious, case example. The underlying idea is that genotypes moderate the effects of environmental context on the development of psychopathology, and vice versa (i.e., environmental factors moderate genetic effects on mental disorder). With profound implications for DP, this subfield erupted, 15 years ago, with core publications by Caspi and colleagues (Caspi et al., 2002, 2003). However, such widely cited findings have been subject to meta-analyses, which initially challenged the robustness of such results regarding interactions of the

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serotonin transporter gene with maltreatment or stressful life events (e.g., Risch et al. 2009) and then subsequently upheld the initial results when all relevant investigations were included (Karg, Burmeister, Shedden, & Sen, 2011). Both statistical power and selection biases are major factors in all such investigations. In a commentary, Caspi, Hariri, Holmes, Uher, and Moffitt (2010) made the point that interactive effects are accentuated in smaller-sample investigations that feature viable measures of environmental stress—highlighting the importance of precise measures of both the genetic (Dick et al., 2015) and the contextual side of the equation. Similar but greatly expanded perspectives have been provided by Dick et al. (2015), who outline essential recipes for avoiding the major issue of false positive findings in research on gene × environment interactions; and by Keller (2014), who adds to the cautionary note that many gene × environment researchers will overestimate such interactive power lest they explicitly take into account potentially confounding effects of passive gene-environment correlation. Furthermore, Bakermans-Kranenburg and van IJzendoorn (2015), Belsky and Pluess (2009), and Ellis, Boyce, Belsky, Bakermans-Kranenburg, and van IJzendoorn (2011) argue that some “vulnerability” genes are actually “susceptibility” genes, exquisitely responsive to either extremely good or poor environments—with the latter contentions also challenged by a range of artifacts that can produce false-positive findings. In fact, the potential confounding of genetic and environmental contributions to behavior through gene-environment correlation is unquestioned, which is why contributions such as Harold et al. (2013)—who demonstrated reciprocal and transactional effects of child ADHD symptoms and negative parenting with respect to continuations of child behavior in adoptive samples, in which parents and children are biologically unrelated—are essential from a DP perspective. The bottom line is that increasingly sophisticated investigations, with careful attention paid to selection of genes, selection of environments, and careful consideration of a host of design and statistical issues, are needed to elucidate and validate specific ways in which genetic variation may be accentuated or unleashed in particular environmental contexts. In cutting-edge research on DP, the Journal of Abnormal Psychology recently published a special section of articles on ontogenic process models in the field, with special emphasis on investigations focused on the integration of (a) geneenvironment interplay, (b) neuroimaging correlates, and (c) contextual factors that may elicit pathological outcomes across development. I was asked to provide a commentary on these articles, and in doing so I noted that in many ways they represent the cutting edge of the field, largely related to such integration (Hinshaw, 2015). Commenting on only a subset (see also Hankin et al., 2015; LeMoult et al., 2015; Little et al., 2015; and Vrshek-Schallhorn et al., 2015), I first highlight that Carey et al. (2015) revealed an endocannibanoid polymorphism that interacted with childhood sexual abuse to predict development of cannabis dependence in adolescence. Upping the level of complexity and biological relevance, in one of their samples they also studied basolateral amygdala habituation.

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This investigation added a dynamic neural measure to the usual Gene × Environment interaction paradigm, with findings suggestive of a plausible biological pathway leading to cannabis dependence symptoms. Moreover, Pagliaccio et al. (2015) examined early life stress and genetic risk— indexed by a composite score of 10 polymorphisms in hypothalamic-pituitaryadrenal axis genes (see Nikolova, Ferrell, Manuck, & Hariri, 2011, for information on the amalgamation of “risky” alleles in polygenic risk indices), in relation to both (a) amygdala-related connectivity with other brain regions and (b) downstream anxiety symptoms and emotion regulation skills. Evidence was found for both moderation (of early stress by genetic vulnerability) related to low connectivity, and mediation (whereby such reduced connectivity was linked to poor emotion regulation). In addition, Chhangur et al. (2015) examined interactions of two dopamine receptor alleles with core aspects of parenting (high control, low support) to predict adolescent delinquency, using five waves of adolescent data. One genetic variant (DRD2), in interaction with low parental support, showed the expected interaction. Intriguingly, the shape of the interaction was curvilinear, such that the combination of the DRD2 allele in question (A2A2) with low parental support was associated with quick increases in delinquency across early to mid-adolescence, followed by sharp decreases by late adolescence. It may be the case that different configurations of genes and family environments are needed to explain the pernicious group of youth with persistent antisocial behavior patterns (see Gizer, Otto, & Ellingson, 2016). Finally, as highlighted above with respect to gene × environment research in general, most such investigations are seriously underpowered, so only replication can reveal strong evidence for interactive effects (Dick et al., 2015). Throughout this special section of articles, it was openly admitted by authors that interactive effects are typically of small size regarding typical effect-size metrics. It is noteworthy that Chhangur et al. (2015) were diligent in following the strong advice of Keller (2014) to adjust for potential gene-environment correlations before claiming significant effects of Gene × Environment interactions. In all, the possibility that genetically induced variation in vulnerability to psychopathology is moderated by stressful or downright harmful environmental factors—and conversely, that contextual influences on key outcomes are moderated by genotype—remains a tantalizing and theoretically fascinating possibility, with considerable supportive research evidence amidst a sea of controversy about the entire endeavor (e.g., Bakermans-Kranenburg & van IJzendoorn, 2015; Dick et al., 2015; Keller, 2014). This example of the intersection of biology and context is emblematic of the promise—and problems—of the field in the second decade of the 21st century. In sum, recent investigations in the field are explicitly tying in gene-environment interplay with (a) sensitive measures of brain function and (b) randomized clinical trials (Bakermans-Kranenburg & van Ijzendoorn, 2015), in the attempt to elucidate developmental pathways to psychopathology of various forms. The progenitors of

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DP would probably not, a generation and more ago, have envisioned the extent to which technological advances and conceptual sophistication have propelled the gene-environment field along the lines of core DP axioms and principles, nor the wholesale questioning of the endeavor.

SUMMARY Each of the previous points converges on the core theme that the development of psychopathological functioning is multidetermined, complex, interactive, transactional, and in many instances nonlinear. For those who like problems and solutions wrapped in neat packages, the study of DP will undoubtedly be a frustrating if not unfathomable endeavor. On the other hand, for those who are intrigued by the diverse clinical presentations of various pathological conditions in childhood and adolescence; for those who are fascinated with how much remains to be learned about antecedent conditions and maintaining factors; for those who are possessed by an intense “need to know” about underlying mechanisms of child and adolescent forms of mental illness; and for those who realize the need to consider healthy outcomes and competence as well as maladaptation, the DP perspective is a necessary guide to and framework for the rapidly growing scientific enterprise linking normal and atypical development. Longitudinal, multilevel investigations are typically mandated to gain the types of knowledge needed to understand psychopathology (and competence) from a developmental perspective, with potentially high yield for basic developmental science; for elucidation of highly impairing behavioral, emotional, and developmental conditions; and for informing prevention and intervention efforts. The study of DP is ever expanding, engaging scientists from multiple disciplines and perspectives. Progress is emerging quickly, but the territory to explore remains vast.

CHAPTER CONTENTS In our instructions to the volume’s contributors, we asked for up-to-date material that is simultaneously developmentally based, clinically relevant, and directly inclusive of the types of psychobiological formulations gaining ascendancy in the mental-health enterprise. In other words, our aim for each chapter was presentation of state-of-the-art, DP-laden information, full of complexity but presented in a manner facilitating comprehension and integration. Specifically, for chapters dealing with particular disorders and dimensions of psychopathology, we requested coverage of historical context, epidemiology, diagnostic issues, sex differences, etiology (including psychobiological and contextual factors, as well as RDoC considerations when possible), developmental processes, cultural variables, and synthetic comments to illuminate the pathology under discussion. We clarified that emphasis on neural and neurophysiological processes must not be reductionistic. Indeed, psychosocial and family factors—which served

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as the predominant modality throughout much of the past century—interact and transact with biological vulnerabilities to produce both maladaptation and healthy adaptation throughout development (Beauchaine & Hinshaw, 2016; Beauchaine & McNulty, 2013). There is no escaping the need for integrative and integrated models as the field moves forward. Thus, we asked contributors to consider multilevel models and transactional processes. Indeed, as noted above, modern views of behavioral and molecular genetics have placed into sharp relief the unique and interactive roles that environmental and cultural forces exert on development (e.g., Belsky & Pluess, 2009; Dodge & Rutter, 2011; Hyde, 2015). Given page limitations and our desire for focused rather than exhaustive coverage, each chapter is relatively brief. Our goal is that readers can use these contributions as a springboard for additional exploration of conceptual frameworks, empirical research on mechanisms of interest, and building blocks for a new generation of evidence-based prevention and treatment efforts. As can be seen, the early chapters pertain to core conceptual and developmental issues and factors, and later chapters cover specific dimensions and disorders of interest. Immediately following this introductory chapter, Theodore Beauchaine and Daniel Klein (Chapter 2) provide crucial material spanning categorical (i.e., DSM) empirically based (e.g., the Child Behavior Checklist; Achenbach, 2009), and continuous (i.e., RDoC) methods and models for conceptualizing psychopathology. Certainly, dimensional/continuous accounts are gaining traction, yet at the same time clinical needs call for categorical diagnoses. Integrating these overarching frameworks is therefore necessary. The material in this chapter provides needed context for each of the remaining entries. Next, in Chapter 3 Beauchaine, Lisa Gatzke-Kopp, and Ian Gizer discuss crucial concepts related to gene-environment interplay in the genesis of psychopathology. This chapter exemplifies what is now a truism: genes and environments must not be viewed as separable, independent factors influencing mental disorders, as their effects are tightly intertwined in reciprocal and transactional fashion. In keeping with current trends in DP, this chapter conveys core material from both behavioral genetic and molecular genetic perspectives and discusses rapidly evolving research on epigenetic processes through which environmental experiences alter DNA expression, with possible implications for psychological adjustment. It does not shy away from either promise or controversy regarding this endeavor. Bruce Compas, Meredith Gruhn, and Alexandra Bettis (Chapter 4) present essential material on risk and resilience, providing a needed set of concepts and principles related to the potential for better-than-expected outcomes for subsets of vulnerable and high-risk youth. We must remember that not all children who express biological vulnerabilities and/or grow up with exposure to environmental risk develop pathological outcomes; indeed, one of the core DP principles noted above pertains to multifinal outcomes resulting from adverse early experiences. This chapter challenges conceptions of inevitable pathology from early vulnerability and risk.

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In Chapter 5, Sara Jaffee covers the crucial area of child maltreatment, providing needed integration of psychosocial and psychobiological mechanisms through which maltreatment confers risk for a wide range of pathological outcomes. This chapter is a paragon of integrated and integrative perspectives on this prevalent and potentially devastating set of risk factors; compared to earlier formulations on maltreatment, her coverage of biological processes shows an explosion of growth in this arena. Chapter 6, written by Emily Neuhaus and Theodore Beauchaine, covers impulsivity and vulnerability to psychopathology, viewing impulse-control problems as an underlying dimension that confers vulnerability to a range of mental disorders. Such risk is “expressed,” however, in the context of often-toxic environments, whether in the form of maladaptive parenting, less-than-responsive schools, or violent neighborhoods. In other words, transactional models, spanning biological vulnerability and environmental risk, are necessary for considerations of the development of psychopathology, particularly for the next generation of ontogenic process models in the DP field. Chapter 7, written by Jerome Kagan, deals with the temperamental construct of behavioral inhibition, emphasizing its predictive power for pathological outcomes in some but not all cases. Written with flair, it provides both historical and current perspectives on links between temperament and environment. In Chapter 8, Bruce Ellis, Marco Del Giudice, and Elizabeth Shirtcliff cover the highly relevant constructs of allostasis and biological sensitivity to context, topics that are receiving increasing coverage in the research literature each year. Notable here are both the complexity of the relevant biological mechanisms involved and the inherent interplay between genes, biological substrates, and environmental inputs intricately involved in these phenomena. They contrast their adaptive calibration model to the earlier construct of allostatic load per se, arguing for the greater predictive and explanatory power of adaptive calibration. Chapter 9, written by Lauren Doyle, Nicole Crocker, Susanna Fryer, and Sarah Mattson, covers the important area of exposure to teratogens (chemicals ingested by pregnant mothers) that confer risk for physical malformations as well as behavioral and emotional sequelae for the child, once born. As all students of pharmacology know, the placenta provides a completely permeable border for any and all drugs ingested by the mother, and the fetus’s organs for metabolizing foreign substances are slow to develop—potentially providing for a host of teratogenic exposures. Consequences for developmental psychopathology are profound. Next, in Chapter 10, Peter Arnett, Jessica Meyer, Victoria Merritt, Lisa Gatzke-Kopp, and Katherine Shannon Bowen write about brain injury as a risk factor for psychopathology. The multiple ways in which the developing brain can receive insults—and the complex pathways through which such injury affects development—are staggering. This chapter provides information about which many readers will have relative unfamiliarity; we are glad to have included these essential perspectives in our third edition.

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Immediately following is Chapter 11 by Pamela Cole, Sarah Hall, and Nastassia Hajal on the still-growing topic of emotion regulation and dysregulation. Clearly, this chapter moves “up” a level from Chapters 9 and 10 in terms of levels of analysis, as the former chapters are heavily biological. Indeed, the ways in which intraindividual vulnerability and contextual risk shape individuals’ abilities to recognize, process, and act on emotions (their own and those of others) are fascinating and of real importance to psychopathology. Finally, rounding out the early “conceptual” chapters, in Chapter 12 Wesley Jennings and Nicholas Perez move up another level again, considering effects of neighborhoods on psychopathology, particularly externalizing behaviors. As in each of the other chapters, transactional processes are highly salient, as this analysis clarifies ways in which systems-level influences represented by neighborhood-level effects interact with individual vulnerabilities and risk factors to shape the most pronounced cases of antisocial behavior. Beginning the section of chapters on disorders and dimensions of salience to psychopathology, Joel Nigg (Chapter 13) presents an elegant, integrative view on the development of attention-related and impulse-control problems (categorized as ADHD). Despite the strongly heritable nature of such symptoms, other biological-level influences as well as contextual processes are central to their developmental unfolding, as portrayed in this state-of-the-art chapter. Then, in Chapter 14, Benjamin Lahey and Irwin Waldman present, in a parallel framework, interconnected processes related to development of aggression and antisocial behavior—which are tremendously costly to property, lives, and the economy as a whole. Once again, multiple levels of analysis and transactional processes are on center stage in this synthetic chapter, which features intensive discussion of important subfacets of externalizing behavior patterns. In Chapter 15, Sandra Brown, Kristin Tomlinson, and Jennifer Winward discuss the topic of substance use disorders in adolescence and beyond. Because the major impairments—physical, emotional, economic—linked to substance abuse are legion, this chapter will be of interest to readers from multiple disciplines and perspectives. In addition to elucidating developmental pathways and mechanisms, the chapter authors also feature biological effects of substances on the developing brain, a vital issue not often sufficiently emphasized. Next, Carl Weems and Wendy Silverman use Chapter 16 to convey essential, developmentally relevant information on anxiety disorders, which are prevalent and frequently devastating in the impairments they “carry.” As the field moves from a multiple-categories conception of anxiety conditions, embodied by the DSM approach, to more current formulations informed by developmental psychopathology and transactional models, this chapter provides essential reading. Chapter 17, by Emily Ricketts, Deepika Bose, and John Piacentini, covers obsessive-compulsive conditions and disorders, including OCD, body dysmorphic disorder, hair-pulling disorder, hoarding disorder, and skin-picking disorder. As noted by their placement in a separate chapter, these conditions reveal different

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developmental processes and pathways from other anxiety-related disorders. Biological and environmental mechanisms underlying symptom display are emphasized. In Chapter 18, authored by Daniel Klein, Brandon Goldstein, and Megan Finsaas, the subject matter is the highly prevalent and severely impairing spectrum of depressive disorders. The evolving picture of biological vulnerability and psychosocial risk related to depression in youth—operating transactionally and in equifinal fashion—provides fertile testing ground for many core tenets of DP. Indeed, the chapter features the heterotypically continuous manifestations of depressive disorders across the lifespan, shaped by biological vulnerability and contextual risk. Erin Kaufman, Sheila Crowell, and Mark Lenzenweger (Chapter 19) write about the related but partially independent topics of borderline personality configurations and self-injury. In intriguing ways, these areas signify the confluence of internalizing and externalizing tendencies in the same youth; massive increases in rates of self-harm, along with its undoubted psychobiological and psychosocial roots, make this chapter another fulcrum point for a large number of DP principles and processes. Chapter 20 features the contentious and clinically important topic of traumarelated disorders, authored by Bruce Perry. Here again is an area in which genetic vulnerabilities are accentuated in the face of traumatic life events—and in which long-term consequences of trauma are experienced in both biological systems and a range of psychological and emotional symptoms. Then, in Chapter 21, Joseph Blader, Donna Roybal, Colin Sauder, and Gabrielle Carlson take on the controversial topic of bipolar-spectrum disorders, which continue to be a source of contention in the field (i.e., does bipolar illness exist in children—and if so, what forms does it take)? Issues of heritability along with psychosocial stressors, and of “kindling” across the lifespan—such that episodes potentially become more self-generating and frequent over time—are salient in this chapter. Chapter 22 authored by Susan Faja and Geraldine Dawson, features the crucial topic of autism spectrum disorders. The fast rise in diagnosed prevalence, the serious impairments accruing from the symptoms, the early age of onset in most cases, and the controversies over effective intervention strategies render many issues in this area contentious—and of major clinical and scientific importance. The biological explosion of knowledge about this area is featured in this chapter. Robert Asarnow and Jennifer Forsyth, in Chapter 23, deal with the low-prevalence but clinically and scientifically fascinating area of schizophrenia spectrum disorders in children and adolescents, long a source of diagnostic controversy. Their formulations, steeped in psychobiological vulnerability in transaction with stressful family environments, provide an authoritative account, revealing the importance of this topic for modern conceptions of early-onset schizophrenia. Finally, Chapter 24, authored by Eric Stice and Deanna Linville, takes on the area of eating disorders. In writing about an area associated with intensive pain

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for individuals and family members alike, the authors add binge eating disorder to the traditional syndromes of anorexia nervosa and bulimia nervosa for this current synthesis. In sum, each chapter features complex, interactive processes spanning psychobiological vulnerabilities and psychosocial risk factors, while providing strong emphasis on a developmental neuroscience perspective. Overall, the study of atypical development is fascinating, complex, and clinically as well as scientifically essential. It carries major potential for elucidating processes through which normal development occurs, at the same time that it highlights both expected and unexpected pathways to potentially devastating behavioral and emotional outcomes. As the 21st century continues its lightning-fast progressions into multilevel, integrative models of risk and resilience (and of health and pathology), it is heuristic to consider, simultaneously, the major progress made each year in the field along with the fundamental ignorance the field still possesses of the relevant variables, principles, and pathways linked to impairing mental disorders. We hope that you, the readers, are enticed by the clinical and scholarly puzzles that remain to be solved as well as humbled by the huge clinical need that remains in place for every single child, adolescent, family, and community experiencing the isolation, pain, and impairment related to mental disorder. The best minds of the next generations of scientists, clinicians, and policy makers need to become deeply engaged in the long journey that remains in front of us.

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adolescence: Influence of gender, development, genetic susceptibility, and peer stress. Journal of Abnormal Psychology, 124, 803–816. Harold, G. T., Leve, L. D., Barrett, D., Elam, K., Neiderhiser, J. M., Natsuaki, N. M, . . . Thapar, A. (2013). Biological and rearing mother influences on child ADHD symptoms: Revisiting the developmental interface between nature and nurture. Journal of Child Psychology and Psychiatry, 54(10), 1038–1046. Haslam, N., & Kvaale, E. P. (2015). Biogenetic explanations of mental disorder: The mixed- blessings model. Current Directions in Psychological Science, 24(5), 399–404. Hinshaw, S. P. (2002). Intervention research, theoretical mechanisms, and causal processes related to externalizing behavior patterns. Development and Psychopathology, 14, 789–818. Hinshaw, S. P. (2007). The mark of shame: Stigma of mental illness and an agenda for change. New York, NY: Oxford University Press. Hinshaw, S. P. (Ed.) (2008a). Breaking the silence: Mental health professionals disclose their personal and family experiences of mental illness. New York, NY: Oxford University Press. Hinshaw, S. P. (2008b). Developmental psychopathology as a scientific discipline: Relevance to behavioral and emotional disorders of childhood and adolescence. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Child and adolescent psychopathology (pp. 3–26). Hoboken, NJ: Wiley. Hinshaw, S. P., & Kranz, R. (2009). The triple bind: Saving our teenage girls from today’s pressures. New York, NY: Ballantine. Hinshaw, S. P. (2013). Developmental psychopathology as a scientific discipline: Rationale, principles, and recent advances. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Child and adolescent psychopathology (2nd ed., pp. 1–18). Hoboken, NJ: Wiley. Hinshaw, S. P. (2015). Developmental psychopathology, ontogenic process models, gene-environment interplay, and brain development: An emerging synthesis. Journal of Abnormal Psychology, 124, 771–775. Hinshaw, S. P., & Cicchetti, D. (2000). Stigma and mental disorder: Conceptions of illness, public attitudes, personal disclosure, and social policy. Development and Psychopathology, 12, 555–598. Hinshaw, S. P., Owens, E. B., Zalecki, C., Huggins, S. P., Montenegro-Nevado, A., Schrodek, E., & Swanson, E. N. (2012). Prospective follow-up of girls with attention-deficit hyperactivity disorder into young adulthood: Continuing impairment includes elevated risk for suicide attempts and self-injury. Journal of Consulting and Clinical Psychology, 80, 1041–1051. Hinshaw, S. P., & Scheffler, R. M. (2014). The ADHD explosion: Myths, medication, money, and today’s push for performance. New York, NY: Oxford University Press. Hinshaw, S. P., & Stier, A. (2008). Stigma as related to mental disorders. Annual Review of Clinical Psychology, 4, 269–293. Hyde, L. W. (2015). Developmental psychopathology in an era of molecular genetics and neuroimaging: A developmental neurogenetics approach. Development and Psychopathology, 27, 587–613.

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Insel, T., Cuthbert, B., Garvey, M., Heinssen, R., Pine, D. S., Quinn, K., . . . Wang, P. (2010). Research domain criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167(7), 748–751. Karg, K., Burmeister, M., Shedden, K., & Sen, S. (2011). The serotonin transporter promoter variant (5-HTTLPR), stress, and depression meta-analysis revisited: Evidence of genetic moderation. Archives of General Psychiatry, 68(5), 444–454. Keller, M. C. (2014). Gene × environment interaction studies have not properly controlled for potential confounders: The problem and the (simple) solution. Biological Psychiatry, 75(1), 18–24. Kennedy, M., Kreppner, J., Knights, N., Kumsta, R., Maughan, B., Golm, D., . . . Sonuga-Barke, E. J. S. (2016). Early severe institutional deprivation is associated with a persistent variant of adult attention-deficit/hyperactivity disorder: Clinical presentation, developmental continuities and life circumstances in the English and Romanian Adoptees study. Journal of Child Psychology and Psychiatry. Online ahead of print. doi: 10.1111/jcpp.12576 Kessler, R. C., Berglund, P., Demler, O., Jin, R., & Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey replication. Archives of General Psychiatry, 62, 593–602. Kraemer, H. C., Kazdin, A. E., Offord, D. R., Kessler, R. C., Jensen, P. S., & Kupfer, D. J. (1997). Coming to terms with the terms of risk. Archives of General Psychiatry, 54, 337–343. Kraemer, H. C., Stice, E., Kazdin, A., Offord, D., & Kupfer, D. (2001). How do risk factors work together? Mediators, moderators, and independent, overlapping, and proxy risk factors. American Journal of Psychiatry, 158, 848–856. Kreppner, J. M., O’Connor, T. G., Rutter, M., & the English and Romanian Adoptees Study Team. (2001). Can inattention/overactivity be a deprivation disorder? Journal of Abnormal Child Psychology, 29, 513–528. LeMoult, J., Ordaz, S. J., Kircanski, K., Singh, M. K., & Gotlib, I. (2015). Predicting first onset of depression in young girls: Interaction of diurnal cortisol and negative life events. Journal of Abnormal Psychology, 124, 850–859. Lewis, M., & Rudolph, K. D. (Eds.). (2014). Handbook of developmental psychopathology (3rd ed.). New York, NY: Springer. Little, K., Olsson, C. A., Youssef, G. J., Whittle, S., Simmons, J. G., Yucel, M., . . . Allen, N. B. (2015). Linking the serotonin transporter gene, family environments, hippocampal volume, and depression onset: A prospective imaging gene × environment analysis. Journal of Abnormal Psychology, 124, 834–849. Luthar, S. S. (2006). Resilience in development: A synthesis of research across five decades. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 739–795). Hoboken, NJ: Wiley. Martinez, A., & Hinshaw, S. P. (2016). Mental health stigma: Theory, developmental issues, and research priorities. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk resilience, and intervention (3rd ed., pp. 997–1039). Hoboken, NJ: Wiley.

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Martinez, A., Piff, P. K., Mendoza-Denton, R., & Hinshaw, S. P. (2011). The power of a label: Mental illness diagnoses, ascribed humanity, and social rejection. Journal of Social and Clinical Psychology, 30, 1–23. Mash, E. J., & Barkley, R. A. (Eds.). (2006). Treatment of child disorders (3rd ed.). New York, NY: Guilford Press. Mash, E. J., & Barkley, R. A. (Eds.). (2007). Assessment of child disorders (4th ed.). New York, NY: Guilford Press. Mash, E. J., & Barkley, R. A. (Eds.). (2014). Child psychopathology (3rd ed.). New York, NY: Guilford Press. Masten, A. S, & Cicchetti, D. (2016). Resilience in development: Progress and transformation. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 271–333). Hoboken, NJ: Wiley. McLaughlin, K. A., Zeanah, C. H., Fox, N. A., & Nelson, C. A. (2012). Attachment security as a mechanism linking foster care placement to improved mental health outcomes in previously institutionalized children. Journal of Child Psychology and Psychiatry, 53, 46–55. Meza, J., Owens, E. B., & Hinshaw, S. P. (2016). Response inhibition, peer preference and victimization, and self-harm: Longitudinal associations in young adult women with and without ADHD. Journal of Abnormal Child Psychology, 44(2), 323–334. Moffitt, T. E. (2006). Life course persistent versus adolescence limited antisocial behavior. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 570–598). New York, NY: Wiley. Muthén, B. O., Brown, C. H., Masyn, K., Jo, B., Khoo, S. T., Yang, C. C., . . . Liao, J. (2002). General growth mixture modeling for randomized prevention trials. Biostatistics, 3, 459–475. Nelson, C. A., Zeanah, C. H., Fox, N. A., Marshall, P. J., Smyke, A. T., & Guthrie, D. (2007). Cognitive recovery in socially deprived young children: The Bucharest Early Intervention Project. Science, 318, 1937–1940. Neese, R. M. (2005). Evolutionary psychology and mental health. In D. Buss (Ed.), Handbook of evolutionary psychology (pp. 903–927). Hoboken, NJ: Wiley. Nigg, J. T., Hinshaw, S. P., & Huang-Pollack, C. (2006). Disorders of attention and impulse regulation. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 358–403). Hoboken, NJ: Wiley. Nikolova, Y. S., Ferrell, R. E., Manuck, S. B., & Hariri, A. R. (2011). Multilocus genetic profile for dopamine signaling predicts ventral striatum reactivity. Neuropsychopharmacology, 36(9), 1940–1947. O’Connor, T. G. (2006). The persisting effects of early experiences on psychological development. In D. Cicchetti & D. Cohen (Eds.), Developmental psychopathology: Vol. 3. Risk, disorder, and adaptation (2nd ed., pp. 202–234). Hoboken, NJ: Wiley. Pagliaccio, D., Luby, J. L., Bogdan, R., Agrawal, A., Gaffrey, M. S., Belden, A. C., . . . Barch, D. M. (2015). Amygdala functional connectivity, HPA axis genetic

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variation, and life stress in children and relations to anxiety and emotion regulation. Journal of Abnormal Psychology, 124, 817–833. Pescosolido, B. A., Martin, J. K., Long, J. S., Medina, T. R., Phelan, J. C., & Link, B. G. (2010). “A disease like any other”? A decade of change in public reactions to schizophrenia, depression, and alcohol dependence. American Journal of Psychiatry, 167, 1321–1330. Phelan, J. C., Link, B. G., Stueve, A., & Pescosolido, B. A. (2000). Public conceptions of mental illness in 1950 and 1996: What is mental illness and is it to be feared? Journal of Health and Social Behavior, 41, 188–207. Richters, J. E. (1997). The Hubble Hypothesis and the developmentalist’s dilemma. Development and Psychopathology, 9, 193–229. Risch, N., Herrell, R., Lehner, T., Liang, K. Y., Eaves, L., Hoh, J., . . . Merikangas, K. R. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. Journal of the American Medical Association, 301(23), 2462–2471. Rutter, M., Dunn, J., Plomin, R., Simonoff, E., Pickles, A., Maughan, B., . . . Eaves, L. (1997). Integrating nature and nurture: Implications of person-environment correlations and interactions for developmental psychopathology. Development and Psychopathology, 9(2), 335–364. Rutter, M., & Sroufe, L. A. (2000). Developmental psychopathology: Concepts and challenges. Development and Psychopathology, 12, 265–296. Sawyer, M. G., Whaites, L., Rey, J. M., Hazell, P. L., Graetz, B. W., & Baghurst, P. (2002). Health-related quality of life of children and adolescents with mental disorders. Journal of the American Academy of Child and Adolescent Psychiatry, 41(5), 540–537. Scott, K. M., Lim, C., Al-Hamzawi, A., Alonso, J., Bruffaerts, R., Caldas-de-Almeida, J. M., . . . Kessler, R. C. (2016). Association of mental disorders with subsequent chronic physical conditions: World mental health surveys from 17 countries. JAMA Psychiatry, 73(2), 150–158. Serafica, F. C., & Vargas, L. A. (2006). Cultural diversity in the development of child psychopathology. In D. Cicchetti & D. J. Cohen (Eds.), Developmental psychopathology: Vol. 1. Theory and method (2nd ed., pp. 588–626). Hoboken, NJ: Wiley. Sonuga-Barke, E., Bitsakou, P., & Thompson, M. (2010). Beyond the dual pathway model: Evidence for the dissociation of timing, inhibitory, and delay-related impairments in attention-deficit/hyperactivity disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 49, 345–355. Sroufe, L. A. (1997). Psychopathology as an outcome of development. Development and Psychopathology, 9, 261–268. Sroufe, L. A., & Rutter, M. (1984). The domain of developmental psychopathology. Child Development, 55, 17–29. Swanson, E. N., Owens, E. B., & Hinshaw, S. P. (2014). Pathways to self-harmful behaviors in young women with and without ADHD: A longitudinal investigation of mediating factors. Journal of Child Psychology and Psychiatry, 44, 505–515.

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Visser, S. N., Danielson, M. L., Bitsko, R. H., Holbrook, J. R., Kogan, M. D., Ghandour, R. M., . . . Blumberg, S. J. (2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003–2011. Journal of the American Academy of Child and Adolescent Psychiatry, 53, 34–46. Vrshek-Schallhorn, S., Stoud, C. B., Mineka, S., Zinborg, R. E., Adam, E. K., Redei, E. E., . . . Craske, M. G. (2015). Additive genetic risk from five serotonin system polymorphisms interacts with interpersonal stress to predict depression. Journal of Abnormal Psychology, 124, 776–790. Vul, E., Harris, C., Winkielman, P., & Pashler, H. (2009). Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspectives on Psychological Science, 4(3), 274–290. Wadsworth, M. E., Evans, G. W., Grant, K., Carter, J. S., & Duffy, S. (2016). Poverty and the development of psychopathology. In D. Cicchetti (Ed.), Developmental psychopathology: Vol. 4. Risk, resilience, and intervention (3rd ed., pp. 136–179). New York, NY: Wiley. Wakefield, J. C. (1992). Disorder as harmful dysfunction: A conceptual critique of DSM-III-R’s definition of mental disorder. Psychological Review, 99(2), 232–247. Whiteford, H. A., Ferrari, A. J., Degenhardt, L., Feigin V., & Vos, T. (2015). The global burden of mental, neurological, and substance use disorders: An analysis from the Global Burden of Disease Study 2010. PLoS One, 10(2), e0116820. World Health Organization. (2014). Health for the world’s adolescents: A second chance in the second decade. Geneva, Switzerland: Author.

CHAPTER 2

Classifying Psychopathology The DSM, Empirically Based Taxonomies, and the Research Domain Criteria THEODORE P. BEAUCHAINE AND DANIEL N. KLEIN

A

ll scientific disciplines have rules for classifying phenomena and events that fall within their purview. Chemistry, for example, among the more advanced physical sciences, has fundamental laws that describe what constitutes an element (i.e., the number of protons in an atomic nucleus), what gives rise to similarities among elements (e.g., common bonding properties), how elements differ from one another (e.g., solubility vs. inertness), and how elements interact across levels of analysis to create what might otherwise be inexplicable phenomena (e.g., the high boiling point of water conferred by hydrogen bonds). For chemistry, these and other properties are summarized in the periodic table, which represents a taxonomy of elements. Although issues of taxonomy in chemistry are far more complex than this brief description implies, the example illustrates how important precise classification is in any discipline. Accurate classification ultimately leads to better prediction and control of external events, which are primary objectives of science (Braithwaite, 1953; see also Beauchaine, Gatzke-Kopp, & Mead, 2007). In chemistry, control of chemical reactions and molecular compounds has led to astounding advances in processes such as water purification, improving quality of life for millions. As outlined in Chapter 1 [Hinshaw], a major goal of developmental psychopathology is to improve prediction and control of mental illness, which should ultimately lead to more effective prevention and intervention programs, alleviating considerable human suffering (see also Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008). Taxonomies of diseases, including psychopathology, are often referred to as nosologies. In this chapter we describe the predominant classification system of psychopathology in the United States—the Diagnostic and Statistical Manual of 33

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Mental Disorders (5th ed.; DSM-5; American Psychiatric Association [APA], 2013). In doing so we (a) outline the history of the DSM; (b) highlight important issues and difficulties that emerge when diagnosing psychopathology; and (c) juxtapose the DSM-5 and its limitations with alternative perspectives and theoretical orientations, including empirically derived taxonomies and the Research Domain Criteria (RDoC). The latter is a fairly new approach to characterizing psychopathology that is currently being developed by the National Institute of Mental Health (2015a).

HISTORICAL CONTEXT Unlike the physical sciences, such as physics, chemistry, and geology, clinical psychology and psychiatry are relatively new. In fact, the first well-organized attempt in the United States at devising a classification system of psychopathology occurred only 64 years ago with publication of the first edition of the DSM (APA, 1952). As a result, psychology and psychiatry still struggle with unresolved taxonomic issues,1 some of which are specific to children and adolescents (see e.g., Achenbach & Rescorla, 2006; Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009; Eaton, Krueger, South, Simms, & Clark, 2011; Jensen, Knapp, & Mrazek, 2006; Krueger et al., 2011; World Health Organization, 1996). These issues are described in sections to follow.

Early Versions of the DSM The current version of the DSM is the DSM-5 (APA, 2013), which is actually the eighth in a series of DSMs, including both major and minor revisions, dating to 1952 (DSM-I, 1952; DSM-II, 1968; DSM-II, seventh printing, 1974; DSM-III, 1980; DSM-III-R, 1987; DSM-IV, 1994; DSM-IV-TR, 2000; DSM-5, 2013). Below we provide brief descriptions of each DSM, list the primary objectives of the American Psychiatric Association in undertaking each revision, and outline major changes in each new edition. DSM-I. The DSM-I (APA, 1952) was an effort by the APA to produce a single nomenclature for psychopathology. Prior to the DSM-I, there were several alternative classification systems, none of which was used consistently across the United States (see Blashfield, 1998). The DSM-I was influenced strongly by Adolph Meyer’s psychobiology, which characterized psychopathology as a reaction 1. We are not suggesting that taxonomic questions have been resolved in other sciences. In fact, issues of classification continue to be debated in many other fields, including evolutionary biology (see e.g., Laurin, 2010) and paleontology (see Beauchaine, 2003).

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to stress (e.g., Meyer, 1934). Hence, all disorders included “reaction” in their titles (e.g., depressive reaction). In formulating the DSM-I, the APA relied on the collective opinion of its membership. To do so, it sent detailed questionnaires to 10% of members, from which proposed categories of psychopathology were derived. Three broad classes of psychopathology emerged, including organic brain syndromes, functional disorders, and mental deficiency. Within these broad classes, 108 specific diagnoses were created (depending on the method of counting), only one of which could be applied specifically to children (adjustment reaction of childhood/adolescence). Final approval of psychiatric classes and specific diagnoses was obtained through a vote of the full APA membership. As this description implies, the DSM-I had little if any basis in empirical research. DSM-II. The DSM-II (APA, 1968), which contained about 182 diagnoses (again, depending on the method of counting), was published with few changes in process or philosophy. A major goal in formulating the DSM-II was to improve communication among mental health professionals—especially psychiatrists (e.g., Scotti & Morris, 2000). The DSM-II had strong psychoanalytic overtones, reflecting the training of most psychiatrists at the time. Major diagnostic classes of psychopathology were expanded from 3 to 11, and a number of childhood and adolescent disorders were added, including group delinquent reaction, hyperkinetic reaction, overanxious reaction, runaway reaction, unsocialized aggressive reaction, and withdrawing reaction. Since publication of the DSM-II, international treaty has dictated that the DSM and the International Classification of Diseases (ICD) be compatible. The ICD, published by the World Health Organization (WHO), is the classification system used in most other countries to diagnose mental illness. Some changes made to the DSM-II were needed to render it more similar to the ICD-8 (WHO, 1966). Currently, the ICD is in its 10th edition—revised (ICD-10; WHO, 2008). The ICD-11 is expected in 2018 (WHO, 2015). DSM-II, Seventh Printing. In the seventh printing of the DSM-II (APA, 1974), homosexuality was removed as a mental disorder, following protests by gay rights activists at the 1970 Annual Convention of the APA in San Francisco and a subsequent vote of the membership. This landmark event illustrates several important and interrelated points about diagnosis of mental illness. First, diagnostic systems such as the DSM, which are constructed by social institutions, always reflect social values (see e.g., McCarthy & Gerring, 1994). Second, psychiatry and related disciplines at times reinforce prevailing social value systems, which can lead to stigmatization of certain members of society, with considerable potential for negative effects on mental health (see e.g., Prilleltensky, 1989). Finally, as a social institution, the APA is not indifferent to sociopolitical influence.

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Removing homosexuality from the DSM-II also foreshadowed struggles to deal with validity of psychiatric diagnosis more broadly, a major issue confronted in later revisions of the DSM, as described below.

Reliability, Validity, and Subsequent Versions of the DSM In contrast to the DSM-I (APA, 1952) and the DSM-II (APA, 1968), the DSM-III (APA, 1980) was designed to be descriptive and largely atheoretical, so it would appeal and be useful to professionals from disciplines and conceptual orientations beyond psychiatry. Research on clinical features and etiologies of major forms of psychopathology were also weighted heavily in formulating the DSM-III—a major shift from the consensus opinion approach to constructing its earlier versions (see above). Thus, introduction of the DSM-III in 1980 was a watershed event in modern classification of psychopathology. Prior to 1970, most mental health professionals in the United States were not especially concerned with psychiatric diagnosis. The dominant paradigm was psychoanalysis, which did not place much stock in diagnosis. However, in the 1960s a new paradigm, often referred to as biological psychiatry, challenged and ultimately supplanted psychoanalysis as the dominant perspective in the United States. One agenda of biological psychiatry proponents was to make the discipline more scientific by increasing its emphasis on empirical research, particularly on the biological bases and treatment of psychopathology, thereby bringing psychiatry into mainstream modern medicine. Diagnosis played a central role in this agenda, as a reliable and valid classification system was necessary for the enterprise. Indeed, how successful could research on biological causes/correlates of psychopathology be if the major independent variable—diagnosis—was unreliable or invalid? Because diagnosis was a cornerstone of well-developed specialties in medicine (e.g., Engel, 1977), emphasis on reliable diagnosis was paramount. However, there was a major obstacle: limited evidence of interrater reliability of psychiatric diagnosis (e.g., Spitzer & Fleiss, 1974). Problems with reliability were hard to ignore. First, rates of various diagnoses differed dramatically between the United States and most European countries. For example, the rate of schizophrenia was many times higher in the United States than in the United Kingdom. In order to address this issue, a team of researchers in the United States and United Kingdom launched the Cross-National Diagnostic Project (for a description see Gurland, 1976). Using the same diagnostic criteria and assessment procedures, they found that differences in clinical diagnoses between hospitals in New York and London were attributable entirely to different diagnostic practice; patients’ symptoms were virtually identical in both cities. Furthermore, clinical diagnoses by British psychiatrists corresponded more closely to patients’ actual clinical presentations than those by American psychiatrists, who greatly overdiagnosed schizophrenia and underdiagnosed mood disorders. Second, almost all studies that addressed diagnostic reliability during that era indicated very low interrater agreement. Spitzer and Fleiss (1974) aggregated

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data from these studies, and calculated interrater reliability using the kappa (κ) statistic, which measures the degree of association between categorical constructs such as presence vs. absence of a diagnosis, correcting for chance agreement. In general, κs ranging from 0 to .20 indicate slight agreement, .21 to .40 fair agreement, .41 to .60 moderate agreement, .61 to .80 substantial agreement, and .81 to 1.0 excellent agreement (Landis & Koch, 1977). Spitzer and Fleiss reported that κs from previous interrater reliability studies were .41 for depression, .33 for mania, .45 for anxiety neurosis, .57 for schizophrenia, and .71 for alcoholism. Only the latter could be considered adequate. Spitzer and Fleiss (1974) attributed low interrater reliability to two sources: criterion variance and information variance. Criterion variance refers to diagnosticians’ reliance on different criteria when making a diagnosis, whereas information variance refers to collection of different data (see below). With respect to criterion variance, if one clinician diagnoses schizophrenia on the basis of even mild indications of cognitive slippage (a form of thought disorder), whereas another reserves the diagnosis only for patients who exhibit severe delusions or hallucinations, agreement will be low. In this regard, the DSM-I (APA, 1952) and DSM-II (APA, 1968) were not helpful because their diagnostic criteria were vague. Each diagnosis was described in several sentences listing characteristic signs and symptoms, yet there was no specification of how many symptoms were required, how long a symptom had to be present, or whether other symptoms might rule out a diagnosis (e.g., in a patient with visual hallucinations, could schizophrenia be diagnosed in the context of acute alcohol withdrawal?).

Operationalizing Diagnostic Criteria: Reducing Criterion Variance The criterion variance problem was addressed initially by Mandel Cohen, who was interested in developing a more empirical approach to studying psychopathology. Cohen conducted several pioneering studies of mood, anxiety, and somatoform disorders. These involved formulating very careful criteria for diagnosis, applying them to what at the time were large samples of patients, and examining patients’ clinical presentations, family histories, and clinical course (see Healy, 2002). Psychiatric journals were not particularly interested in this work, so most of Cohen’s papers were published in medical journals (e.g., Cohen, Cassidy, Flanagan, & Spellman, 1937; Cohen, Robins, & Purtell, 1952), with very little effect on psychiatry or psychology. One of Cohen’s students was Eli Robins, who became chair of the Psychiatry Department at Washington University in St. Louis. Throughout the 1960s, Robins and several colleagues, including Samuel Guze and George Winokur, applied Cohen’s approach in a series of landmark studies of psychopathology (e.g., Arkonac & Guze, 1963; Reich, Clayton, & Winokur, 1969). One of the hallmarks of the Washington University approach was development of systematic operational (i.e., explicit) diagnostic criteria for a selected group of diagnoses.

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This approach was explicated by E. Robins and Guze (1970), who published a brief yet highly influential paper in which they advanced a five-step process toward ensuring that psychiatric classes were specific, objective, and nonarbitrary. Using the example of schizophrenia, Robins and Guze suggested that diagnostic validity can be established only when a clinical syndrome is characterized by (1) a cluster of covarying symptoms and etiological precursors (obtained from clinical description); (2) reliable physiological, biological, and/or psychological markers (obtained from laboratory studies); (3) readily definable exclusionary criteria; (4) a predictable course (assessed through follow-up studies); and (5) increased rates of the same disorder among first-degree relatives (assessed through family studies). The Robins and Guze method was soon used by Feighner et al. (1972) to develop the first set of psychiatric disorders that were validated systematically. Associated symptom lists are now referred to as the Feighner Criteria. Although the primary motivation in formulating the Feighner Criteria was to validate psychiatric disorders (see Kendler, Munoz, & Murphy, 2009), doing so required specification of explicit operational criteria, as noted above. Soon after the Feighner Criteria (1972) were published, the NIMH sponsored the Collaborative Study of the Psychobiology of Depression, a multisite investigation of the clinical features, family history, biological correlates, and course of depression (see Katz, Secunda, Hirschfeld, & Koslow, 1979). As part of this study, the NIMH contracted with Spitzer and Endicott to develop a revised version of the Feighner criteria, which came to be known as the Research Diagnostic Criteria (RDC; Spitzer, Endicott, & Robins, 1978). Thus, by the late 1970s, the importance of specifying operational criteria for psychiatric disorders was widely recognized among the psychopathology research community, which strongly influenced development of the DSM-III (APA, 1980) and all subsequent versions of the DSM (see e.g., Cloninger, 1989; Kendler et al., 2009), including the DSM-5 (APA, 2013).

Structured Interviews: Reducing Information Variance With the goal of reducing information variance, a major task of the US-UK Cross-National Project was to standardize collection of data on symptoms, assessed by British and American clinicians. Accordingly, Wing, Cooper, and Sartorius (1974) developed a standardized clinical interview that provided (a) specific questions to be asked by the interviewer, (b) specific rating scales for each symptom, (c) conventions for making ratings, and (d) a detailed glossary defining each symptom. This instrument was called the Present State Examination (PSE), which was designed to allow experienced clinicians to obtain a systematic assessment of patients’ current symptoms. It did not collect information on previous course or history and therefore could not be used to make diagnoses. However, it was an important advance in standardizing collection of information across clinicians and sites.

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At the same time, psychiatrists at Washington University developed a semistructured diagnostic interview for use in various research projects being conducted in their department. Like the PSE, it included standardized questions and rating scales. However, it also provided a systematic assessment of the development and course of psychopathology, rather than focusing only on the patient’s current state (Woodruff, Goodwin, & Guze, 1974). Thus, it included all information necessary to make diagnoses according criteria established at the time (see above). Soon afterward, as part of their role in the NIMH Collaborative Study of the Psychobiology of Depression Study, Endicott and Spitzer (1978) developed a semistructured diagnostic interview called the Schedule for Affective Disorders and Schizophrenia (SADS). This interview allowed trained clinicians to collect systematic and reliable data on both current symptoms and history of most major psychiatric disorders. Thus, use of the SADS also allowed clinicians to make specific diagnoses. By the time the DSM-III was published in 1980, structured diagnostic interviews were accepted as state-of-the-art in psychiatric assessment. However, both the PSE and SADS were quite time consuming, and neither matched the DSM-III. Hence, Spitzer and Williams (1983) developed a new instrument, the Structured Clinical Interview for DSM-III (SCID), which eventually assessed all major disorders in the DSM-III and later the DSM-III-R (e.g., Spitzer, Williams, Gibbon, & First, 1990), DSM-IV (e.g., First, Spitzer, Gibbon, & Williams, 2002), and DSM-5 (First, Williams, Karg, & Spitzer, 2015). One objective was that the SCID be sufficiently user-friendly to be adopted in routine clinical practice in addition to research, although such adoption is extremely limited. Another major development in structured interviewing was construction of the Diagnostic Interview Schedule (DIS; Robins, Helzer, Croughan, & Ratcliff, 1981), by Lee Robins (not to be confused with E. Robins, her spouse), a sociologist at Washington University who pioneered research on antisocial personality disorder (see, e.g., Dishion & Hiatt Racer, 2013). The impetus for development of the DIS was a report by the Carter Administration’s Presidential Commission on Mental Health, which stressed the need to collect better data on the prevalence of mental disorders in the United States. This report led to the NIMH Epidemiological Catchment Area (ECA) survey, the largest epidemiological study of mental disorders ever conducted at that time (see Regier et al., 1984). When designing this study, it was clear that hiring trained clinicians to conduct diagnostic interviews with over 18,000 participants would be prohibitively expensive. L. Robins and colleagues therefore developed the DIS so it could be used by lay interviewers with no previous training in psychopathology. Because it was designed for use by nonclinicians, it is much more structured than other diagnostic interviews, and, unlike the PSE, SADS, and SCID, it leaves no room for interviewer judgment in formulating questions and rating symptoms. With these latter instruments, the interviewer is expected to probe

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respondents’ answers until confident they understand the question and are reporting a clinically significant experience that is relevant to the construct being assessed. In contrast, with the DIS, interviewers take the respondents’ report at face value. Thus, it is a respondent-based, as opposed to an interviewer-based interview (Angold & Fisher, 1999). Diagnoses are derived by computer using DSM criteria. In order to assess rates of psychopathology in large epidemiological samples of children and adolescents, the NIMH later developed the Diagnostic Interview Schedule for Children (DISC; Costello, Edelbrock, Dulcan, Kalas, & Klaric, 1984). The current version of the DISC assesses 30 DSM-IV-TR (APA, 2000) psychiatric disorders. It is designed for use with parents of children, ages 6–17, and with both children and adolescents, ages 9–17. There is currently no DISC for the DSM-5, although one is being constructed. This tardiness may be of limited consequence for most childhood disorders, as changes to the DSM-5 were minimal (see below). Two exceptions are disruptive mood dysregulation disorder and intermittent explosive disorder—new diagnoses that are not represented in previous instantiations of the DSM (see e.g., Beauchaine & McNulty, 2013; Leibenluft & Stoddard, 2013). Like the DIS, the DISC is respondent-based, and can be administered by lay interviewers (Shaffer, Fischer, Lucas, Dulcan, & Schwab-Stone, 2000). Both the DIS and DISC have been controversial, with some questioning the validity of diagnoses so completely based on self-report—especially among youth (see e.g., Renou, Hergueta, Flament, Mouren-Simeoni, & Lecrubier, 2004). Indeed, adolescents who suffer from externalizing behavior disorders such as ADHD and conduct disorder often underreport their symptoms (e.g., Sibley et al., 2010). It is therefore routinely recommended that adult informants (parents) also provide data for such conditions. Nevertheless, considerable evidence points toward reliability of the DISC (see Shaffer et al., 2000), and its use in research settings is now commonplace. Finally, semistructured, interviewer-based diagnostic interviews have also been developed to assess psychopathology among children and adolescents (Dougherty, Klein, Olino, & Laptook, 2008). The most widely used of these is a downward extension of the SADS—the Kiddie SADS (Kaufman et al., 1997).

The DSM-III, DSM-III-R, DSM-IV, and DSM-5 DSM-III. Following from his extensive work on psychiatric diagnosis outlined above, Spitzer was chosen to lead on revisions to the DSM-III. Rather than continuing with tradition, he looked toward the Feighner et al. (1972) criteria and the RDC (Spitzer, Endicott, & Robins, 1978) as a means of solving the problem of criterion variance. The DSM-III therefore became the first official classification system in psychopathology that used specific symptoms, including inclusion, exclusion, and duration criteria for each diagnosis. This effort required a major expansion of the

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Feighner criteria and the RDC, which at the time covered no more than about 15 disorders. The DSM-III (APA, 1980) also introduced multiaxial classification. Thus, in addition to classifying major psychiatric syndromes (Axis I), separate axes were created for personality disorders (Axis II); physical conditions that are relevant to understanding a person’s presenting problem (Axis III); psychosocial and environmental stressors and problems (Axis IV); and overall severity, or global assessment of functioning (GAF; Axis V). Use of multiple axes was a means of addressing patients’ uniqueness in making a diagnosis: not every patient with the same diagnosis is the same in all respects. This is a particularly important consideration in developmental psychopathology research (see Chapter 1 [Hinshaw]), which emphasizes equifinality and contextual influences on the development of mental illness (see Chapters 1 [Hinshaw] and 4 [Compas, Gruhn, & Bettas]). DSM-III-R. A revised version of the DSM-III (APA, 1987) was published only seven years later. In large part because so little new research was available, changes were minimal, and the revision was not extensive enough to warrant being called a fourth edition. The rationale for the revision was that some diagnostic criteria were inconsistent, unclear, or contradicted by subsequent research (APA, 1987). Despite almost no alterations to diagnostic criteria, one set of changes had major consequences. Following publication of the DSM-III (APA, 1980), several studies were published questioning widespread use of exclusion criteria. Exclusion criteria are a means of implementing diagnostic hierarchies, which serve to simplify diagnosis. Patients typically present with a wide array of symptoms. Traditionally, a major task of diagnosing has been differential diagnosis—deciding what the most appropriate diagnosis is among many possibilities suggested by the patient’s clinical presentation. Diagnostic hierarchies are useful in differential diagnosis because they indicate which symptoms should receive priority. Prior to the DSM-III-R, organic mental disorders (syndromes attributable to central nervous system disease, brain trauma, or significant substance abuse) were at the top of the diagnostic hierarchy. Next came schizophrenia. Then came major mood disorders, with neurotic and personality disorders at the bottom. Thus, in the absence of organic factors, schizophrenia symptoms were accorded priority in diagnosis, regardless of the presence of major mood, neurotic, and/or personality disorder features. In the absence of both organic factors and schizophrenia symptoms, mood disorder symptoms took precedence regardless of neurotic and personality disorder features. Finally, neurotic and personality disorder diagnoses were only considered if organic, schizophrenia, and mood disorder features were absent. Several studies in the early 1980s demonstrated that exclusion criteria in the DSM-III (APA, 1980) were often arbitrary and caused a loss of significant information. For example, family histories of patients with major depression and panic

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disorder differed from those of patients with major depression alone (Leckman, Weissman, Merikangas, Pauls, & Prusoff, 1983). Hence, comorbid panic disorder appeared to be important, and excluding the panic disorder diagnosis among patients with major depression represented a loss of potentially important information. In light of these considerations, exclusion criteria were largely abandoned from the DSM-III-R (APA, 1987) onward, except those used to rule out organic (general medical or substance-induced) causes of disorder. As might be expected, eliminating exclusion criteria led to a significant increase in rates of comorbidity—the co-occurrence of two or more disorders (see Klein & Riso, 1993). As a consequence, understanding comorbidity has been a top agenda item in psychopathology research ever since (see e.g., Angold, Costello, & Erkanli, 1999; Beauchaine & Cicchetti, 2016a, 2016b; Beauchaine, Hinshaw, & Pang, 2010; Klein & Riso, 1993). At the same time, reduction of hierarchical exclusion criteria has resulted in a diminished role for differential diagnosis in diagnostic practice. DSM-IV. In 1994 the DSM-IV (APA, 1994) was published. One motivation for publishing a new version so soon was the international treaty requirement that the DSM be consistent with the ICD (see above), which was undergoing revision. Although content changes were again relatively minor, the process through which DSM-IV revisions were derived witnessed a marked change. Revisions were driven much more by data than before, and the process was more systematic and better documented. As outlined in the DSM-IV itself: (a) review papers were commissioned by the APA addressing relevant literature for almost all existing and proposed categories; (b) the NIMH funded 12 multisite field trials to collect data to inform decisions about revisions to criteria; (c) the MacArthur Foundation provided funding for several investigators to reanalyze existing data sets, thereby providing additional data relevant to proposed revisions, and; (d) the literature reviews, results from field trials, reanalyses, and rationales for all revisions were published in a multivolume DSM-IV Sourcebook (e.g., APA, 1996). A similar process was carried forward to the DSM-5, as described below. DSM-IV-TR. In the text revision to the DSM-IV, published in 2000 (APA, 2000), diagnostic categories and their criteria were left almost completely unchanged. Instead, factual errors were corrected; sections of text describing each diagnostic category, associated features, advances in laboratory and clinical research, and so on were revised based on new research; and diagnostic codes that had changed in the latest edition of the ICD were updated. DSM-5. The revision process for the DSM-5 (APA, 2000) began in 1999 with an informal discussion about the need to improve validity of psychiatric diagnosis between Steven Hyman, director of the NIMH; Steven Mirin, medical director of

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the APA; and David Kupfer, chair of the APA Committee on Psychiatric Diagnosis and Assessment at the NIMH (APA, 2012a). This discussion spawned the initial DSM-5 Research Planning Conference in 1999, sponsored by both the APA and the NIMH. Participants invited to this conference included experts in behavioral genetics, molecular genetics, neuroscience, life-span development, cognition, and behavior. Notably, many of those involved in the DSM-IV revision were not invited, with the explicit purpose of encouraging new thinking. The Committee commissioned a series of white papers to identify (a) areas of needed research, (b) cross-cutting unresolved issues in psychiatric diagnosis, (c) ways in which the burgeoning research base in neuroscience could inform psychiatric diagnosis, and (d) issues of culture in psychopathology, among others. Soon after the conference, Darrel Regier was recruited to coordinate development of the DSM-5. Regier became vice chair of the DSM-5 Task Force, which was chaired by David Kupfer. A first set of white papers appeared in 2002 (Kupfer, First, & Regier, 2002), and a second set appeared in 2007 (Narrow, First, Sirovatka, & Regier, 2007). These edited volumes identified specific areas in which new research was needed. Between 2004 and 2008, 13 conferences were held among experts at the NIMH, the APA, the WHO, the American Psychiatric Institute for Research and Education, the National Institute on Drug Abuse, and the National Institute on Alcoholism and Alcohol Abuse. Participants from both the United States and other nations wrote a series of review papers, from which more specific research agendas were developed (APA, 2012b). In 2006, Kupfer and Regier nominated chairs of the diagnostic work groups for the DSM-5 Task Force, who were approved by the APA Board of Trustees in 2007. These chairs then recruited leading experts in their fields to populate individual work groups, which were approved by the APA in 2008, after they had begun meeting. Thirteen work groups were formed, representing major diagnostic categories in the DSM-IV-TR (APA, 2000). As with previous revisions (see above), the DSM-5 Task Force implemented a series of field trials, this time to ascertain the validity, reliability, feasibility, and clinical utility of proposed criteria, including new dimensional indices—an approach never used in previous versions of the DSM. A goal of the field trials was to develop diagnostic criteria that are useful in both research and clinical settings. However, the design and implementation of the field trials were controversial, and the reliability of a number of criterion sets proved to be disappointing (Frances & Widiger, 2012; Regier et al., 2013), although they led to some revisions of criteria (APA, 2012c). The personality disorders (PD) section was one of the most controversial parts of DSM-IV, and significant changes to PDs were anticipated in DSM-5. Indeed, the PD Work Group proposed a hybrid categorical/dimensional approach to diagnosis that required meeting overarching criteria for PD including impairment in self and interpersonal functioning. It also added five higher-order pathological trait dimensions

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(negative affectivity, detachment, antagonism, disinhibition, psychoticism) and 25 lower-order facets on which all individuals would be rated. In addition, they recommended retaining only six of the 10 specific PD diagnoses: obsessive-compulsive, narcissistic, schizotypal, avoidant, antisocial, and borderline, with revisions of specific criteria for these diagnoses to reflect the pathological traits noted above (see Klein, Bufferd, Dyson, & Danzig, 2014 for a discussion of the application of these criteria in youth). The four PDs with the smallest databases—paranoid, schizoid, histrionic, and dependent—were to be dropped. These changes would have been a marked departure from the DSM-IV-TR, which used a categorical system in which PDs were grouped into three clusters (Cluster A, paranoid, schizoid, schizotypal; Cluster B, antisocial, borderline, histrionic, narcissistic; Cluster C, avoidant, dependent, obsessive-compulsive) and did not include overarching criteria for PD or trait dimensions. Despite recommendations of the PDs Work Group, these changes were not implemented, and the PDs section of the DSM-5 was left unchanged from DSM-IV-TR. Proposed changes offered by the DSM-5 PDs Work Group appear in Section III of the manual (emerging measures and models) and are being used by researchers, but it is unlikely that this system will be used in clinical practice. In contrast, changes were made to a number of other sections. Here we focus on the most notable of these changes. Interested readers are referred to Beauchaine and Hayden (2016), and to specific chapters in this volume, for more detailed accounts. A major change was elimination of the DSM-IV multiaxial system of diagnosis (see above). The rationale for this change stemmed from the conceptual overlap between the major Axis I clinical syndromes and the Axis II personality disorders, as many Axis I disorders share the hallmarks of personality disorders—early-onset, persistence, and pervasive impact on functioning (Klein et al., 2014). In addition, Axes III, IV, and V were often if not usually ignored in applied settings. Several changes, albeit minor, were made to ADHD. The DSM-IV-TR included three ADHD subtypes, including primarily hyperactive-impulsive, primarily inattentive, and combined. This subtyping scheme was dropped from the DSM-5 in favor of presentations, which specify whether criteria have been met for hyperactivity/impulsivity, inattention, or both (i.e., combined)—specifically in the past 6 months. This change follows from recognition that many children move in and out of subtypes over time (e.g., Todd et al., 2008). In addition, the DSM-5 no longer includes ADHD among the disruptive behavior disorders, but instead moves it to the neurodevelopmental disorders section, which includes intellectual disabilities, communication disorders, autism spectrum disorder, specific learning disorder, and motor disorders. This decision was based on (a) evidence for aberrant neural responding and functional connectivity across several brain regions/networks among children, adolescents, and adults with ADHD (see e.g., Chapter 13 [Nigg]; Diamond, 2005; Fair et al., 2013; Plichta & Scheres, 2014; Rubia, 2011), and (b) hope that classifying ADHD as a neurodevelopmental disorder will lead to early

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diagnosis, more thorough assessment, easier access to intervention, and more research on effects of comorbid inattention and learning disabilities on academic achievement (see Tannock, 2013). In addition, the age of onset criterion for impairing symptoms was increased from under 7 to under 12 years of age, and symptom thresholds were reduced somewhat for adult diagnoses. More radical changes, such as expanding the number of impulsivity-related symptoms, were not adopted. Changes were also made to the mood disorders section. In contrast to the DSM-IV-TR, which had one mood disorders section, the DSM-5 differentiates between unipolar and bipolar disorders by parsing the categories into two sections, in order to acknowledge the link between bipolar disorder and schizophrenia spectrum disorders. In addition, the exclusionary criterion for bereavement is removed for major depressive disorder (MDD), given little evidence for meaningful differences between depressive episodes following loss compared with those that occur in other contexts (e.g., Kendler, Myers, & Zisook, 2008; although see Wakefield, 2013 for an opposing view). A new category of mood disorder, persistent depressive disorder, subsumes DSM-IV-TR chronic MDD and dysthymic disorder, given limited evidence of meaningful differences between the two syndromes (e.g., Klein, 2010; Klein, Shankman, Lewinsohn, Rohde, & Seeley, 2004). More fundamental changes were made to the anxiety disorders section. Panic disorder and agoraphobia are now separate disorders, and posttraumatic stress disorder is moved from the anxiety disorders chapter into a new section, trauma and stressor-related disorders, given evidence of partially distinct etiologies (e.g., Stein, Craske, Friedman, & Phillips, 2011). Perhaps the largest change is elimination of OCD from the anxiety disorders section, which follows from emerging evidence that anxiety disorders and OCDs exhibit different patterns of comorbidity and arise from partially independent neural substrates (e.g., Stein et al., 2010; although see Abramowitz & Jacoby [2015] for a dissenting view). Finally, the DSM-5 no longer distinguishes between anxiety disorders of childhood vs. adulthood, given limited evidence validity of such distinctions (e.g., Bögels, Knappe, & Clark, 2013). Thus, separation anxiety can be diagnosed at any age. In addition to changes made to existing disorders, several new disorders were added to the DSM-5, a few of which are especially relevant for children and adolescents (although most also apply to adults). Disruptive mood dysregulation disorder (DMDD), which is characterized by severe tantrums accompanied by persistent dysphoric mood, was added to the depressive disorders section in DSM-5. This diagnosis was created, in large part, to reduce rampant overdiagnosis of pediatric bipolar disorder (see e.g., Batstra et al., 2012), given evidence that most children with severe mood dysregulation are not on the bipolar spectrum (see Chapter 21 [Blader, Roybal, Sauder, & Carlson]; Carlson & Klein, 2014). However, studies of the course and validity of DMDD are only beginning to appear (e.g., Dougherty et al., 2014). Another new diagnosis is intermittent explosive disorder (IED), which is characterized by severe emotional lability (particularly anger and aggression). IED differs

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from DMDD in that it does not require persistent dysphoria between outbursts, or a childhood onset. IEE has a lifetime prevalence rate of almost 8% among adolescents (McLaughlin et al., 2012). A third addition to the DSM-5 is nonsuicidal self-injury (NSSI), which is listed as a condition for further study. Adding NSSI follows from recognition that (a) its prevalence rate has increased in recent years (Nock 2010); (b) it is exhibited by a large proportion of depressed adolescents, especially girls (e.g., Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer, 2011); (c) it is often a developmental precursor to borderline personality disorder (e.g., Crowell, Beauchaine, & Linehan, 2009); (d) it is associated with altered patterns of central nervous system activity (e.g., Sauder, Derbidge, & Beauchaine, 2015), peripheral nervous system activity (e.g., Crowell et al., 2005), neuroendocrine responding (Beauchaine, Crowell, & Hsiao, 2015), and serotonergic function (e.g., Crowell et al., 2008); and (e) it marks considerable functional impairment, both concurrently and prospectively, and predicts future suicide attempts better than any other independent variable (e.g., Klonsky, May, & Glenn, 2012; Nock 2010).

THE DSM AND DEVELOPMENTAL PSYCHOPATHOLOGY Although it is important for any student of psychopathology to understand the history behind, rationale for, and use of the predominant classification system of mental disorders in the United States, it is equally important to understand limitations of that system. Indeed, several departures in philosophy between the DSM approach and the developmental psychopathology approach to characterizing mental health are apparent. Historically, criticisms of the DSM have come from both within and outside psychiatry (see e.g., McCarthy & Gerring, 1994; van Praag, 2010), with developmental psychopathologists providing some of the most incisive critiques (e.g., Richters & Cicchetti, 1993). We and others have summarized these critiques, and provided a few of our own elsewhere (e.g., Beauchaine, 2003; Beauchaine et al., 2009; Cummings, Davies, & Campbell 2000; Hinshaw & Park, 1999; Hudziak, Achenbach, Altoff, & Pine, 2007). Here we provide an overview of such criticisms, some of which are specific to the DSM-5, but most of which apply to the overall philosophy that undergirds—oftentimes implicitly—categorical diagnostic systems.

Problems With Changes to the DSM-5 Even though most changes to the DSM-5 were minor, it will take years of research to determine how effective this newest revision will be in increasing the validity of psychiatric diagnosis—a major objective of the DSM-5 Task Force, the APA, and other interested parties (see e.g., Kraemer, Kupfer, Narrow, Clarke, & Regier, 2010). It is

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likely, however, that several decisions made by the DSM-5 Task Force will interfere with this objective. Although the Task Force explicitly charged DSM-5 workgroups with proposing changes that were founded in empirical research, the Task Force ultimately ignored several of these recommendations. For example, despite strong evidence that several PDs can be diagnosed reliably in adolescence and that developmental precursors to these PDs exist (see e.g., Chapter 19 [Kaufman, Crowell, & Lenzenweger]; Beauchaine et al., 2009; Crowell, Kaufman, & Beauchaine, 2014; Klein et al., 2014), the DSM-5 proscribes PD diagnoses among those who are under age 18 years. Second, the DSM-5 retains all DSM-IV-TR PDs, despite little evidence for the validity of several and almost no evidence for validity of the A, B, and C clustering structure outlined above (see Beauchaine et al., 2009). The decision to move ADHD into the neurodevelopmental disorders section and out of the disruptive behavior disorders section is also problematic in some ways. As noted earlier, this decision was based largely on practical grounds, such as hopes for earlier diagnosis, more thorough assessment, easier access to intervention, and more research on effects of comorbid inattention and learning disabilities on academic achievement (Tannock, 2013). Notably, such considerations were not applied to other disorders. If they had been, one could argue convincingly that conduct disorder (CD) should have also been moved, since ADHD and CD share common neurodevelopmental substrates and psychopathological endpoints (see Chapter 13 [Nigg]; Beauchaine & McNulty, 2013; Diamond, 2005; Fair et al., 2013; Gatzke-Kopp, 2011; Gatzke-Kopp et al., 2009; Kopp & Beauchaine, 2007; Rubia, 2011). Thus, moving ADHD to a different section of the DSM obscures its etiological connections with CD and other disruptive behavior disorders (see Beauchaine & Hayden, 2016; Beauchaine & Hinshaw, 2016; Beauchaine, Zisner, & Sauder, 2017). Of course, there is not complete correspondence between (a) ADHD and (b) CD and other antisocial-spectrum conditions (for a historical overview, see Hinshaw, 1987; see also Ahmad & Hinshaw, 2016), but placing ADHD in the neurodevelopmental disorders section may not be conceptually clarifying in all respects. From a developmental psychopathology perspective, the decision to drop the multiaxial structure that characterized the DSM-III, DSM-III-R, DSM-IV, and DSM-IV-TR, particularly Axis IV (psychosocial and environmental stressors), is also unfortunate. De-emphasizing psychosocial and contextual factors downplays the important role that environment plays in shaping almost all forms of mental illness—even those with strong genetic underpinnings (see Chapters 1 [Hinshaw] and 3 [Beauchaine, Gatzke-Kopp, & Gizer] Beauchaine et al., 2017).

Additional Criticisms of the DSM Approach Problems With Construct Validity. Although application of the Feighner Criteria and the RDC to some (though not nearly all) disorders represents an attempt to ensure

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diagnostic validity (see above), reliability has been of far greater concern from the DSM-III onward (APA, 1980; see e.g., Kraemer, Kupfer, Narrow, Clarke, & Regier, 2010). It is important to note that reliability is necessary for validity but does not ensure validity. To use a somewhat hyperbolic example, separate raters can agree with very high precision that a person is over 6′ 5′′ (reliability), but such agreement says nothing about height being a symptom of mental illness (validity). Indeed, any such assertion would be fully arbitrary—a situation that applied to sexual orientation before the seventh printing of the DSM-II, when homosexuality was considered a mental disorder (see above). In developmental psychopathology research, construct validity refers to the extent to which symptoms of a diagnosis mark an objective, nonarbitrary entity that relates to mental health outcomes. Construct validity should be considered whenever the cause of a trait cannot be observed directly (Cronbach & Meehl, 1955), which is usually the case for psychopathology. To borrow an example we have used elsewhere (Beauchaine & Marsh, 2006), consider the difference between a medical syndrome such as pancreatic cancer and a common psychiatric condition such as MDD. In the former case, a patient presents at his/her physician’s office with a collection of symptoms, which might include weight loss, dark urine, nausea, and abdominal pain. This collection of symptoms, or manifest indicators, leads to a hypothesis on the part of the physician regarding its unobserved, or latent cause. Importantly, for a medical condition such as pancreatic cancer, the hypothesis is confirmed or disconfirmed by a biopsy or other diagnostic test. If the biopsy is positive, the cause of the disorder becomes known. If the biopsy is negative, a new hypothesis is generated and tested. Compare this with a depressed individual, who also presents with a collection of symptoms, including depressed mood, anhedonia, fatigue, weight loss, and insomnia. In contrast to the case of pancreatic cancer, there are no diagnostic tests that can identify most causes of depression (although certain medical conditions such as hypothyroidism can be identified and should therefore be ruled out). Thus, we are left with a somewhat tautological definition of depression: The patient is depressed because s/he presents with a collection of symptoms, and the patient presents with a collection of symptoms because s/he is depressed. We are therefore forced to infer psychopathology with no gold standard or pathognomonic sign of disease state (see Beauchaine & Thayer, 2015). Under such conditions, difficulties posed for construct validation of psychiatric disorders are often formidable. Prior to publication of the DSM-III (APA, 1980), almost no evidence existed for the construct validity of any diagnostic category (Kendell, 1989), because all were derived clinically rather than through systematic research (see above). At present, even after decades of relevant research, unanswered questions about the construct validity of many psychiatric disorders abound. For example, in research on pediatric bipolar disorder, issues regarding proper diagnostic cutoffs and delimitation from other disorders including

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ADHD have not been addressed fully (see Chapter 21 [Blader, Roybal, Sauder, & Carlson] Carlson & Klein, 2014). Heterogeneity Within Diagnostic Classes. A related issue follows from the observation that diverse etiologies often result in what appears to be a single disorder, a phenomenon known as equifinality (see Chapter 1 [Hinshaw]). For example, impulsivity may arise from one of several sources, each of which may be expressed behaviorally as ADHD (see Chapters 6 [Neuhaus & Beauchaine], 13 [Nigg], & 10 [Arnett et al.]; Castellanos-Ryan & Séquin, 2015; Zisner & Beauchaine, 2015). However, since DSM diagnoses are all derived syndromally (i.e., from symptoms with little if any regard to etiology or pathophysiology), different underlying causes of a disorder may never be ascertained, even when it is possible to do so. Both treatment and prevention are improved when pathophysiological and etiological diagnosis are used rather than syndromal diagnosis (see Beauchaine et al., 2008; Preskorn & Baker, 2002). For example, if hypothyroidism is identified in the pathophysiology of depression, treatment follows a very different course (synthetic thyroxine treatment) than antidepressant use and/or psychotherapy. Although this example may seem extreme, potentially meaningful distinctions among depression subtypes are underemphasized in the DSM-5. For example, melancholia—a subtype of depression that appears to arise from different etiological mechanisms than nonmelancholic depression (see Leventhal & Rehm, 2005)—may confer increased risk of adverse long-term functional outcomes including suicide (e.g., Carroll, Greden, & Feinberg, 1980; Coryell & Schlesser, 2001), yet it is not classified as a separate mood disorder, even though some argued ardently for doing so in the DSM-5 (e.g., Parker et al., 2010). Categorical Versus Dimensional Measurement. One of the most persistent criticisms of the DSM is that all disorders are diagnosed categorically (i.e., present vs. absent), even though overwhelming research evidence indicates that most forms of psychopathology (a) reflect extreme expressions of continuously distributed traits (see e.g., Haslam, Holland, & Kuppens, 2012; Hudziak et al., 2007; Krueger & Tackett, 2015; Krueger, Watson, & Barlow, 2005; Trull & Durrett, 2005), and (b) are rooted in interactions among neural systems that subserve overlapping behavioral and emotional functions (see e.g., Beauchaine, 2015; Beauchaine & Thayer, 2015). Even in rare exceptions when psychiatric vulnerability may be distributed categorically (e.g., schizotypy; see Lenzenweger, McLachlan, & Rubin, 2007), individual differences in symptom expression are nevertheless observed and meaningful functionally (Beauchaine, Lenzenweger, & Waller, 2008). They also provide key information about current functioning and long-term prognosis. Other adverse consequences of categorizing dimensions include difficulty ascertaining optimal diagnostic cutoffs (e.g., 95th percentile? 98th percentile?

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see Meehl, 1995), and loss of statistical information (see MacCallum, Zhang, Preacher, & Rucker, 2002). Individuals in need of intervention may also be turned away because they fail to meet diagnostic criteria even though they suffer considerable impairment. To address such problems, hybrid classification systems have been proposed in which both presence vs. absence and severity of psychopathology are assessed (e.g., Hudziak et al., 2007). As outlined above, such an approach was recommended by the PDs Work Group for the DSM-5, but was ultimately rejected. Notably, dimensional assessment has long been used in child psychopathology research, even when applying DSM criterion sets (e.g., Achenbach & Edelbrock, 1991; Conners, Sitarenios, Parker, & Epstein, 1998; Gadow & Sprafkin, 1997; Robinson, Eyberg, & Ross, 1980). Such is not the case in adult psychopathology research. Failure to Consider Development. Developmental psychopathologists have been especially critical of the DSM because it fails to consider issues of development in diagnosis (e.g., Beauchaine et al., 2017; Richters & Cicchetti, 1993; Sroufe, 1997). With few exceptions (e.g., early-onset conduct disorder; see Chapter 14 [Lahey & Waldman]), child and adolescent psychopathology are assessed and diagnosed without consideration of normative developmental trends in behavior, and without acknowledgement that single behavioral traits—including those that confer vulnerability to psychopathology—may be expressed differently at different ages. Heterotypic continuity refers to such changes in the behavioral expression of psychopathology across development (see Chapter 1 [Hinshaw]). As an example, we have known for over 50 years that delinquent adult males almost invariably traverse a developmental pathway that begins with severe hyperactivity/impulsivity as early as toddlerhood, followed in rough temporal sequence by oppositional defiant disorder (ODD; Chapter 14 [Lahey & Waldman]) in preschool, early-onset conduct disorder (CD; Chapter 14 [Lahey & Waldman]) in elementary school, substance use disorders (SUDs; Chapter 15 [Brown, Tomlinson, & Winward]) in adolescence, and antisocial personality disorder (ASPD) in adulthood (see e.g., Beauchaine & Hinshaw, 2016; Beauchaine & McNulty, 2013; Beauchaine et al., 2017; Loeber & Hay, 1997; Lynam, 1998; Robins, 1966). Thus, even though continuity in externalizing conduct is common among those on this trajectory, specific behaviors vary considerably across development (Ahmad & Hinshaw, 2016; Beauchaine, Shader & Hinshaw, 2015; Hinshaw, Lahey, & Hart, 1993). Among other consequences, failure to consider heterotypic continuity results in (a) a research literature that is fractionated based on topographies of behavior (e.g., tantrums in toddlerhood, truancy in elementary school, substance use in adulthood) rather than etiology, (b) alternative treatment strategies for conditions such as CD and SUDs that are not informed by one another when they would benefit from being so (see Beauchaine et al., 2008), and (c) faulty conclusions about etiology and comorbidity of externalizing disorders (see Beauchaine et al., 2010). Finally, there is growing evidence that many

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preschool-aged children meet DSM criteria for psychiatric disorders (Bufferd, Dougherty, Carlson, Rose, & Klein, 2012). However, it is often unclear where to draw the line between developmentally normative and pathological behavior in early childhood, and whether diagnostic criteria developed for older children, adolescents, and adults are appropriate for preschoolers (Bufferd, Dyson, Hernandez, & Wakschlag, 2016). Failure to Consider Culture and Other Contextual Issues. In general, the DSM is indifferent to both (a) culturally induced individual differences in behavior that might be mistaken for psychopathology (see e.g., Marsella & Yamada, 2010), and (b) cultural, socioeconomic, and other contextually driven individual differences in the expression of psychopathology (see e.g., Lewis-Fernández et al., 2010; Gone & Kirmayer, 2010). As a result, strict adherence to DSM criterion sets without consideration of race, ethnicity, and class can lead to both false positive and false negative conclusions regarding the presence versus absence of psychopathology. One objective of the developmental psychopathology approach is to construct a discipline that acknowledges the role of context in shaping behavior, and that does not assume—even implicitly—that group differences in behavior between members of the dominant social class and other cultural subgroups always imply deficits in functioning among the latter (e.g., Garcia-Coll, Akerman, & Cicchetti, 2000; Cicchetti & Toth, 2009; see also Chapter 1 [Hinshaw]).

EMPIRICALLY DERIVED CLASSIFICATION SYSTEMS Early on, the DSM was, and in many ways remains, a top-down, deductive approach to classifying psychopathology. Opinions of experts are still weighed heavily in the revision process, and empirical findings are sometimes eschewed, despite explicit calls for, both within and outside DSM workgroups, a research-based taxonomy of mental illness (see above). In stark contrast to this approach, developmental psychopathologists have a long history of constructing and using, in both research and clinical settings, bottom-up, inductive systems of classification and assessment that derive almost fully from empirical interrelations among symptoms of psychopathology. The earliest and most renowned of these is the parent-report Child Behavior Checklist (CBCL; Achenbach & Edelbrock, 1983), which was later expanded to include both teacher (Teacher Report Form [TRF]; Achenbach & Edelbrock, 1986) and self-report versions (Youth Self-Report [YSR]; Achenbach & Edelbrock, 1987). Collectively, these instruments, along with more newly developed adult versions, comprise the Achenbach System of Empirically Based Assessment (ASEBA; Achenbach, 2009). The CBCL and its successors were derived from factor analyses of large sets of symptoms of psychopathology. These studies, and subsequent factor-analytic evaluations of adult psychopathology (e.g., Krueger, 1999), demonstrated a remarkably consistent hierarchical latent structure of mental illness in which two higher-order,

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internalizing

internalizing spectrum disorder 1

internalizing spectrum disorder 3

internalizing spectrum disorder 2

externalizing

externalizing spectrum disorder 1

externalizing spectrum disorder 3

externalizing spectrum disorder 2

Figure 2.1 A simplified depiction of the hierarchical latent structure of psychopathology. Adapted from Beauchaine and Thayer (2015).

latent factors, internalizing and externalizing, account for much of the covariation among first-order factors (i.e., behavioral syndromes).2 This hierarchical latent structure of psychopathology is depicted in Figure 2.1. First-order internalizing factors include constructs such as anxious/depressed, withdrawn/depressed, and somatic complaints, whereas first-order externalizing factors include constructs such as impulsivity, rule-breaking behavior, and aggression. When using the CBCL and related empirically based assessment instruments, children and adolescents (and/or parent and teachers) rate each symptom, and these ratings are summed to provide scores on individual first-order syndromes. Syndrome scores are then added to compute broad-band (i.e., higher-order) internalizing and externalizing scores. There are several advantages of empirically based assessment, compared with the approach to diagnosis represented in the DSM. First, raters are not forced to render dichotomous diagnostic decisions. Rather, each individual receives a set of scale scores, the severity of which can be evaluated vis-à-vis national norms. Oftentimes, children who score at or above the 95th percentile are considered to be clinically impaired. Lower but elevated scores, such of those above 85th percentile, may also be flagged for concern. Second, empirically based assessment does not 2. Factor analysis is a mathematical approach to reducing large numbers of items (in this case, symptoms), into a smaller number of factors, each of which consists of items that share common variance. Although most factor analyses of psychopathology allow for correlated factors, correlations of items within factors exceed correlations of items across factors. Interested readers are referred elsewhere for detailed accounts of factor analysis (e.g., Thompson, 2004).

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force diagnosticians, even implicitly, to choose one disorder over others. Rather, elevated scores both within and across internalizing and externalizing domains are observed and expected, which “carves nature at its joints” more effectively than assigning a single disorder. For example, adolescents with conduct disorder (CD), although likely to experience symptoms of ADHD, are often diagnosed only with the former disorder, which may interfere with treatment and obscure etiological relations between the two conditions (e.g., Beauchaine et al., 2010; 2017). Third, such systems are more sensitive to capturing heterotypic comorbidity, whereby an individual with a primary externalizing disorder, for example, also displays—often subclinically—symptoms of an internalizing disorder (see, e.g., Zisner & Beauchaine, in press). Based on these considerations and others, empirically based assessment is used in almost all research contexts among developmental psychopathologists, even when DSM-derived diagnoses are also evaluated.

THE RESEARCH DOMAIN CRITERIA In 2009, the NIMH, as part of its Strategic Plan (NIMH, 2015a), launched a new initiative, the Research Domain Criteria (RDoC; e.g., Cuthbert & Insel, 2013; NIMH, 2015b; Sanislow et al., 2010), to provide an alternative framework, particularly for research purposes, of studying and ultimately classifying psychopathology. RDoC was developed out of frustration with the slow pace in understanding the etiopathogenesis of, and development of effective treatments for, mental disorders, and a sense that the DSM has not adequately facilitated and may have hindered such progress (Insel et al., 2010). RDoC acknowledges that nearly all current DSM-defined clinical phenotypes are etiologically heterogeneous and lack neurobiological validity, and that information about core etiological mechanisms is needed to identify more homogeneous, biologically valid phenotypes (see also Beauchaine & Thayer, 2015)—a precondition for specifying molecular genetic substrates of psychopathology (see Chapter 6 [Neuhaus & Beauchaine]). Furthermore, RDoC assumes that key etiological influences, and ultimately clinical phenotypes, take the form of dimensions rather than discrete classes, an observation that has proven almost axiomatic in psychopathology research (see e.g., Forbes, Tackett, Markon, & Krueger, in press; Krueger & Tackett, 2015; Krueger et al., 2002). RDoC descends from biobehavioral motivational systems perspectives, which were advanced initially in the mid- to late 20th century by distinguished investigators including Jeffrey Gray (see e.g., Gray, 1987) and Peter Lang (e.g., Lang, Bradley, & Cuthbert, 1992). These investigators identified broad, neurally mediated activation/approach and inhibition/withdrawal systems, which predispose to individual differences in dispositional responding to specific classes of stimuli (e.g., Beauchaine & Thayer, 2015; Fowles, 1988). RDoC, which is intended to be an evolving project that integrates research across human and infrahuman species, posits the existence of five major domains of behavior, which should be studied across multiple units of analysis, ranging from genes to molecules to cells to neural

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circuits (e.g., emotion-modulated startle) to physiology (e.g., heart rate) to behavior (naturalistic observation or in particular tasks) to self-reports (interviews, questionnaires). These five domains, each of which includes a number of subdomains, were selected for their potential relevance to psychopathology, and because aspects of their neural circuitry are already understood. The domains include negative valence systems (acute threat [or fear], potential threat [or anxiety], sustained threat, loss, and frustrative nonreward); positive valence systems (e.g., initial responsiveness to reward, sustained responsiveness to reward, reward learning); cognitive systems (e.g., attention, perception, cognitive control, working memory), systems for social processes (e.g., affiliation and attachment, social communication, perception and understanding of the self, perception and understanding of others), and arousal and regulatory systems (arousal, circadian rhythms, sleep and wakefulness) (NIMH, 2015b). These five domains and their subdomains are presented in a series of rows, and units of analysis head a series of columns, which together comprise the RDoC matrix (Morris & Cuthbert, 2012). Ultimately, cells in the matrix will be filled with measures of constructs in each domain, at each unit of analysis (e.g., fear-potentiated startle is a measure of the acute fear subdomain at the unit of circuits). Following from Cronbach and Meehl’s (1955) classic construct validation framework, the goal is to develop and test a “nomological network” of hypotheses about interrelations among measures at various levels of analysis for each construct represented in the domains and subdomains. The RDoC matrix also includes a column for paradigms, referring to tasks that are particularly useful in assessing the domain construct (National Advisory Mental Health Council Workgroup on Tasks and Measures for Research Domain Criteria, 2016). Finally, two important dimensions that are recognized as being critically important but are not formally included in the matrix are environmental influences and development (Casey, Oliveri, & Insel, 2014). Despite its significant effect on funding priorities in the United States, RDoC is still very much under development and faces a number of questions and challenges. First, it is not clear how thoroughly and systematically development, the course of psychopathology, and environmental influences (including culture) will be incorporated, given that these are not formally represented in the matrix. Second, the construct validity of the domains and subdomains is only partially established. For example, it must be determined whether RDoC should include all of the most crucial domains and subdomains, and whether the convergent and discriminant validity of the domains and subdomains are consistent with the structure posited in the matrix. Third, even if phenotypes are defined on the basis of underlying processes rather than clinical presentation, it is likely that complex behaviors reflect interactions among multiple domains and subdomains (multifinality), and that particular domains and subdomains contribute to many different patterns of behavior (equifinality) (Beauchaine & Thayer, 2015). Fourth, measures for many of the cells in the matrix have yet to be identified, and the construct validity of many (if not most) of the candidate measures is only partially established. Moreover, related

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to the previous point, it is likely that most of the endophenotypes/intermediate phenotypes that populate the cells are themselves highly complex (Iacono, Vaidyanathan, Vrieze, & Malone, 2014; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]), and may reflect effects of multiple domains and subdomains. Fifth, magnitudes of associations between measures at different levels of analysis are often very modest, making it difficult to demonstrate construct validity (Patrick et al., 2013). Sixth, there are significant conceptual challenges to understanding relationships between and across units of analysis (Cicchetti, 2008; Meehl, 1977; Miller, 2010). Seventh, despite efforts not to privilege lower units of analysis, there are concerns that it may be susceptible to biological reductionism (e.g., Beauchaine et al., 2017; Berenbaum, 2010). Finally, and perhaps most importantly, the RDoC matrix does not include clinical phenotypes to classify patients and provide targets for clinical research and treatment. Although this omission raises questions about clinical relevance, it is central to the entire endeavor. RDoC assumes that biologically valid phenotypes are likely to be narrower than, or cut across, diagnostic constructs in the DSM. Thus, a major goal of the RDoC initiative is to identify phenotypes that are related to impairment in core domains of biobehavioral functioning. Just as 35 years ago the field assumed that introduction of operational diagnostic criteria in DSM-III (APA, 1980) would increase reliability, thereby leading to more valid phenotypes, enhanced understanding of etiopathogenesis, and the development of more effective treatments (see above), proponents of RDoC are wagering that research elucidating core biobehavioral systems across multiple units of analysis will yield more valid phenotypes and better understanding of the causes and treatment of mental disorders.

CONCLUSIONS In this chapter, we reviewed historical developments in psychiatric diagnosis and identified core issues confronted by those who seek to classify psychopathology. As our review indicates, the history of the DSM, RDoC, and the complexities behind their development are far more intricate than might be surmised at first glance. Although considerable efforts of many talented scientists have contributed to revising the DSM, longstanding issues of validity (and to a lesser extent reliability) remain to be addressed fully. Among the most important limitations of the DSM framework are its failures to (a) capture developmental processes underlying current and future risk for psychopathology, (b) specify pathophysiological and etiological mechanisms of psychopathology, (c) map broad biobehavioral traits that predispose to psychopathology across traditional diagnostic boundaries, and (d) account fully for contextual influences such as ethnicity and culture on the development of psychopathology. Although the RDoC initiative addresses some of these limitations, it ignores others—particularly those related to development, environment, and culture. These and other issues, which are central to the developmental psychopathology perspective (Chapter 1 [Hinshaw]), are addressed in chapters to follow.

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Kopp, L., & Beauchaine, T. P. (2007). Patterns of psychopathology in the families of children with conduct problems, depression, and both psychiatric conditions. Journal of Abnormal Child Psychology, 35, 301–312. Kraemer, H. C., Kupfer, D. J., Narrow, W. E., Clarke, D. E., & Regier, D. A. (2010). Moving toward DSM-5: The field trials. American Journal of Psychiatry, 167, 1158–1160. Krueger, R. F. (1999). The structure of common mental disorders. Archives of General Psychiatry, 56, 921–926. Krueger, R. F., Eaton, N. R., Clark, L. A., Watson, D., Markon, K. E., Derringer, J., . . . Livesley, W. J. (2011). Deriving an empirical structure of personality pathology for DSM-5. Journal of Personality Disorders, 25, 170–191. Krueger, R. F., Hicks, B. M., Patrick, C. J., Carlson, S. R., Iacono, W. G., & McGue, M. (2002). Etiologic connections among substance dependence, antisocial behavior, and personality: Modeling the externalizing spectrum. Journal of Abnormal Psychology, 111, 411–424. Krueger R. F., & Tackett, J. L. (2015). The externalizing spectrum of personality and psychopathology: An empirical and quantitative alternative to discrete disorder approaches. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Oxford handbook of externalizing spectrum disorders (pp. 79–89). New York, NY: Oxford University Press. Krueger, R. F., Watson, D., & Barlow, D. H. (2005). Toward a dimensionally based taxonomy of psychopathology [Special section]. Journal of Abnormal Psychology, 114, 491–569. Kupfer, D. J., First, M. B., & Regier, D. A. (2002). A research agenda for DSM-V. Washington, DC: American Psychiatric Association. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33, 159–174. Lang, P. J., Bradley, M. M., & Cuthbert, B. N. (1992). A motivational analysis of emotion: Reflex-cortex connections. Psychological Science, 3, 44–49. Laurin, M. (2010). The subjective nature of Linnaean categories and its impact in evolutionary biology and biodiversity studies. Contributions to Zoology, 79, 131−146. Leckman, J. F., Weissman, M. M., Merikangas, K. R., Pauls, D. L., & Prusoff, B. A. (1983). Panic disorder and major depression: Increased risk of depression, alcoholism, panic, and phobic disorders in families of depressed probands with panic disorder. Archives of General Psychiatry, 40, 1055–1060. Leibenluft, E., & Stoddard, J. (2013). The developmental psychopathology of irritability. Development and Psychopathology, 25, 1473–1487. Lenzenweger, M. F., McLachlan, G., & Rubin, D. B. (2007). Resolving the latent structure of schizophrenia endophenotypes using expectation-maximization-based finite mixture modeling. Journal of Abnormal Psychology, 116, 16–29. Leventhal, A. M., & Rehm, L. P. (2005). The empirical status of melancholia: Implications for psychology. Clinical Psychology Review, 25, 25–44. Lewis-Fernández, R., Hinton, D. E., Laria, A. J., Patterson, E. H., Hofmann, S. G., Craske, M. G., . . . Liao, B. (2010). Culture and the anxiety disorders: Recommendations for DSM-V. Depression and Anxiety, 27, 212–229.

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Rubia, K. (2011). “Cool” inferior frontostriatal dysfunction in ADHD versus “hot” ventromedial orbitofrontal-limbic dysfunction in conduct disorder: A review. Biological Psychiatry, 69, e69–e87. Sanislow, C. A., Pine, D. S., Quinn, K. J., Kozak, M. J., Garvey, M. A., Heinssen, R. K., . . . Cuthbert, B. N. (2010). Developing constructs for psychopathology research: Research Domain Criteria. Journal of Abnormal Psychology, 119, 631–639. Sauder, C. L., Derbidge, C. M., & Beauchaine, T. P. (2015). Neural responses to monetary incentives among self-injuring adolescent girls. Development and Psychopathology. Epublished ahead of print. Scotti, J. R., & Morris, T. L. (2000). Diagnosis and classification. In M. Hersen & R. T. Ammerman (Eds.), Advanced abnormal child psychology (pp. 15–32). Mahwah, NJ: Erlbaum. Shaffer, D., Fischer, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child and Adolescent Psychiatry, 39, 28–38. Sibley, M. H., Pelham, W. E., Molina, B. S. G., Waschbusch, D. A., Gnagy, E. M., Babinski, D. E., & Biswas, A. (2010). Inconsistent self-report of delinquency by adolescents and young adults with ADHD. Journal of Abnormal Child Psychology, 38, 645–656. Spitzer, R. L., Endicott, J., & Robins, E. (1978). Research Diagnostic Criteria. Archives of General Psychiatry, 35, 773–782. Spitzer R. L., & Fleiss, J. L. (1974). A re-analysis of the reliability of psychiatric diagnosis. British Journal of Psychiatry, 125, 341–347. Spitzer, R. L., & Williams, J. B. W. (1983). The DSM-III classification of affective disorders. Acta Psychiatrica Scandinavica, S310, 106–116. Spitzer, R. L., Williams, J. B. W., Gibbon, M., & First, M. B. (1990). Structured Clinical Interview for DSM-III-R, Patient Edition/Non-patient Edition (SCID-P/SCID-NP). Washington, DC: American Psychiatric Press. Sroufe, L. A. (1997). Psychopathology as an outcome of development. Development and Psychopathology, 9, 17–29. Stein, D. J., Craske, M. G., Friedman, M. J., & Phillips, K. A. (2011). Meta-structure issues for DSM-5: How do anxiety disorders, obsessive compulsive and related disorders, post-traumatic stress disorders, and dissociative disorders fit together? Current Psychiatry Reports, 13, 248–250. Stein, D. J., Fineberg, N. A., Bienvenu, O. J., Denys, D., Lochner, C., Nestadt, G., . . . Phillips, K. A. (2010). Should OCD be classified as an anxiety disorder in DSM-5? Depression and Anxiety, 27, 495–506. Tannock, R. (2013). Rethinking ADHD and LD in DSM-5: Proposed changes in diagnostic criteria. Journal of Learning Disabilities, 46, 5–25. Thompson, B. (2004). Exploratory and confirmatory factor analysis: Understanding concepts and applications. Washington, DC: American Psychological Association.

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Todd, R. D., Huang, H., Todorov, A. A., Neuman, R. J., Reiersen, A. M., Henderson, C. A., . . . Reich, W. C. (2008). Predictors of stability of ADHD subtypes from childhood to young adulthood. Journal of the American Academy of Child and Adolescent Psychiatry, 47, 76–85. Trull, T. J., & Durrett, C. A. (2005). Categorical and dimensional models of personality disorder. Annual Review of Clinical Psychology, 1, 355–380. van Praag, H. M. (2010). No functional psychopharmacology without functional psychopathology. Acta Psychiatrica Scandinavica, 122, 438–439. Wakefield, J. C. (2013). The DSM-5 debate over the bereavement exclusion: Psychiatric diagnosis and the future of empirically supported treatment. Clinical Psychology Review, 33, 825–845. Wilkinson, P., Kelvin, R., Roberts, C., Dubicka, B., & Goodyer, I. (2011). Clinical and psychosocial predictors of suicide attempts and nonsuicidal self-injury in the adolescent depression antidepressants and psychotherapy trial (ADAPT). American Journal of Psychiatry, 168, 495–501. Wing, J. K., Cooper, J. F., & Sartorius, N. (1974). The measurement and classification of psychiatric symptoms. London, UK: Cambridge University Press. Woodruff, R. A., Goodwin, D. W., & Guze, S. B. (1974). Psychiatric diagnosis. New York, NY: Oxford University Press. World Health Organization. (1966). ICD-8: International statistical classification of diseases and related health problems (10th rev. ed.). Geneva, Switzerland: Author. World Health Organization. (1996). Multiaxial classification of child and adolescent psychiatric disorders. Cambridge: Cambridge University Press. World Health Organization. (2008). ICD-10: International statistical classification of diseases and related health problems (10th ed., rev.). Geneva, Switzerland: Author. World Health Organization. (2015). Classifications: International statistical classification of diseases. Retrieved from http://www.who.int/classifications/icd/en/ Zisner, A., & Beauchaine, T. P. (2016). Midbrain neural mechanisms of trait impulsivity. In T. P. Beauchaine and S. P. Hinshaw (Eds.), Oxford handbook of externalizing spectrum disorders (pp. 184–200). New York, NY: Oxford University Press. Zisner, A., & Beauchaine, T. P. (in press). Common neural circuitry for trait impulsivity, irritability, and anhedonia: A mechanism of heterotypic comorbidity among externalizing disorders and unipolar depression. Development and Psychopathology.

CHAPTER 3

Genetic, Environmental, and Epigenetic Influences on Behavior THEODORE P. BEAUCHAINE, LISA GATZKE-KOPP, AND IAN R. GIZER

HISTORICAL CONTEXT

T

heories regarding causes of psychopathology span much of written history. In the 2nd century A.D., Galen—extending the writings of Hippocrates—attributed temperamental characteristics to individual differences in four bodily humors. According to his account of human behavior, melancholia—or depression—resulted from excess black bile, whereas emotional volatility resulted from excess yellow bile. Although Galen’s theory placed the locus of mental illness within the individual, other historically influential accounts of psychopathology emphasized the role of environment in shaping behavior. Perhaps the most famous of these is Freud’s psychoanalytic theory, which attributed causes of mental illness to intrapsychic conflicts among the id, ego, and superego. According to Freud, both the ego and superego derived their relative strength or weakness almost exclusively from early experience. Although extracted from very different historical epochs, these examples reflect a clear difference in beliefs about the importance of nature versus nurture in the development of mental illness. Until the 20th century, such differences in opinion were irresolvable because formal scientific methods had not been applied to the study of psychopathology, and because appropriate technological and methodological tools had not been developed to effectively parse the relative contributions of heritable and environmental influences on behavior. Toward the end of the century, however, advances in molecular genetics, along with refinements in both behavioral genetics and statistical modeling, provided means for resolving longstanding questions about the etiology of psychopathology (see e.g., Rende & Waldman, 2006). Yet despite these breakthroughs, disagreements over the relative contributions of genes and environment in explaining psychopathology lingered 68

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(see e.g., Albee & Joffe, 2004; Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008; Rutter, 2014). Indeed, preferred explanations for individual differences in behavior have waxed and waned between genes and environment several times during the past 50 years, often influenced as much by political considerations as by scientific discovery and innovation (see Rutter, Moffitt, & Caspi, 2006). In the past 20 or so years, a far more balanced perspective has emerged. Theoretical advances, the capacity to conduct genome-wide scans, and widespread use of the advanced methods mentioned above have confirmed that both genetic and environmental influences play significant roles in the expression of almost all behavioral traits—including those linked to psychopathology—and that the nature versus nurture question is misleading because it forces us to choose between influences that are almost always interdependent. In fact, environment can affect development by altering gene expression through epigenetic processes, which we describe in later sections. Epigenetic effects blur traditional boundaries between genes and environments in shaping behavior. Furthermore, although genetic and environmental influences on behavior have often been treated as separate, a growing body of research indicates that Gene × Environment interactions are often more important in determining behavior then either factor alone (see Moffitt, Caspi, & Rutter, 2006; Rutter, 2014). It has long been known, for example, that impulsivity is a highly heritable trait (e.g., Hinshaw, 2002, 2003; see also Beauchaine & Gatzke-Kopp, 2012; Beauchaine, Hinshaw, & Pang, 2010; Gizer, Otto, & Ellingson, 2015; Chapter 6 [Neuhaus & Beauchaine]), which confers vulnerability to a host of behavioral disorders including delinquency, antisocial behavior, and both alcohol and substance dependencies (see, e.g., Beauchaine & Cicchetti, 2016a; Beauchaine & McNulty, 2013; Krueger et al., 2002). However, impulsive boys and girls are more likely to develop these conditions in neighborhoods with high rates of drug use, violence, and criminality (Lynam et al., 2000; Meier, Slutske, Arndt, & Cadoret, 2008), and/or when maltreated by caretakers ( Jaffee et al., 2005; Chapter 5 [Jaffee]). Furthermore, genetically vulnerable individuals may evoke reactions from others that exacerbate their inherited susceptibilities to psychopathology, exemplifying evocative gene-environment correlation (e.g., Burt, 2008; O’Connor, Deater-Deckard, Fulker, Rutter, & Plomin, 1998). Thus, combinations of genetically conferred vulnerabilities and environmentally mediated risk factors result in worse outcomes than either influence alone (Beauchaine, Zisner, & Sauder, 2017). When vulnerability and risk interact in such a way, studying either in isolation causes us to underestimate their combined importance (see Beauchaine, Neuhaus et al., 2008). Our primary objective in writing this chapter is to provide an integrated account of the interplay of heritable and environmental influences on psychopathology across the lifespan. We focus primarily on broad conceptual issues given that findings specific to particular forms of psychopathology are presented in later chapters. Our approach is informed considerably by the work of Rutter and others, who have written extensively about the mutual interplay of genes and environment in shaping human development and behavior (see e.g., Moffitt, 2005; Rutter, 2010, 2014; Rutter, Moffitt et al., 2006).

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THE DEVELOPMENTAL PSYCHOPATHOLOGY PERSPECTIVE As outlined in Chapter 1 [Hinshaw], contents of this book are organized around the developmental psychopathology perspective, an approach to studying mental illness that emerged in the past 35 years. The developmental psychopathology framework is advantageous for studying the emergence of behavior disorders because it integrates strengths of numerous other disciplines, including psychiatric genetics, child clinical psychology, child psychiatry, developmental psychology, epidemiology, and clinical neuroscience, among others (see e.g., Beauchaine & McNulty, 2013). Developmental psychopathologists seek to characterize the course of mental illness as precisely as possible, across all relevant levels of analysis. Levels of analysis refer to different systems through which a psychopathological trait is expressed, spanning genes to behavior to broad cultural factors (see Beauchaine & Gatzke-Kopp, 2012; Cicchetti, 2008; Gottlieb, 2007; Hinshaw & Beauchaine, 2015). The advantage of a multiple-levels-of analysis approach to understanding psychopathology is exemplified in research on schizophrenia, an oftentimes progressively degenerative disorder in which afflicted individuals experience delusions, exhibit odd behaviors, and become isolated and avolitional (see Chapter 23 [Asarnow & Forsyth]). Although vulnerability to schizophrenia is highly heritable, most likely through combinations of both vulnerability genes (see Gottesman & Gould, 2003), and rare structural genetic variants (Costrain et al., 2015; Walsh et al., 2008), the exact genetic mechanisms have only begun to be identified. However, it is important to note that identifying all such genes will not result in a full understanding of the disorder, because genes do not affect behaviors—including those related to psychopathology—directly (see Rutter, Moffitt et al., 2006). Rather, they code for variations in protein expression that lead to structural and functional variations in the central nervous system and other organ systems. In isolation, these structural and functional variations are typically not necessary or sufficient to cause psychopathology. For example, traits associated with genetic vulnerability to schizophrenia, including neuromotor abnormalities, eye tracking dysfunction, and abnormal activity in the prefrontal cortex during working memory tasks, can be present in vulnerable individuals whether or not they manifest the disorder (Callicott et al., 2003; Erlenmeyer-Kimling, Golden, & Cornblatt, 1989; Glahn et al., 2003; Lenzenweger, McLachlan, & Rubin, 2007; Ross, 2003). As is the case with many psychiatric conditions, progression from genetic predisposition to manifestation of schizophrenia is affected profoundly by environmental influences (Cannon et al., 2002). Conversely, protective familial environments can both improve the course of the disorder, and in some cases prevent onset of illness (e.g., Cornblatt, 2001; see also Chapter 23 [Asarnow & Forsyth]). This example illustrates the importance of incorporating information from genetic, neurological, behavioral, and environmental levels of analysis toward understanding

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the complexity of debilitating conditions such as schizophrenia. Specifying determinants of psychopathology across all relevant levels of analysis and understanding interactions and longitudinal transactions across levels is therefore a primary objective of developmental psychopathology research (see Beauchaine & Cicchetti, 2016a; Beauchaine & Gatzke-Kopp, 2012; Beauchaine & McNulty, 2013; Cicchetti, 2008).

TERMINOLOGICAL AND CONCEPTUAL ISSUES Our main goals in writing this chapter are to (a) describe the interactive roles of heritability and environment in shaping behavior, particularly psychopathology, and (b) present important principles for interpreting more specific findings presented in later chapters and in the literature more broadly. Toward addressing these objectives, we first consider distinctions between genotypes, phenotypes, and endophenotypes, important constructs in behavioral and molecular genetics research. Although our descriptions are necessarily brief, they provide a foundation for understanding contents presented later in this volume.

Genotypes, Phenotypes, and Endophenotypes Genotype. The word genotype refers to structural composition of DNA as it exists within an individual. The human genome is largely fixed in the population, but approximately 0.2% of the genome varies across individuals. The term genotype is sometimes used to refer to an individual’s entire genetic sequence, and at other times in reference to a single gene, or more frequently, to describe an individual’s genetic sequence at a single point of variation. Of most interest to psychiatric geneticists is variation in DNA that influences functions or regulation of genes. Genes are composed of DNA, which guides synthesis of messenger RNA through a process called transcription. In turn, messenger RNA guides production of polypeptides through a process called translation. These polypeptides are the building blocks of proteins, or gene products. Differences in genetic sequence, referred to as allelic variants, give rise to individual differences in the volume or functionality of gene products. Some of these individual differences influence behavior. For example, some genetic variants give rise to individual differences in synthesis, reuptake, and catalysis of neurotransmitters that subserve mood, self-regulation, and motivation. When compromised, these neurotransmitter systems, including serotonin, dopamine, and norepinephrine, among others, may confer vulnerability to mood disorders, impulse control problems, and asociality (see Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011). Traditionally, all genetic variation was assumed to be inherited fully and fixed across the lifespan. It was also believed that heritable genetic variation encoded

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psychiatric disorders directly, through either single or multiple loci—assumptions referred to as monogenic and polygenic determinism, respectively. These assumptions imply (at least for serious mental disorders) that particular genes or patterns of genes always result in psychopathology, regardless of environmental input (see Rutter, Moffitt et al., 2006). However, it is now recognized that a number of intervening influences—many of which fall under environmental control—affect gene transcription, translation, and promotion, thereby altering gene expression. Although we discuss some of these intervening influences in later sections, for now it is sufficient to state that (a) both genes and environments are implicated in the expression of almost all forms of psychopathology, (b) there are no genes “for” particular behaviors or disorders (although rare, single-gene neurological disorders do exist), (c) environments can alter gene expression, and (d) many people who are genetically vulnerable never develop mental illness (see Kendler, 2005; Plomin, 1989)—a phenomenon known as incomplete penetrance. Phenotype. The term phenotype refers to observable characteristics—both physical and behavioral—that result from the interplay between an organism’s genes and the environment. The phenotype concept stems from early work in Mendelian genetics, whereby physical characteristics of an organism are reliable, outwardly measurable indicators of underlying genotypes. In Mendel’s experiments on flower color and pea pod shape, phenotypes were dictated almost exclusively by inherited pairs of dominant and recessive genes, with very limited environmental influence except in cases of severe deprivation (Hartl & Jones, 2002). Such is the case when a phenotype is determined monogenically. In contrast, polygenic traits are influenced by many genes, so correspondences between genotypes and phenotypes are far from 1:1. Furthermore, with multiple genetic influences, there are many opportunities for both gene-gene interactions and environmental regulation of gene expression (see below). This far-from-complete correspondence between genotypes and behavioral phenotypes presents formidable obstacles for psychiatric genetics (see Gatzke-Kopp, 2011), another topic we return to below. Endophenotype. As defined by Gottesman and Gould (2003), endophenotypes are “measurable components unseen by the unaided eye along the pathway between disease and distal genotype” (Gottesman & Gould, 2003, p. 636). In this sense, endophenotypes are a special case of phenotypes, as they are also measurable physical, physiological, or behavioral traits. However, they are presumed to be closer to the functional output of gene(s) in question (see Beauchaine, 2009; Gould & Gottesman, 2006; Lenzenweger, 2014). This closer proximity to genes makes carefully chosen endophenotypes valuable to psychiatric geneticists in their attempts to identify (a) specific alleles associated with psychopathology and (b) genetically vulnerable individuals who have not yet developed psychopathology (see Castellanos & Tannock, 2002; Gizer et al., 2015; Skuse, 2001).

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In psychiatric genetics, it is important to distinguish between endophenotypes and other types of biomarkers (see Beauchaine, 2009; Lenzenweger, 2014). At the broadest level, biomarkers are measureable characteristics that indicate either vulnerability to, or direct manifestation of, mental illness. For example, preliminary evidence suggests that choline concentrations in the anterior cingulate cortex correlate with depression severity among patients with bipolar disorder, as indicated by positron emission tomography (Moore et al., 2000). Choline concentrations may therefore serve as an objective biomarker of both clinical state and treatment response. Although such information may be quite useful in understanding the neural bases of mood and mood state among affected individuals, to qualify as an endophenotype a biomarker must be state independent. In other words, an endophenotype must mark genetic vulnerability, whether or not an individual is currently symptomatic, in order to be maximally useful to geneticists in the search for genes that contribute vulnerability to mental disorders. To qualify as an endophenotype, a biomarker must (a) segregate with illness in the general population, (b) be heritable, (c) be state independent, (d) cosegregate with disorder within families, (e) be present at higher rates in affected families than in the general population, and (f) be measured reliably and specifically (Gould & Gottesman, 2006). Thus, although the terms biomarker and endophenotype are often used interchangeably, the latter are a subset of the former, with much greater specificity and utility in genetics research (Beauchaine, 2009). To date, a limited number of reliable endophenotypes have been identified in the psychopathology literature. A particularly good example comes from research on schizophrenia. As noted above, patients with schizophrenia experience irregularities in smooth-pursuit eye tracking of moving stimuli, as measured by sophisticated eye-tracking devices (see e.g., Gooding & Basso, 2008). Although the pathophysiology of such deficiencies is not fully understood, about 80% of patients with schizophrenia exhibit the trait, as do about 45% of their first-degree relatives, compared with only 10% of those in the general population (Gottesman & Gould, 2003; see also Kathmann, Hochrein, Uwer, & Bondy, 2003). Importantly, this 10% figure matches the population prevalence rate of schizophrenia liability.1 Thus, eye tracking dysfunction segregates with illness in the general population, is heritable, is state independent, cosegregates within families, is observed at higher rates in affected families than in the population, and is specific to schizophrenia liability. It is therefore quite useful in identifying those with a genetic predisposition to the disorder, even if they have not developed outwardly expressed symptoms. Detecting premorbid vulnerability among such individuals may have important implications for prevention, particularly when early identification improves long-term prognosis (see McGorry et al., 2002). 1. As most readers are likely aware, the prevalence of schizophrenia is about 1% in the population. However, about 10% carry genetic vulnerability. This is an example of incomplete penetrance, described above.

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PSYCHIATRIC GENETICS Broadly speaking, there are two overarching objectives of psychiatric genetics. The first is to parse variability in behavioral traits (e.g., impulsivity, aggression, anxiety) within populations into portions accounted for by (a) heritable influences, (b) environmental influences, and sometimes (c) Gene × Environment (G × E) interactions. This goal is accomplished through behavioral genetics research. The second is to identify specific alleles or other gene variants that confer vulnerability to psychopathology. This is accomplished though molecular genetics research. Thus, behavioral genetics and molecular genetics research provide information about psychopathology at different levels of analysis.

Behavioral Genetics Despite its name, behavioral genetics research does not involve direct measurement of genes, and in fact emerged as a discipline well before technological advances that allow for mapping of the human genome. Traditionally, behavioral genetics studies have applied statistical modeling techniques to parse sources of variance in observed behavior (phenotypes) into three broad classes, including additive genetic effects, shared environmental effects, and nonshared environmental effects. Additive genetic effects encompass all sources of variance in a behavioral trait that are accounted for by heritable mechanisms within a population. Although potentially confusing to explain, these “genetic effects” can arise from both genetic and nongenetic (although heritable) sources. For example, some genes are activated (“turned on”) among offspring only when their mothers are exposed to particular environments, oftentimes prenatally (see Rutter, Moffitt et al., 2006). Such maternal programming effects may increase vulnerability to, or protect against, the emergence of psychopathology through epigenetic mechanisms, described in more detail below. These effects are not purely genetic, yet they are often subsumed within the additive genetic component in behavioral genetics studies. Accordingly, to avoid confusion in this chapter we refer to heritable effects on behavior when the source of heritability cannot be attributed unambiguously to main effects of genes. In the shorthand of behavioral genetics, heritable effects are denoted A, shared environmental effects are denoted C, and nonshared environmental effects are denoted E (hence the term ACE model). When squared, each term signifies a proportion of variance in behavior accounted for. In theory, these sources sum to 1.0, accounting for all variance within a population for a behavioral (or other phenotypic) trait (a2 + c2 + e2 = 1.0). Parsing a behavioral trait into heritable, shared environmental, and nonshared environmental components is accomplished through twin, family, and adoption studies, by comparing correspondences between behavioral traits among

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individuals who are and are not related genetically. For example, in a basic twin design, the following assumptions are made in estimating A, C, and E: 1. Monozygotic (MZ) twins who are raised in the same family share 100% of their genes (A) and 100% of their common environment (C). Thus, the squared correlation (r2 ) between phenotypes of MZ twins provides an estimate of a2 + c2 (r2 MZ = a2 + c2 ). 2. Dizygotic (DZ) twins who are raised in the same family share 50% of their genes (A), and 100% common environment (C). Thus, the squared correlation between phenotypes of DZ twins provides an estimate of 1/2 a2 + c2 . Rearranging algebraically, a2 = 2(r2 MZ − r2 DZ ), and c2 = r2 MZ − a2 . 3. Residual error variance provides an estimate of e2 via the formula e2 = 1 − (a2 + c2 ). Note, however, that such nonshared environmental variance includes both environmental effects unique to siblings, and measurement error. Behavior genetics can also be conducted using nontwin designs, following the assumptions that (a) sibling-sibling pairs share 50% of their genes, but twin sibling pairs share more common environment than nontwin sibling pairs; (b) parents share 50% of their genes with each of their children; and (c) relatives such as aunts, uncles, and grandparents share 25% of their genes. It is important to note that these genetic relatedness percentages are true at the aggregate (population) level. Thus, any given individual may share slightly more or less than 50% of his or her genes with a sibling due to random allocation of each parent’s genes during embryogenesis. Accordingly, accuracy of ACE models depends, in part, on sample size. Accurate estimates of A, C, and E also rest on two additional assumptions. The first is that parents mate randomly. Assortive mating refers to situations in which reproduction does not occur randomly. Rather, heritable traits of individuals attract them to similar individuals, and these similar traits are passed on to offspring (e.g., shy individuals may be attracted one another). When this occurs, estimates of C are spuriously increased, and estimates of A are spuriously decreased (see Dhamija, Tuvblad, & Baker, 2015). Assortive mating has been demonstrated for several psychiatric disorders, including ADHD (e.g., Boomsma et al., 2010). The second assumption is that environmental similarity does not differ for MZ vs. DZ twin pairs—termed the equal-environments assumption. One method used to test this assumption is to compare two sets of MZ twin pairs: one set raised by parents who correctly believed they were MZ twins, and one set raised by parents who incorrectly believed they were DZ twins during childhood. If these sets of twins are equally similar behaviorally, parents are probably not engaging in behavior that makes MZ twins more similar than DZ twins, which supports the equal environments assumptions (e.g., Scarr & Carter-Saltzman, 1979). Alternative approaches used to test the equal environments assumption also support it. Even so, greater

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genetic differences between DZ twins can lead these twin pairs to experience more divergent environments relative to MZ twins, but since this results from genetic differences, it is not considered a violation of the equal environments assumption. This point is discussed in more detail in subsequent sections on gene-environment interplay. Although variations in behavioral genetics designs are sometimes used, most are built around these assumptions.2 Interested readers are referred to Plomin, DeFries, Knopik, and Neiderhiser (2013) for a more comprehensive account of behavioral genetics research. Behavioral genetics studies have evaluated heritabilities of a wide range of psychiatric disorders. For example, symptoms of hyperactivity/impulsivity among children with ADHD consistently yield heritability coefficients in the .70–.75 range (see Dhamija et al., 2015), and are therefore attributable largely to heritable effects (A). Most of the remaining variance is attributable to nonshared environmental effects (E), with negligible if any contribution from shared environments (C) (Burt, 2009). Heritability coefficients this large are rarely observed so early in life. By adulthood, however, many psychiatric disorders yield similarly high heritabilities, a point we turn to next. Complexities and Limitations of Behavioral Genetics. Several complexities and caveats should be considered in any discussion of the behavioral genetics of psychopathology. Perhaps most important, heritable vulnerabilities and environmental risk factors often interact to affect both age of onset and severity of psychopathology. For example, in many cases genetic liability is insufficient to result in schizophrenia (see above). Rather, vulnerability is translated into illness only when coupled with significant environmental risk (Gottesman & Gould, 2003). When heritable vulnerabilities (G) and environmental risk factors (E) mutually influence the course of psychopathology, a Heritability × Environment (G × E) interaction is observed (see below). Crucially, G × E interactions cannot be disentangled from pure heritability effects in behavioral genetics studies unless the specific environmental variable that interacts with genetic vulnerability is quantified precisely. In most behavioral genetics studies, effects of environment are inferred from residual variance, not measured variance (see above). Under such conditions, unmeasured G × E interactions are subsumed within the heritability coefficient (see Rutter, 2014). Thus, developmental increases in the heritability of almost all forms of psychopathology (Bergen, Gardner, & Kendler, 2007; see below) in part reflect accumulating effects of environmental risk exposure interacting with genetic vulnerability across the lifespan.

2. An alternative to the ACE model is the ADE model, in which D represents nonadditive, or dominant genetic effects. Dominance is indicated when MZ correlations are more than double those obtained for DZ/full sibling pairs. ACE and ADE models can be compared statistically to see which provides a better fit. In a recent meta-analysis, Nikolas and Burt (2010) found that the ADE model provides a better fit for symptoms of inattention, whereas the ACE model provides a better fit for symptoms of hyperactivity.

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As a result, behavioral genetics studies in which specific environmental influences are not measured overestimate main effects of heritability on emerging mental illness. The likely end result may be a literature-wide overestimation of main effects of heritability in the pathogenesis of psychopathology. This is by no means a trivial point because the uniformly high heritability coefficients of adult psychiatric disorders (e.g., Shih, Belmonte, & Zandi, 2004) are sometimes interpreted as evidence that environment contributes little to the expression of psychopathology. In recognition of this limitation, increasingly sophisticated efforts to quantify G × E interactions have appeared in recent years. These studies, some of which are described below, suggest that G × E interactions may be more common than thought previously (e.g., Caspi et al., 2002; Caspi et al., 2003; Caspi et al., 2010; Dodge & Rutter, 2011; Moffitt, 2005). However, most G × E interactions have been tested in molecular genetics research (see below), not behavioral genetics research. Moreover, many such findings need to be replicated to confirm their validity (see Duncan & Keller, 2011). As just noted, it has become apparent in recent years that heritability coefficients of almost all behavioral traits—including those associated with psychopathology— increase substantially from childhood to adulthood (see Beauchaine, Neuhaus et al., 2008; Bergen et al., 2007). This general pattern applies to almost all forms of psychopathology that have been assessed at different points in development, including antisocial behavior, anxiety, depression, eating disorders, and substance dependences (Bergen et al., 2007; Hicks et al., 2007; Klump, McGue, & Iacono, 2000; Lyons et al., 1995). Moreover, although environmental effects such as peer influences affect age of onset of smoking and drinking behaviors, both smoking maintenance and heavy drinking are accounted for almost exclusively by heritable effects (e.g., Boomsma, Koopsman, Van Doormen, & Orlebeke, 1994; Koopsman, Slutske, Heath, Neale, & Boomsma, 1999; Koopsman, van Doornen, & Boomsma, 1997; McGue, Iacono, Legrand, & Elkins, 2001; Viken, Kaprio, Koskenvuo, & Rose, 1999). Psychopathology researchers have offered a number of potential explanations for increasing heritability coefficients. These include suggestions that the nature of psychopathology may be different among children than among adults (e.g., Klein, Torpey, Bufferd, & Dyson, 2008), that different genetic factors operate in childhood versus adolescence (e.g., Kendler, Gardner, & Lichtenstein, 2008), and that differences in heritability may indicate diverse equifinal pathways to psychopathology (e.g., Silberg, Rutter, & Eaves, 2001). Notably, evocative and active gene-environment correlations (rGE), discussed below, also contribute to increases in heritabilities of behavioral traits across the lifespan. This occurs because small initial behavioral differences between DZ twins become magnified over time as genetic differences cause twin pairs to provoke and seek out different environments, which then amplify phenotypes, increasing behavioral differences. In contrast, MZ twin pairs remain relatively similar over time, and, when compared to the declining similarity of DZ twin pairs, cause heritability estimates to increase (Beam & Turkheimer, 2013).

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Although some or all of these mechanisms are likely to be operative, important artifactual influences should also be considered. For example, developmental increases in heritability are a mathematical necessity in twin and adoption studies whenever there are individual differences in age of onset, even when underlying causal processes of psychiatric disturbance are similar (see Beauchaine, Neuhaus et al., 2008). Heritability is estimated from concordance of psychopathology among twin pairs. Differences in age of onset—which could be caused by environmental insults or stochastic (chance, random) effects—necessarily produce increasing concordance over time, thereby increasing heritability. Importantly, even when they are reared in very similar environments, phenotypic variation among MZ twins is observed, highlighting the importance of nonshared environment (Wong, Gottesman, & Petronis, 2005). Regardless of the causes, moderate to high heritabilities are observed for almost all adult psychiatric disorders, with smaller but significant nonshared environmental effects and negligible if any shared environmental effects. This fact led Turkheimer (2000) to coin the three laws of the behavior genetics: (1) all human behavioral traits are heritable, (2) effects of being raised in the same family are smaller than effects of genes, and (3) a substantial portion of variation in complex human behavioral traits is not accounted by effects of genes or families. It is also important to note that behavioral genetics studies are usually conducted with large samples recruited through twin registries. Ideally, these samples are representative of the population from which they are drawn. As a result, behavioral genetics analyses parse mostly normal variation in individual differences. This variation is analyzed by constructing structural models to evaluate linear associations between heritable influences and behavior and between environmental influences and behavior. However, linear associations do not always represent processes that operate at the extremes of a distribution—the very region where psychopathology is represented (see e.g., Beauchaine, 2003). According to most definitions, psychopathology is limited to the upper (or lower) extremes of a normal distribution, usually defined as the 95th or 98th percentile (or the 2nd or 5th percentile). Mechanisms of behavior can be quite different at the extremes of a distribution than near the mean of a distribution (see Beauchaine, Lenzenweger, & Waller, 2008; Plichta & Scheres, 2014). Thus, gene-behavior and environment-behavior relations among individuals with psychopathology can be swamped in behavioral genetics analyses by mostly normative variation in individual differences, thereby going undetected. Finally, behavioral genetics studies are nonspecific, providing broad information about heritable versus environmental risk but yielding no information about particular genes that contribute to phenotypes. As a result, these studies cannot be used to identify disease processes or mechanisms of psychiatric disturbance. Behavioral genetics models contribute most effectively to informing theoretical frameworks from which hypotheses can be derived for specific genetics testing, a topic discussed more thoroughly below.

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Molecular Genetics Linkage Studies. In contrast to behavioral genetics, molecular genetics studies identify specific genetic polymorphisms (i.e., allelic variants) that confer vulnerability to psychopathology. There are two broad types of molecular genetics approaches: linkage and association studies. Linkage studies scan broad sections of the genome, and require large samples of families with two or more children affected by psychopathology. Genetic data are collected from family members, and searches are conducted for genetic markers with known chromosomal locations. Notably, linkage studies are ideally suited for identifying rare genetic variants (described below) of moderate to large effect sizes. They are therefore quite effective at identifying risk loci for monogenic or oligogenic traits (i.e., traits determined by one gene or a small number of genes). For example, the gene responsible for cystic fibrosis was found by “linking” the disease to a genetic variant on the long arm of Chromosome 7 within affected families. This discovery was followed by several subsequent linkage analyses that identified the specific chromosomal location (see Bolsover, Hyams, Jones, Shepard, & White, 1997). In contrast, successful application of linkage analysis to complex and highly polygenic traits has proved to be far more difficult. In child psychopathology research, linkage analyses have been applied to families of sibling pairs with autism to identify susceptibility loci for the disorder (see Chapter 22 [Faja & Dawson]). These studies specify Chromosomes 7, 8, and 9 as likely locations of autism susceptibility genes, with additional markers on Chromosomes 4 and 11 for females and males, respectively (see Lowe, Werling, Constantino, Cantor, & Geschwind, 2015; Schellenberg et al., 2006). Specification of multiple susceptibility loci indicates that autism is a multifactorial disorder. Although these investigations provide insights into the pathogenesis of autism, specific genes or combinations of genes necessary for developing the disorder have yet to be identified conclusively. Nevertheless, information obtained from linkage studies can narrow the list of candidate genes considerably. Linkage studies have also been used to identify susceptibility loci for externalizing behavior disorders (see e.g., Gizer et al., 2016; Jain et al., 2007). To date, linkage studies of psychiatric disorders have produced more failures to replicate than replications, with surprisingly few exceptions (see Craddock & Forty, 2006; Duncan & Keller, 2011; Stein & Gelernter, 2010; Zhou et al., 2008). A likely explanation is that most psychiatric traits are polygenic and multifactorial. Effect sizes attributable to identified variants are therefore small. For example, early linkage studies of autism spectrum disorder (ASD) were conducted under the assumption that 10–20 genes might contribute to vulnerability. However, current studies suggest that as many as 1,000 different genes may be involved (Sanders et al., 2012). If true, this highly polygenic architecture may explain why linkage studies fail to replicate. If rare variants in each of these genes contribute to ASD vulnerability, many will apply to just a handful of families, making it difficult to replicate linkage to a given locus. Thus, linkage studies of complex traits require large collections of families in order for signals to be detected. Importantly,

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advances in genetic sequencing make it more feasible to measure rare genetic variation, which has led to renewed interest in linkage analysis. Studies of ASD provide one example of how aggregating samples can increase statistical power and lead to identification of vulnerability loci. As noted above, early linkage studies of ASD identified a susceptibility locus on the long arm of Chromosome 7 (e.g., Ashley-Koch et al., 1999). Subsequent meta-analyses confirmed this result (Badner & Gershon, 2002; Trikalinos et al., 2006), and fine mapping studies implicate variation in the contacting-associated protein-like 2 (CNTNAP2) gene as one source of this linkage signal (Alarcón et al., 2008; Arking et al., 2008). Notably, this conclusion is supported by a recent sequencing study that identified a rare, functional variant in CNTNAP2 as a susceptibility locus for ASD (O’Roak et al., 2011). It is important to note, however, that this is one of only a few early success stories. Much linkage work remains toward identifying genes that confer susceptibility to psychopathology. Association Studies. In contrast to linkage studies, which attempt to identify allelic variants that cosegregate with the trait of interest within families, genetic association studies attempt to identify allelic variants that segregate with the trait of interest in the general population. Early association studies took the form of candidate gene studies in which specific genes that were hypothesized to be involved in neurobiological vulnerability to psychiatric disorders were examined for evidence of relations with those disorders. In such studies, allelic frequencies of one or more variants of the candidate gene are compared among individuals with and without the disorder, using classic case-control designs. Genetic associations for dichotomous traits (disordered vs. not disordered) are expressed as odds ratios, which compare the likelihood that a person with a candidate polymorphism has a target disorder with the likelihood that a person without a candidate polymorphism has a target disorder. Odds ratios > 1 indicate higher likelihood of illness among those with versus without the candidate allele. Results for continuous traits can be expressed using effect size statistics that indicate the proportion of variance explained, such as R2 . As genotyping technologies evolved, it became feasible to cost-effectively genotype hundreds of thousands of genetic variants. This was a necessary development for genome-wide association studies (GWAS), in which genetic markers that are spread across the entire genome are tested individually for association with the trait of interest, with no assumptions about which biological systems or genes might contribute to susceptibility (see Pearson & Manolio, 2008). Notably, very large samples (beginning at >30,000 cases and controls) are required to achieve adequate power given the severe correction required for multiple testing. When these sample sizes are achieved, GWAS can lead to significant advances in our understanding of genetic influences on psychopathology. Such is the case for schizophrenia, for which more than 100 susceptibility loci have been identified (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014).

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Although there are multiple methods for conducting either type of association study (see Cordell & Clayton, 2005), two are most common. In case-control designs, allelic frequencies of candidate genes are compared among those with and without psychopathology in a population. Although case-control studies can provide large numbers of participants and are therefore powerful statistically, they are vulnerable to population stratification, which refers to differences in allelic frequencies across ethnicities and geographical locals. These can introduce confounds into case-control designs, producing false positive results. Current case-control association studies, and GWAS in particular, rely on large sets of randomly selected genetic variants that are used to determine an individual’s ancestry. Population stratification can also be avoided with family-based designs in which parents serve as controls. The most commonly used of these designs is the transmission disequilibrium test (TDT; Spielman, McGinnis, & Ewens, 1993), which quantifies transmission of genetic alleles from parents to offspring. Affected offspring, along with both biological parents, are genotyped. If a parent is heterozygous for the variant of interest (i.e., “Aa” rather than “AA” or “aa” genotype, to use a simple example), offspring will have a 50–50 chance of inheriting either allele. If a candidate polymorphism confers risk for psychopathology, however, the risk allele should be transmitted from parent to offspring at a rate that exceeds 50%. An important drawback of this approach is that only heterozygous parents are informative for the analysis, and thus, the final sample size is substantially reduced because only a fraction of parents possess this genotype. As noted above, association studies, particularly case-control designs, can detect genetic effects that account for far less variance in behavior than linkage studies. However, well-articulated theories are required to identify candidate genes for analysis, and large samples are required to detect variants given the small effect sizes typically associated with them (odd ratios < 1.1 or R2 < 0.01). As described in Chapter 6 (Neuhaus & Beauchaine), for example, contemporary neural theories of impulsivity implicate the mesolimbic and mesocortical dopamine (DA) systems (see Beauchaine & Gatzke-Kopp, 2012; Gatzke-Kopp, 2011; Gatzke-Kopp et al., 2009). Given that impulsivity is a highly heritable trait that confers vulnerability to almost all externalizing disorders (Beauchaine & McNulty, 2013; Beauchaine et al., 2010, 2016), genes involved in synthesis, reuptake, and metabolism of DA should be associated with at least some of these conditions. Consistent with this supposition, association studies have identified several candidate alleles involved in DA neurotransmission, including variations in the DRD4 gene (Chromosome 11p15.5) and the DAT1 gene (Chromosome 5p15.3), among others (see Castellanos & Tannock, 2002; Gizer, Ficks, & Waldman, 2009; Gizer et al., 2016). Rare Structural Variants. A somewhat recent development in psychiatric genetics is the use of genome-wide scans to identify rare structure variants, or copy-number variants (CNVs), including both microduplications and microdeletions. As these terms imply, such variants result from either more than or fewer

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than the normal number of genes being inserted on certain chromosomes. In addition, CNVs can be caused by inversion and translocation of genes. Rare structural variants can be either inherited or arise de novo via mutation, and are implicated in the pathogenesis of autism (Sebat et al., 2007; Chapter 22 [Faja & Dawson]), schizophrenia (e.g., Rees et al., 2014; Walsh et al., 2008; Chapter 23 [Asarnow & Forsyth]), and ADHD (Lesch et al., 2011). In fact, Walsh et al. (2008) demonstrated duplications and deletions of genes among 20% of individuals with early-onset schizophrenia versus 5% of controls. Moreover, for both autism and schizophrenia, implicated genes disproportionately affect neurodevelopment (see Grayton, Fernandesa, Rujescub, & Collier, 2012). In addition to their direct effects, structural variants may interact with other susceptibility genes to “push” certain individuals over the threshold for developing psychopathology. Complexities and Limitations of Molecular Genetics. Even though molecular genetics studies are far more specific than behavioral genetics studies, they are not without limitations. Perhaps the biggest is the small amount of variance in behavior for which most identified variants account. For example, although behavioral genetics studies routinely yield heritability coefficients that explain close to 80% of the variance in impulsivity (see e.g., Dhamija et al., 2015; Nikolas & Burt, 2010), specific variants identified in molecular genetics studies account for a very small fraction of this effect, a situation that applies to all psychiatric disorders. This is often referred to as the missing heritability problem (see Slatkin, 2009). Furthermore, nonreplications across studies are common (Gizer et al., 2009, 2016). A key reason is assumed to be the multifactorial nature of most psychiatric disorders, including ADHD (see e.g., Swanson & Castellanos, 2002). Nevertheless, considerable work remains toward mapping genetic substrates of almost all behavioral traits that confer vulnerability to psychopathology—and how they interact with both one another and the environment.

Heterogeneity of Phenotypes The search for candidate genotypes through selection of homogenous phenotypes presents significant challenges to psychiatric genetics research. Success of this approach is maximized when the association between genotype and phenotype is 1:1, which is almost never the case for psychopathology or any complex trait (see above). Indeed, most disorders are multifactorially determined, and most diagnostic criteria are specified solely at the behavioral level of analysis, and are therefore affected by nongenetic influences. Thus, in addition to the small effect sizes described above, phenotypic heterogeneity presents a second obstacle to identifying genetic substrates of psychopathology (see Rende & Waldman, 2006; Skuse, 2001). Such heterogeneity can arise from three sources. First, criteria used for symptom assessment and participant selection often differ across studies. Fore example, some labs prefer broader

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definitions that include both aggressive and nonaggressive antisocial activities, whereas others confine their definitions to overtly aggressive and violent offenses. In general, narrower definitions yield higher heritability coefficients in behavioral genetics studies and more replicable evidence for linkage and/or association in molecular genetics studies (e.g., Eley, Lichtenstein, & Moffitt, 2003; Waldman et al., 1998)—presumably because narrow phenotypes identify individuals with similar genetic vulnerabilities. Second, the same or similar symptoms can develop through different etiological pathways, a phenomenon known as equifinality (Cicchetti & Rogosch, 1996; see also Chapter 1 [Hinshaw]). For example, some cases of depression are influenced more by biological vulnerability and less by environmental risk, whereas others are influenced less by biological vulnerability and more by environmental risk (see e.g., Beauchaine, Crowell, & Hsiao, 2015; Cicchetti & Rogosch, 2002; Harrington, Rutter, & Fombonne, 1996). Thus, different combinations of liability and risk can give rise to very similar behavioral syndromes. Third, diagnostic syndromes are highly complex and are often defined by constellations of symptoms. Most diagnostic criteria require only a subset of these symptoms to meet threshold, allowing for a single diagnostic label to apply to a multitude of symptom profiles. Within a given syndrome, a certain set of symptoms may derive from greater genetic influence than another set. For example, evidence suggests that melancholia—a more severe form of depression with a particularly insidious course—is more heritable and results from different genetic vulnerabilities than other forms of mood disorder (Eaves et al., 2005; Willeit et al., 2003). Despite such indications that melancholia is distinct from other forms of depression, until quite recently most genetic studies of depression—both behavioral and molecular—lumped all participants who met DSM criteria into a single group for analysis, with no effort to stratify by subtype. One consequence is to water down and obscure genetic effects on specific depression subtypes, resulting in small effect sizes and failures to replicate (see Castellanos & Tannock, 2002; Skuse, 2001). This pattern has led to numerous calls for the use of carefully chosen endophenotypes to differentiate between subgroups with distinct heritable vulnerabilities (e.g., Gottesman & Gould, 2003; Hasler, Drevets, Manji, & Charney, 2004). In the case of melancholia, abnormal hypothalamic-pituitary-adrenal axis reactivity, which is associated with a polymorphism in the promoter of the serotonin transporter gene, has emerged as a potential endophenotype (see e.g., Coryell, & Schlesser, 2001), although additional research is needed. This example illustrates why tightened definitions of psychopathology are required to specify genetic vulnerabilities more precisely.

GENE-ENVIRONMENT INTERDEPENDENCE Gene-environment interdependence occurs when heritable and environmental influences either correlate or interact with one another to explain more variance in behavior than their combined main effects (Rutter, 2014). There are several forms of

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gene-environment interdependence, each of which is outlined briefly below. More comprehensive accounts can be found in Duncan and Keller (2011), Moffitt et al. (2006), Rutter (2006, 2010), and Rutter, Moffitt et al. (2006).

Gene-Environment Interaction As mentioned above, gene-environment interaction (G × E) refers to situations in which environments moderate effects of genes on behavior or (equivalently) genes moderate effects of environments on behavior.3 Among the most renowned examples of a G × E interaction, demonstrated by Caspi et al. (2003), is the finding that polymorphisms in the promoter region of the serotonin transporter gene (5-HTTLPR) moderate effects of stressful life events—including maltreatment between ages 3 and 11—on adult depression. Individuals with two copies of the short allele (s/s homozygous) are more likely to experience adult depression following child adversity than individuals with two copies of the long allele (l/l homozygous). Those who are heterozygous (s/l) are intermediately vulnerable. Similar findings for the 5-HTTLPR gene have since been reported by others (Eley et al., 2004; Kaufman et al., 2004; see also Chapter 18 [Klein, Goldstein, & Finsaas]). Polymorphisms in the 5-HTTLPR gene (s/s) also moderate effects of stressful life events on development of drinking and drug use (Covault et al., 2007). Although findings of 5-HTTLPR × Stress interactions have been disputed by some (e.g., Risch et al., 2009), this moderating effect remains the most studied example of a G × E interaction in the psychiatric genetics literature, with many well controlled studies continuing to suggest that the s allele indeed confers susceptibility to depression following adversity, particularly when environmental influences are measured with precision (Caspi et al., 2010; Starr, Hammen, Conway, Raposa, & Brennan, 2014). Importantly, although the main effect of maltreatment in predicting depression in the Caspi et al. (2003) study was significant, the main effect of 5-HTTLPR variation was not. Thus, had the G × E interaction not been assessed, variation in the 5-HTTLPR allele would have appeared to be unrelated to adult depression. This example argues strongly for careful consideration of environment in psychiatric genetics research, and illustrates how failure to assess interaction effects can lead to incorrect inferences about the importance of heritability in the expression of psychopathology (see Beauchaine, Neuhaus, et al., 2008; Crowell et al., 2008). 3. Deciding whether genes moderate effects of environment on behavior or whether environments moderate effects of genes on behavior is dictated by theoretical considerations. For example, Caspi et al. (2002) demonstrated that maltreated children become violent later in life only if they carry a specific variable number tandem repeat in the promoter region of the monoamine oxidase A gene. This could be viewed as a case of genetic variation moderating effects of maltreatment or as a case of maltreatment moderating effects of genetic variation. Analytically, the decision is arbitrary, as the mathematics are identical. In both cases, the effect of one variable differs as a function of the other—the statistical definition of interaction. Our preference is to consider genetic variation the predictor and environment the moderator because genetic variation precedes maltreatment.

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Gene-Environment Correlation Gene-environment correlation (rGE) refers to situations in which (a) heritable traits of parents affect their child’s exposure to adverse environments (also known as an indirect genetic effect), or (b) heritable traits of children affect their own exposure to adverse environments. Such correlations come in three forms, including active, evocative, and passive (see Plomin, DeFries, & Loehlin, 1977). Active rGE. Active rGE occurs when a child’s heritable vulnerabilities influence his or her selection of environments. For example, a primary neural substrate of impulsivity is deficient mesolimbic DA activity (see Beauchaine & McNulty, 2013; Beauchaine et al., 2016; Gatzke-Kopp, 2011; Gatzke-Kopp et al., 2009). This DA dysregulation predisposes to sensation-seeking behaviors, including early initiation and sustained use of substances, association with delinquent peers, and other high-risk activities (see Chapter 6 [Neuhaus & Beauchaine]). Thus, genetically vulnerable children and adolescents are predisposed to seek risky environments and experiences, some of which may compound vulnerability. For instance, vulnerable individuals are more likely to engage in high-risk behaviors such as substance use. Such experiences can canalize trajectories toward psychopathology directly through pharmacological effects of drugs on developing mesocortical and mesolimbic brain systems (e.g., Catlow & Kirstein, 2007; see Chapter 15 [Brown, Tomlinson, & Winward]), and/or indirectly through exposure to antisocial peer influences and subsequent restriction of access to prosocial peer groups (e.g., Dishion, Kim, & Tein, 2015). In this manner, active rGE associated with externalizing behavior can feed back to exacerbate preexisting heritable compromises in avolition and self-control (Beauchaine & McNulty, 2013; Beauchaine et al., 2017). Similar active rGE has been described for other traits including anxiety (Fox, Hane, & Pine, 2007). Evocative rGE. Evocative rGE occurs when genetically influenced behaviors elicit reactions from others that interact with and exacerbate existing vulnerabilities. As outlined immediately above, one behavioral trait that can evoke environmental risk is impulsivity. Impulsive children present with challenging behaviors that elicit and reinforce ineffective parenting, which in turn amplifies risk for progression of ADHD to more serious externalizing behaviors (e.g., Patterson, DeGarmo, & Knutson, 2000). O’Connor, Deater-Deckard et al. (1998) reported an evocative rGE in a sample of children at high genetic vulnerability for externalizing behaviors who were adopted at birth. Despite being raised by adoptive parents, these children received more negative parenting than those in a matched control group. Because the adoptive parents’ behaviors could not be explained by shared genetic risk with the child, these data provide strong evidence for evocative rGE. Similar findings have since been reported by Neiderhiser et al. (2004) and Harold et al. (2013). Evoked negative responses from others can then amplify a child’s ineffective self-control, thereby increasing his or her externalizing behaviors and eliciting

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further negative parental responses. Over time, evoked cycles of negativity may affect developing neural systems through mechanisms of neural plasticity, with potential long-term consequences for adjustment (see Beauchaine, Neuhaus et al., 2008; Beauchaine et al., 2011; Pollak, 2005). In this manner, evocative rGE may amplify and solidify behavior patterns that were once malleable (see Fishbein, 2000). As noted previously, this type of reciprocal feedback, as well as that described for active rGE, provides a potential explanation for the increasing heritability of behavioral traits typically observed across the lifespan (Beam & Turkheimer, 2013). Passive rGE. Passive rGE occurs when genetic factors that are common to both a parent and child influence parenting behaviors or home environments more generally. This process can be associated with either positive or negative outcomes. For example, an intelligent parent may purchase more books for her child and read to her more often than most mothers read to their children. In this case, a genetic advantage is correlated with environmental opportunity. Parents can also confer both genetic vulnerability to their offspring and provide risky rearing environments. For instance, twin studies indicate that genes play a significant role in the intergenerational transmission of depression (e.g., Rice, Harold, & Thapar, 2005; Rice, Lewis, Harold, & Thapar, 2013), yet overwhelming evidence also demonstrates that maternal depression adversely affects parenting (Lovejoy, Graczyk, O’Hare, & Neuman, 2000; Rice et al., 2013). Given such findings, it may be tempting to infer passive rGE as a mechanism of intergenerational transmission of depression. However, passive rGE cannot be disentangled from shared environmental effects in ordinary behavioral genetics designs (for a discussion of the difference between rGE and shared environmental effects, see Rutter, Moffitt et al., 2006). Rather, sophisticated analyses of data collected from pairs of twin parents are required. No such studies have been conducted to demonstrate passive rGE for maternal depression. However, Neiderhiser et al. (2004) used a twin parent design to identify passive rGE for positive but not negative aspects of maternal parenting behavior in a normative sample. Because these are the only conclusive data demonstrating passive rGE for parenting behavior, further research is needed. Notably however, recently developed statistical models may provide for parsing passive rGE and G × E without twin parent designs (Price & Jaffee, 2008).

EPIGENESIS As alluded to above, a range of exogenous influences—including trauma, adverse rearing conditions, prenatal exposure to stress hormones, diet, and even cultural factors experienced early in life—can alter gene expression, with effects on neural development, neurotransmitter function, and behavior (see, e.g., Masterpasqua,

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2009; Roth, 2013; Tremblay & Côté, 2009). The term epigenesis refers to changes in gene expression that are environmentally mediated (i.e., “regulated,” “activated,” “turned on,” “silenced,” etc.). These occur through structural changes to DNA molecules that do not affect DNA sequence (Hartl & Jones, 2002). Notably, our understanding of the many epigenetic mechanisms that underlie this process is limited. Methylation processes (conversion of cytosine to 5-methylcytosine), which alter accessibility of DNA to mRNA in modulating gene expression, represent a well-studied mechanism, although our understanding of this phenomenon is rapidly evolving. More advanced knowledge is critically important as recent studies demonstrate that epigenetically induced changes to DNA structure are far more dynamic than imagined even a decade ago. In a now famous paper, Fraga et al. (2005) reported increasingly divergent patterns of DNA methylation over the lifespans of monozygotic twins, which indicates that epigenetic marks derived from nonshared environmental exposures accumulate over our lifetimes. Epigenetic effects on behavior were first reported in the animal literature. For example, Weaver et al. (2004) discovered epigenetically induced genetic variation in hippocampal glucocorticoid receptors among rat pups that experienced high levels of maternal caretaking, including licking, grooming, and arched-back nursing compared with pups that experienced low levels of such behaviors. This epigenetic effect transmits adaptive variations in stress responding to offspring (see Meany, 2007). Rat pups reared in high-risk environments, where such maternal caretaking behaviors are altered, have more reactive hypothalamic-pituitaryadrenocortical responses and are consequently more fearful, leaving them better prepared for the high-risk environment they are likely to face as they mature. Although epigenetic changes in gene expression clearly occur in humans, demonstrating their effects on behavior is more difficult than among animals because it requires random assignment of groups to different rearing environments (e.g., impoverished vs. enriched; see Rutter, 2014), an ethically indefensible practice. However, indirect evidence of epigenesis can be gleaned by (a) measuring methylation of genes within target tissues and (b) correlating methylation with adversity. Such studies have become common in recent years following articulation of rich theoretical models invoking epigenetic processes in the emergence of several forms of psychopathology. In fact, epigenetic mechanisms are implicated in an ever-broadening list of psychiatric syndromes, including antisocial behavior (e.g., Tremblay, 2005), schizophrenia (e.g., Roth, Lubin, Sodhi, & Kleinman, 2009), bipolar disorder (e.g., Petronis, 2003), autism (e.g., Shulha et al., 2011), addiction (e.g., Renthal & Nestler, 2008), ADHD (e.g., Mill & Petronis, 2008), PTSD (Smith et al., 2011), suicide (Poulter et al., 2008), and depression (e.g., Schroeder, Krebs, Bleich, & Frieling, 2010). Interestingly, administration of antidepressants to rodents induces epigenetic changes in the P11 promoter, which modulates serotonin receptor (5-HT1B ) function (Svenningsson et al., 2006) and is implicated in the pathophysiology of depression

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in humans (Melas et al., 2011). Other studies link childhood adversity to epigenetically induced changes in glucocorticoid receptor genes (e.g., Tyrka, Price, Marsit, Walters, & Carpenter, 2012), which modulate stress responding. The epigenome appears to be especially vulnerable to environmental insults that occur prenatally or very early in postnatal life (Roth, 2013). Diet, teratogen exposure, and in utero stress exposure are among the most common triggers of epigenesis during these developmental periods. From an evolutionary perspective, some degree of flexibility in responding to a range of environments has clear survival advantages. However, behaviors that are advantageous in some contexts may be disadvantageous in others, conferring vulnerability to psychopathology (e.g., Mead, Beauchaine, & Shannon, 2010). Thus, activation and deactivation of genes via epigenetic processes may play important roles in both vulnerability and resilience to psychopathology (see Kramer, 2005; Rutter, 2005; Rutter, Moffitt et al., 2006). Our ability to evaluate epigenetic changes in brain tissue among live humans is quite limited. For example, although animal models suggest that epigenetic alterations in hippocampal glucocorticoid receptors should be found among humans who incur adversity early in life, measuring methylation in such specific brain tissues is not possible (Davies et al., 2012). Nevertheless, postmortem studies indicate increased cytosine methylation of the glucocorticoid receptor (NR3C1) promoter in the hippocampi of those who were exposed to child abuse (McGowan et al., 2009). Furthermore, some theorize that for certain disorders, the most relevant epigenetic changes may be systemic in nature and might therefor be observed across tissue types. As evidence, some recent studies indicate substantial correlations between epigenetic marks in multiple tissues (Davies et al., 2012; Tylee, Kawaguchi, & Glatt, 2013). Thus, use of peripheral tissues such as blood as proxies for brain tissue does have some support.

GENETICS OF COMORBIDITY The term comorbidity refers to the co-occurrence of more than one psychiatric disorder within an individual. Although many subtypes and causes of comorbidity have been described (see Beauchaine & Cicchetti, 2016a, b; Caron & Rutter, 1991; Klein & Riso, 1993), two broad forms are important for this discussion. Homotypic comorbidity refers to co-occurrence of multiple externalizing disorders or internalizing disorders within an individual. For example, externalizing disorders including ADHD, oppositional defiant disorder (ODD), conduct disorder (CD), antisocial personality disorder (ASPD), and substance use disorders (SUDs) often co-occur, particularly as development proceeds from childhood through adulthood (see Beauchaine & McNulty, 2013; Beauchaine et al., 2010, 2016; Lewinsohn, Shankman, Gau, & Klein, 2004). Comorbidity of internalizing disorders, including depression, dysthymia, and anxiety disorders is also high (Angold & Costello, 1993; Brady & Kendall, 1992; Ferdinand, Dieleman, Ormel, & Verhulst, 2007).

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In contrast to homotypic comorbidity, heterotypic comorbidity refers to the co-occurrence of at least one externalizing disorder and at least one internalizing disorder within an individual (e.g., CD and depression). This form of comorbidity is more perplexing because many (though not all) symptoms appear to overlap minimally (see Sauder, Beauchaine, Gatzke-Kopp, Shannon, & Aylward, 2012; Kopp & Beauchaine, 2007; Zisner & Beauchaine, 2016). For example, depression includes symptoms of sadness, anhedonia, and feelings of guilt and/or worthlessness, whereas CD is characterized by sensation seeking, lying, property destruction, and aggression. Despite these apparently distinct presentations, rates of comorbidity of CD and depression are much higher than expected by chance (Angold & Costello, 1993; Essau, 2003; Zisner & Beauchaine, 2016).

Behavioral Genetics of Comorbidity Comorbid disorders have often been treated as distinct yet co-occurring conditions with different etiologies (see Beauchaine, 2003; Kopp & Beauchaine, 2007), yet behavioral genetics studies suggest common heritable substrates for both homotypic and heterotypic comorbidity. Biometric modeling of latent associations between supposedly distinct syndromes has advanced our understanding of comorbidity, as described next. Homotypic Comorbidity. Behavioral genetics analyses indicated that most disorders within the externalizing spectrum share a common heritable vulnerability, with similar findings reported for disorders within the internalizing spectrum (Baker, Jacobson, Raine, Lozano, & Bezdjian, 2007; Kendler, Prescott, Myers, & Neale, 2003; Krueger et al., 2002; Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011; Tambs et al., 2009). For example, about 80% of shared variance in disinhibition, conduct problems, antisocial personality, alcohol dependence, and drug dependence is accounted for by a single latent impulsivity trait (Krueger et al., 2002). Yet each specific category of externalizing conduct is influenced strongly by environment. Thus, trait impulsivity arising primarily from heritable predispositions manifests differently depending on environmental opportunities (see Beauchaine & Cicchetti, 2016a; Beauchaine & McNulty, 2013; Beauchaine et al., 2017; Lynam et al., 2000, Chapter 6 [Neuhaus & Beauchaine]). Following from these models, molecular genetic and neurobiological research targeting the common heritable vulnerability of trait impulsivity—as opposed to specific psychiatric syndromes—may provide advances in our understanding of etiology (see, e.g., Forbes, Tackett, Markon, & Krueger, in press), whereas research on environmental risk mechanisms may be more beneficial if focused on factors that differentially contribute to specific syndromes. Heterotypic Comorbidity. Behavioral genetics studies also suggest common heritability across the internalizing and externalizing spectra (see e.g., Lahey

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et al., 2011). For example, O’Connor, McGuire, Reiss, Hetherington, and Plomin (1998) reported that 45% of the covariation between depressive and antisocial symptoms was accounted for by common genetic liability among 10- to 18-year-old twins. Similar findings have since been reported by others in both adolescent and adult samples (e.g., Burcusa, Iacono, & McGue, 2003; Caspi et al., 2014; Kendler et al., 2003). Such findings offer an explanation of comorbidity not as diagnostic co-occurrence of supposedly independent conditions, but rather as covariation of related syndromes stemming from common heritable vulnerabilities. The general latent structure of psychopathology, as determined from twin and family studies, is depicted in Figure 3.1. Recently, Caspi et al. (2014) reported that a general, higher-order psychopathology factor, which they labelled p, accounted in part for observed comorbidity among internalizing and externalizing disorders, both concurrently and across 20 years spanning adolescence to midlife, in the Dunedin Multidisciplinary Health and Development Study. High p scores were heritable, and associated with greater life impairment and poor executive function. Thus, p appears to confer general vulnerability to psychopathology via deficiencies in top-down, prefrontal control over behavior, which interacts with bottom-up, subcortical vulnerabilities to eventuate in specific manifestations of psychopathology (see Beauchaine, 2015; Beauchaine & Thayer, 2015). This “bifactor” model of psychopathology, which has been replicated in several independent samples of children, adolescents, and adults

internalizing

anxiousmisery

major depression

fear

simple phobia

dysthymia

generalized anxiety

externalizing

social phobia

panic disorder

agoraphobia

alcohol dependence

drug dependence

antisocial personality

Figure 3.1 The general latent structure of psychopathology, as determined by twin, adoption, and population-based studies. Adapted from Krueger (1999)

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general vulnerability factor (prefrontal function)

Level of Analysis

behavioral genetic vulnerability trait/ cortical neural substrate

behavioral genetic vulnerability trait

behavioral syndrome

internalizing vulnerability (trait anxiety)

internalizing spectrum disorder 1

internalizing spectrum disorder 3

externalizing vulnerability (trait impulsivity)

externalizing spectrum disorder 1

internalizing spectrum disorder 2

subcortical neural substrate

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septo-hippocampal function (BIS) (5HT, NE)

externalizing spectrum disorder 3

externalizing spectrum disorder 2

psychotic disorder 1

psychotic disorder 3

psychotic disorder 2

septo-hippocampal function (BIS) (5HT, NE)

mesolimbic function (BAS) (DA)

Figure 3.2 The bifactor structure of psychopathology, including a superordinate general vulnerability factor (see Caspi et al., 2014; Lahey et al., 2011; Lahey et al., 2012).

(e.g., Lahey et al., 2011; Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2012), appears in Figure 3.2.

Molecular Genetics of Comorbidity Homotypic Comorbidity. Recall that molecular genetics studies benefit from and in some cases require sound theory to guide the search for candidate genes. As noted above, modern accounts of impulsivity implicate mesolimbic DA dysfunction (Beauchaine & Gatzke-Kopp, 2012; Beauchaine et al., 2017; Gatzke-Kopp et al., 2009; Chapter 6 [Neuhaus & Beauchaine]). In fact, aberrant neural responding in the mesolimbic DA system, including the ventral tegmental area and its projections to the nucleus accumbens, the caudate, and the putamen, is a core neural substrate of vulnerability to all or most externalizing behaviors (Beauchaine & McNulty, 2013; Gatzke-Kopp et al., 2009). Furthermore, studies using both positron

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emission tomography and functional magnetic resonance imaging indicate that low levels of neural activity in the DA-mediated primary reward centers of the brain predispose to sensation seeking, irritability, negative affectivity, and low motivation—core symptoms of externalizing psychopathology (Durston, 2003; Laakso et al., 2003; Leyton et al., 2002; Scheres, Milham, Knutson, & Castellanos, 2007; Zisner et al., 2016). These findings suggest that genes involved in synthesis, catalysis, and reuptake of DA should be candidates in molecular genetics studies of externalizing behavior patterns. Chapter 6 [Neuhaus & Beauchaine] summarizes studies implicating numerous genes involved in DA neurotransmission (e.g., DAT1, DrD4, dopamine-𝛽-hydroxylase, monamine oxydase, catechol-O-methyl transferase) in expression of impulsivity and related externalizing psychopathology. In sum, central DA dysfunction may account for much of the shared vulnerability for externalizing disorders. In contrast, vulnerability to anxiety disorders is conferred largely through behavioral inhibition, which has been linked closely with serotonergic and noradrenergic neurotransmission and (see e.g., Gray & McNaughton, 2000; Chapter 7 [Kagan]). Heterotypic Comorbidity. Studies of overlapping vulnerabilities for conduct problems and depression provide potential insights into why heterotypic comorbidity is so common. At the symptom level, both internalizing and externalizing disorders are characterized by negative affectivity, irritability, and anhedonia (see Zisner & Beauchaine, 2016). Neurally, these symptoms are subserved by the same DA deficiencies just described and detailed in Chapter 6 [Neuhaus & Beauchaine] for externalizing disorders (Forbes et al., 2006; Nestler & Carlezon, 2006; Shankman, Klein, Tenke, & Bruder, 2007). In fact, neuroimaging studies reveal blunted activation within DA-mediated brain regions during reward tasks among externalizing children/adolescents and among those with depression (see Durston, 2003; Epstein et al., 2006; Forbes et al., 2006; Sauder, Derbidge, & Beauchaine, 2016; Scheres et al., 2007). Thus, externalizing and internalizing disorders appear to share a common neural deficiency that accounts, at least in part, for overlap in symptoms. This conclusion is consistent with results outlined above from behavioral genetics studies indicating a common heritable vulnerability for depression and antisocial behavior (Burcusa et al., 2003; Kendler et al., 2003; Lahey et al., 2011; O’Connor, Neiderhiser, Reiss, Hetherington, & Plomin, 1998). Importantly, deficiencies in DA-mediated reward circuitry are moderated by other biologically influenced traits to affect behavior (Corr & McNaughton, 2016). One such trait is behavioral inhibition (see Figure 3.1), which differentiates between those who present principally with CD and those who present principally with depression (see e.g., Beauchaine, 2001; Chapter 7 [Kagan]). Thus, high trait anxiety predisposes to depression among those with blunted reward systems, whereas low trait anxiety predisposes to delinquency. Trait anxiety is modulated by an entirely different (primarily serotonergic) neural network, often referred to as the septo-hippocampal

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system (Gray & McNaughton, 2000). This is an example of two heritable traits interacting to affect behavior (i.e., a Trait × Trait interaction; see Derryberry, Reed, & Pilkenton-Taylor, 2003).

GENETICS OF CONTINUITY Whereas homotypic continuity describes the unfolding of a single class of behavioral/emotional disturbance over time (e.g., aggression), heterotypic continuity refers to the sequential development of different (but related) internalizing or different externalizing behaviors or disorders across the lifespan (see Beauchaine & McNulty, 2013; Beauchaine et al., 2010, 2016; Ferdinand et al., 2007; Rutter, Kim-Cohen, & Maughan, 2006). For example, delinquent adult males are likely to have traversed a developmental pathway that began with tantrums and hyperactive/impulsive behaviors in toddlerhood, followed by ODD in preschool, early-onset CD in elementary school, SUDs in adolescence, and ASPD in adulthood (see Beauchaine et al., 2010; Loeber & Hay, 1997; Lynam, 1996).4 Developmental trajectories of internalizing disorders in which infant reactivity and early shyness mark liability for later anxiety and depression have also been described (Kagan, Snidman, Kahn, & Towsley, 2007; see also Rutter, Kim-Cohen, & Maughan, 2006; Chapter 7 [Kagan]). Few studies have addressed either the behavioral genetics or the molecular genetics of heterotypic continuity. Although some inferences can be offered from cross-sectional studies outlined above addressing homotypic comorbidity, longitudinal studies are required to make strong statements about the stability of behavior disorders over time or about heritable versus environmental bases of behavioral stability (see Rutter, Kim-Cohen et al., 2006). In one such behavioral genetics analysis, heritable factors accounted for much of the stability in antisocial behavior, depressive symptoms, and their co-occurrence over a 3-year interval among 10to 18-year-olds (O’Connor, Neiderhiser et al, 1998). Although molecular genetics studies addressing heterotypic continuity have not appeared in the literature to date, several genes that predispose to ADHD also predispose to conduct problems and SUDs (see Gizer et al., 2016), consistent with findings from behavioral genetics research implicating common genes for different externalizing disorders (see above). Genes associated with ADHD are also associated with ASPD (Martin, Hamshere, Stergiakouli, O’Donovan, & Thapar, 2015). Recently, we articulated an ontogenic process model of externalizing psychopathology in which the well-characterized developmental progression from early life ADHD to increasingly serious forms of problem behavior across development— including oppositional defiant disorder, conduct disorder, antisocial personality disorder, and substance abuse and dependence—are explained by passive 4. This does not mean that all or even most children with ADHD eventually develop antisocial behavior. Although children with ADHD are vulnerable to more serious externalizing conduct across development, many desist. Nevertheless, most antisocial adult males began as hyperactive-impulsive preschoolers.

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and evocative G × E (Ahmad & Hinshaw, 2016; Beauchaine & McNulty, 2013; Beauchaine et al., 2010, 2016, 2017). Individuals who inherit trait impulsivity and are exposed to coercive parenting are likely to generalize maladaptive social behaviors to extrafamilial settings and develop conduct problems. Once these emerge, deviant peer group affiliations are likely. In such peer groups, exposure to substances of abuse and opportunities to engage in crime are common. Consistent with the developmental perspective of this chapter and this entire volume, this a decidedly transactional model in which individual-level vulnerabilities eventuate in recalcitrant externalizing conduct only among those who are exposed to high-risk environments. Even though much progress has been made toward specifying behavioral and molecular genetic bases of psychopathology, considerable work remains on questions of comorbidity and continuity (for extended discussions, see Beauchaine & Cicchetti, 2016a, b; Rutter, Kim-Cohen et al., 2006). Nevertheless, investigations conducted to date suggest that mechanisms of both comorbidity and continuity are likely to result from broad vulnerability traits such as impulsivity and anxiety. This supposition is consistent with recent behavioral genetics approaches that have identified general internalizing and externalizing heritable vulnerabilities that account for more variance in psychopathology than do clusters of symptoms specific to any single disorder (e.g., Kendler et al., 2003; Krueger et al., 2002; Skuse, 2001).

SUMMARY AND CONCLUSIONS Despite expanded acknowledgment of the importance of both genes and environments in the development of psychopathology, much work remains toward uncovering specific mechanisms through which “nature” and “nurture” interact to affect behavior. Although behavioral genetics studies parse phenotypic variance into heritable versus environmental influences, genes are not measured in such studies. Rather, phenotypic similarities between related individuals are used to model heritable effects, which have both genetic and nongenetic origins. The considerable distance between genotypes and phenotypes, along with various interdependencies among genotypes, phenotypes, and environments, can lead to inflated and misleading estimates of heritability. Furthermore, molecular genetics studies aimed at identifying specific allelic variants that are associated with psychological dysfunction often fail to account for environmental moderators of vulnerability. More mechanistic studies, including experiments with animals, can uncover complex patterns of environmentally mediated gene expression and function. Such epigenetic processes, which have drawn considerable attention in recent years, are likely to be implicated in expression of numerous psychiatric disorders and may explain some of the large gap in variance (i.e., missing heritability) explained by molecular genetics versus behavioral genetics studies. Although epigenetic processes are difficult to study in humans, they should nevertheless be included in emerging models of developmental psychopathology.

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Most researchers now reject dichotomizing genetic and environmental influences on behavior (nature versus nurture). Indeed, boundaries between nature and nurture continue to dissolve as we increase our understanding of the interplay between heritable and experiential factors affecting psychopathology. Given the mutual interdependence of genes and environments in affecting behavior, it is no longer tenable to study psychopathology from strictly biological or environmental perspectives (see e.g., Beauchaine et al., 2017). The next generation of mental health professionals must reject false reductionism and be facile in their thinking about psychopathology across all relevant levels of analysis including genes, neural systems, family environments, neighborhoods, and even broader social systems (see Cicchetti, 2008). Breakthroughs in the understanding of and treatment of psychopathology are unlikely to occur by considering any single level of analysis in isolation.

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P A R T II

VULNERABILITIES AND RISK FACTORS FOR PSYCHOPATHOLOGY

CHAPTER 4

Risk and Resilience in Child and Adolescent Psychopathology BRUCE E. COMPAS, MEREDITH GRUHN, AND ALEXANDRA H. BETTIS

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he concepts of risk and resilience are cornerstones of the field of developmental psychopathology, with research on these constructs playing a central role in understanding the etiologies of various psychological symptoms and disorders. Risk research is concerned with factors and processes that are associated with increased probability of the development of psychopathology, whereas resilience research focuses on factors and processes that are associated with decreased probability of psychopathology among individuals who have been exposed to known vulnerabilities or risk factors. In this chapter, we provide an overview of these constructs in developmental psychopathology by first reflecting on the history of risk and resilience research, next reviewing contemporary issues in their definitions and conceptualization, and then presenting evidence for stress as a pervasive source of risk—and processes of coping and emotion regulation as important of processes of resilience. Finally, we provide a salient example of current research on processes of risk and resilience among children of depressed parents.

HISTORICAL CONTEXT Research and theory on processes of risk and resilience have long and rich histories, with the first large-scale programs of research on the interplay of these processes emerging in the behavioral sciences in the early 1970s. However, because the concept of resilience is dependent on identification of sources of risk, it is not surprising that studies examining sources of risk for psychopathology among children and adolescents preceded research on resilience. Work in this area stemmed from the traditions of risk research established in public health and focused on three general questions: (1) Who gets sick, and who doesn’t get sick? (2) What are risk factors 113

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for illness? and (3) What can be done to make illness less common? (Gruenberg, 1981; Masten, 2013; Masten et al., 1990). Early studies were largely correlational, linking various risk factors to poor outcomes in a variety of populations, including children of parents with psychopathology, children living in poverty, and children exposed to trauma or disaster (e.g., Anthony & Koupernik, 1974; Garmezy, 1974; Rutter, 1979). Although cross-sectional studies were valuable in providing the first indication of possible risk factors in these populations, a vital approach to identification of risk is found in large-cohort longitudinal studies. Several studies exemplify this approach, including (among many others) the Isle of Wight Study (e.g., Rutter, Tizard, Yule, Graham, & Whitmore, 1976), the Dunedin Longitudinal Study (e.g., Poulton, Moffitt, & Silva, 2015), and more recently the Bucharest Early Intervention Project (e.g., Nelson, Fox, & Zeanah, 2014; Zeanah et al., 2003). However, the origin of risk research in psychopathology is best reflected in early studies of children at high risk due to parental schizophrenia. Historically, any influence—whether exogenous or endogenous—that increased the likelihood of psychopathology, was considered to be a risk factor (Kraemer, Kazdin, Offord, Kessler, Jensen, & Kupfer, 1997). More recently, a distinction has been made in developmental psychopathology research between risk factors as external influences on an individual’s adjustment and vulnerabilities as biological predispositions that interact with external risk factors to eventuate in adverse mental health outcomes (see e.g., Shannon, Beauchaine, Brenner, Neuhaus, & Gatzke-Kopp, 2007). In this chapter, we maintain this distinction between vulnerabilities and risk factors, as doing so facilitates discussion of complex Biology × Environmental interactions in the emergence of psychopathology. Such interactions are far more common than once supposed (Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Four pioneering programs of research on risk shared a number of common features, and shaped research in this area for decades to come—-the Mauritius/Danish High Risk Project directed by Salvador Mednick and F. Schulsinger (Mednick & Schulsinger, 1968); Project Competence at the University of Minnesota headed by Norman Garmezy and colleagues (Garmezy, 1972, 1974; Garmezy & Streitman, 1974); the UCLA Family High Risk Project directed by Michael Goldstein and Eliot Rodnick (e.g., Jones, Rodnick, Goldstein, McPherson, & West, 1977; Rodnick & Goldstein, 1974); and the Rochester Longitudinal Study directed by Arnold Sameroff, Ronald Seifer, and Melvin Zax (e.g., Sameroff, Seifer, & Zax, 1982; Siefer, Sameroff, & Jones, 1981). All of these studies were concerned with children at extraordinarily high vulnerability/risk for psychopathology as conferred by parental schizophrenia. These studies investigated to varying degrees the role of genetic, psychological, and interpersonal/familial mechanisms of vulnerability and risk that distinguished these children from those of parents without psychopathology. One of the most striking findings of this work was the unexpected observation that in spite of exposure to significant risks associated with parental schizophrenia, a surprising number of children did not evidence psychopathology and, in fact, their

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development was characterized by high levels of competence. The observation that parental schizophrenia, which confers both genetic vulnerability and environmental risk, did not result in uniformly poor outcomes across individuals was an important impetus for research on the concept of resilience. Garmezy, Rutter, Werner, and others argued for the equal importance of understanding pathways to positive as well as to negative adjustment (e.g., Garmezy & Rutter, 1983; Rutter, 1987; Werner & Smith, 1982). As a result, a parallel set of questions was added to those put forth by Gruenberg (1981), including the following: (a) Who becomes healthy, stays healthy, recovers well, and develops well? (b) Why do some individuals display patterns of healthy development in spite of exposure to risk? (c) What can we do to promote and protect health or positive development and facilitate recovery? (Masten, 2013). Since the initial investigation of these questions in the 1970s, resilience research has moved through several waves. Investigators first worked toward defining, operationalizing, and measuring the concepts of risk and resilience with the goal of identifying factors within an individual that promote positive adjustment in the face of adversity (see Masten & Garmezy, 1985). Early work focused on dramatic individual cases of resilience, with an emphasis on observing differences in psychosocial outcomes among individuals in high vulnerability/risk groups (e.g., Anthony & Koupernik, 1974; Garmezy, 1985; Garmezy & Nuechterlein, 1972; Garmezy & Rutter, 1983; Rutter, 1979, 1985; Werner & Smith, 1982). As noted above, investigations of children of mothers suffering from schizophrenia played a crucial role in the emergence of research on resilience in childhood and adolescence (e.g., Garmezy, 1974; Garmezy & Streitman, 1974; Masten et al., 1990). Evidence that many of these children thrived despite high genetic vulnerability and environmental risk led to increasing empirical efforts to understand individual variations in response to adversity, such as differences in social competence (Garmezy, 1974). Garmezy’s seminal work also led to studies investigating factors of resilience in other domains, such as children affected by war, famine, poverty, and other disasters (Masten & Cicchetti, 2012). A second wave of resilience research was marked by an increasing recognition of the contribution of external factors in promoting positive adjustment in the face of risk. In the 1980s and early 1990s, research evolved to incorporate factors thought to be central in the development of resilience, including individual factors, familial factors, and extrafamilial support factors (Masten & Garmezy, 1985; Werner & Smith, 1982, 1992). Individual factors included qualities related to temperament, reflectiveness in meeting new situations, responsiveness to others, and cognitive skills. Family factors were marked by caregiver warmth, family cohesion, parents’ concern for their child’s well-being, and the presence of a caring adult in the absence of responsive parents. Finally, extrafamilial factors included a person who provides support and guidance to the child, such as a teacher or social worker (see also Luthar & Brown, 2007). More recently, risk and resilience research has shifted from identifying internal and external protective factors to understanding underlying protective processes that might account for resilient outcomes in children and adolescents (e.g.,

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Cicchetti, 2013; Cicchetti & Rogosch, 2012; Masten, 2014; Rutter, 2012). This shift in focus has led to a deeper exploration of how the three factors cited above (individual, familial, and support) may contribute to positive outcomes over time. Drawing on this increased focus on processes, we now examine some contemporary issues and challenges in the study of vulnerability, risk, and resilience.

CONTEMPORARY TERMINOLOGICAL AND CONCEPTUAL ISSUES As outlined above, processes of vulnerability, risk, and resilience as they relate to psychopathology in childhood and adolescence have been central to the study of developmental psychology for many years. However, there has been considerable debate regarding definitions of these constructs. Vulnerability and Risk. As alluded to above, the terms vulnerability and risk refer to increased probabilities of a negative developmental outcome in a specified population (Kraemer, 2003; Kraemer et al., 1997; Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001). Thus, they are quantitative concepts reflected as either odds ratios when outcomes are measured categorically or as some variant of a regression weight when outcomes are continuous or quantitative. A vulnerability is an endogenous characteristic of the individual (e.g., genetic, neural, hormonal) that increases the likelihood of psychopathology, particularly in contexts of environmental adversity. In parallel, a risk factor is an exogenous agent or characteristic of the environment related to the increased probability of a negative outcome. A landmark report by the National Research Council and the Institute of Medicine (NRC & IOM; 2009) further distinguished between risk factors that are specific to a particular outcome (e.g., depression) versus those that are nonspecific, or transdiagnostic and are related to a number of outcomes (e.g., depression, anxiety, eating disorders). In addition to distinguishing levels of vulnerability and risk, temporal precedence must be established between risks and outcomes; that is, the presence of or exposure to the risk factor must precede evidence of the development of the outcome. Kraemer et al. (2001) address the issue of temporal precedence within a typology of risk factors. If a risk factor is simply associated with an outcome at a single point in time, it is identified as a correlate. A correlate that precedes an outcome is a risk factor, and a risk factor that can be changed or changes with development is a variable risk factor. Finally, if manipulation of the risk factor changes an outcome, it is a causal risk factor. Rutter (2012) raised several cautions in interpretation of findings from risk research. According to his account, risk may be genetically rather than environmentally mediated (i.e., risk might represent reverse causation in which a disorder has led to the hypothesized risk factor, rather than the reverse). This point is one reason to distinguish between vulnerability and risk, as we do throughout this volume.

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Furthermore, risk processes might also reflect social selection or processes by which individuals select or shape their environments (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Cumulative Risk. Cumulative risk refers to the co-occurrence of more than one risk factor for a given individual or within a population (Sameroff, 2006). For example, poverty and economic hardship are associated with multiple additional risks factors, including neighborhood crime and violence, lack of access to quality schools, living in a single parent home, and heightened family conflict (e.g., Chen, 2007; Evans & Cassells, 2014; Evans, Li, & Whipple, 2013; Evans & Wachs, 2010; Miller, Chen, & Parker, 2011). Similarly, parental psychopathology, another important risk factor throughout childhood and adolescence, is linked with family conflict and marital discord as well as potential genetic vulnerability to psychopathology (e.g., Goodman et al., 2011; Karg, Burmeister, Shedden, & Sen, 2011). The probability of negative outcomes may increase additively or exponentially as the number of vulnerabilities and risk factors—or cumulative risk—increases. The effects of risk factors can also be nonlinear. Kraemer et al. (2001) spell out conditions in which one risk factor (A) moderates the effects of a second risk factor (B) on an outcome (O). For A to function as a moderator of B, A must precede B, A and B must not be correlated, and A cannot influence B directly. However, the strength of the effect of B on O must be affected by the level of A. For example, consider the interaction between sex and pubertal timing in predicting depression in adolescence, such that girls (but not boys) with early onset puberty have an increased likelihood of a major depressive episode (Negriff & Susman, 2011). Following the principles outlined by Kraemer et al. (2001), in this case sex (A) precedes pubertal timing (B), sex is uncorrelated with pubertal timing, and both are related to depression (O). However, the strength of the association between early onset puberty and depression is greater for girls than for boys; that is, pubertal timing moderates the relation between sex and depression (Copeland et al., 2010). Thus, pubertal timing is a source of increased vulnerability to depression among girls but not among boys. Greater precision of the relations among vulnerabilities and risk factors and their moderating effects will contribute to greater clarity in distinguishing between risk factors and sources of vulnerability and, ultimately, in devising strategies to promote resilience. Resilience. As noted above, the concept of resilience is linked closely to and has grown from research on risk. In the broadest sense, resilience refers to positive adaption despite significant experiences of adversity; however, there is considerable variation in how resilience is defined, conceptualized, and operationalized. Masten (2013) discussed four main controversies in resilience research. First, there is an ongoing debate as whether various definitions of resilience classify it as a process, capacity, trait, outcome, or adaptive pattern. Researchers initially defined

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and measured resilience as an outcome of interest (e.g., a given at-risk individual did not develop schizophrenia and achieved a lasting romantic relationship). However, it is argued that this approach fails to recognize individuals and their circumstances as constantly changing and interacting and that achieving a “positive outcome” involves a wide array of processes. Second, debate exists over whether there is a resiliency trait (Masten, 2012; Riley & Masten, 2005). Researchers have attempted to develop measures to assess resiliency as a trait; however, there is not yet convincing evidence of such an innate or stable characteristic (Masten, 2013), even though significant heritability has been observed for resilience processes (Kim-Cohen, Moffitt, Caspi, & Taylor, 2004). Third, investigators have asked whether the concept of resilience adds more to the field than simply being the positive reframing of risk and vulnerability—i.e., that protective factors are simply the polar opposites of risk factors (e.g., good vs. poor family functioning). Resilience investigators have defended this concept by pointing to the shift in focus of attention, research, and intervention incited by this concept (e.g., Luthar, 2006; Masten, 2011; Rutter, 2006). The fourth issue outlined by Masten (2013) involves criteria and standards of measurement. One main question of interest under this topic asks whether an individual needs to function positively in a specific domain, or across all possible areas of functioning, to meet criteria for being labeled “resilient.” A common criterion used is absence of psychopathology; however, there is growing recognition that individuals with serious psychopathology can function quite well given a well-established repertoire of coping strategies, indicating that resilience should not solely be defined by the absence of psychopathology. Luthar and colleagues (Luthar, 2006; Luthar & Cicchetti, 2000) assert that indicators used to represent resilience should parallel the adversity examined in terms of the domain assessed and the stringency of criteria used, yet other researchers suggest that defining “positive adaption” may be dependent on other factors, such as the sociocultural context in which an individual operates rather than solely the adversity faced (Clauss-Ehlers, 2008; Mahoney & Bergman, 2002; Waller, 2001). A final area of controversy in measuring this construct is a lack of consensus on the pattern of adaption that resiliency follows. Masten and Narayan (2012) propose an outline of patterns of adaptation in response to either acute trauma, or severe and chronic adversity, highlighting the importance of temporality. In this model, responses to acute trauma include (a) resistance or maintenance of function, (b) decline in functioning followed by recovery, and (c) functioning that improves over baseline as a result of the stressful experience. The question, then, is whether recovery after an initial drop in well-being meets criteria for resilience. Bonanno, Westphal, and Mancini (2005) argue that recovery after the initial decline is often incomplete. However, Luthar and Brown (2007) state that there is no

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good evidence to suggest that “resistance” and “recovery” groups of resilient individuals are qualitatively different or that these groups have distinct antecedents or consequences. Again, this distinction depends on the time frame used to characterize resilience. As the time frame increases, it is more likely that many individuals will recover, indicating again the necessity of longitudinal studies of resilience (Rutter, 2012). Despite the aforementioned debates in the field, researchers are united by the common goal of identifying mechanisms and processes that underlie positive adaption in the presence of vulnerability and risk. Current resilience research focuses on a process of positive adaptation in the presence of vulnerability and risk that may be the result of individual factors (e.g., how an individual copes with stress, genes), environmental factors (e.g., parental warmth, community support), or the interplay of the two (Cicchetti, 2013; Luthar, 2006; Luthar & Cicchetti, 2000; Rutter, 2012). Identification of processes that promote resilience in the face of risk is vital to creation of promising preventive interventions. Risk and Resilience. Although there is merit to understanding risk and resilience as distinct concepts, they may be better conceptualized as existing along a continuum. In many instances, high levels of a factor or process protect individuals from risk whereas low levels of the same factor or process amplify risk (Luthar, Sawyer, & Brown, 2006). For example, high IQ may serve as a protective factor in the face of socioeconomic adversity, whereas low IQ may increase the potency of the effects of poverty. Thus, IQ may both increase and decrease vulnerability associated with socioeconomic hardship. However, there are also instances where high levels of a factor are protective, but low levels are neutral or benign in relation to the source of risk. For example, temperamental characteristics of negative affectivity and positive affectivity, respectively, are vulnerability/risk and resilience factors for emotional problems (Compas, Connor-Smith, & Jaser, 2004). However, these traits are relatively independent, as low negative affectivity does not denote positive affectivity. Thus, low negative affectivity indicates the absence of this vulnerability factor but it does not necessarily serve as a protective factor. The situation is further complicated because some vulnerability/risk and protective factors are stable, whereas others change with development. For example, some temperamental characteristics emerge in infancy and remain stable throughout childhood and adolescence. Stable individual differences in temperament may function as either vulnerability or protective factors in adolescence, depending on the characteristic in question. Similarly, some features of the environment may be stable sources of risk or protection throughout childhood and adolescence (e.g., chronic poverty or a supportive and structured family environment), even though negative patterns of family interaction have been shown to be changeable with evidence-based intervention. Other factors

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may emerge during adolescence as sources of risk and protection and can be defined as developmental risk and protective factors. For example, some aspects of cognitive and brain development change dramatically during early adolescence and mark this as a period of heightened vulnerability for many adolescents (Albert, Chein, & Steinberg, 2013; Casey, Getz, & Galvan, 2008; Powers & Casey, 2015; Spear, 2011; Steinberg, 2008, 2015). Similarly, it appears that effects of certain types of stressful events are relatively benign during childhood but are much more likely to be associated with negative outcomes during adolescence (Hankin & Abramson, 2001). In human research, the final step in vulnerability/risk research is likely to involve preventive interventions designed to change established vulnerability and risk factors to determine their possible causal role and prevent the onset of psychopathology.

UNIFYING CONCEPTS FOR UNDERSTANDING RISK AND RESILIENCE: CURRENT PERSPECTIVES ON STRESS, COPING, AND EMOTION REGULATION Research on exposure to stressful events and circumstances and ways in which children and adolescents respond to and cope with stress are central to understanding processes of risk and resilience for psychopathology among young people. Specifically, exposure to stressful events and circumstances, including generation of stressors in neighborhood, school, peer, and family environments, are primary risk factors that exert effects on child and adolescent mental (and physical) health. Furthermore, individual differences in coping and related processes of stress reactivity and emotion regulation are crucial sources of resilience in the face of both distal and proximal sources of stress.

Stress and Emotions In spite of strong criticisms of the construct (e.g., Lazarus, 1993), stress remains a central factor in understanding risk for psychopathology. Prevailing definitions of stress include environmental circumstances or conditions that threaten, challenge, exceed, or harm the psychological or biological capacities of the individual. Grant, Compas, Stuhlmacher, Thurm, and McMahon (2003, p. 449) defined stressors during childhood and adolescence as “environmental events or chronic conditions that objectively threaten the physical and psychological health or well-being of individuals of a particular age in a particular society.” This definition is consistent with traditional objective measures and stimulus-based definitions of stress (e.g., Rudolph & Hammen, 2000). At the same time, events or chronic circumstances can threaten the well-being of an individual without leading to negative outcomes. Thus, stressful events and conditions are defined independently of their effects or outcomes. This definition allows for positive outcomes in the face of objectively threatening circumstances; that is, it allows for resilience.

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In a series of reviews, Grant and colleagues (Grant et al., 2003; Grant et al., 2004; Grant et al., 2006; McMahon, Grant, Compas, Thurm, & Ey, 2003) identified several overarching findings from research on stress and psychopathology among children and adolescents. First, as noted above, Grant et al. (2003) suggest that stress is best conceptualized in terms of the occurrence of acute events or chronic conditions or circumstances (referred to as stressors) that threaten the physical or mental health of the child or adolescent. The nature of events (e.g., parental divorce, family move) and chronic conditions (e.g., poverty, chronic parental conflict and discord) that constitute sources of stress vary as a function of children’s development and social context. Second, more than 50 prospective longitudinal studies have provided evidence that exposure to stressful events and chronic adversity predict increases in both internalizing and externalizing symptoms over time (Grant et al., 2004). Such longitudinal prediction suggests that stressors play a causal role in development of both types of symptoms. Thus, stressful events in the lives of children and adolescents meet criteria for risk factors (and even causal risk factors) as outlined by Kraemer et al. (2001). Third, consistent with a heuristic model of Nolen-Hoeksema and Watkins (2011), exposure to stressful life events functions as a distal risk factor for internalizing and externalizing symptoms, potentially mediated by more proximal family characteristics, including disrupted parenting and parent–child relationships (Grant et al., 2003; Grant et al., 2006). Evidence is particularly strong for poverty and economic disadvantage as distal risk factors that affect child/adolescent internalizing and externalizing symptoms through their effects on parenting (Grant et al., 2003). Finally, McMahon et al. (2003) concluded that exposure to stressful events and chronic sources of adversity plays a role in virtually all types of psychopathology including internalizing and externalizing problems, as well as more specific symptoms of depression, anxiety, eating disorders, aggressive behavior problems, conduct problems, substance use and abuse, and somatization. McMahon et al. (2003) note that across various stressors examined, the most consistent evidence for specificity is for the association of sexual abuse with internalizing symptoms, PTSD, and sexual acting-out symptoms. Subsequent research indicates specificity among a wider set of psychosocial risk factors that include but are not limited to stressful events (Shanahan, Copeland, Costello, & Angold, 2008). In contrast, evidence from the National Comorbidity Survey Replication (Green et al., 2010; McLaughlin et al., 2012) suggests that childhood adversities, including interpersonal loss (parental death, parental divorce, and other separation from parents or caregivers), parental maladjustment (mental illness, substance abuse, criminality, and violence), maltreatment (physical abuse, sexual abuse, and neglect), life-threatening childhood physical illness, and extreme childhood family economic adversity are associated with all types of psychopathology in adulthood. Thus, stressful life events and circumstances of adversity are broad, nonspecific risk factors for a wide range of co-occurring patterns of symptoms and disorders in childhood and adolescence—revealing clear evidence for multifinality (see Chapter 1 [Hinshaw]).

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The large body of research on the role of stressors in child and adolescent psychopathology could suggest that research in this area has reached its zenith and that there is little new to be learned. However, several new perspectives have emerged with strong potential to expand on the decades of research on stress and coping that inform understanding of processes of risk and resilience. Specifically, research in psychopathology has been reshaped by an emphasis on underlying processes as reflected in the Research Domain Criteria (RDoC) from the National Institute of Mental Health (Casey, Oliveri, & Insel, 2014; Insel & Cuthbert, 2015). A similar approach is needed to provide a better understanding of processes that reflect the foundations and substrates of stress and coping. Moreover, the construct of allostatic load highlights the importance of biological processes of stress. Allostatic load refers to the cost or wear and tear on biological and psychological systems as a result of chronic or repeated exposure to significant stress (McEwen & Stellar, 1993). Underlying biological systems can become dysregulated as a result of prolonged exposure to stressful events or conditions leading to behavioral, emotional, and biological dysfunction (Juster, McEwen, & Lupien, 2010). The concept of allostatic load has added value for understanding the role of stress in developmental psychopathology in part because unlike traditional research on stressful life events, it emphasizes the integration of multiple levels of analyses, including genetic and other neurobiological processes, developmental history, and current context and experience (Cicchetti, 2011). Drawing on studies of humans and animals, such multiple-levels-of-analysis research holds promise for delineating processes through which exposure to stressful events and circumstances contribute to development of psychopathology (see Chapter 1 [Hinshaw]). Research guided by the allostatic load framework has generated a number of findings that are potentially important to child and adolescent psychopathology. For example, initial conceptualizations of allostatic load emphasized the effects of chronic stress on activation and dysregulation of the hypothalamic-pituitary-adrenal axis and the production of cortisol. However, Beauchaine, Neuhaus, Zalewski, Crowell, and Potapova (2011) note the additional importance of dysregulation in monoamine neural systems including dopamine, norepinephrine, and serotonin. For example, repeated and prolonged exposure to stress often alters central serotonin expression through epigenetic mechanisms, conferring lifelong risk for anxiety, depression, and other adverse outcomes. Research on allostatic load highlights the important role of chronic exposure to stress as a major source of risk for other biological systems and behaviors as well. For example, repeated exposure to violence alters neurodevelopment in the hippocampus and prefrontal cortex, conferring risk for learning and memory difficulties, disrupted social affiliation, and substance use and abuse (Mead, Beauchaine, & Shannon, 2010). Research on the effects of stressful life events has typically focused on the occurrence of events within a specified and relatively recent period of time (e.g., the prior 6 months). In contrast, more recent research places greater emphasis on the developmental timing of exposure to stress, with evidence accumulating for the long-term significance of early exposure to stress and adversity. For example,

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work by Evans and colleagues documents long-term effects of growing up with the chronic stress of poverty on later psychological and physical health (e.g., Evans et al., 2010; Evans et al., 2013). Extensive research also indicates that exposure to abuse and neglect early in development is related to increased risk for subsequent psychopathology in childhood and adolescence (e.g., Heleniak, Jenness, Vander Stoep, McCauley, & McLaughin, 2016). Research on effects of stressful events and chronic stress has been enriched by a focus on the role of emotions that arise in response to sources of stress in the environment. Emotion is defined broadly as a person-environment interaction requiring attention that involves considerable personal significance and evokes complex, continuously evolving responses (Gross & Thompson, 2007). The environment may include external stimuli or internal representations involving thoughts and memories. Emotions have historically been divided into primary emotions (including anger, sadness, fear, happiness, disgust, surprise) and secondary emotions (e.g., shame, pride). Whereas primary emotions are direct responses to environmental stimuli and constitute a biological preparation for appraisal and response (Izard, 2002), secondary emotions occur as a result of primary emotions. Seminal work by Zajonc (1980) posited the primacy of emotions in human thought and behavior patterns. That is, personal beliefs about one’s likes and dislikes are based in automatic affective responses and do not require higher-order cognitive processes. The importance of emotions in the development of psychopathology is further reflected in the concept of emotion regulation, which we now consider along with the parallel construct of coping.

Coping and Emotion Regulation Given the significant role of stress in psychopathology during childhood and adolescence, coping with and regulating emotions in response to stress is a key feature of resilience. Skills needed to cope with stressful events and chronic adversity and to regulate emotions, including emotions that arise in response to stress, are fundamental aspects of development. The most widely cited definition of coping is that of Lazarus and Folkman (1984), which is derived from their appraisal-based model of stress and coping. They define coping as “constantly changing cognitive and behavioral efforts to manage specific external and/or internal demands that are appraised as taxing or exceeding the resources of the person” (Lazarus & Folkman, 1984, p. 141). Thus, coping is viewed as an ongoing dynamic process that changes in response to changing demands of a stressful encounter or event and includes purposeful responses. Definitions of coping following from the seminal work of Lazarus and Folkman have shifted toward a developmental emphasis (Compas, Connor-Smith, Saltzman, Thomsen, & Wadsworth, 2001; Eisenberg, Fabes, & Guthrie, 1997; Skinner, Edge, Altman, & Sherwood, 2003). For example, Skinner and Wellborn (1994) conceptualized coping as “action regulation under stress” and defined it as “how people mobilize, guide, manage, energize, and direct behavior, emotion, and orientation,

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or how they fail to do so” (p. 113). Compas et al. (2001) defined coping as “conscious volitional efforts to regulate emotion, cognition, behavior, physiology, and the environment in response to stressful events or circumstances” (p. 89). Building on work by Lazarus and Folkman, these definitions focus on effortful, purposeful responses to acute or chronic stressful events or circumstances. In other words, coping refers to processes that are enacted in response to stress, with an increasing emphasis on coping as a form of regulation in response to stress. Regulation of a wider range of functions, including emotion, behavior, cognitions, physiology, and the environment, is now included within the sphere of coping. There has been little consensus regarding the nature and dimensions or types of coping and emotion regulation in childhood and adolescence. Skinner, Edge, Altman, & Sherwood (2003) identified more than 400 categories or types of coping that have been represented in research on this construct. Previous categories include problem-versus emotion-focused coping, approach versus avoidance, and active versus passive coping. Although the problem-and emotion-focused distinction may be important historically, an alternative three-factor control-based model of coping has been validated successfully in several samples that are diverse with regard to ethnicity, nationality, and type of stress (Compas et al., 2001; Compas et al., 2014; Connor-Smith, Compas, Thomsen, Wadsworth, & Saltzman, 2000; Rudolph, Dennig, & Weisz, 1995). Within this model, responses to stress are first distinguished along the dimension of automatic versus controlled processes; coping responses are considered controlled, volitional efforts to regulate cognition, behavior, emotion, and physiological processes, as well as aspects of the environment in response to stress. Coping responses are further distinguished as primary control engagement (problem solving, emotional modulation, emotional expression), secondary control engagement (acceptance, cognitive reappraisal, positive thinking, distraction), or disengagement (cognitive and behavioral avoidance, denial, wishful thinking). This model is supported by at least seven confirmatory factor analytic studies with children, adolescents, and adults exposed to and coping with a wide range of stressors (e.g., peer stressors, war-related stressors, family stressors, economic stressors, chronic pain), from diverse socioeconomic and cultural backgrounds and international samples (e.g., Euro-American, Native American Indian, Spanish, Bosnian, Chinese), using multiple informants (Benson et al., 2011; Compas et al., 2006; Connor-Smith et al., 2000; Connor-Smith & Calvete, 2004; Wadsworth, Reickmann, Benson, & Compas, 2004; Yao et al., 2010). Research in emotion regulation has been carried out relatively separately from research on coping among children and adolescents. Emotion regulation research is organized around modulation of specific emotions (e.g., sadness, fear, anger), and includes any efforts to up or down regulate both positive and negative emotions (see Chapter 11 [Cole, Hall, & Hajal]). A widely accepted definition of emotion regulation has been offered by Thompson (1994): “The extrinsic and intrinsic processes responsible for monitoring, evaluating, and modifying emotional reactions, especially their intensive and temporal feature, to accomplish one’s goals”

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(pp. 27–28). This definition includes the set of processes that allows for the increase, decrease, or maintenance of an affective state (Davidson, Putnam, & Larson, 2000). According to Gross (1998) emotion regulation is a multistage process that includes generation and modulation of emotions, which can occur during several sequential steps (Gross, 1998). The phases during which specific emotions are regulated include both antecedent-focused strategies, which influence an emotion before it is formed fully, and response-focused strategies, which influence an emotion once it has been developed. In this process model, antecedent-focused strategies include situation selection, situation modification, attentional deployment, and cognitive change, and response-focused strategies include response modulation (e.g., Gross, 2001; Gross & Jazaieri, 2014; Gross, Sheppes, & Urry, 2011). Similar to the state of research on coping, a number of different emotion regulation strategies have been identified (e.g., cognitive reappraisal, emotional suppression) and there consensus has not been reached on the underlying structure of these strategies (e.g., Aldao, Nolen-Hoeksema, & Schweizer, 2010). In spite of considerable overlap in conceptualization and measurement of coping and emotion regulation, the literatures on these two constructs have developed quite independently, with the former largely preceding the latter. We see this situation as problematic, as a richer understanding of adaptation to stress will result from integration of these lines of work. To that end, we now consider recent work on emotion regulation and coping. Coping and emotion regulation have both shared and nonshared features. Importantly, both constructs emphasize processes of regulation—whether regulation of emotions specifically (emotion regulation) or regulation of a broader set of responses to stress (coping). Furthermore, processes of regulation may be in response specifically to a stressor (coping) or occur across both positive and negative situations (emotion regulation). Several strategies appear common to both. For example, cognitive restructuring, as viewed in the context of coping as efforts to actively reinterpret stressful or negative events in more neutral or positive terms, overlaps heavily with the cognitive reappraisal form of emotion regulation. Implementation of this strategy is linked to reduced physiological and emotional arousal when an individual is presented with an emotional stimulus (e.g., Oschner, Bunge, Gross, & Gabrieli, 2002); it is used clinically as part of evidence-based cognitive behavioral therapy treatments for several disorders (e.g., Stark, Krumholz, Ridley, & Hamilton, 2009). Deficits in the use of adaptive strategies in response to stress and emotions have been tied to significant emotional and behavioral problems including symptoms of mood and anxiety disorders among children and adolescents (e.g., Bettis et al., 2016). Deficits in emotion regulation in the presence of stress have been tied to many DSM diagnoses among adults, including mood, anxiety, eating, and substance use disorders, as well as personality disorders (e.g., Campbell-Sills & Barlow, 2007; Gross & Levenson, 1997; Miller, Rathus, & Linehan, 2007). In addition, deficits in regulating negative emotions have been linked to internalizing and externalizing disorders among children and adolescents (Beauchaine, 2015). Therefore, strategies of

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coping and emotion regulation may be important sources of resilience for children and adolescents, particularly those at high risk for internalizing and externalizing psychopathology.

RISK AND RESILIENCE: CHILDREN OF DEPRESSED PARENTS To further exemplify stress and coping processes in child and adolescent vulnerability, risk, and resilience for psychopathology, we focus here on examples from research on development of depression during childhood and adolescence. Depression provides a useful example of these constructs because it increases dramatically in prevalence over the course of childhood and adolescence and because there is now a substantial body of work identifying stress as a significant source of risk, stress reactivity as a potential vulnerability, and coping as a source of resilience. Children and adolescents whose parents experience one or more episodes of depression are exposed to a significant source of vulnerability to and risk for depression and other mental health problems. Research on children of depressed parents builds on early vulnerability, risk, and resilience research on children of parents with schizophrenia, but depression has a far higher prevalence than schizophrenia. Indeed, the high prevalence of depression in the general population represents a significant mental health problem (Chapter 18 [Klein, Goldstein, & Finsaas]). As reported in the National Comorbidity Survey Replication, Kessler et al. (2003) found the lifetime prevalence of major depressive disorder to be 16.9%. It is expected that 32 to 35 million adults in the United States will experience an episode of depression over the course of their lifetime. Depression increases significantly from childhood to adolescence. Longitudinal studies suggest that middle adolescence (ages 15 to 16 years old) is a time of substantially increased risk for the onset of major depression (e.g., Hankin et al., 1998). Depression is also a highly recurrent disorder, as individuals who suffer from a first depressive episode have a 40% chance experiencing a subsequent episode, individuals with two episodes have an approximate 60% chance of a recurrence, and individuals with three episodes have 90% risk (e.g., Moffitt et al., 2010). Furthermore, an initial onset of depression during adolescence predicts a severe and recurrent course of disorder, with high levels of impairment (e.g., Hammen, Brennan, Kennan-Miller, & Herr, 2008). Rates of depression among women are highest in young adulthood, during childbearing years, and among women with children (Kessler et al., 2003). Among males, rates of depression are higher in those younger than age 45 than in those age 45 and older. The former age group of men is likely to have children (Kane & Garber, 2004). Thus, a significant number of children and adolescents are exposed repeatedly to symptoms of depression—both when their parents are in and out of episode. The large number of mothers who experience clinical depression during their children’s lifetimes is particularly problematic, as maternal depression is

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linked to significant negative developmental outcomes among children (Goodman, 2007; Goodman et al., 2011). Children of depressed parents are at high risk for both internalizing and externalizing psychopathology, including a two- to threefold increased risk of developing depressive disorders (NRC & IOM, 2009). As many as 50%–80% of offspring of depressed parents (7.5 to 12 million children) will meet criteria for a psychiatric disorder by young adulthood (NRC & IOM, 2009). Because adolescence marks a particularly important developmental period for increased depression risk, early to middle adolescent offspring (10 to 15 years old) of depressed parents are an ideal target for prevention, given their high risk for depression, anxiety, and externalizing behavior problems (NRC & IOM, 2009). Vulnerability, risk, and protective factors for children of depressed parents include biological, psychological, and interpersonal processes (Goodman & Gotlib, 1999). Indeed, because the heritability of major depression appears to be less than 50% (Sullivan, Neale, & Kendler, 2000), psychosocial risk factors are highly likely to be involved in intergenerational transmission. Having established that parental depression is a significant risk factor for depression and other forms of psychopathology among children and adolescents, it is important to understand mechanisms and processes through which vulnerability and risk affect offspring. In addition, since not all offspring of depressed parents develop psychopathology, understanding protective processes that lead to resilience is also important.

Vulnerability and Risk Processes Among Children of Depressed Parents Effects of parental depression on offspring are likely transmitted through multiple mechanisms, including heritable vulnerability in terms of dysfunctional neuroregulatory mechanisms; exposure to negative maternal cognitions, behaviors, and affect; and the stressful context of the adolescent’s life (Goodman & Gotlib, 1999). Of particular relevance to this chapter are disrupted interpersonal interactions that depressed individuals experience, which contribute to high levels of stress for children and adolescents in families of depressed parents. Specifically, parent–child interactions are critical mechanisms through which children are exposed to risk factors associated with parental depression, as parental depression affects parents’ behaviors and emotions in interactions within the family (Dix & Meunier, 2009; Lovejoy, Graczyk, O’Hare, & Neuman, 2000). Exposure to stressful parent–child interactions is a primary psychosocial mechanism through which parental depression exerts its effects on children (e.g., Gruhn et al., 2016; Jaser et al., 2005; Jaser et al., 2007; Jaser et al., 2008; Langrock, Compas, Keller, Merchant, & Copeland, 2002). Depression impairs parents’ abilities to effectively provide support, nurturance, and structure for their children, leading to disruptions in parenting. Most research on parenting in depressed families is concentrated on parenting difficulties associated with the physical,

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cognitive, and emotional symptoms of depression (e.g., sad mood, irritability, lack of interest, fatigue, or difficulty concentrating; Dix & Meunier, 2009). Parental withdrawal (e.g., avoidance or unresponsiveness to their children’s needs) and intrusiveness (e.g., irritability toward their children or excessive involvement in their children’s lives) are characteristic of depressed parents in their interactions with their children (e.g., Cummings, DeArth-Pendley, DuRocher-Schudlich, & Smith, 2001). Exposure to hostile, disengaged, and inconsistent parenting, as opposed to nurturing parenting, contributes to a chronically stressful and unpredictable environment for children and tends to result in increased symptoms among offspring of depressed parents. Recent studies show that children who are exposed to higher levels of parental intrusiveness/irritability and withdrawal have higher levels of internalizing and externalizing symptoms. For example, Langrock et al. (2002) found that both parental intrusiveness and withdrawal are correlated with higher levels of offspring anxiety/depression and aggression, according to parent reports. Jaser et al. (2005) extended those findings by demonstrating significant positive cross-informant correlations among adolescent reports of parental intrusive behaviors and parent reports of adolescent internalizing and externalizing symptoms. Using direct observations of parents’ behavior in a sample of parents with a history of depression, Gruhn et al. (2016) found that parental depressive symptoms were related to withdrawn parenting for parents of boys and girls and to intrusive parenting for parents of boys only. When covarying for intrusive parenting, parental depressive symptoms were related to withdrawn parenting for parents of boys. Moreover, when adjusting statistically for the other type of problem (i.e., internalizing or externalizing), withdrawn parenting specifically predicted externalizing problems but not internalizing problems among girls. No evidence of specificity was found for boys, suggesting that impaired parenting behaviors are diffusely related to both internalizing and externalizing symptoms for boys. Evidence suggests that behavioral effects of maternal depression are at least in part causal. For example, depressive symptoms among children of depressed mothers decrease when mothers’ depression is in remission, an effect that is partly accounted for by improvements in mothers’ parenting (Weissman et al., 2014). There may be long-lasting effects of disrupted parenting, as discord in mother-child relationships at age 15 predicts depressive symptoms in daughters of depressed mothers at age 20 (Katz, Hammen, & Brennan, 2013). Through several risk processes, offspring of depressed parents are at increased risk for depression and other forms of psychopathology. However, research suggests that even under the stressful circumstances of having a parent with depression, a substantial proportion of children are resilient and adapt successfully. Attempts to explain resilience have focused on potential moderators. Ways that adolescents react to and cope with the stress of living with a depressed parent may serve as both mediators and moderators of the effects of this stress.

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Sources of Resilience Among Children of Depressed Parents Styles of response to stress play critical roles in the effects that stress has on their emotional and psychological well-being (Compas et al., 2001; Compas et al., 2014). First, how do children cope with stress associated with parental depression? Second, how does depression in a parent constrain or alter ways that children cope with stress related to parents’ depression? Third, how do children’s coping responses moderate relations between stress and children’s adjustment in families of depressed parents? Our research group has studied coping and stress responses in three samples of adolescent offspring of depressed parents (Bettis et al., 2016; Dunbar et al., 2013; Fear et al., 2009; Jaser et al., 2005; Jaser et al., 2007; Jaser et al., 2008; Jaser, Champion, Dharamsi, Reising, & Compas, 2011; Langrock et al., 2002). First, we examined these processes in a sample of adolescents whose mother or father had a history of depression and who had experienced at least one episode of depression in the adolescent’s lifetime (Jaser et al., 2005; Jaser et al., 2007; Langrock et al., 2002). In this sample, adolescents’ use of secondary control coping (i.e., positive thinking, distraction, acceptance, and cognitive restructuring) was related to lower symptoms of anxiety and depression, both within and across adolescents’ and parents’ reports of adolescents’ coping and symptoms. Furthermore, higher levels of stress reactivity (emotional and physiological arousal, intrusive thoughts) were related to higher symptoms of anxiety/depression. A troubling pattern was identified in these adolescents. As levels of stress related to parental withdrawal and parental intrusiveness increased, adolescents reported using less secondary control coping and experiencing higher levels of stress reactivity (Jaser et al., 2005; Langrock et al., 2002). This finding is consistent with the notion that stress contributes to dysregulation (heightened stress reactivity) and interferes with controlled self-regulation and coping, both of which lead to increased risk for depressive symptoms (Compas, 2006). Second, we have examined coping and stress responses among adolescents whose mothers had a history of depression and compared them with a demographically matched sample of adolescents whose mothers had no history of depression (Jaser et al., 2008). As expected, the former group were higher in depressive symptoms and externalizing problems than adolescents whose mothers did not have a history of depression. Furthermore, the former group reported higher levels of stress reactivity (e.g., emotional and physiological arousal, intrusive thoughts) than the comparison group. Mothers’ reports of their current depressive symptoms and observations of maternal sadness during parent–child interactions in the laboratory were each related to higher levels of adolescents’ depressive symptoms and externalizing problems, higher stress reactivity, and lower levels of secondary control coping. Finally, adolescents’ use of secondary control coping and stress reactivity accounted for the relation between maternal history of depression and adolescents’ depressive symptoms. These findings replicate those found by Jaser et al. (2005) and Langrock et al. (2002) and extend them by using direct observations to assess parental depressive symptoms and parent-adolescent interactions.

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Third, we have examined stress and coping among adolescent offspring of mothers and fathers with a history of depression (Bettis et al., 2016; Dunbar et al., 2013; Fear et al., 2009). In this sample, our focus was on adolescents’ coping with interparental conflict. Once again, we found support for secondary control coping as a predictor of lower internalizing and externalizing symptoms, after accounting for method variance in adolescent and parent reports of coping and symptoms. Furthermore, secondary control coping partially or fully accounted for the association between interparental conflict and adolescent symptoms (Fear et al., 2009). In addition, Bettis et al. (2016) found that secondary control coping is a transdiagnostic correlate of lower levels of symptoms of anxiety and depression in youth. In contrast, primary control coping was related specifically to lower youth depressive symptoms but not anxiety symptoms. Disengagement coping was not a significant correlate of symptoms of anxiety or depression in youth (Bettis et al., 2016). Researchers have also examined emotion regulation (Compas et al., 2009; see earlier) among children of depressed parents, yet no such studies have been conducted to date among adolescents. The most extensive work has been conducted by Kovacs, Forbes, Silk, and colleagues and has used direct observation methods to assess young children’s (age 3 to 7 years old) emotion regulation in response to laboratory stress tasks, and examined the relation between children’s emotion regulation and depressive symptoms (Forbes, Fox, Cohn, Galles, & Kovacs, 2006; Forbes, Shaw et al., 2006; Silk, Shaw, Forbes, Lane, & Kovacs, 2006; Silk, Shaw, Skuban, Oland, & Kovacs, 2006). Because of the importance of understanding emotion regulation and depression in young people, we review these studies here. They are noteworthy for several reasons, including inclusion of a particularly high-risk sample—children whose mothers themselves first experienced depression during childhood—and use of both direct observations and physiological measures of emotion regulation. Silk, Shaw, Forbes, et al. (2006) observed children’s responses to a delay-ofgratification task as an example of an emotionally arousing (frustration) context for children and their mothers. Children of mothers with childhood-onset depression were more likely to focus on a delay object (a response that may be similar to rumination—as a form of passive engagement with the source of stress or emotional arousal) than children of mothers without a history of depression. Furthermore, use of positive reward anticipation (displays of joy and information gathering, a component of problem solving, a form of primary control engagement coping) was related to fewer internalizing symptoms among children of mothers with childhood-onset depression and current depressive symptoms, but not for children of mothers without a history of depression (Silk, Shaw, Skuban et al., 2006). These studies suggest that processes of coping and emotion regulation may develop during childhood and carry over into adolescence.

Preventing Psychopathology Among Children of Depressed Parents Drawing on evidence for the importance of stress and coping for positive child adjustment in families of depressed parents, Compas, Forehand, Keller

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and colleagues developed and tested the initial efficacy of a family group cognitive-behavioral (FGCB) preventive intervention for parents with a history of depression and their children (Compas, Forehand, & Keller, 2011; Compas, Keller, & Forehand, 2011; Compas, Langrock, Keller, Merchant, & Copeland, 2002). The preventive intervention is designed to reduce stressful parent–child interactions that are associated with parental withdrawal and irritability/intrusiveness and enhance children’s and parents’ use of secondary control engagement coping strategies to reduce the risk for symptoms and disorder in these children. The FGCB intervention is a manualized 12-session program (eight weekly and four monthly follow-up sessions) designed to teach coping skills in a small family group format to families with a parent who has a history of a depressive disorder. Each family group includes four families and is co-led by a mental health professional with extensive training in group facilitation and a doctoral student in clinical psychology. The program is designed for participation by both parents and children. Goals are to educate families about depressive disorders, increase family awareness of the impact of stress and depression on functioning, help families recognize and monitor stress, facilitate the development of children’s adaptive coping responses to stress, and improve parenting skills. Information is presented to group members during eight weekly sessions, practice and discussion of skills are facilitated during the sessions, and all members are given weekly at-home practice exercises. Four monthly follow-up booster sessions are included to provide additional practice and support in continued development and refinement of the skills learned in the initial weekly sessions. The intervention is designed to address hypothesized mediators of the effects of parental depression on children: parental depressive symptoms and negative affect, stressful parent–child interactions, and children’s coping with these stressors. The parenting component of the intervention includes building skills to increase parental warmth and involvement with their children, plus increasing structure and consequences for children’s problem behavior. Children are taught skills to cope with their parents’ depression, including the use of acceptance, distraction, and cognitive reappraisal. The coping skills that are taught and practiced as part of the program are designed to enhance development of secondary control coping strategies (cognitive restructuring, acceptance, distraction) among participants. The research summarized above shows that these strategies are effective in coping with stressful parent–child interactions associated with parental depression. The initial efficacy of the intervention has been tested in a clinical trial in which families were randomized to the FGCB intervention or to a written information (WI) comparison condition. Significant effects on children’s (ages 9 to 15 years) mental health favoring the FGCB intervention were found at 2-, 6-, and 12-month follow-ups (Compas et al., 2009), effects that were generally maintained at 18 months, although some effects dissipated at 24 months (Compas et al., 2011, 2015). The FGCB intervention led to significantly lower levels of Youth Self-Report (YSR) internalizing symptoms at 2, 6, 12, and 18 months and significantly lower externalizing symptoms at 12, 18, and 24 months. There was also an effect for the

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intervention on a specific youth self-report measure of depressive symptoms at 12 months and mixed anxiety-depression symptoms at 2, 6, 12, and 18 months. Effects on parents’ reports of their children’s symptoms were quite limited, with the only significant effect occurring for externalizing symptoms on the Child Behavior Checklist at 12 months. Finally, the FGCB intervention had a significant effect on children’s episodes of major depression as measured with diagnostic interviews with the parents and children. Over the 24 months from baseline, fewer children in the FGCB intervention experienced a major depressive episode (14.3%) than children in the WI comparison condition (32.7%). Mediational analyses examined whether changes in children’s coping and changes in parenting behaviors accounted for the effects of the FGCB on children’s mental health outcomes. Significant effects were found for changes in children’s coping as a mediator of the intervention, as changes in secondary control coping from baseline to 6 months mediated intervention effects on changes in children’s depression, mixed anxiety-depression, internalizing, and externalizing symptoms from baseline to 12-month follow-up. We also tested for possible effects of the intervention on children’s primary control coping, but there were no significant findings. The intervention was specific in its effects on secondary control coping, and strong evidence was found for secondary control coping as a protective factor for both internalizing and externalizing symptoms. These findings support the role of children’s coping and impaired parenting skills in parents suffering from depression as possible causal factors as outlined by Kraemer et al. (2001, 2002).

CONCLUSIONS Exposure to stress and adversity, and the ways that individuals cope with stress, are venerable and well-tested constructs that are central to understanding sources of vulnerability, risk, and resilience to psychopathology in children and adolescents. Stressful life events and chronic adversity, most notably poverty and chronic abuse during development, are powerful, nonspecific predictors of internalizing and externalizing symptoms and disorders. Among some individuals, vulnerability and risk are buffered by resilient qualities, including effective ways of coping with stress. Recent research supports a control-based model of coping among children and adolescents, with protective effects associated with use of primary and secondary control coping methods. The foundation of research on stress and coping is now being extended by research on new iterations on these themes, including the importance of processes of allostatic load, long-term effects of exposure to adversity early in development, and elucidation of specific emotion regulation processes as part of overall efforts to cope with stress. Advances in research on children and adolescents at high risk for depression provide salient examples of risk and resilience processes in this population. Perhaps most importantly, recent evidence suggests that these processes can inform the development of promising interventions to prevent depression in children at high risk. The field is now poised

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to extend research on stress, coping, and emotion regulation to other types of symptoms and disorders to inform the development of preventive interventions and treatments.

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CHAPTER 5

Child Maltreatment and Risk for Psychopathology SARA R. JAFFEE

EPIDEMIOLOGY OF ABUSE AND NEGLECT

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n 2013, the most recent year for which figures are available, approximately 3.9 million children in the United States were investigated as possible victims of abuse or neglect. Twenty percent of these investigations were substantiated (or indicated), representing 679,000 children nationally, or 9.1 per 1,000 children in the population (U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth, and Families, & Children’s Bureau, 2015). Of these children 1,520 died as a result of abuse or neglect. These figures represent a decline in maltreatment rates since the 1990s, although the extent of the decline may be more pronounced in official records as opposed to national surveys of youth or caregivers (Gilbert et al., 2012). As reviewed by Finkelhor and Jones (2006), the decline parallels a downward trend in crime rates overall and may reflect reductions in numbers of unwanted children, growing economic prosperity (with the exception of the Great Recession; Brooks-Gunn, Schneider, & Waldfogel, 2013), and increases in numbers of social workers, child protection workers, and child abuse prevention workers. Of those children who were substantiated victims of abuse or neglect, 79% were victims of neglect and 18% were victims of physical abuse. Neglect is defined as the failure to meet children’s basic physical needs with respect to clothing, hygiene, food, and safety, whereas physical abuse is defined as harm by a caregiver or someone who has responsibility for the child resulting in nonaccidental physical

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injury (from minor bruises to severe fractures or death) (Leeb, Paulozzi, Melanson, Simon, & Arias, 2008). Nine percent of children were victims of sexual abuse and 8.7% were victims of psychological maltreatment, the core feature of which is a pattern of behavior that impairs a child’s emotional development or sense of self-worth (Leeb et al., 2008). Finally, 10% were victims of “other” forms of abuse, which include, for example, “threatened abuse” or a parent’s drug or alcohol abuse (U.S. Department of Health & Human Services et al., 2015). These percentages add up to more than 100%, because of the presence of multiple forms of maltreatment in some youth. According to 2013 statistics, children under the age of 3 years were victimized at higher rates than older children, and children under the age of 1 year were victimized at the highest rates of all (23.1 per 1000 children) (U.S. Department of Health & Human Services et al., 2015). Boys and girls were equally likely to be victims of abuse or neglect, but 14.6 per 1,000 African-American children were victimized, compared with 8.1 per 1,000 White, and 8.5 per 1,000 Hispanic children (U.S. Department of Health & Human Services et al., 2015). Other sociodemographic predictors of maltreatment include family poverty, young motherhood (Mersky, Berger, Reynolds, & Gromoske, 2009; Sedlak & Broadhurst, 1996; Thornberry et al., 2014), parental history of antisocial behavior (Jaffee, Caspi, Moffitt, & Taylor, 2004; Thornberry et al., 2014), and a perpetrator’s history of maltreatment (Conger, Schofield, Neppl, & Merrick, 2013; Herrenkohl, Klika, Brown, Herrenkohl, & Leeb, 2013; Jaffee et al., 2013; Thornberry et al., 2013; Widom, Czaja, & DuMont, 2015). In a follow-up of participants from the Rochester Youth Development Study, there were pronounced effects of cumulative risk: Only 3% of those who were not at risk in any of 10 developmental domains were involved in perpetrating later maltreatment as adults, whereas 45% of those who were at risk in at least nine developmental domains were involved in perpetrating later maltreatment (Thornberry et al., 2014).

MALTREATMENT AND CHILDREN’S RISK FOR PSYCHOPATHOLOGY This section describes studies that have tested whether maltreated children are at elevated risk for psychopathology. The review is focused on the most methodologically rigorous studies, which include the following features: (a) a prospective research design wherein maltreatment predated the onset of psychopathology, (b) a demographically matched control sample or statistical adjustments for variables that could confound the association between maltreatment and risk for psychopathology, and (c) psychometrically valid measures of psychopathology, including (but not limited to) diagnostic measures. In the majority of these studies, information about maltreatment came from Child Protective Services records, although in some studies maltreatment was reported by caregivers. Because

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official records are likely to underestimate the true prevalence of maltreatment, studies that combine official records with informant reports are likely to provide the most valid information (Cohen, Brown, & Smailes, 2001; Widom et al., 2015).

Maltreatment and Risk for Externalizing Psychopathology Victims of maltreatment are at elevated risk for a range of externalizing problems in childhood and adolescence, including attention deficit/hyperactivity disorder (ADHD), conduct disorder (CD), oppositional defiant disorder (ODD; Cohen et al., 2001; Famularo, Kinscherff, & Fenton, 1992), delinquency (Lansford et al., 2007; Stouthamer-Loeber, Loeber, Homish, & Wei, 2001; Widom, 1989; Williams, Van Dorn, Bright, Jonson-Reid, & Nebbitt, 2010), and antisocial behavior (Jaffee et al., 2004; Jonson-Reid et al., 2010; Lansford et al., 2002; Manly, Kim, Rogosch, & Cicchetti, 2001; Moylan et al., 2010). Some studies also identify elevated symptoms of substance use in maltreated versus nonmaltreated youth (Kaufman et al., 2007; Lansford, Dodge, Pettit, & Bates, 2010; Rogosch, Oshri, & Cicchetti, 2010), but others have not observed this pattern (e.g., Cohen et al., 2001). Risk for externalizing problems extends into adulthood, when victims have significantly elevated rates of antisocial personality disorder (ASPD; Johnson, Cohen, Brown, Smailes, & Bernstein, 1999; Luntz & Widom, 1994), self-reported crime (Thornberry, Henry, Ireland, & Smith, 2010), and criminal arrests (Maxfield & Widom, 1996). Findings with respect to drug and alcohol use are mixed, with some studies identifying elevated rates of drug and alcohol use among young adults with a history of maltreatment versus those without such a history (Cohen et al., 2001; Noll, Trickett, Harris, & Putnam, 2009; Scott, Smith, & Ellis, 2010; Thornberry et al., 2010), and others finding that the relation between these problems and child maltreatment is stronger in women than men—or is detectable in middle age, but not in young adulthood (Widom, Ireland, & Glynn, 1995; Widom, Marmorstein, & Raskin White, 2006).

Maltreatment and Risk for Internalizing Psychopathology Victims of maltreatment are at risk for a range of internalizing problems in childhood as well, including major depressive disorder (Brown, Cohen, Johnson, & Smailes, 1999), anxiety disorders (Cohen et al., 2001), post-traumatic stress disorder (PTSD) and symptoms of trauma (Crusto et al., 2010; Famularo et al., 1992; Milot, Ethier, St-Laurent, & Provost, 2010; Putnam, Helmers, & Horowitz, 1995), and internalizing symptoms (Bolger & Patterson, 2001; Lansford et al., 2002; Manly et al., 2001; Moylan et al., 2010). Risk for internalizing disorders associated with child maltreatment extends into adulthood. Victims have significantly elevated rates of major depressive disorder (Brown et al., 1999; Noll et al., 2009; Scott et al., 2010; Widom, DuMont, & Czaja, 2007), depressive symptoms (Thornberry et al., 2010),

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and anxiety disorders (Cohen et al., 2001; Scott et al., 2010) and PTSD (Scott et al., 2010; Widom, 1999) compared to adults without a history of child maltreatment.

Maltreatment and Risk for Personality Disorders, Psychotic Symptoms, and Suicide In adulthood, victims of child maltreatment are also at risk for borderline personality disorder (Johnson et al., 1999; Widom, Czaja, & Paris, 2009), with one study also showing risk for Cluster B (dramatic, emotional, erratic) and C (anxious, fearful) personality disorders more broadly (Johnson et al., 1999). Indeed, borderline personality symptoms linked to maltreatment are already evident in childhood (Hecht, Cicchetti, Rogosch, & Crick, 2014). Moreover, at least one study demonstrates that child victims of maltreatment experience elevated rates of psychotic symptoms in early adolescence compared with nonmaltreated youth (Arseneault et al., 2011). In addition, victims of child maltreatment are at elevated risk for suicide in adolescence and adulthood (Brown et al., 1999; Thornberry et al., 2010), and engage in elevated rates of self-injury (Yates, Carlson, & Egeland, 2008).

IS THE ASSOCIATION BETWEEN MALTREATMENT AND PSYCHOPATHOLOGY CAUSAL? According to conventional wisdom, maltreatment is a cause of psychopathology. However, alternative explanations are possible. One such possibility is that maltreatment correlates with heritable vulnerabilities to psychopathology, which parents transmit to their children (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). A second possibility is evocative effects in which parents become abusive in response to their child’s difficult behavior or other characteristics that are difficult to manage (Belsky, 1993). Because children cannot ethically be randomly assigned to abusive or neglectful families, quasi-experimental, statistical matching, or randomized control trial data are required in attempts to rule out these alternative explanations. Using propensity score matching methods,1 Thornberry et al. (2010) showed that maltreated youth experience significantly more depressive symptoms and suicidal thoughts, substance use problems, and criminal behaviors in young adulthood compared with nonmaltreated youth who were matched on preexisting individual and family characteristics. Using data from a prospective study of twins, Jaffee et al. (2004) provided four pieces of evidence consistent with the hypothesis that maltreatment is a cause of children’s antisocial behavior: (1) abuse was associated with changes over time in children’s antisocial behavior; (2) a dose-response 1. The propensity score method is an econometric technique developed by Rosenbaum and Rubin (1983) to draw causal inferences from observational data. The method is premised on identifying some “treatment” (e.g., being maltreated or not maltreated). It then matches the treated and the untreated on a variety of background and individual characteristics, thus achieving statistically what randomization to treatment and control conditions would achieve by design.

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relation existed between the severity of the abuse and the severity of children’s antisocial behavior; (3) genetic factors accounted for a small, statistically nonsignificant portion of variation (7%) in children’s experience of abuse, suggesting that genetically mediated child behavior problems did not provoke abusive discipline; and (4) abuse remained a significant predictor of children’s antisocial behavior when parental antisocial behavior was covaried. These data are consistent with the hypothesis that characteristics of parents and families, but not children, explain why some children were more likely than others to be abused. Although quasi-experimental designs allow for stronger causal inference about relations between maltreatment and child psychopathology, many such designs are ill-suited for the study of child maltreatment. In adoption designs, for example, investigators can eliminate the possibility that maltreatment is a marker for genetic risk for psychopathology parents transmit to children because parents and children are not related biologically. With good reason, however, adoptive families are screened for being at low risk of maltreatment, so rates of maltreatment in studies of adoptees are low. The co-twin control design in which researchers test whether twins who are discordant for the experience of maltreatment are similarly discordant for psychopathology is another design that allows for strong causal inference, because twins growing up in the same family are exposed to many of the same risk factors for psychopathology (e.g., family poverty, a parent’s mental illness), and they are genetically similar (virtually identical, in the case of monozygotic twins; see Chapter 3 [Beauchaine et al.]). It is rare, however, for one twin but not the other to have been maltreated (Jaffee et al., 2004) and extremely large, high-risk samples are required to identify discordant cases. Research designs that match maltreated children with sociodemographically similar, nonmaltreated youth may be the most feasible way of estimating unique effects of maltreatment on risk for psychopathology. Whenever prospective, longitudinal data are available, such designs can be used in attempts to adjust for child characteristics that pre-date maltreatment, in order to rule out child evocative effects. Although this design has indeed identified effects of maltreatment on risk for psychopathology, it also demonstrates that socioeconomic disadvantage itself is associated with high rates of emotional and behavioral health problems, poor life course outcomes, and risk for maltreatment perpetration (Nikulina, Widom, & Czaja, 2011; Widom et al., 2015).

ETIOLOGICAL FORMULATIONS Researchers have proposed multiple mechanisms through which maltreatment might increase risk for psychopathology, from epigenetic processes and gene expression, to neuroendocrine, immune, and neurotransmitter systems, to brain structure and function, as well as to social cognition. Although it is likely that alterations at one level of the organism have downstream effects, empirical demonstrations of these pathways among humans are rare. In addition, although

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there is growing evidence that maltreated and nonmaltreated individuals differ physiologically and cognitively, the assumption that such differences increase vulnerability to psychopathology among maltreated children or adults with histories of maltreatment is not always tested. Thus, studies on mechanisms of maltreatment are reviewed by level of analysis, with an emphasis on those that cross levels of analysis and test mediational models.

Epigenetic Modifications and Alterations in Gene Expression Epigenetics refers to chemical changes in DNA (not technically nucleotide structure but in associated “hardware”) that alter functional activity, including, among other processes, chromatin remodeling, histone modification, microRNA activity, and methylation, the last of which has been the focus of much research in rats and humans (Szyf & Bick, 2013). In human and animal studies, early caregiving conditions are associated with glucocorticoid receptor (GR) methylation and gene expression profiles in hippocampal tissue, where the GR is expressed highly in rodents and humans (Wang et al., 2013). For example, GR 17 expression is reduced in offspring of rat dams that engaged in low as compared to high levels of licking and grooming, and offspring of low licking and grooming dams show increased methylation of GR 17 relative to offspring of high licking and grooming dams (Weaver et al., 2004). A similar pattern of findings has been observed in GR1F , the human GR17 homologue. In studies of postmortem hippocampal tissue, suicide completers with childhood histories of abuse show reduced expression and increased methylation of GR1F relative to controls and suicide completers without histories of abuse (McGowan et al., 2009). Maltreatment is also associated with increased methylation among children in exons 1D (Tyrka et al., 2015) and 1F (Romens, McDonald, Svaren, & Pollak, 2015; Tyrka et al., 2015) of the glucocorticoid receptor gene NR3C1 in peripheral tissue. Reduced expression of other GR splice variants including GR1B , GR1C , and GR1H is observed among suicide completers with childhood histories of abuse relative to nonabused suicide completers and controls, although a history of abuse is associated with decreases in methylation across GR1H (Labonte, Yerko, et al., 2012). Abuse-related methylation differences are also observed across the genome (Labonte, Suderman et al., 2012). In the first epigenome-wide study of maltreated youth, methylation differences between maltreated and control youth were identified in 2,868 genes, even taking into account multiple testing and sociodemographic characteristics (Yang et al., 2013). The majority of these genes were in intragenic regions and are involved in adversity-related diseases, including cancer (Yang et al., 2013). It is possible that maltreatment-induced epigenetic changes are allele-specific, which could explain why individuals who carry a particular genotype are at elevated risk for psychopathology when they are exposed to maltreatment. For example, men and women who experience childhood sexual and physical abuse

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have significantly lower levels of methylation at CpG sites in intron 7 of FKBP5 rs1360780 compared with men and women who are not exposed to childhood trauma, but only if they carry the risk (A) allele. Among individuals who are homozygous for the protective (G) allele, early trauma is unrelated to methylation levels (Klengel et al., 2013). Demethylation is associated with increased transcription of FKBP5 in response to a synthetic glucocorticoid and to amplification of the ultrashort feedback loop through which FKBP5 transcription results in decreased glucocorticoid receptor activity (Klengel et al., 2013). Evidence of allele-specific glucocorticoid receptor insensitivity among abuse-exposed individuals is provided by the finding that risk allele carriers show weaker correlations between gene expression levels in glucocorticoid responsive genes and plasma cortisol levels compared with protective allele carriers. Moreover, genes that show the greatest FKBP5-dependent effects on GR sensitivity include transcripts in several immune system pathways, potentially explaining observed associations in the literature between exposure to trauma, low-grade inflammation, and dysregulated immune function (Klengel et al., 2013).

Alterations in HPA Axis Function The HPA axis is activated in response to physical and psychosocial stressors such as maltreatment, resulting in release of corticotropin releasing factor (CRF) and vasopressin from the paraventricular nucleus of the hypothalamus. CRF stimulates release of adrenocorticotropic hormone (ACTH) from the anterior pituitary, which in turn stimulates release of cortisol from the adrenal gland. Cortisol terminates the stress response through feedback at the level of the hypothalamus and the pituitary (Gunnar & Vazquez, 2006). The literature on how the HPA axis is shaped by exposure to early life stress is complex, with disparate findings depending on the level of the HPA axis under examination, whether the HPA axis is being stimulated in response to psychosocial or pharmacological challenge (i.e., synthetic glucocorticoids), and whether participants are currently depressed or experiencing symptoms of PTSD. In addition, relatively few studies have tested whether adversity-related alterations in HPA axis function account for observed associations between maltreatment and psychopathology. Nevertheless, there is growing consensus that abuse and neglect are associated with a blunted cortisol response to psychosocial stressors such as the Trier Social Stress Test (TSST) among children and adolescents (MacMillan et al., 2009; Trickett, Gordis, Peckins, & Susman, 2014), although this pattern is not always observed (Linares, Shrout, Nucci-Sack, & Diaz, 2013). Such a pattern of blunted reactivity is pronounced among maltreated youth who carry at least one copy of the G allele of the corticotropic releasing hormone receptor 1 (CRHR1) variant rs110402 (Sumner, McLaughlin, Walsh, Sheridan, & Koenen, 2014). At least two different designs provide evidence that the effect of exposure to abuse and neglect on HPA axis function is likely to be causal. In the Bucharest Early

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Intervention Project, youth who were randomized to remain in institutional care—where they experienced neglect from caregivers—showed significantly less cortisol reactivity to a psychosocial stressor (the Trier Social Stress Test; TSST) than youth who were randomized to foster care or who were raised by biological parents (McLaughlin, Sheridan et al., 2015). A second study compared 12-year-old monozygotic twins who were discordant for the experience of being bullied or maltreated (Ouellet-Morin et al., 2011). This design controls automatically for genetic differences within the pair that could explain differences in cortisol reactivity, as well as experiences shared by twins who grow in the same family that could confound observed associations between violence victimization and cortisol reactivity. Controlling for nonshared characteristics (e.g., birth weight, IQ, early childhood problem behaviors), youth who were exposed to bullying or maltreatment showed a blunted cortisol response to the TSST compared with their nonbullied or nonmaltreated twin (Ouellet-Morin et al., 2011). Moreover, among youth who were bullied or maltreated, lower levels of cortisol reactivity were associated with higher levels of social and behavioral problems (Ouellet-Morin et al., 2011). In addition to studies of cortisol reactivity, investigators have also focused on whether maltreatment is associated with diurnal variation in cortisol. Cortisol levels typically peak approximately 30 minutes after waking and then decline over the course of the day, reaching their lowest levels around bedtime (Gunnar & Vazquez, 2006). Chronic and uncontrollable stressors tend to be associated with a flat pattern of cortisol production across the day, with relatively low morning levels and relatively high evening levels, a pattern that sometimes leads to higher levels of total cortisol output across the day (Miller, Chen, & Zhou, 2007). This pattern can lead to atrophy and, potentially, loss of hippocampal neurons and to metabolic and inflammatory disease (McEwen, 1998). However, the nature of HPA axis dysregulation depends on several factors, including the timing and severity of maltreatment and the co-occurrence of maltreatment with children’s internalizing problems. For example, youth who experience physical or sexual abuse in early childhood (but not youth who experience abuse in later childhood) show attenuated declines in cortisol from morning to afternoon, although this profile may be specific to youth who also have high levels of internalizing problems (Cicchetti, Rogosch, Gunnar, & Toth, 2010). Other studies demonstrate that effects of maltreatment on diurnal variation vary as a function of the type or severity of abuse. For example, severe neglect is associated with lower morning cortisol levels and a flatter cortisol slope across the day (Bruce, Fisher, Pears, & Levine, 2009; Carlson & Earls, 1997; van der Vegt, van der Ende, Kirschbaum, Verhulst, & Tiemeier, 2009; but see Gunnar, Morison, Chisholm, & Schuder, 2001; Kertes, Gunnar, Madsen, & Long, 2008), whereas moderately severe maltreatment and severe emotional abuse (compared with no abuse) are associated with higher cortisol levels and a steeper cortisol slope (Bruce et al., 2009; van der Vegt et al., 2009). In a study of 5- to 13-year-olds, maltreatment status was associated with (a) greater variability in cortisol levels at the start of the study (measured daily around 4 p.m.) and (b) change over time in cortisol levels

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measured over 20 weeks (Doom, Cicchetti, & Rogosch, 2014). Although maltreated children had more internalizing and externalizing problems than nonmaltreated children, individual differences in cortisol variability did not account for this association (Doom et al., 2014). Investigations of children in foster care show that they typically have lower morning cortisol levels and a flatter cortisol slope than controls (Dozier et al., 2006; Fisher, Stoolmiller, Gunnar, & Burraston, 2007), although interventions to promote sensitive caregiving in a foster care context may normalize diurnal variation in cortisol (Dozier et al., 2006; Fisher et al., 2007; Fisher, Van Ryzin, & Gunnar, 2011). Interestingly, dysregulation of diurnal variation in cortisol is more pronounced among young children who continue to live with their birth parents after investigation by Child Protective Services than in children who are placed in foster care (Bernard, Butzin-Dozier, Rittenhouse, & Dozier, 2010).

Alterations in Immune System Activity Developmental psychopathologists have identified strong associations between early experiences of adversity such as abuse or neglect and both physical and mental health problems later in life, some of which are hypothesized to result from proinflammatory responses (Miller, Chen, & Parker, 2011). Although adversity is most often conceptualized as low childhood socioeconomic position, at least a few studies show that exposure to maltreatment is associated with immune biomarkers such as C reactive protein in adulthood (Danese, Pariante, Caspi, Taylor, & Poulton, 2007) and in 12-year-olds who were physically maltreated and currently depressed, but not in physically maltreated, nondepressed children (Danese et al., 2011). In another study, severe adverse life events at 7 and 8 years of age (e.g., abuse, taken into foster care, separation from mother or father) and cumulative adversity from birth to middle childhood were associated with elevated IL-6 and CRP levels at 10 and 15 years (Slopen, Kubzansky, McLaughlin, & Koenen, 2013). A third study identified an interaction between maltreatment timing and CRH genotype on CRP levels. In a study of 489 children, ages 8 to 12 years, those who carried at least one copy of the A allele of rs1417938 demonstrated significantly higher CRP levels than those who were homozygous for the T allele, but only if they had a recent onset of maltreatment. Among nonmaltreated youth and among those who were maltreated in early childhood (regardless of whether maltreatment persisted), genetic differences in CRP levels were nonsignificant (Cicchetti, Handley, & Rogosch, 2015). In addition, among youth who were maltreated recently, higher levels of CRP were associated with higher levels of internalizing symptoms (Cicchetti et al., 2015). According to inflammation models of mental and physical health problems, dysregulation of immune function is a causal risk factor for depression (Miller, Maletic, & Raison, 2009). Proinflammatory biomarker levels are correlated significantly with depression symptoms in clinical and community samples of adults (Howren, Lamkin, & Suls, 2009), although findings from a small number of

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prospective, longitudinal studies are mixed as to the direction of this association (Copeland, Shanahan, Worthman, Angold, & Costello, 2012). Of the four prospective, longitudinal studies involving children and adolescents, all demonstrate that depressive episodes or symptoms predict later inflammatory biomarkers such as CRP (Copeland et al., 2012; Miller & Cole, 2012; Slopen, Kubzansky, & Koenen, 2013) and IL-6 levels (Miller & Cole, 2012), although one identified an association between depression and subsequent natural killer cell function among older girls only (Caserta, Wyman, Wang, Moynihan, & O’Connor, 2011). Only one of the four also showed that inflammation prospectively predicts the emergence of depression (Miller & Cole, 2012). There, as in other studies (Danese et al., 2011), clustering of depression and inflammation was most pronounced for youth who experienced multiple adversities (Miller & Cole, 2012). Data are consistent with the possibility that effects of adversity on inflammatory markers are mediated by symptoms of depression. However, the only study to test this hypothesis did not detect significant indirect effects of childhood adversity on CRP or IL-6 levels in late childhood or adolescence via symptoms of depression (Slopen, Kubzansky, McLaughlin, & Koenen, 2013).

Alterations in Brain Structure and Function Recent reviews describe research on associations between maltreatment and brain structure and function among both children and adults (Lim, Radua, & Rubia, 2014). Here I focus on studies of children and on studies showing that maltreatment-related alterations in brain structure and function are associated with child problem behaviors. Maltreatment Is Associated With Alterations in Brain Structure. Abuse and neglect are hypothesized to affect regions of the brain that are involved in emotion processing and regulation, including the amygdala, hippocampus, and prefrontal cortex (for a recent review, see Beauchaine, 2015). Although the evidence base is relatively small, research consistently shows that children who have been maltreated have smaller-than-average prefrontal cortical volumes (Lupien, McEwen, Gunnar, & Heim, 2009). At least one study demonstrates that smaller prefrontal cortex volumes in physically abused versus nonabused youth (specifically in the right orbitofrontal cortex) are associated with difficulties in social functioning within the family and in school (Hanson et al., 2010) There is less consistent evidence that maltreatment is associated with amygdala or hippocampal volumes. A meta-analysis of studies of children with maltreatment-related PTSD found no associations between maltreatment and hippocampal volumes (Woon & Hedges, 2008). A more recent meta-analysis also showed that maltreatment was not associated with hippocampal volume in children, but a childhood history of maltreatment was associated with reduced

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hippocampal volumes among adults (Riem, Alink, Out, Van Ijzendoorn, & Bakermans-Kranenburg, 2015). Evidence with respect to maltreatment-related variation in amygdala volumes is similarly mixed, with some studies showing no association (De Brito et al., 2013) and others showing that institutionalization in early childhood is associated with greater amygdala volumes (Mehta et al., 2009; Tottenham et al., 2010). Such inconsistencies might be accounted for by the wide age range of children included across these studies and by cross-study variation in amygdala quantification (Hanson et al., 2015). A recent study that involved careful hand tracing of amygdala and hippocampus brain regions showed that (a) children (ages 9 to 15 years) who were exposed to various forms of early life stress, including early neglect (in the form of institutionalization), physical abuse, and low socioeconomic status had significantly smaller left amygdala volumes and (b) physically abused and low SES children had significantly smaller right hippocampal volumes compared with middle-class control children, with more extensive lifetime exposure to stressors inversely correlated with the size of these regions (Hanson et al., 2015). Mediational analyses suggested that children who were exposed to these various forms of early life stress had elevated rates of behavioral problems, partly because of their smaller hippocampal (but not amygdala) volumes (Hanson et al., 2015). Beyond volumetric differences, maltreated and nonmaltreated adolescents also differ with respect to cortical thickness in right hemispheric prefrontal regions, surface area in the medial temporal area and the left lingual gyrus, and local gyrification in two left hemisphere clusters (Kelly et al., 2013). In another study, effects of early institutional deprivation on increased symptoms of ADHD were accounted for by the fact that institution-reared youth showed reduced cortical thickness in orbitofrontal cortex, insula, supramarginal gyrus, precuneus, superior temporal gyrus, inferior parietal cortex, superior temporal cortex, fusiform gyrus, and lingual gyrus (McLaughlin et al., 2014). Maltreatment Is Associated With Alterations in Brain Function. Maltreatment-related differences in brain function have also been observed. Children who are exposed to family violence (including maltreatment) show greater right amygdala activation in response to angry (but not sad) versus neutral faces compared with nonexposed children (McCrory et al., 2011). Such differences are observed even when emotion expressions are presented subliminally (McCrory et al., 2013). Another study showed that postinstitutionalized youth showed greater amygdala reactivity than control youth to fearful versus neutral faces (Tottenham et al., 2011). Consistent with the findings related to anger stimuli, event related potential studies indicate that children with a history of physical abuse are more attentive to angry cues (vs. other negatively valenced cues) and have more difficulty disengaging from angry versus happy cues (Cicchetti & Curtis, 2005; Curtis & Cicchetti, 2011;

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Pollak, Klorman, Thatcher, & Cicchetti, 2001; Pollak & Tolley-Schell, 2003; Shackman, Shackman, & Pollak, 2007). Such hyperattention to threat mediates associations between a history of maltreatment and current symptoms of anxiety (Shackman et al., 2007). Although maltreated youth are more reactive to negative stimuli than nonmaltreated youth, such responses can be modulated. For example, although adolescents who self-report physical or sexual abuse show greater left and right amygdala activation in response to negative versus neutral images compared with control youth, they also show greater activation of prefrontal cortex during trials in which they are instructed to decrease their emotional response to negative stimuli, thus erasing maltreatment-related differences in amygdala activation on those trials (McLaughlin, Peverill, Gold, Alves, & Sheridan, 2015). The role of amygdala and cognitive control regions (e.g., anterior cingulate cortex and dorsolateral prefrontal cortex) in detection and regulation of emotional conflict has been further explored in tasks in which presentation of emotional expressions are superimposed with emotion terms that are either congruent (e.g., the word “happy” appears over a happy face) or incongruent (e.g., the word “happy” appears over a fearful face). When asked to identify the emotion expression of the face, individuals tend to be slower and less accurate during incongruent versus congruent trials. In a study of 51 children and adolescents (ages 9 to 16 years), youth who were exposed to various forms of trauma (abuse, neglect, domestic violence, repeated separations from parents) were not only slower and less accurate during incongruent versus congruent trials than nonexposed youth, but they also showed greater right and left amygdala activation (Marusak, Martin, Etkin, & Thomason, 2015). In addition, although repeated exposure to incongruent trials resulted in improvements in reaction time and accuracy for non–trauma-exposed participants, such adaptive gains were not observed for trauma-exposed participants, with the failure to improve accuracy correlated with heightened dorsolateral prefrontal cortex activity and reduced coupling of amygdala and anterior cingulate cortex (Marusak et al., 2015). Finally, heightened amygdala activation during incongruent (vs. congruent) trials mediated the association between trauma exposure and lower reward sensitivity (Marusak et al., 2015). During a social rejection paradigm (cyberball), adolescents who were separated permanently in early childhood from their biological parents (often as a result of physical or emotional neglect) and subsequently adopted showed reduced activation in dorsal anterior cingulate cortex (dACC), dorsolateral prefrontal cortex (dlPFC), and anterior insula; heightened activation in left middle temporal gyrus; and reduced connectivity between dACC and dlPFC (Puetz et al., 2014). Youth who experienced early separation reported feeling more excluded during the cyberball game than youth who did not experience early separation, an association partly mediated by dlPFC activation (Puetz et al., 2014).

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Maltreatment-related alterations in neural function have parallels in rodent models, which hold constant genetic and pre- and postnatal environmental conditions that may be confounded with maltreatment among humans (see, e.g., Mead, Beauchaine, & Shannon, 2010). For example, after some of the bedding from a mouse’s cage is removed, mouse dams engage in inconsistent caregiving, which may be homologous to some conditions experienced in orphanage rearing (Cohen et al., 2013). To further simulate the experience of human adoption from an institutional setting to a more enriched environment, removal of bedding was restricted to the preweaning period of postnatal days 2 through 21, after which normal cage conditions were restored. In paradigms in which (a) mice and (b) human children had to inhibit a fear response in favor of goal-directed behavior, both stress-exposed mice and humans were slower to approach the goal than their nonstressed counterparts, where the goal for mice was getting a drink of sweetened milk in a novel and well-lit cage and the goal for human adolescents was detecting a neutral stimuli that was embedded among rare threat nontarget cues. In addition, stress-exposed mice and humans showed greater neural activity in the amygdala (greater c-Fos expression in the basolateral amygdala for mice and higher levels of blood oxygen level-dependent activity in humans; Cohen et al., 2013). Maltreatment Is Associated With Developmental Alterations in Brain Connectivity. Institutionalization in early life has also been associated with accelerated development of connections between the amygdala and medial prefrontal cortex (mPFC). In typically developing children, negative functional connectivity between the amygdala and the mPFC—a prerequisite of effective top-down modulation of anxiety (see Beauchaine, 2015)—is not fully mature until adolescence. In contrast, postinstitutionalized children (ages 6.5 to 10.4 years) show more negative amygdala-mPFC coupling than control children and similar levels of negative amygdala-mPFC coupling as postinstitutionalized and control adolescents (10.5 to 17.6 years; Gee et al., 2013). Parallel findings related to accelerated development are observed in rodent models involving maternal separation (Caldji et al., 1998). Moreover, although postinstitutionalized children exhibited more symptoms of anxiety than control youth, within the postinstitutionalized group, more negative amygdala-mPFC coupling was associated with fewer symptoms of anxiety (Gee et al., 2013). In contrast to accelerated development of amygdala-mPFC coupling, postinstitutionalized adolescents show delayed maturation of the ventral striatum, for which reactivity of the nucleus accumbens to incentives and other pleasurable stimuli typically peaks in adolescence. In contrast to this typical trajectory, postinstitutionalized adolescents show reduced reactivity of the nucleus accumbens in response to happy (vs. neutral) faces than controls (Goff et al., 2013). The postinstitutionalized adolescents also had more symptoms of depression than controls (Goff et al., 2013).

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Importantly, among both children and adults, unipolar depression is associated with reduced nucleus accumbens activity to pleasurable stimuli (e.g., Forbes & Dahl, 2012; Pizzagalli et al., 2009). Consistent with such findings, low levels of nucleus accumbens reactivity were correlated with depressive symptoms in the sample overall, even though nucleus accumbens activity did not mediate the association between early life stress and depressive symptoms (Goff et al., 2013). An adequately powered sample would be needed to determine whether this was a false negative finding (Goff et al., 2013). Importantly, blunted activity to incentives in the striatum, particularly the nucleus accumbens, is associated with symptoms of anhedonia in both externalizing disorders and depression (see Zisner & Beauchaine, in press). Thus, findings of reduced striatal responding to incentives among maltreated children and adolescents may reflect a mechanism of heterotypic comorbidity, which characterizes this population. Analysis of resting state functional connectivity shows that among 18-yearolds, higher scores on a retrospective measure of child maltreatment (Childhood Trauma Questionnaire; CTQ) are associated with lower connectivity (a) between the right amygdala and ventromedial (vm)-PFC, an effect that is more pronounced in females than males, and (b) between the left amygdala and vmPFC (Herringa et al., 2013). Reduced resting state functional connectivity mediated effects of CTQ scores on internalizing symptoms (Herringa et al., 2013). Maltreatment Is Associated With Cognitive, Behavioral, and Socioemotional Processes. Maltreated youth are characterized by a range of cognitive, behavioral, and socioemotional problems that account for their increased risk for externalizing and internalizing problems. These include social-cognitive biases, problems in emotion recognition, understanding, and regulation, and problems in the conceptualization of the self. Externalizing Problems. As described above, physically abused youth selectively attend to angry stimuli compared with nonabused youth. Consistent with these findings, physically abused youth have a tendency to attribute hostile intent to others’ behavior and to respond accordingly (Dodge, Bates, & Pettit, 1990), which can make such youth likely to exhibit chronic aggression. In addition, maltreated youth have difficulty regulating their own emotions (as captured by appropriate displays of emotion, emotional awareness, and empathy). Such emotion dysregulation, in turn, is associated with peer rejection and increased externalizing problems over time (Kim & Cicchetti, 2006). Maltreated preschoolers also have difficulty with emotion understanding, as evidenced by inability to match positive, negative, and neutral events with positive and negative emotions (Perlman, Kalish, & Pollak, 2008). This deficit may affect their ability to predict reactions that their own negative behaviors will elicit from others. Maltreated youth also have more positive beliefs

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about violence, which leads them to antisocial peer groups and increases their risk for violent behavior in adolescence (Herrenkohl, Huang, Tajima, & Whitney, 2003). Physically abused children allocate more attention to angry faces than nonabused children, which may explain their angry affect and aggressive behavior directed toward peers in a subsequent task (Shackman & Pollak, 2014). They also allocate more attention to task-irrelevant angry auditory cues than nonabused children, which may explain their symptoms of anxiety (Shackman et al., 2007). Maltreatment may also lead to behavioral trajectories toward increasingly high-risk conduct. For example, youth who run away from home to escape abuse are likely to drop out of school and become homeless (Paradise & Cauce, 2002; Stein, Leslie, & Nyamathi, 2002). With nowhere to live and no formal education, some of these youth may resort to prostitution (Wilson & Widom, 2009) and other criminal behaviors as a means of supporting themselves (Kim, Tajima, Herrenkohl, & Huang, 2009). This constellation of problem behaviors in adolescence and young adulthood accounts for the association between child maltreatment and women’s illicit drug use in middle age (Wilson & Widom, 2009). Elevated rates of drug abuse among women with childhood histories of abuse and neglect is also explained by the fact that they are more likely than women without such histories to live in neighborhoods characterized by relatively high rates of social disorder and social disadvantage (Chauhan & Widom, 2012). Internalizing Problems. Childhood sexual abuse in particular is linked to a range of problems in self-functioning, defined in terms of self-coherence, self-continuity, self-affectivity, and self-agency (Stern, 1985). Some studies show that abused and neglected youth report elevated symptoms of dissociation compared with nonmaltreated youth (Macfie, Cicchetti, & Toth, 2001), particularly among those who feel shame and blame themselves for the abuse (Feiring, Cleland, & Simon, 2010; Feiring, Taska, & Lewis, 1996). Moreover, when combined with high levels of arousal and avoidant coping, symptoms of dissociation measured during or immediately after disclosure of sexual abuse accounted for substantial variation in symptoms of PTSD among sexually abused youth (Kaplow, Dodge, Amaya-Jackson, & Saxe, 2005). The tendency to feel shame and self-blame is also associated with internalizing and externalizing problems (Feiring & Cleland, 2007; McGee, Wolfe, & Olson, 2001) and, in the short term, low self-esteem (Feiring, Taska, & Lewis, 2002). Sexually abused and neglected children are more likely than nonabused children to develop an external locus of control, with perceived external control accounting substantially for their elevated symptoms of internalizing problems (Bolger & Patterson, 2001). Exposure to Maltreatment Is Associated With Lower Levels of Social Support. Although some studies demonstrate that a supportive adult buffers youth from effects of maltreatment on risk for psychopathology and other undesirable outcomes

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(Spaccarelli & Kim, 1995), maltreated youth tend to have smaller social networks (Negriff, James, & Trickett, 2015) and perceive lower levels of support from individuals within those networks (Lamis, Wilson, King, & Kaslow, 2014; Pepin & Banyard, 2006). Some studies show that low levels of perceived support account for observed associations between a history of abuse and risk for psychopathology (Lamis et al., 2014; Pepin & Banyard, 2006), although perceived support from adults is not consistently associated with psychopathology and other forms of psychosocial adjustment (Ezzell, Swenson, & Brondino, 2000). Efforts to understand whether social support has broadly promotive versus protective functions in the context of maltreatment would be advanced by better data on sources of support (e.g., parental vs. extraparental figures) and by more careful parsing of social support dimensions, such as network size, network quality (e.g., youth perceptions of how much network members care about them), and type of social support. In addition, recent data suggest that genetic factors influence the extent to which maltreated youth perceive support. For example, a variant of the oxytocin receptor gene (rs53576) that is associated with prosocial behavior is also associated with maltreated youths’ perceptions of social support. Those who are homozygous for the major allele (G/G) not only perceive higher levels of social support compared to youth who carry at least one copy of the A allele, but they also have similarly low levels of psychopathology compared to controls (Hostinar, Cicchetti, & Rogosch, 2014). Such findings suggest that some children may be more predisposed biologically to perceive and benefit from social support than other children.

MODERATORS OF CHILD MALTREATMENT Moderators of maltreatment can be conceptualized as factors that exacerbate effects of maltreatment on risk for psychopathology (e.g., in a diathesis-stress framework) or factors that promote competence in mental health, academic, or interpersonal domains despite exposure to maltreatment (e.g., in a resilience framework). In studies where resilience to maltreatment is stringently defined as competence that is sustained over time across more than one domain, between 12–22% of individuals who are maltreated as children are resilient (Cicchetti, Rogosch, Lynch, & Holt, 1993; Jaffee & Gallop, 2007; Kaufman, Cook, Arny, Jones, & Pittinsky, 1994; McGloin & Widom, 2001). In a 2013 review, we concluded that the effect of maltreatment on risk for psychopathology is largely similar across demographic groups (e.g., sex, race/ethnicity) (Jaffee & Maikovich-Fong, 2013). Similarly, evidence that the association between maltreatment and risk for psychopathology varies as a function of maltreatment subtype was weak and inconsistent. In contrast, chronic maltreatment is associated more strongly with risk for psychopathology than maltreatment that occurs sporadically or is confined to a single developmental

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period (Jaffee & Maikovich-Fong, 2011). With respect to age-at-onset, although maltreatment that is first experienced in childhood is associated more strongly with internalizing outcomes than maltreatment that is first experienced in adolescence, the reverse is true for externalizing problems (Kaplow & Widom, 2007; Thornberry et al., 2010). Since our 2013 review, the research base on genetic moderators of maltreatment effects has expanded exponentially, so we focus our review on this topic. A separate section is devoted to a discussion of psychosocial factors as a moderator of maltreatment.

Genetic Moderators of Maltreatment Effects Although there are dozens of reports of Genotype × Maltreatment interactions predicting psychopathology, this review is focused on the best-replicated findings. These include studies testing whether the monoamine oxidase A (MAOA) genotype moderates effects of maltreatment on risk for antisocial behavior, and studies testing whether 5HTTLPR genotype moderates effects of maltreatment on risk for depression. MAOA × Maltreatment. The first published report of an interaction between the MAOA genotype and maltreatment came from Caspi et al. (2002), who showed that men in the Dunedin Longitudinal Study birth cohort who experienced childhood maltreatment (as indicated by retrospective reports of sexual and physical abuse and prospective reports of maternal rejection, harsh parenting, and multiple caregiver changes) exhibited elevated levels of childhood conduct and adult antisocial behavior problems only if they carried the low activity variant of a 30 base-pair variable number tandem repeat polymorphism of the MAOA gene. Men who carried the high activity variant of the MAOA gene were not at elevated risk for antisocial outcomes regardless of their exposure to childhood maltreatment. This finding was supported in a 2007 meta-analysis of eight studies that tested for MAOA × Maltreatment interactions among males (Taylor & Kim-Cohen, 2007). A more recent meta-analysis of 27 studies tested whether the interaction was (a) specific to maltreatment versus adverse life events more generally and (b) specific to males versus females (Byrd & Manuck, 2014). This meta-analysis also supported the association between early life maltreatment and antisocial outcomes for males who carried the low versus the high activity variant of the MAOA gene. In the 11 female cohorts, maltreatment was more strongly associated with antisocial outcomes among carriers of the high versus the low activity variant, although this effect was not robust to sensitivity checks (Byrd & Manuck, 2014). A future direction for research is to identify why males who carry the low activity variant are at elevated risk for antisocial outcomes when they are exposed

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to maltreatment. A growing number of genetic imaging studies suggest possible mechanisms through which the low activity variant increases risk for antisocial behavior. For example, the low activity variant is associated with (a) low resting state activity in areas of the brain related to executive function and inhibitory control (Clemens et al., 2015); (b) poor self-reported anger control and activation of the dorsal anterior cingulate cortex and the amygdala in response to insult (Denson, Dobson-Stone, Ronay, von Hippel, & Schira, 2014); and (c) enhanced amygdala activity in response to negative versus neutral stimuli and reduced coupling of prefrontal cortex and amygdala, which is often interpreted as an indicator of emotion regulation (Buckholtz et al., 2008; Meyer-Lindenberg et al., 2006). Moreover, low MAOA activity in cortical and subcortical regions as measured by positron emission topography is associated with heightened levels of trait aggression (Alia-Klein et al., 2008). In addition, epigenetic studies show that hypermethylation in CpG sites in the promoter region of MAOA in incarcerated men with ASPD, compared with age- and sex-matched nonoffender controls, is associated with decreased promoter activity in functional follow-up assays and lower levels of circulating serotonin (Checknita et al., 2015). Research is needed that shows how maltreatment interacts with these processes to increase risk for antisocial behavior. 5HTTLPR × Maltreatment. The first published report of an interaction between 5HTTLPR genotype and maltreatment also came from the Dunedin Longitudinal Study, and showed that among individuals who were homozygous for the short (s) form of the serotonin transporter allele, a childhood history of maltreatment was associated with elevated risk for depression and depressive symptoms in adulthood (Caspi et al., 2003). This reported interaction was embedded within a broader analysis of an interaction between 5HTTLPR and stressful life events. Two of three subsequent meta-analyses have tested whether the effect of stressful life events (rather than maltreatment specifically) is moderated by 5HTTLPR genotype, and have concluded that evidence for an interaction effect is weak (Munafo, Durrant, Lewis, & Flint, 2009; Risch et al., 2009). In contrast, the meta-analysis by Karg, Burmeister, Shedden, & Sen (2011) stratified studies according to type of stressful life event and found that 5HTTLPR genotype moderated the effect of maltreatment (as opposed to other stressful life events) on risk for depression. Risk for depression may be elevated among individuals who carry the 5HTTLPR s allele because these individuals have a more pronounced physiological response to stress than l allele carriers. For example, a meta-analysis showed that s/s homozygotes mount a significantly greater cortisol response to acute stressors than l-allele carriers (Miller, Wankerl, Stalder, Kirschbaum, & Alexander, 2013), although it is not clear whether s and l allele carriers differ in their physiological response to chronic stress, such as ongoing abuse or neglect. In addition, s allele carriers show enhanced amygdala reactivity to negatively valenced stimuli compared with

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individuals who are homozygous for the l allele (Munafo, Brown, & Hariri, 2008). A handful of investigations also show that, among s allele carriers, (a) elevated cortisol reactivity to acute stressors and (b) amygdala reactivity to negative emotional stimuli is most pronounced among those who have experienced numerous stressful life events (Alexander et al., 2009; Alexander et al., 2012; Williams et al., 2009; but see Canli et al., 2006 and Mueller et al., 2011 for an alternative interaction pattern), thus demonstrating that potential endophenotypes for depression are also predicted by Genotype × Environment interactions. Along with the genetic imaging studies, there is evidence that the s allele is associated with cognitive vulnerabilities to depression, particularly under stressful conditions (Gibb, Beevers, & McGeary, 2013). For example, compared with individuals who are homozygous for the l allele, healthy adults who carry the s allele take longer to disengage attention from facial expressions of emotions (Beevers, Wells, Ellis, & McGeary, 2009), and to appraise recent stressful life events as being more negative (Conway et al., 2012), with negative appraisals correlated with elevations in depressive symptoms (Conway et al., 2012). Children who carry the s allele also show enhanced memory for negative (vs. positive) self-descriptive traits (Hayden et al., 2013)—a cognitive vulnerability for depression. A few studies show that coping and/or perceptions of coping are associated with the 5-HTTLPR genotype. For example, among adults who were asked to recall recent situations in which they had felt strong emotions of fear, sadness, or joy, those who carried the s allele reported that they felt less able to cope with situations that evoke strong feelings of sadness or fear than individuals who were homozygous for the l allele (Szily, Bowen, Unoka, Simon, & Keri, 2008). In another study, healthy young adults who were homozygous for the s allele less frequently endorsed the use of cognitive reappraisals to deal with negative emotions and events (e.g., “When I want to feel less negative emotion, I change what I’m thinking about” or “I look for the positive side of the matter”) than l allele carriers. In turn, less frequent use of cognitive reappraisal strategies explained why individuals who carried two copies of the s allele had more symptoms of social anxiety (Miu, Vulturar, Chis, Ungureanu, & Gross, 2013). Finally, in a sample of 156 healthy adults, Wilhelm et al. (2007) reported that s allele carriers used fewer problem-solving coping strategies in response to a stressor than l carriers. Although the literature provides a growing number of clues as to why s allele carriers may be at heightened risk of depression in the face of stressors like maltreatment, there have been few tests of formal mediation of Genotype × Environment effects. In one of the first such tests, Cline et al. (2015) showed that cumulative exposure to risk indicators (low parental warmth, harsh parental discipline, traumatic life events) was associated with elevations in internalizing symptoms for youth who were homozygous for the s allele but not for youth who carried the l allele. Moreover, among youth who were homozygous for the s allele, those who experienced more cumulative risk indicators less frequently used distraction coping strategies, such

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as playing a video game or playing sports, which partly explained their elevated symptoms of internalizing problems (Cline et al., 2015).

Psychosocial Moderators of Maltreatment Children who are resilient to maltreatment tend to exhibit high ego control and ego resiliency, high self-esteem, high self-reliance, and the tendency to attribute successes to their own efforts (Cicchetti et al., 1993; Feiring et al., 2002; Moran & Eckenrode, 1992). In addition, above-average intelligence emerges as a protective factor in some studies (Herrenkohl, Herrenkohl, & Egolf, 1994; Jaffee, Caspi, Moffitt, Polo-Tomas, & Taylor, 2007) but not others (Cicchetti & Rogosch, 1997; DuMont, Widom, & Czaja, 2007). Such individual characteristics may be protective only as long as children are not exposed to a multitude of stressors in addition to maltreatment (DuMont et al., 2007; Jaffee et al., 2007). Although socially supportive relationships are hypothesized to buffer youth from the adverse effects of maltreatment (Cicchetti, 2013), there is relatively little research on this question that involves children. There is, however, evidence that nonparental mentoring relationships matter for youth who are in foster care, with one study showing that those who report being mentored before the age of 18 are more likely to participate in higher education, have better self-reported physical health, and are less likely to be diagnosed with a sexually transmitted infection, engage in suicidal ideation, or hurt someone in a fight than those who do not report the presence of a mentor (Ahrens, DuBois, Richardson, Fan, & Lozano, 2008).

CONCLUSIONS Maltreatment is a significant public health problem. From a basic research perspective, there is a need for more prospective, longitudinal data on maltreatment to better understand courses of resilience and dysfunction over time, and long-term effects of maltreatment on mental and physical health. A mix of research strategies and research models is needed to understand mechanisms through which neglect and abuse influence basic biological and psychological processes. Research on the biology of maltreatment would benefit from larger and more representative samples, whereas research on psychological sequelae of maltreatment would benefit from designs that allow for stronger causal inference about potential mediators of maltreatment. An integrative, multilevel perspective is needed to trace effects of maltreatment on pathways from genes to brain to behavior. From a clinical perspective, more research is needed to better evaluate treatment efficacy for maltreated children, to improve access to services and the quality of services for maltreated children, and to understand why some maltreated children respond better to treatment than others.

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Tottenham, N., Hare, T. A., Quinn, B. T., McCarry, T. W., Nurse, M., Gilhooly, T., . . . Casey, B. J. (2010). Prolonged institutional rearing is associated with atypically large amygdala volume and difficulties in emotion regulation. Developmental Science, 13, 46–61. Trickett, P. K., Gordis, E., Peckins, M. K., & Susman, E. J. (2014). Stress reactivity in maltreated and comparison male and female young adolescents. Child Maltreatment, 19, 27–37. Tyrka, A. R., Parade, S. H., Eslinger, N. M., Marsit, C. J., Lesseur, C., Armstrong, D. A., . . . Seifer, R. (2015). Methylation of exons 1(D), 1(F), and 1(H) of the glucocorticoid receptor gene promoter and exposure to adversity in preschool-aged children. Development and Psychopathology, 27, 577–585. U.S. Department of Health and Human Services, Administration for Children and Families, Administration on Children, Youth, and Families, and Children’s Bureau. (2015). Child maltreatment 2013. Retrieved from http://www.acf.hhs.gov/ sites/default/files/cb/cm2013.pdf Van der Vegt, E. J. M., van der Ende, J., Kirschbaum, C., Verhulst, F. C., & Tiemeier, H. (2009). Early neglect and abuse predict diurnal cortisol patterns in adults: A study of international adoptees. Psychoneuroendocrinology, 34, 660–669. Wang, Q., Van Heerikhuize, J., Aronica, E., Kawata, M., Seress, L., Joels, M., . . . Lucassen, P. J. (2013). Glucocorticoid receptor protein expression in human hippocampus: Stability with age. Neurobiology of Aging, 34, 1662–1673. Weaver, I. C. G., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., . . . Meaney, M. J. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7, 847–854. Widom, C. (1989). The cycle of violence. Science, 244, 160–166. Widom, C. (1999). Posttraumatic stress disorder in abused and neglected children grown up. American Journal of Psychiatry, 156, 1223–1229. Widom, C., Czaja, S. J., & DuMont, K. A. (2015). Intergenerational transmission of child abuse and neglect: Real or detection bias? Science, 347, 1480–1485. Widom, C., Czaja, S. J., & Paris, J. (2009). A prospective investigation of borderline personality disorder in abused and neglected children followed up into adulthood. Journal of Personality Disorders, 23, 433–446. Widom, C., DuMont, K., & Czaja, S. (2007). A prospective investigation of major depressive disorder and comorbidity in abused and neglected children grown up. Archives of General Psychiatry, 64, 49–56. Widom, C., Ireland, T., & Glynn, P. J. (1995). Alcohol abuse in abused and neglected children followed-up: Are they at increased risk? Journal of Studies on Alcohol, 56, 207–217. Widom, C., Marmorstein, N. R., & Raskin White, H. (2006). Childhood victimization and illicit drug use in middle adulthood. Psychology of Addictive Behaviors, 20, 394–403. Wilhelm, K., Siegel, J. E., Finch, A. W., Hadzi-Pavlovic, D., Mitchell, P. B., Parker, G., & Schofield, P. R. (2007). The long and short of it: Associations between 5-HTT genotypes and coping with stress. Psychosomatic Medicine, 69, 614–620.

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CHAPTER 6

Impulsivity and Vulnerability to Psychopathology EMILY NEUHAUS AND THEODORE P. BEAUCHAINE

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erms such as impulsive, disinhibited, and hyperactive have long been used to describe individuals with insufficient control over their behaviors. Although normative variation in these traits can reflect spontaneity, exuberance, and extraversion (Sagvolden, Johansen, Aase, & Russell, 2005), individuals who display extreme impulsivity, or fail to acquire age-appropriate self-regulation as they mature, are vulnerable to a host of maladaptive outcomes. According to developmental psychopathology models of externalizing behavior, extreme impulsivity during the preschool years often represents the first stage in a trajectory that can progress, via potentiating and mediating variables, to early onset delinquency and other antisocial behaviors (Ahmad & Hinshaw, 2016; Beauchaine, Gatzke-Kopp, & Mead, 2007; Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013; Beauchaine, Shader, & Hinshaw, 2015; Campbell, Shaw, & Gilliom, 2000; Hinshaw, Lahey, & Hart, 1993; Patterson, DeGarmo & Knutson, 2000). Indeed, trait impulsivity underlies most if not all disorders that fall along the externalizing spectrum, including attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD), antisocial personality disorder (ASPD), and substance use disorders (see Barkley, 1997; Beauchaine & Hinshaw, 2015; Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009; Krueger et al., 2002). In other cases, especially among girls, temperamental impulsivity marks the beginning stages of a developmental trajectory that culminates in self-harm, depression, and internalizing psychopathology (Beauchaine et al., 2009; Hinshaw et al., 2012; Hirshfeld-Becker et al., 2002). Thus, impulsivity observed early in life portends vulnerability to a wide range of adverse, multifinal outcomes. In this chapter, we consider impulsivity as a dimensional construct that represents a biologically based vulnerability trait. In combination with a variety of 178

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environmental and family risk factors, discussed below, impulsivity forms the core of a spectrum of psychological disorders (Beauchaine & McNulty, 2013; Beauchaine et al., 2010; Beauchaine et al., 2015). This form of impulsivity cuts across traditional diagnostic boundaries and derives largely from subcortical substrates of approach behavior. Among impulsive individuals, functioning of this neural system, and its interactions with (a) other subcortical neural systems of emotion generation, and (b) cortical systems of emotion regulation, yield a behavioral predisposition that encourages reward-seeking behaviors that interfere with long-term goals and well-being (see Beauchaine, 2015). We begin our chapter by placing impulsivity within historical context and contrasting different approaches to defining the construct. We then review genetic and neurobiological underpinnings of the trait. We close with a review of environmental risk factors that moderate and/or mediate links between impulsivity and distal psychological outcomes.

HISTORICAL CONTEXT As with nearly all psychological phenomena, ideas about the nature and etiology of impulsivity evolved considerably over the 19th and 20th centuries. Early neurobiological theories of behavioral control focused on frontal regions of the brain. These theories derived largely from observations of altered behavior among those who suffered from traumatic brain injuries, such as Phineas Gage, a railroad foreman (see Chapter 10 [Arnett, Meyer, Merritt, Gatzke-Kopp, & Shannon Bowen]). In 1848, Gage suffered a severe brain injury when a blasting charge propelled an iron rod through his eye socket and out the frontal part of his skull. Despite full recovery of motor and sensory functions, Gage’s personality transformed radically following his injury (Macmillan, 1992). Although previously “quiet and respectful,” he became “gross, profane, coarse, and vulgar to such a degree that his society was intolerable to decent people” (Bigelow, 1850 cited in Macmillan, 1992, p. 86). He was further described as “impatient of restraint or advice when it conflicts with his desires, at times pertinaciously obstinate, yet capricious and vacillating, devising many plans of future operation, which are no sooner arranged than they are abandoned in turn for others appearing more feasible” (Harlow, 1868, cited in Macmillan, 2004). Thus, the most striking result of Gage’s injuries was marked behavioral disinhibition that contrasted starkly with his socially appropriate demeanor prior to the injury. Consistent with theories of the time, explanations of Gage’s behavior relied upon two assumptions. First, particular brain regions located in the frontal lobe were assumed to support specific behavioral traits, and damage to these regions undermined those traits. In Gage’s case, the shift in behavior was attributed to damage to the “regions of the organs of BENEVOLENCE and VENERATION” (Harlow, 1868, cited in Macmillan, 1992). Second, it was assumed that competing factors were at work within the mind, with behavior resulting from the equilibrium established between them. Damage to the brain disrupted this equilibrium, and the changing balance affected behavior. In the absence of the inhibiting influence of the damaged

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areas, the balance between Gage’s “intellectual faculties and his animal propensities” was destroyed (Harlow, 1868, cited in Macmillan, 2004), resulting in impulsive behavior. As later sections of this chapter reveal, this theme—that behavior derives from a relative equilibrium between self-gratifying and precautious motivations—is also central to contemporary theories of impulsivity. Twentieth-century conceptualizations of impulsivity continued to look toward imbalances in competing neurobiological systems. Eppinger and Hess (1915) argued that vagotonia, an imbalance within the autonomic nervous system favoring the parasympathetic over the sympathetic division, accounted for a number of medical and psychological phenomena. They described vagotonia as a chronic disposition caused by an “abnormal irritability of all or only a few autonomic nerves” (p. 39), including the 10th cranial (vagus) nerve, which provides parasympathetic cardiac influence. Occurring relatively frequently in young individuals, vagotonia was hypothesized to cause neurasthenia, hysteria, and nervousness. Eppinger and Hess described patients with vagotonia as “hasty and precipitous” (p. 40), foreshadowing links that would later be made between this condition and hyperactivity. Although the vagotonia hypothesis has since been refuted (see Beauchaine, 2001), by the middle of the 20th century it was a putative cause of restlessness and hyperactivity in children, and was considered a possible predictor of later antisocial behavior (e.g., Venables, 1988). More recent sources indicate compromised sympathetic and parasympathetic functioning among impulsive children and adolescents (Beauchaine & Gatzke-Kopp, 2012; Beauchaine, Katkin, Strassberg, & Snarr, 2001; Beauchaine et al., 2007; Crowell et al., 2006). At about the same time the vagotonia hypothesis emerged, the encephalitis epidemics of 1918 yielded a group of children who displayed marked impulsivity, hyperactivity, inattention, aggression, and impairments in judgment (Carlson & Rapport, 1989; Schachar, 1986). Neurologists of the time attributed these behaviors to some kind of underlying neurological disturbance, and used the term minimal brain damage (later softened to minimal brain dysfunction, or MBD) to describe such children, as well as those with learning disabilities and other problems (Hässler, 1992). Theories varied with respect to which region(s) of the brain was injured, but impulsivity and hyperactivity were assumed to result from brain damage of some sort, even among children with no documented history of head trauma or illness (Lyon, Fletcher & Barnes, 2003). Although problem behaviors included under MBD shifted over the next few decades, variations of the term and concept remained popular until recently (Hässler, 1992). It was not until the DSM-III was published in 1980 that the category of MBD was dropped, and children with learning difficulties were distinguished officially from those with behavioral difficulties (Lyon et al., 2003).

TERMINOLOGICAL AND CONCEPTUAL ISSUES Despite the centrality of trait impulsivity to current theories of ADHD, CD, antisocial behavior, and substance use disorders, the construct lacks both a consistent

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operational definition and a standard method of measurement. Although impulsivity has been defined traditionally by behavioral symptoms, some researchers have attempted to refine these definitions based on results from neuropsychological tests. For example, reaction time during verbal tasks has been used to assess the degree of “short-circuiting of analytic or reflective thought processes” (Oas, 1985, p. 141). Alternatively, errors in maze solving have been interpreted as inattention to detail, carelessness, and lack of planning (Porteus, 1965). Perseverative errors during set-shifting tasks such as the Wisconsin Card Sorting Test have also been attributed to impulsivity (e.g., Avila, Cuenca, Félix, Parcet, & Miranda, 2004), as have errors due to overly quick responding and lack of reflection during match-to-sample tasks (Oas, 1984). Among the most popular neuropsychological measures of impulsivity are drawing tasks such as the Bender Gestalt (Bender, 1938) and the Draw-A-Person test (Koppitz, 1968). With these tests, impulsivity is assessed by scoring drawings on the basis of variables such as completion time, overall quality, omissions, asymmetry, detailing, and shading (Oas, 1984). Continuous performance tests (e.g., Conners & MHS Staff, 2000; Gordon, 1988) are also purported to assess impulsivity by indexing errors of commission, when participants fail to inhibit inappropriate responses. Although these measures provide tightly operationalized definitions of impulsivity, they do not speak to the neural mechanisms underlying the construct, nor do they fully explain relations between impulsivity and psychopathology (see Gatzke-Kopp, 2011). Many of these formulations describe impulsivity in highly cognitive terms, linking it to specific executive functions such as inhibitory control (the ability to interrupt an ongoing action or prevent a prepotent reaction; Kenemans et al., 2005) and effortful control (the ability to control attentional processes and behavior to inhibit a dominant response in favor of a nondominant response; Rothbart & Bates, 1998), two closely related constructs. Although it remains to be determined how such cognitive constructs relate to trait impulsivity, they are likely to show some degree of overlap, as different measures of inhibitory and effortful control correlate with various facets of impulsivity and problem behavior (e.g., Enticott, Ogloff, Bradshaw, 2006; Murray & Kochanska, 2002). More recent cognitive models of disinhibition integrate multiple components of the trait, suggesting several alternative brain mechanisms that may be responsible for impulsive behavior, exemplifying equifinality (see Chapter 1 [Hinshaw]). Nigg (2000, 2005; see also Chapter 13), for example, has suggested that impulsivity results from dysfunction in at least one of two inhibitory systems. He distinguishes between motivational inhibition, which results from behavioral suppression in the context of anxiety-provoking cues, and executive inhibition, or the deliberate process of stopping or suppressing a prepotent but task-inappropriate response. Barkley (1997) also characterizes faulty inhibition, positing a hierarchical inhibitory structure in which behavioral inhibition consists of three subprocesses (inhibiting of prepotent responses, stopping ongoing responses, and controlling interfering stimuli), each supporting a number of executive functions that allow for effective goal-directed behavior.

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Behaviorally, impulsivity has been described as “behavior that is socially inappropriate or maladaptive and is quickly emitted without forethought” (Oas, 1984; 1985). This behavioral rather than neuropsychological definition has a number of strengths. Although distinct from the more heavily cognitive formulations of disinhibition, it does not rule out cognitively mediated mechanisms. Furthermore, it emphasizes impulsivity as a maladaptive trait, distinguishing it from other qualities, such as spontaneity, which are viewed more positively. Finally, it does not include causal assumptions regarding the etiology of disinhibition, allowing for both psychological and biological contributions. Our preferred definition of impulsivity is that described in the DSM-5 (American Psychiatric Association, 2013) as a component of the hyperactive/impulsive presentation of ADHD. According to this definition, impulsivity is demonstrated by “hasty actions that occur in the moment without forethought and have high potential for harm to the individual . . . a desire for immediate rewards or an inability to delay gratification . . . and/or as making important decisions without consideration of long-term consequences” (p. 61). This conceptualization is similar to that of Sagvolden and colleagues (2005), who describe impulsivity as taking action without forethought and failing to plan ahead. Inherent in these definitions is the premise that impulsivity becomes pathological when it interferes with social, academic, and/or occupational functioning. Thus, the degree, context, and consequences of an individual’s behavior are essential considerations, as they differentiate impulsivity from partially related but more normative behaviors such as risk taking, novelty seeking, and sensation seeking (see also Hirshfeld-Becker et al., 2002).

ETIOLOGICAL FORMULATIONS As should be apparent from our discussion, the behavioral (phenotypic) expression of impulsivity may derive from one or more of several sources (see also Sonuga-Barke, 2005). Well-characterized influences on impulsive behavior include brain injuries, which may result from head trauma, hypoxia, or other central nervous system insults (Chapter 10 [Arnett, Meyer, Merritt, Gatzke-Kopp, & Shannon Bowen]); exposure to teratogens such as alcohol, stimulant drugs of abuse, and/or lead (Chapter 9 [Doyle, Mattson, Fryer, & Crocker]); early traumatic experiences including social deprivation, child abuse, and neglect (Lucas et al., 2004; Poeggel et al., 1999; Chapter 5 [Jaffee]); and genetic vulnerabilities that give rise to deficient executive control over behavior (Chapter 13 [Nigg]). Although this list is certainly not exhaustive, it illustrates the heterogeneous nature of broad behavioral traits such as impulsivity (see Beauchaine et al., 2010; Beauchaine & Marsh, 2006). Rather than describing each of these mechanisms in detail, we begin by focusing on neurobiological substrates that (a) give rise to individual differences in impulsivity that are temperamental, present very early in life, and often emerge before

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conditions such as ADHD can be diagnosed; (b) are supported by voluminous literatures derived from both animal models and humans; and (c) confer vulnerability to externalizing disorders across the lifespan, particularly in the context of high-risk environments characterized by violence, trauma, and emotional lability. This focus on temperamental impulsivity is consistent with our main objective in writing this chapter: to describe early-onset impulsivity as a vulnerability for later psychopathology. Readers should note, however, that it may be difficult in clinical practice to distinguish between children who are impulsive due to an inherited temperamental trait versus children who are impulsive due to other etiological influences such as prenatal exposure to various toxic substances or head injuries (e.g., Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011; Gatzke-Kopp, 2011). Most modern accounts of temperamental disinhibition emphasize structural and functional abnormalities in phylogenetically old brain regions including the mesolimbic dopamine system and the basal ganglia, overlapping neural networks that mature very early in life and subserve individual differences in approach behavior throughout the lifespan (see Beauchaine et al., 2001; Beauchaine et al., 2010; Beauchaine et al., 2012; Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007; Sagvolden et al., 2005). Here, we emphasize vulnerability to psychopathology that is conferred by compromises in functioning of subcortical mesolimbic brain regions, rather than on cortical (prefrontal) theories of impulsivity. Prefrontal brain regions mature throughout adolescence and into the early 20s and therefore contribute less to individual differences in trait impulsivity very early in life (see Beauchaine & McNulty, 2013; Halperin & Schulz, 2006). Nevertheless, we acknowledge the importance of frontal mechanisms of impulsivity, and refer readers to Castellanos-Ryan and Séguin (2016) for an excellent review. It is also important to note that neurodevelopment of frontal regions—through mechanisms of neural plasticity, programming, and pruning—is shaped by early experiences that are themselves a product of subcortically mediated impulsivity (Beauchaine, Neuhaus, Brenner, & Gatze-Kopp, 2008; Sagvolden et al., 2005; see also Shannon, Sauder, Beauchaine, & Gatzke-Kopp, 2009). Thus, heritable compromises in functioning of early maturing brain regions that give rise to impulsivity can alter neurodevelopment of later maturing brain regions that are responsible for executive functioning and planning—especially in high-risk environments. This model highlights the transactional, or ontogenic process nature of brain-behavior linkages (Beauchaine & McNulty, 2013; Beauchaine, Shader, & Hinshaw, 2016). Recognition and description of such transactions between individuals and environments are tenets of the developmental psychopathology perspective (see Beauchaine & Gatzke-Kopp, 2012; Cicchetti, 2006; Hinshaw, 2013; Rutter & Sroufe, 2000; Sroufe & Rutter, 1984; Chapter 1 [Hinshaw]). In later sections, we describe neurodevelopmental mechanisms through which early impulsivity may potentiate vulnerability to deficient executive functioning later in life.

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Temperamental Impulsivity and Central Dopamine Functioning Theories of impulsivity have long focused on the mesolimbic dopamine (DA) system, including the ventral tegmental area and its projections to ventral striatum, including the nucleus accumbens, caudate, and putamen (Swartz, 1999), and on other dopaminergic networks within the central nervous system (Beauchaine & Gatzke-Kopp, 2012; Castellanos, 1999; Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007, Kalivas & Nakamura, 1999; Sagvolden et al., 2005). Many of these theories follow from research on reinforcement motivation and substance dependence conducted with rodents and nonhuman primates. This research demonstrates that (a) electrical and pharmacological stimulation of dopaminergically mediated mesolimbic structures is reinforcing, such that trained animals engage in prolonged periods of operant behaviors (e.g., lever pressing) to obtain these incentives (see Milner, 1991); (b) neural activity increases within mesolimbic structures during both reward anticipation and reward-seeking behaviors and following administration of DA agonists (see Knutson, Fong, Adams, Varner, & Hommer, 2001; Phillips, Blaha, & Fibiger, 1989; Schott et al., 2008); and (c) DA antagonists attenuate—and in extreme cases block—the rewarding properties of food, water, and stimulant drugs of abuse (e.g., Rolls et al., 1974). Based on this set of observations, several authors have offered theories of impulsivity and personality that explain individual differences in approach behavior as variations in activity of mesolimbic structures. Perhaps the most prominent of these theories is that offered by Gray (1987a, 1987b), in which he proposed a mesolimbic behavioral approach system (BAS) as the neural substrate of appetitive motivation. Soon afterward, clinical scientists interested in impulsivity co-opted dopaminergic theories of approach motivation to explain the unbridled reward-seeking behaviors observed in ADHD, CD, and related externalizing disorders (e.g., Fowles, 1988; Rogeness, Javors, & Pliszka, 1992; Quay, 1993). Although early theories correctly identified mesolimbic neural structures implicated in the expression of impulsivity, most researchers at the time subscribed to the face-valid assumption that excessive dopaminergic activity led to impulsive behavior. In other words, they assumed a positive correspondence between neural responding and behavior. This assumption is evident in the formulation of measures such as the BIS/BAS scales (Carver & White, 1994), which presuppose a direct relation between impulsive behaviors and BAS activity (see Brenner, Beauchaine, & Sylvers, 2005). However, several clear and consistent findings present intractable problems for theories that link excessive mesolimbic DA activity to impulsivity. First, several studies indicate that impulsive preschoolers, middle-schoolers, and adolescents display reduced sympathetic nervous system (SNS)-linked cardiac reactivity to reward (Beauchaine et al., 2001; Beauchaine et al., 2007; Crowell et al., 2006). These findings are significant because (a) SNS-linked cardiac reactivity to incentives serves as a peripheral index of central DA responding under such stimulus conditions (Brenner et al., 2005; Brenner & Beauchaine, 2011) and (b) infusions of DA into mesolimbic structures produce SNS-mediated increases in cardiac output (van den

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Buuse, 1998). Thus, reduced cardiac reactivity to reward among impulsive children is likely to signal diminished DA responding—directly opposite to expectations based on the excessive DA theory. Second, studies using both single photon emission computed tomography and positron emission tomography demonstrate that the primary mechanism of action of methylphenidate and related DA agonists is to increase neural activity in the ventral striatum, located within the mesolimbic reward pathway (e.g., Vles et al., 2003; Volkow, Fowler, Wang, Ding, & Gatley, 2002). Thus, pharmacological interventions that increase mesolimbic DA activity by inhibiting reuptake decrease hyperactivity, impulsivity, and related aggressive behaviors (e.g., Hinshaw, Henker, Whalen, Erhardt, & Dunnington, 1989; MTA Cooperative Group, 1999). Theories of excessive DA as a mechanism of impulsivity predict the opposite effect (i.e., increasing striatal DA activity should worsen impulsivity). Finally, infusions of DA into mesolimbic structures are experienced as pleasurable, and individual differences in central DA expression predict trait positive affectivity (see Ashby, Isen, & Turken, 1999; Berridge, 2003; Forbes & Dahl, 2005). In contrast, PET studies indicate that low levels of striatal DA activity are associated with trait irritability (Laakso et al., 2003). When interpreted in the context of positive relations between externalizing behaviors and both negative affectivity and irritability (e.g., Martel & Nigg, 2006; Mick, Spencer, Wozniak, & Biederman, 2005), these findings suggest diminished rather than excessive DA functioning among at least some impulsive individuals. These converging sources of evidence for reduced mesolimbic DA function as a neural substrate of impulsivity led to a reformulation of first-generation models. We and others have suggested that underactivation of striatal DA leads to increased behavioral responding, which functions to raise activation levels within the mesolimbic system (Beauchaine et al., 2007; Beauchaine et al., 2012; Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007; Sagvolden et al., 2005; Volkow et al., 2009). Thus, what has been mistaken for hypersensitivity is more likely to be reward insensitivity, which results in increased impulsive behavior in an effort to temporarily alleviate a chronically aversive mood state—the affective consequence of an underactive mesolimbic DA system (Ashby et al., 1999; Forbes & Dahl, 2005; Laakso et al., 2003). In addition to the literature cited above, this interpretation is supported by research indicating (a) associations between low basal DA activity/blunted DA reactivity and a propensity to use DA agonist drugs of abuse (De Witte, Pinto, Ansseau, & Verbanck, 2003; Laine, Ahonen, Räsänen, & Tiihonen, 2001; Martin-Soelch et al., 2001; Martinez et al., 2007); (b) significant correlations between blunted DA responses to amphetamine administration and the personality trait of novelty seeking (Leyton et al., 2002); and (c) well-replicated neuroimaging studies indicating reduced striatal activity during reward tasks among children and adolescents with ADHD and CD (e.g., Carmona et al., 2012; Durston et al., 2003; Plichta, & Scheres, 2014; Vaidya et al., 1998). Thus, overwhelming evidence

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now supports the hypothesis that trait impulsivity results at least in part from abnormally low mesolimbic DA activity.

GENETICS AND HERITABILITY There are two general approaches to studying the genetic bases and heritability of any behavioral trait—behavioral genetics and molecular genetics (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Each approach offers unique insights into genetic influences relevant to impulsivity, its origins, and its expression over the course of development.

Behavioral Genetics of Impulsivity Behavioral genetics studies are used to parse variability in a behavioral trait into heritable (both genetic and nongenetic) and nonheritable (environmental) components. Impulsivity is among the most heritable of all behavioral traits. Behavioral genetics studies comparing concordance rates of impulsivity and ADHD for monozygotic and dizygotic twins produce heritability coefficients (h2 ) that approach and sometimes exceed .8, indicating that as much as 80% of the variance in impulsive behavior is accounted for by heritable factors (e.g., Levy, Hay, McStephen, Wood, & Waldman 1997; Price, Simonoff, Waldman, Asherson, & Plomin, 2001; Sherman, Iacono, & McGue, 1997; Wood, Rijsdijk, Saudino, Asherson, & Kuntsi, 2008). Furthermore, Krueger et al. (2002) identified a common vulnerability for a wide range of externalizing symptoms including disinhibition, conduct problems, antisocial personality, alcohol dependence, and drug dependence among a sample of 1,048 participants in the Minnesota Twin Family Study. This latent vulnerability for externalizing disorders, which is likely to reflect trait impulsivity (see Beauchaine & Marsh, 2006; Beauchaine & McNulty, 2013; Beauchaine et al., 2016), was 81% heritable. Similar findings have since been reported in child samples (Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011; Tuvblad, Zheng, Raine, & Baker, 2009). Still, specific categories of externalizing behavior are influenced strongly by environmental effects. This well-replicated finding is important because it demonstrates that a common genetic vulnerability can result in divergent, multifinal outcomes depending on environmental experience (Beauchaine et al., 2010; Beauchaine & Kopp, 2012), a point which we return to below.

Molecular Genetics of Impulsivity Molecular genetics approaches, including both linkage and association studies, are designed to identify specific genes that contribute to the expression of a trait or disorder (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Linkage studies search for chromosomal regions that are shared more often than expected among large numbers of families with two or more affected children, thereby “linking”

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the disorder or trait to those chromosomal regions (Faraone & Mick, 2010). Because linkage analyses scan broad sections of the genome, the approach works best when very few genes with large effects contribute to a behavioral trait or disease— a rare precondition for psychiatric disorders, which are usually multifactorial in nature. Nonetheless, a genome scan meta-analysis combining seven datasets supported a significant linkage for ADHD on chromosome 16, with possible linkages within a number of other regions (Zhou et al., 2008). Chromosome 16 has also been linked with a variety of other neurodevelopmental disorders (e.g., autism, epilepsy; Ramalingam et al., 2011), supporting impulsivity as a trait that cuts across diagnostic boundaries. Despite this finding, no specific gene has been identified through linkage analyses, and failures to replicate continue to plague psychiatric genetics research (see e.g., Gizer, Otto, & Ellingson, 2016). In contrast to linkage studies, genetic association studies begin with a candidate gene that is believed to play an etiological role in the expression of a disorder (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Using this approach, allelic frequencies of specific genetic polymorphisms are compared among those with and without the condition under study. Association studies can be used to detect genes that account for much smaller amounts of variance in behavior. Given well-articulated theories specifying altered DA functioning as a pathophysiological determinant of impulsivity (see above), association studies are well suited for use with this behavioral trait (Galili-Weisstub & Segman, 2003). Not surprisingly, association studies far outnumber linkage studies of impulsivity and ADHD. Historically, the consistency of results and effect sizes from these studies has been mixed, but recent reviews (e.g., Faraone, Bonvicini, & Scassellati, 2014) highlight the DRD4 and DAT1 genes as the most likely and consistent genetic candidates. Vulnerability alleles including the DRD4 gene (chromosome 11p15.5), which codes for DA receptors located throughout the central and peripheral nervous systems, have been associated with higher levels of trait impulsivity and ADHD (Benjamin et al., 1996; Faraone & Mick, 2010; Li, Sham, Owen, & He, 2006; Schilling, Kuhn, Sander, & Gallinat, 2014). They also correspond to less consistent performance on cognitive tasks among individuals with ADHD (Kebir & Joober, 2011) and predict response to methylphenidate administration (Bruxel et al., 2014). The DAT1, or dopamine transporter gene (chromosome 5p15.3), regulates synaptic levels of DA, the principal target of psychostimulants used to treat ADHD (Grace, 2002). Individuals who are homozygous for the DAT1 10-repeat allele demonstrate reduced cortical thickness in prefrontal areas, as well as reduced volume and activation within mesolimbic and prefrontal structures (Durston, 2010; Fernandez-Jaen et al., 2015). In addition, DAT1 vulnerability alleles are associated with increased DAT binding in the caudate in both typical development and ADHD (Spencer et al., 2013). Recent data suggest that DAT1 may be most influential in combination with specific environment risk factors such as familial risk for ADHD and prenatal substance exposure (Durston et al., 2008; Faraone & Mick, 2010; Laucht et al., 2007; Neuman et al., 2007). Impulsive children who carry vulnerability alleles for both DRD4 and DAT1 appear

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to be at particularly high risk for disruptive behavior, suggesting a cumulative effect of genetic vulnerabilities (Kotte, Faraone, & Biederman, 2013). In addition to genes that are involved directly in DA expression, association studies also suggest roles for genes that are involved in synthesis and metabolism of DA—processes that also affect synaptic activity and reuptake. Candidate genes include those that encode for dopamine-𝛽-hydroxylase (DBH), which converts DA to norepinephrine; and both monoamine oxidase (MAO) and catechol-O-methyl transferase (COMT), enzymes involved in DA (and other monoamine neurotransmitter) degradation. Association studies involving these genes have been few and conflicting. With regard to DBH, allelic status has been linked with ADHD in some samples (Carpentier et al., 2013; Hess et al., 2009), but meta-analyses cast doubt on the reliability and strength of this finding (Gizer, Ficks, & Waldman, 2009). Current evidence suggests that polymorphisms in both the MAOA gene (Xp11.23–11.4) and the COMT gene are associated with antisocial behavior among impulsive individuals, particularly in the context of environmental adversity, including problematic parenting (Caspi et al., 2002; Qian et al., 2009; Thapar et al., 2005; Vanyukov et al., 2007). Emerging evidence also indicates that reduced neural responses to reward and decreased white matter connectivity are associated with particular alleles of MAOA (A allele) and COMT (Met allele of the Val158Met polymorphism), respectively (Hong et al., 2015; Nymberg et al., 2013). However, associations between these genes and ADHD are inconsistent in both direction and effect size (see Faraone & Mick, 2010), and MAO may have differential effects on impulsivity according to sex (indeed, it is X-linked; see Biederman et al., 2008). Taken together, DBH, MAOA, and COMT may be less associated with trait impulsivity per se than with externalizing sequelae that arise from Gene × Environment interactions (see Beauchaine et al., 2009). To summarize, behavioral genetics studies indicate impressively high heritabilities, and suggest that impulsivity contributes to a number of externalizing outcomes. However, despite this high heritability, candidate genes identified to date account for very little variance in impulsive behavior. This “missing heritability” problem plagues psychiatric genetics (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Clearly, considerable work remains in our attempts to understand the genetic bases of impulsivity.

IMPULSIVITY AND VULNERABILITY TO PSYCHOPATHOLOGY In developmental psychopathology, a distinction is often made between vulnerabilities and risk factors for psychiatric disorders (e.g., Shannon, Beauchaine, Brenner, Neuhaus, & Gatzke-Kopp, 2007; Luthar, 2006). Vulnerabilities are usually assumed to be biologically based traits that render individuals susceptible to psychopathology, whereas risk factors are environmental influences that interact with vulnerabilities to potentiate psychopathology. For example, it is now known that distressing experiences (risk factors) elicit posttraumatic stress disorder mainly

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among genetically predisposed (vulnerable) individuals (e.g., Orr et al. 2003; Stein, Jang, Taylor, Vernon, & Livesley, 2002). Although the distinction between vulnerabilities and risk factors breaks down when we consider the interactive roles that genetically influenced traits play in eliciting specific environments (evocative effects; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]) and that environments play in the expression of genes (see Moffitt, 2005; Shannon et al., 2007), we maintain traditional use of the terms in upcoming sections, where we outline factors that amplify the likelihood of psychopathology among impulsive and therefore vulnerable individuals. Before proceeding, however, it should be noted that temperamental impulsivity is usually not enough (except in perhaps the most extreme cases) to result in psychopathology in the absence of additional vulnerabilities and/or risk factors. Research with impulsive preschoolers indicates that many do not progress to severe externalizing behavior as they mature (e.g., Campbell et al., 2000; Lahey et al., 2016). In sections to follow, we summarize several additional vulnerabilities and risk factors that interact with temperamental impulsivity to increase the likelihood of later psychopathology.

Behavioral Inhibition In addition to impulsivity, a second well-characterized temperamental trait is behavioral inhibition. This term refers to a general tendency to be wary in novel situations, to be “slow to warm up,” and to avoid overly stimulating environments. Kagan, Reznick, and Snidman (1988) identified a group of 3-year-olds who displayed high degrees of behavioral inhibition in unfamiliar laboratory settings. These children avoided approaching and interacting with unfamiliar children and adults, remained in close proximity to their mothers, and ceased vocalizing in the presence of strangers. When they were reassessed at age 7, they remained quiet, cautious, and socially avoidant. Thus, like trait impulsivity, behavioral inhibition can be detected early in life and is stable (although not invariant) across development. It is also mediated largely by heritable factors (see Chapter 7 [Kagan]). It has often been assumed that trait inhibition and trait impulsivity mark extremes along a bipolar continuum of behavioral control. Yet the neural substrates of the two traits are almost completely nonoverlapping. In contrast to impulsivity, behavioral inhibition, which renders individuals vulnerable to anxiety disorders, is mediated by the septo-hippocampal system, a primarily serotonergic network (see e.g., Beauchaine, 2015; Gray & McNaughton, 2000). Moreover, the two systems evolved to subserve distinct functions: approach behaviors promote survival by ensuring engagement in activities such as eating, drinking, and mating, whereas avoidance behaviors promote survival by reducing exposure to danger. In fact, Gray and others (Gray & McNaughton, 2000; McNaughton & Corr, 2004) have argued convincingly that the functional role of the septo-hippocampal system is to suppress approach behaviors under conditions of threat and uncertainty.

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This conceptualization, in which approach tendencies are actively suppressed by avoidance tendencies, is supported by a large literature on experiments with animals and has direct implications for psychopathology (see Beauchaine, 2001; Beauchaine et al., 2011). Given that the approach and avoidance systems operate with substantial independence, an individual can be high or low on either or both dimensions. A person who is temperamentally impulsive due to a heritable DA deficiency may be protected from severe psychopathology if he or she is also high on behavioral inhibition. Although this might seem implausible at first glance, symptoms of anxiety are surprisingly common among impulsive children with ADHD (Angold, Costello, & Erkanli, 1999; MTA Cooperative Group, 1999), and in the absence of additional comorbidities, such children are more responsive to behavioral interventions than their nonanxious counterparts (Jensen et al., 2001). Furthermore, older externalizing youth with comorbid anxiety are less physically aggressive, regarded less negatively by peers, and experience fewer police contacts than those without anxiety symptoms (Walker et al., 1991). Such findings are precisely what one would expect from a more responsive septo-hippocampal system. Consistent with this interpretation, in a structural neuroimaging study, interactions between trait anxiety and trait impulsivity predicted individual differences in gray matter volumes in both septo-hippocampal and mesolimbic brain regions among children with ADHD (Sauder, Beauchaine, Gatzke-Kopp, Shannon, & Aylward, 2012). Those with ADHD who experienced comorbid anxiety showed normal gray matter volumes in these brain regions compared with controls, whereas those who experienced low levels of anxiety exhibited reduced gray matter volumes. As this discussion implies, an impulsive person who is low on trait anxiety may be especially vulnerable to developing more serious externalizing disorders. Psychopathy, a behavior pattern characterized by manipulation of others, superficial charm, callousness, and lack of remorse, is probably the most intractable form of externalizing conduct (see Lykken, 2006). As several authors have noted, individuals who score high on psychopathy measures exhibit excessive approach behaviors coupled with a disturbing lack of fear and anxiety (see Fowles & Dindo, 2006). Thus, their impulsive tendencies are not inhibited by impending consequences, presumably because they are very low on behavioral inhibition. As a result, the condition is largely unresponsive to treatment. Given that temperamental impulsivity and inhibition are both largely heritable, individuals with psychopathy appear to be “doubly vulnerable” to psychopathology. This situation might best be considered a Trait × Trait interaction, with two largely independent heritable attributes contributing to behavioral functioning (see also Derryberry, Reed, & Pilkenton-Taylor, 2003). Although such models are rare in psychopathology research, recent advances in molecular genetics make it much easier to study interactions among underlying genes that potentiate psychiatric morbidity (see e.g., Beauchaine et al., 2009).

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Environmental Risk There is also considerable evidence that environmental risk factors lead to more severe psychopathology among impulsive children, including those with ADHD. These youth are more likely than their non-ADHD peers to develop ODD, CD, antisocial personality disorder, and in some cases substance abuse later in life (see Barkley, 2003; Beauchaine & McNulty, 2013). Longitudinal studies suggest that for many children, hyperactivity/impulsivity constitutes the first stage in a trajectory that progresses via mediating environmental risk factors to antisocial behaviors, eventually culminating in early-onset delinquency (see Beauchaine & McNulty, 2013; Beauchaine et al., 2010; Beauchaine et al., 2016). We outline some of these risk factors below. Parenting. One of the most thoroughly studied environmental correlates of externalizing behavior is parenting. Numerous studies demonstrate that parents of impulsive and aggressive children are more negative, lax, verbose, and overreactive in their discipline practices than parents of control children (Arnold, O’Leary, Wolff, & Acker, 1993; Barkley, Karlsson, & Pollard, 1985). In a groundbreaking longitudinal study of impulsive boys, Patterson et al. (2000) demonstrated that coercive parental discipline fully mediated the longitudinal association between hyperactivity and antisocial behavior. Hyperactivity led to more serious externalizing behaviors only when parents consistently nagged their children and were explosive in their discipline practices. Parental psychopathology, including ADHD and conduct problems, potentiates risk for externalizing symptoms among impulsive and hyperactive children (Biederman et al., 1995; Biederman et al., 1996; Tandon, Tillman, Spitznagel, & Luby, 2014; Tung, Brammer, Li, & Lee, 2015), and this risk is mediated by both heritable factors and parenting practices. Coercive family interaction patterns—those in which both children and their parents escalate aversive behaviors and negative affect in order to assert their respective wills—promote physical aggression, conduct problems, and delinquency (Snyder, Edwards, McGraw, Kilgore, & Holton, 1994; Snyder, Schrepferman, & St. Peter, 1997). Developmental models suggest that these frequently repeated episodes of affective and behavioral escalation also shape and maintain emotional lability, emotion dysregulation, and physiological reactivity, which increase vulnerability to severe conduct problems (Beauchaine & Zalewski, 2015; Beauchaine et al., 2007; Crowell, Beauchaine, & Linehan, 2009; Chapter 14 [Lahey & Waldman]). This coercive model has been extended to the development of self-inflicted injury among female adolescents (Beauchaine et al., 2009), based on the premise that impulsivity is a core underlying trait in the development of self-injurious behavior (see also Meza, Owens, & Hinshaw, 2015). Negative interaction patterns within mother-daughter dyads appear to interact with daughters’ peripheral serotonin levels to account for much of the variance in

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self-inflicted injury (Crowell et al., 2008). Such findings highlight the importance of Biology × Environment interactions, and the potential moderating role of sex in the developmental trajectory of impulsivity. Indeed, emerging research indicates that impulsive girls are vulnerable to developing self-injurious behaviors in adolescence and young adulthood (Hinshaw et al., 2012; Swanson, Owens, & Hinshaw, 2014; Chapter 19 [Kaufman, Crowell, & Lenzenweger]). Although this body of research has been interpreted by some as evidence of direct environmental effects, it is also likely that heritable genetic vulnerabilities shared by parents and children drive coercive behaviors observed by both parties (an example of passive gene-environment correlation; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). However, genetic versus environmental hypotheses cannot be disambiguated without true experiments in which impulsive children are assigned randomly to coercive and noncoercive caretakers—an ethically indefensible practice. Nevertheless, in a randomized clinical trial, Hinshaw et al. (2000) found that reductions in negative/ineffective discipline among parents of youth with ADHD mediated school-based reductions in disruptive behavior and improvements in social skills, with effects most pronounced for families who received the multimodal combination of medication and intensive behavior therapy. Similarly, a recent randomized clinical trial demonstrated improved parenting and reduced externalizing behavior among preschool children with ADHD following an empirically supported parent intervention (Webster-Stratton, Reid, & Beauchaine, 2011, 2012). Moreover, interventions that successfully reduce coercive parenting behaviors also reduce delinquency (e.g., Hartman, Stage, & Webster-Stratton, 2003; Martinez & Forgatch, 2001; Piquero, Farrington, Welsh, Tremblay, & Jennings, 2009). Child Abuse and Neglect. Those who study child maltreatment have traditionally considered social mechanisms of risk and intergenerational transmission (see Cicchetti & Valentino, 2006). We therefore include child abuse and neglect as environmental risk factors. However, we note that genetic and temperamental factors also appear to play roles in determining who engages in child abuse and neglect and in influencing the likelihood that a person who experiences abuse will become a future offender (Farrington, Jolliffe, Loeber, Stouthamer-Loeber, & Kalb, 2001; Chapter 5 [Jaffee]). On average, maltreated children are more impulsive than nonmaltreated children (Famularo, Kinscherff, & Fenton, 1992), and histories of abuse are associated with higher levels of externalizing symptoms among male children with ADHD (Briscoe-Smith & Hinshaw, 2006). For girls with ADHD, outcomes are decidedly in the direction of internalizing behaviors and suicidality (Guendelman, Owens, Galan, Gard, & Hinshaw, 2015). Such findings suggest that impulsive children may be at higher risk for child abuse and neglect than their nonimpulsive peers. Furthermore, behavior genetics studies indicate that physical abuse amplifies risk

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for conduct problems and often plays a direct role in the development of antisocial behavior among children (Trouton, Spinath, & Plomin, 2002). This effect is particularly marked among children who are genetically vulnerable (Jaffee et al., 2004). For instance, males who are maltreated as children are at especially high risk for adult antisocial behavior if they carry the low-activity allele of the MAOA gene, a prime example of a Gene × Environmental interaction (Caspi et al., 2002). Neighborhood Effects. A third environmental risk factor that interacts with trait impulsivity is neighborhood context (Chapter 12 [Jennings & Perez]). Several studies indicate that impulsive children who are reared in high-risk neighborhoods (typically defined by such factors as low socioeconomic status (SES), high rates of violence and criminality, and low community involvement) are more prone to engage in antisocial behavior than impulsive children reared in low-risk neighborhoods (Meier, Slutske, Arndt, & Cadoret, 2008; Trentacosta, Hyde, Shaw, & Cheong, 2009; Zalot, Jones, Kincaid, & Smith, 2009). For example, Lynam et al. (2000) found that impulsive boys, as assessed by a number of neuropsychological tests and self-report measures, are at higher risk than nonimpulsive boys for engaging in both status offenses and violent crimes, yet only when they live in neighborhoods of low socioeconomic status and high delinquency. No such effects are observed in high SES neighborhoods (see Zimmerman, 2010, for a different pattern of findings). Taken together, these findings exemplify a Trait × Environment interaction, and illustrate the importance of environmental opportunities in the expression of temperamental risk.

Epigenetic Effects Epigenetic effects refer to alterations in gene expression that result from changes in DNA structure or function rather than changes in DNA sequence (Hartl & Jones, 2002; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). These alterations are mediated by methylation processes (in addition to other, less well-documented mechanisms) that are triggered by environmental events. For example, Weaver et al. (2004) demonstrated epigenetically transmitted differences in the glucocorticoid receptor gene promoter in the hippocampi of rat pups that received high levels of maternal licking, grooming, and arched-back nursing compared with pups that experienced low levels of these maternal behaviors. This epigenetic effect transmits adaptive variations in stress responding to offspring. Rat pups reared in hazardous environments where maternal behaviors are compromised have more reactive hypothalamic-pituitary-adrenocortical (HPA) responses and are consequently more fearful and wary. They are therefore better prepared for the hazardous environment into which they are born. Mammals are particularly susceptible to such alterations

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in gene expression (Hartl & Jones, 2002), and increasingly divergent patterns of DNA methylation emerge over the lifetimes of monozygotic twin pairs (Fraga et al., 2005). Accordingly, several authors have emphasized the importance of epigenetic effects for child psychopathology research (e.g., Beauchaine et al., 2011; Kramer, 2005; Rutter, 2005; Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]), and theoretical models of antisocial behavior that include epigenetic effects are increasingly common. In the future, greater understanding of processes and timing of epigenetic effects may help in formulating targeted interventions for vulnerable children. Numerous studies illustrate epigenetic effects related to the emergence of impulsivity. The expression of brain-derived neurotrophic factor, which is involved in differentiation of DA neurons in developing mesolimbic structures and is implicated in the pathogenesis of impulsivity, is susceptible to paternally mediated epigenetic effects (Kent et al., 2005). Offspring of male mice that are exposed repeatedly to ethyl alcohol prior to mating display altered DAT expression within the striatum and prefrontal cortex, again suggesting paternally mediated epigenetic effects (Kim et al., 2014). Similarly, the expression of several DA genes within mesolimbic regions is influenced by extreme birth weight (markedly small or large for gestational age; Grissom & Reyes, 2013). Epigenetically mediated effects on impulsivity have also been observed for risk factors including prenatal smoking, childhood exposure to tobacco smoke, exposure to polychlorinated biphenyls (PCBs) and synthetic glucocorticoids, maternal stress during pregnancy, and family distress (Babenko, Kovalchuk, & Metz, 2015; DasBanerjee et al., 2008; Elia, Laracy, Allen, Nissley-Tsiopinis, & Borgmann-Winter, 2011; Kapoor, Petropoulos, & Matthews, 2007; Pagani, 2014).

Neural Plasticity In addition to epigenetic effects, several other mechanisms of neural programming link early impulsivity to later psychopathology. Neural plasticity refers to experience-dependent functional changes in neural networks, including their efficiency, sensitivity, and time course of responding (Pollak, 2005). These experience-dependent changes occur in several neural systems including mesolimbic DA structures (see Beauchaine et al., 2011). For example, Lucas et al. (2004) reported decreased DA transporter densities in mesolimbic brain regions of male rats exposed repeatedly to more dominant males in a stress-inducing paradigm. Similarly, repeated episodes of maternal separation early in the lives of rat pups produce long-term decreases in DA transporter expression (Meaney, Brake, & Gratton, 2002). These effects result in greater sensitivity to behavioral effects of cocaine and amphetamines later in life. Although similar experiments clearly cannot be conducted with humans, such findings illustrate the exquisite sensitivity of the mesolimbic DA system to early experience and suggest the possibility that experience-dependent changes in DA functioning may predispose affected individuals to stimulant use and/or abuse (see Gatzke-Kopp, 2011).

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Perhaps more troubling, stimulants (e.g., cocaine) themselves induce experiencedependent changes in neural function that are similar to those observed following stress exposure. Through this mechanism, alterations in DA expression lead to sensitization and addiction to stimulants including nicotine, amphetamines, and cocaine (e.g., Saal, Dong, Bonci, & Malenka, 2003; Taylor & Jentsch, 2001; Thomas, Beurrier, Bonci, & Malenka, 2001). Chronic elevation of DA neural firing in the nucleus accumbens by stimulants has two other problematic effects. First, it down-regulates basal DA activity (Scafidi et al., 1996), which may worsen impulsive tendencies that emerge from mesolimbic hyporesponding (see above). Second, it suppresses the strength of connections between the mesolimbic DA system and the prefrontal cortex (Thomas et al., 2001), which may alter development of executive functioning and long-term planning, thereby inhibiting a developmental shift from “bottom-up” neural processing in phylogenetically old limbic structures to “top-down” neural processing in phylogenetically newer cortical structures as individuals mature. In typically developing adolescents and adults, these frontal (mesocortical) structures inhibit reward-related behaviors when it is advantageous to do so (Taylor & Jentsch, 2001). Environmental risk factors including stress and drug exposure may prevent this maturational process from unfolding, resulting in an underdeveloped mesocortical DA system that predisposes to further stimulant use and abuse (Prasad, Hochstatter, & Sorg, 1999), and to potential long-term sequelae of early impulsivity, including conduct problems, delinquency, and antisocial personality development. It is important to note, however, that sensitization appears to be limited to early exposure to drugs of abuse and does not appear to extend to the therapeutic use of stimulant medications among children with ADHD. Instead, therapeutic use of stimulants appears to neither increase nor decrease the likelihood of future substance use disorders, although interpretations are limited because naturalistic investigations comprise the database (Humphreys, Eng, & Lee, 2013; Molina & Pelham, 2014; Volkow & Swanson, 2008). Increased rates of substance abuse among individuals with ADHD are likely attributable to the presence of comorbid antisocial behaviors (Mannuzza et al., 2008).

Implications for Learning As many readers may be aware, the same mesolimbic and to a lesser extent mesocortical structures discussed in this chapter are also recruited for associative learning processes (see Berridge & Robinson, 2003; Sagvolden et al., 2005). Thus, alterations in DA responding that arise from genetic, epigenetic, and experience-dependent effects are likely to influence efficiency of knowledge acquisition. This process might occur through at least three mechanisms: (1) reward-seeking tendencies that reduce motivation for learning “mundane” information; (2) reduced efficacy of associative learning due to dampened activation of mesolimbic structures; and (3) compromised executive functioning. Although we do not have space to review the learning

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literature in further detail, these implications underscore the importance of early intervention for impulsive children who may be on an externalizing trajectory.

RESEARCH DOMAIN CRITERIA FRAMEWORK It should be clear that our conceptualization of trait impulsivity exemplifies a core objective of the National Institute of Mental Health’s Research Domain Criteria (RDoC): to identify and characterize transdiagnostic dimensions of observable behavior, to tie them explicitly to neurobiological and genetic bases, and to explore their interactions with other such systems and with environmental factors that shape brain and behavior over the lifespan (Cuthbert, 2014). Along with increasing precision in our behavioral descriptions of impulsivity, research has refined our knowledge of genetic and neural contributors to impulsivity. Similarly, a range of environmental influences, spanning families to cultures, affect the development of impulsivity and its multifinal outcomes, including the unfortunate trajectory to more intractable externalizing behaviors described above. Interactions between neural circuits underlying impulsivity and those associated with other traits, including anxiety and emotion regulation, can act to either curtail or promote externalizing behaviors (Beauchaine, 2015; Beauchaine & Thayer, 2015).

SYNTHESIS AND FUTURE DIRECTIONS In this chapter, we describe (a) heritable biological mechanisms of vulnerability that lead to impulsivity among affected children; (b) environmental risk factors that can potentiate vulnerability, leading to more serious externalizing behaviors that are especially difficult to treat; and (c) the potential importance of gene-environment correlations and Gene × Environment interactions in the expression and development of externalizing behaviors among impulsive children. Although discussion of environmental, epigenetic, and experience-dependent risk factors for delinquency is sobering, it is worth repeating that only about half of impulsive preschool children develop more serious externalizing behaviors (Campbell et al., 2000). Furthermore, progress over the past decade in the specification of mechanisms through which impulsive behaviors escalate is truly astounding. Modern neuroscientific methods provide insights into the development of externalizing behaviors that were unimaginable just a few years ago. When considered in conjunction with findings from more traditional approaches, it becomes apparent that some children face a cascade of cumulative vulnerability and risk that is increasingly difficult to reverse across development. In the worst cases, impulsive children are raised by impulsive parents who, in addition to conferring genetic liability, transmit risk through inconsistent and stressful caretaking during infancy, along with the potential for child maltreatment and coercive, labile parenting (see Beauchaine & McNulty, 2013; Beauchaine et al., 2011). Further accumulation of risk may occur via exposure to violence in high-risk neighborhoods, early escalation

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of substance use, low motivation, and learning difficulties. By middle childhood and adolescence, exposure to stimulant drugs of abuse compromises the development of executive functions and self-regulation, compounding problem behaviors. In contrast, an impulsive child who is raised in a maximally protective environment faces few or none of these additional risk factors and may develop both psychological and biological resilience given enriched educational experiences and competent parenting that teaches strong emotion regulation skills (Beauchaine et al., 2007; Raine et al., 2001). Parenting interventions have proven quite effective in reversing risk for conduct problems, especially when delivered early in childhood (Beauchaine, Webster-Stratton, & Reid, 2005; Nock, 2003; Piquero et al., 2009). Such interventions are even accompanied by improvements in neurobiological indicators of vulnerability among preschoolers (Beauchaine et al., 2014). Thus, there is reason to be optimistic. It is our hope that our knowledge of risk and resilience will continue to grow, and that science will influence public policy so more children on externalizing trajectories receive evidence-based preventive services.

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CHAPTER 7

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability to Psychopathology JEROME KAGAN

HISTORICAL CONTEXT

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sychopathology is no exception to the rule that history continually alters the meanings of words. During most of the 19th century, Americans and Europeans restricted the term psychopathology to a small number of deviant profiles—usually symptoms defining schizophrenia, bipolar disorder, or autism—that disrupted community harmony and/or prevented individuals from carrying out their responsibilities. Freud introduced a seminal change by insisting that a child’s early experiences within the family made a critical contribution to a collection of symptoms in which anxiety was the central feature. This idea, new at the turn of the last century, became popular because historical changes that brought a faster pace of life and the empowerment of women created a vague mood of uncertainty. Freud supplied the public with a source of this feeling by suggesting that its origin lay with improper socialization of children. Freud’s emphasis on early experience, rather than prudishness over sexuality, was an important reason why his ideas were popular among Americans, who celebrated an egalitarian ethos that favored the belief that life history, not a special biology, determined a person’s level of adaptation. This premise meant that everyone was potentially vulnerable to acquiring symptoms of a neurosis. The emphasis on environmental causation, also promoted by behaviorists, motivated psychologists and psychiatrists to conduct studies designed to prove that a child’s early socialization was the main determinant of later problems. At the center of this mission was the belief that a mother’s care for and love of her child were the most important protections against future pathology. John Bowlby 213

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strengthened this premise with a landmark trilogy of books on attachment (Bowlby, 1969, 1973, 1980). The confluence of these events persuaded psychologists and psychiatrists who were active during the half-century between 1910 and 1960 that children and adolescents who developed mental illness probably suffered from insufficient maternal affection. A confident cohort of geneticists, neuroscientists, and molecular biologists, armed with novel methods, interrupted this narrative by reasserting the 19th-century assumption that inherited biological conditions were more important causes of psychopathology. The search for genetic bases of symptoms found a receptive audience in the 1980s because social scientists who advocated a formative role for experience failed to prove that experience alone—independent of a person’s biology and cultural setting—could generate symptoms of psychopathology. A reluctance to blame the rearing practices of poor, less educated parents for their children’s pathology, which followed civil rights legislation of the 1960s, contributed to the political attractiveness of genetic etiologies. No parent can be blamed for transmitting risk genes to their offspring. The media cooperated with these geneticists and neuroscientists, hyping the significance of their findings by telling a vulnerable public that their fate rested with their biology. One trio of experts on psychiatric illness announced the mantra: “The diseases we treat are diseases of the brain” (Ross, Travis, & Arbuckle, 2015). This claim preserves the premise, popular among Western scholars since Democritus, that material entities are the foundation of all natural phenomena. The attractiveness of this idea is exemplified by the receptiveness in the 1830s to Franz Gall’s claim that patterns of bumps on the skull, reflecting variation in the underlying brain tissue, were a clue to a person’s character traits. These and other historical events catapulted biological processes to an alpha position, over complaints of a dwindling number of psychologists who believed that experiences had the power to create symptoms among those without a special biological vulnerability. The pendulum had swung so far toward biological determinism that most investigators who look for a genetic correlate of a symptom or illness category accept the diagnostic category as a biological unity and rarely gather additional psychological information that would strengthen their results. Had they obtained more information on each patient’s history and current circumstances, they may have been able to parse a group of depressed adults into several etiologically distinct categories. Advocates of a determinism between brain states and psychological properties often borrow predicates, such as fear or compute, which assume that humans are the noun agents, attribute the term to one or more brain sites, and imply that the meaning of the predicate has not changed significantly. The practice of equating human psychological states with brain circuits is common among investigators who attribute anxiety to the brain of a rat that fails to explore a brightly lit alley, and fear to the brain of a rat that becomes immobile to a conditioned signal for shock. This prose implies implicitly that the state of anxiety or fear in rats resembles the state experienced by anxious or fearful humans.

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 215 Tovote, Fadoka, and Luthi (2015) suggested that fear is a brain state. They wrote, “Fear and anxiety are brain states,” when they should have written, “Fear of falling while looking down from a tall building, and anxiety over losing a romantic relationship, are human psychological states that emerge from different profiles of brain activity.” When a discipline is young and without a consensual theory, most concepts belong to more than one network of terms and, therefore, have more than one meaning. The social sciences do not have a consensual theory. Therefore, many concepts belong to several networks and possess more than one meaning because sources of evidence introduce terms into the network that influence meaning. The concept of fear provides an example. The meanings of fear are dissimilar when a verbal report, blood flow to the amygdala, potentiated startle, time freezing to a conditioned signal, and rise in cortisol are sources of evidence. LeDoux (2014) agrees with this position for he suggests that defensive, not fear, is the proper name for the circuit that generates several seconds of freezing when a rat is exposed to a conditioned cue for shock. Neuroscientists who study animals should replace the terms fear and anxiety with descriptions of the behaviors that specific circuits generate to various incentives. For example, the bed nucleus of the stria terminalis is the source of an inhibitory, GABA-ergic influence on exploratory behavior. This fact may explain why rats with an excitable bed nucleus do not spend much time in the center of an open field. This description is more faithful to the phenomenon observed than one that attributes anxiety to a rat that fails to explore the center of an unfamiliar open area. The nucleus accumbens provides a second example. The nucleus accumbens receives inputs from many sites that contain representations of the current event in its setting, as well as past experiences in similar settings. These varied inputs create a neural state in the accumbens that biases its output to motor targets that favor one response over alternatives when there is uncertainty over the adaptive action (Floresco, 2015). The accumbens, like the hand that hovers before picking one piece of chocolate from a full box, is not a source of hedonic pleasure (Berridge & Kringelbach, 2015). It is only a critical way station on the way to pleasure. Einstein’s insight in special relativity was that a statement about the simultaneity of two events had to assume a frame of reference. Analogously, a frame of reference is needed to understand the meaning of many predicates that refer to psychological states. The noun representing the agent of these predicates provides the needed frame of reference. The meaning of compute, therefore, depends on whether the noun agent is a collection of neurons, a migrating bird, a chimpanzee gazing at fruit high on a tree, or an engineer working on a laptop. The brain does not enjoy a privileged frame for all psychological outcomes, even though neuroscientists may wish this were the case. The neuroscientists’ desire to award special power to their evidence compared with behavioral or verbal data is reminiscent of the psychologist’s wish, a century ago, to separate their nascent field from philosophy. Although philosophers, psychologists, and neuroscientists have

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each claimed that they provide the most illuminating account of mind, none of these disciplines has been able to explain why or what 3-year-olds mean when they say, “I don’t like myself,” “What’s that?” or “ I love you, mama.”

DIAGNOSTIC ISSUES Three issues are relevant to the task of arriving at the most fruitful diagnostic categories: (1) specifying biological vulnerabilities that predispose a person to psychopathology, (2) specifying experiences that contribute to symptom expression, and (3) determining the validity of the evidence used to infer risk conditions and to describe symptoms. All three remain controversial. Although investigators agree that many genes contribute to vulnerability (see Beauchaine, Gatzke-Kopp, & Gizer, this volume), there is little agreement on specific genes that render a person susceptible to particular forms of psychopathology. This issue is complicated by the discovery of reversible, nonheritable epigenetic modifications of coding or regulatory genes that are induced by a variety of experiences that silence or promote the expression of particular genes in specific tissues. Psychologists, as well as neuroscientists, would profit from adopting the biologist’s expectation of extremely specific consequences of an incentive. For example, the molecule bisphenol A, used in the manufacture of plastics, administered to pregnant mice, methylates specific cytosine bases in particular genes in specific brain sites in male but not female fetuses (Kurdakovic et al., 2015). A person’s genome can restrict ranges of possible brain states but cannot determine any particular state. A person’s life history and the local setting select the psychological state most likely to be actualized at that moment. The fact that individuals who possess the same risk gene can develop different symptoms implies that DSM-5 categories do not carve nature at its joints (Gershon & Grennan, 2015; see also Beauchaine & Klein, Chapter 2, this volume). Most investigators and clinicians emphasize risk events that a child or adolescent experiences directly, such as parental abuse, bullying, rape, serious illness, and persistent poverty, and ignore popular forms of child rearing and historical changes in social conditions. A majority of American, middle-class White children born between 1950 and 1980 experienced a relatively gentle childhood marked by parental indulgence and encouragement to perfect the self. Compared to many adults who belong to the generation born between 1920 and 1950, who suffered through the Great Depression and World War II, many in these later generations escaped serious stressors of war, harsh socialization, demeaning acts of prejudice, and economic hardship. As a result, a number of adolescents born after 1980 were not prepared to cope with the anxiety over gaining admission to a favored college, maintaining high grades, cyberbullying, temptations for promiscuous sex, binge drinking, betrayals by close friends, or the burden of deciding which actions were moral and which were immoral because of the loss of consensus on moral standards that earlier generations enjoyed. These conditions rendered them vulnerable to

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 217 a bout of anxiety or depression when one or more of these events occurred. The dramatic rise in student visits to college mental health facilities, and the tripling of prescriptions for anxiolytics by British general practitioners to adolescents from 2003 to 2011, imply that both the public and clinicians have been persuaded that a bout of worry is a sign of an illness that requires medical treatment (John et al., 2015). A serious problem trailing most conclusions about experiences that contribute to pathology, as well its presence, is the fact that evidence is based only on verbal reports, usually on questionnaires or interviews, without additional behavioral or biological measures. This strategy is questionable because every verbal description of a trait, behavior, mood, or memory of a past experience can originate in more than one biological state and life history. Women who grew up in disadvantaged homes are at a higher than average risk for an anxiety disorder (Kagan, 2012). However, when New England mothers from varied class and ethnic groups are asked to rate their daughter’s anxiety level, girls from the most economically disadvantaged homes are described as less anxious than girls from more affluent families (Mian, Wainwright, Briggs-Gowan, & Carter, 2011). A person’s verbal descriptions of his or her past or present traits, or those given by an informant, are phenomena to be understood and explained, rather than valid indices of traits or circumstances described. Leading journals often publish papers claiming relations between parental practices and child outcomes that are based only on the parent’s answers to questionnaires. This is not a new criticism of verbal reports. Rosen (1956) made the same claim over 60 years ago. Rorschach inkblots and the Thematic Apperception Test were popular from about 1930 to 1960 because psychologists hoped these indirect measures would be more sensitive indices of a person’s traits than verbal replies. Unfortunately, these techniques proved wanting. But rather than try to invent more sensitive procedures, many psychologists returned to questionnaires with a naïve faith in their validity. Few six-year-olds in affluent, affectionate, two-parent families would tell an examiner that their father was dirtier, more dangerous, and reminded them more of dark angular designs than their mother. But they offered these replies when shown pairs of objects or animals that differed on the dimensions dirty-clean, dangerous-safe, dark-light, and angular-curved and asked to say which picture reminded them of their father and which their mother (Kagan, Hosken, & Watson, 1961). George Beam, a critic of surveys, captured the problem with verbal reports by suggesting that if we want to know what’s going on, we shouldn’t ask (Beam, 2012).

THE ETIOLOGICAL ROLE OF TEMPERAMENTS Heritable brain structures and functions render some children especially susceptible to feeling states or actions that, in combination with life history, become personality traits, and in more extreme cases symptoms of psychopathology. These collections of

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feelings and behaviors are called temperaments. Analogous collections are present in varied dog breeds. Because we do not yet know the genes and accompanying physiologies that are the bases of the many human temperaments, psychologists have to define a temperament as a pattern of behaviors to specific incentives. Most temperaments appear early in development, and are sculpted by experiences into a large, but nonetheless limited, number of personality traits. The patterns of behavior called extraversion, conscientiousness, and impulsivity in older children are personality dimensions that are the joint products of temperamental bias and personal history. None is a temperament. Temperamental contributions to a trait or symptom are not easily detected in older children or adults with current methods. A temperamental bias, therefore, can be likened to a drop of black ink placed in a vessel containing glycerin. The ink becomes invisible after the liquid is stirred, but it is still present in the vessel. Some infant temperamental biases include prolonged irritability to pain, cold, or hunger compared with a brief interval of crying followed by rapid soothing; high or low levels of vigorous motor movements to varied events; frequency of babbling or smiling, whether spontaneous or to incentives; reactions to sweet and sour tastes; and very short or prolonged attention to salient events. This list is too small considering the number of possible brain profiles that could serve as a foundation of a temperamental bias. If each gene that made a contribution to temperament had an average of five alleles, there could be as many as 3 × 10750 possible neurochemical combinations that provide the foundation of a temperamental bias (Irizarry & Galbraith, 2004). Even if a majority of these profiles had no relevance for temperament, the large number of remaining patterns implies that future scientists will discover many hundreds of temperaments that remain undiscovered.

Genes, Neurochemistry, and Temperaments It is generally assumed, but not yet proven, that heritable neurochemical patterns are the biological foundations of most but not all human temperaments. This hypothesis, which was anticipated more than a century ago (McDougall, 1908; Rich, 1928), was prominent in the writings of ancients, who posited melancholic, sanguine, choleric, and phlegmatic temperamental types, derived from the balance of the four body humors present in each person. There are more than 150 different molecules that, along with the density and locations of more than 2,000 types of receptors, have the potential to influence feelings and behaviors that define human temperaments. These include norepinephrine, dopamine, epinephrine, serotonin, corticotropin releasing hormone (CRH), glutamate, gamma aminobutyric acid (GABA), opioids, vasopressin, oxytocin, prolactin, monoamine oxidase (MAO-A), catechol-O-methyltransferase, (COMT), neuropeptide S, and the sex hormones (Hartl & Jones, 2005). The genes that code for these molecules and their receptors usually have a number of polymorphisms in one or more of the gene’s exons, introns, promoters, or enhancers. Promoters control

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 219 the level of transcription of a coding gene (exon) into messenger RNA; enhancers determine where and when transcriptions occur. The immaturity in our current understanding of relations among genes, brain chemistry, experience, and behavior frustrates scientists seeking reliable relations between a gene, or set of genes, and a temperamental bias or form of pathology. It is chastening to recognize that patterns of hundreds of genes, across many chromosomes, contribute to a person’s height. No single gene has much power, and its heritability varies with the person’s sex and ethnicity. If this is the state of affairs for height, patterns for temperaments and symptoms of mental illness are apt to be far more complicated. Investigators interested in the genetic bases of a form of psychopathology should read papers describing epistatic interactions among the many genes that contribute to formation and function of melanocytes in skin, hair, and eyes. Reflection on these discoveries would protect us from announcing overly optimistic assumptions about the genes that place a person at risk for any DSM-5 category (see also Beauchaine, Gatzke-Kopp, & Gizer, this volume).

Reactions to the Unexpected or Unfamiliar Two classes of behavior that emerge during the final months of the first year and appear to be products of temperamental biases have been studied more extensively than most. These reactions involve the contrast between a restrained, cautious, avoidant posture to unfamiliar or unexpected objects, events, people, or settings— called behaviorally inhibited—and one marked by a spontaneous approach, called uninhibited (Asendorpf, 1989, 1991; Bates, 1989; Buss, 2011; Kagan, 1994; Volbrecht & Goldsmith, 2010). Events that are violations of expectations are a primary source of many biological and psychological reactions, and children vary in their response to such violations (Schomaker & Meeter, 2015). Both inhibited and uninhibited behaviors to unfamiliar or unexpected events are moderately stable over time, relatively easy to measure, and have modest heritabilities (Bartels et al., 2004; Birn et al., 2014; Degnan et al. 2014; Kagan & Saudino, 2001; Muris, Hendriks, & Bot, 2016; Robinson, Kagan, Reznick, & Corley, 1992). These two behavioral profiles are observed within every mammalian species studied, even though the biological bases for these biases are not the same across species (Schneirla, 1959; Scott & Fuller, 1962). Because tameness in different mammalian species is the result of different genes, it is likely that the same is true for inhibited and uninhibited behaviors in different species (see Clauss, Avery, & Blackford, 2015; Fox et al., 2005; and Kagan & Snidman, 2004 for reviews of behavioral inhibition). Particular life settings select the behavioral phenotype likely to develop from one of these biases. The social class of a child’s family, which is a proxy for a host of experiences, represents one of the most important influences on the behavioral phenotype. Social class remains the best predictor of developing an anxiety, depressive, conduct, or addictive disorder as defined by the DSM-5, as well as academic skills,

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teenage pregnancy, values, and occupation (Pickett & Wilkinson, 2010; Werner & Smith, 1982; Xin, Zhang, & Liu, 2010).

HIGH- AND LOW-RISK INFANTS: DEVELOPMENTAL PROGRESSION Some children who display an inhibited or uninhibited profile acquired their behavioral pattern without the contribution of a temperamental bias. In order to discover children whose inhibited or uninhibited profile originated in a specific temperamental bias, rather than experience alone, we studied a large cohort of middle-class, healthy, Caucasian infants from age 4 months to 18 years in order to discover whether any psychological properties of young infants predicted each profile. We restricted the sample to healthy Caucasian infants born to financially secure, two-parent families because of differences in the genomes of Caucasians, Asians, and Africans (Chen, Burton, Greenberger, & Dmitrieva, 1999). Four-month-old infants born in Beijing, as well as Chinese-American infants born in the United States, are less irritable than Caucasian infants, and newborn Chinese-Americans are less reactive than Caucasian newborns (Freedman & Freedman, 1969; Kagan et al., 1994; Liu, unpublished manuscript). In addition, we suspected that infants born to economically disadvantaged single parents might experience conditions that favored development of one of these behavioral patterns. The central hypothesis guiding our infant assessments was that inherited variation in excitability of the amygdala would be accompanied by distinct patterns of motor activity and distress to unexpected and unfamiliar events. The brain circuits responsible for control of motor activity and crying to unexpected events mature after 16 weeks of age. The amygdala consists of a number of neuronal clusters, called the lateral, basal, cortical, medial, intercalated, and central regions. Each cluster has a distinct set of connections, neurochemical patterns, and functions (Stefanacci & Amaral, 2002). Thresholds of excitability in these clusters are influenced by a large number of molecules, including GABA, glutamate, opioids, serotonin, norepinephrine, dopamine, vasopressin, and oxytocin (Kirsch et al., 2005; Nuss, 2015). The balance among the concentrations of these molecules and the activity of their receptors, combined with inputs from other sites, determine the excitability of each cluster. Projections from the basolateral nucleus of the amygdala, which is responsive to unfamiliar or unexpected events, disinhibit the tonically inhibited central nucleus, which, in turn, activates sites that generate a vigilant state and appropriate actions should the event pose a threat (Fitzgerald, Angstadt, Jelsone, Nathan, & Phan, 2006). Hence, infants with an excitable amygdala might be more likely than others to become inhibited children. A variety of factors could create a more excitable amygdala. One is a compromise in the usual suppression of the central nucleus by the medial prefrontal cortex (mPFC). A second possibility is suppression of the intercalated cell mass within the amygdala, which normally inhibits the central nucleus.

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 221 Newborn infants whose rate of sucking increases dramatically following an unexpected change in taste sensation from water to sweet are more inhibited during the second year than infants who show a minimal increase in sucking rate following the same change in taste (LaGasse, Gruber, & Lipsitt, 1989). The unexpected change in taste sensation activates the medial and central nuclei of the amygdala, which is followed by activation of motor centers that control sucking. Infants with more excitable amygdalar nuclei should have larger increases in sucking rates. The more critical fact for our purposes is that activation of the amygdala of many species is accompanied by vigorous limb movements, back arching, and distress cries (Pitkanen, 2000). Human infants display all three behaviors. Infants who possess more excitable amygdalar nuclei should display more vigorous limb activity, more arches of the back, and frequent crying to unfamiliar events, compared with infants born with a different neurochemistry that renders the amygdala less excitable. The display of arches of the back is particularly significant because this response is mediated by a circuit from the central nucleus of the amygdala to the central gray. Hence, frequent arches implies a more active amygdala. This pattern of behavior is in accord with Rothbart’s (1989) emphasis on variation in reactivity as a basic temperamental category. We coded from film records the frequency of vigorous limb movements, back arching, fretting, and crying, along with babbling, smiling, and heart rate in over 450 healthy, 16-week-old Caucasian, middle-class infants during a battery that included presentation of unfamiliar, colorful objects moving back and forth in front of the face, recordings of speech emanating from a schematic face with no human present, and a cotton swab that had been dipped in dilute alcohol applied to the nostrils. These events were unexpected but not sources of pain or serious threat. The 20 percent of infants who showed a pattern that combined high levels of limb activity, back arching, and crying were called high reactive. The 40 percent who showed a pattern of minimal motor activity, few arches, and little crying were called low reactive. It is unlikely we would have detected these two groups if we had interviewed the mothers or given them a questionnaire that asked them to describe their infant, since most parents attend to their infant’s smiling and crying, but do not notice the pattern of actions to unfamiliar events that define high- or low-reactive infants (Bornstein et al 2015.) A small group of 4-month-old infants who combined high levels of limb activity with babbling and smiling, but little or no crying, were biased to become exuberant 5-year-olds (Degnan et al., 2011). A few studies suggest that fetuses who become active or respond with a large change in heart rate to maternal arousal are biased to become high-reactive 4-month-olds (Dipietro, Ghera, & Costigan, 2008). We assessed the high- and low-reactive infants on seven occasions through 18 years of age. Detailed results of these evaluations are summarized elsewhere (Kagan, 1994; Kagan & Snidman, 2004; Kagan, Snidman, Kahn, & Towsley, 2007). A brief summary of findings detailed in these sources follows.

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Child and Adolescent Evaluations. High reactives observed in a laboratory at 14 and 21 months were more avoidant of and fearful to a series of 17 unfamiliar social or nonsocial incentives than low reactives. Fox has affirmed this relation in an independent sample (Fox, Snidman, Haas, Degnan, & Kagan, 2015). The 20 percent of high reactives who were not exceptionally fearful in the second year presumably had experiences that allowed them to gain a measure of control over the behavioral expression of fear. These children were likely to have parents who did not protect them from every minor threat (Lewis-Morrarty et al., 2012). More high- than low-reactive 2-year-olds had narrow faces (ratio of width at the bizygomatic over the length of the face; Arcus & Kagan, 1995). Men with broad faces are a little more aggressive than others (Hasselhuhn, Ormiston, & Wong,2015), and children as well as adults rate photos of men with broad faces as more competent than men with narrow faces (Antonakis & Dalgas, 2009). Moreover, capuchin monkeys with broad faces are most likely to hold the alpha status in the troop (Lefevre et al., 2014). Adults with narrow faces are more likely to possess an ectomorphic body build, which is often a feature of patients with panic disorder (Pailhez, Rosado, Baeza-Velasco, & Bulbena, 2014). High- and low-reactive temperaments contribute to behaviors that 1-year-olds display in the Strange Situation. Some high reactives cry so intensely when the mother leaves them in an unfamiliar room that they are difficult to soothe when she returns. Hence, they are classified as insecure-resistant. Some low reactives are so unperturbed by the maternal departure that they do not cry when the mother leaves and do not rush toward her when she returns. As a result, these children are labeled insecure-avoidant (Marshall & Fox, 2005). Close to half of high-reactive 4-year-olds, compared with 10% of low reactives, were behaviorally inhibited when they played with two unfamiliar children of the same age and sex in an unfamiliar room (Kagan, Snidman, & Arcus, 1998). About one half of high-reactive 7-year-olds were afraid of animals, darkness, thunderstorms, and/or unfamiliar people and places, compared with fewer than 10% of low reactives. It is worth noting that more high than low reactives had an atopic allergy, usually hay fever or eczema (Kagan, 1994), and that behaviorally inhibited macaque monkeys have a hyperresponsive airway tract (Chun, Miller, Schlegle, Hyde & Capitanio, 2013). Eleven- and 15-year-old high reactives displayed fewer spontaneous comments and smiles than low reactives during a laboratory session designed to measure four biological reactions that are indirect signs of an excitable amygdala. One of these measures is asymmetry of activation in the frontal lobe. More high than low reactives displayed greater activation of the right compared with the left frontal lobe at ages 11 and 15. Right frontal activation, defined by less alpha band power in the right compared with the left frontal lobe during a resting baseline, has modest stability and modest relations with an unpleasant sensory state in infants, susceptibility to an anxious or depressed mood in adults, a less effective response to an antidepressant,

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 223 and behavioral signs of fear in animals, although not every study confirms these generalizations (Adamec, Blundell, & Burton, 2005; Blackhart, Minnix, & Kline, 2006; Bruder et al., 2008; Davidson, 2003; Davidson, Jackson, & Kalin, 2000; Degnan et al 2011; Fox, Calkins, & Bell, 1994; Fox et al., 2005; Peltola et al., 2014; Schmidt, 2008; Smit, Posthuma, Boomsma, & De Geus, 2007). Because the amygdala projects ipsilaterally to sites in the frontal cortex, greater activity in the right amygdala is accompanied by greater activation of the right frontal area (Cameron, 2002). Visceral feedback from the body to the central nucleus is greater to the right than to the left amygdala. Therefore, children who experience more frequent visceral activity should have a more active right amygdala and display right, rather than left, frontal activation. High-reactive boys who reported that they often feel bad when a parent criticized them showed right frontal activation (Kagan & Snidman, 2004). More high than low reactives showed a larger brain stem auditory-evoked response from the inferior colliculus at ages 11 and 15 years. Because the amygdala sends projections to the inferior colliculus, this result implies that high reactives possess a more excitable amygdala (Baas, Milstein, Donlevy, & Grillon, 2006; Brandao, Coimbra, & Osaki, 2001). High-reactive adolescents also showed larger N400 waveforms to discrepant visual scenes (for example, a chair with one leg) and greater sympathetic compared with parasympathetic tone in the cardiovascular system (Kagan & Snidman, 2004; see Fox et al., 2005; Movius & Allen, 2005; Schmidt, Fox, Schulkin, & Gold, 1999 for replications of some of these results). However, only a small number of high reactives displayed all of these biological features. This is why investigators must gather a pattern of outcome measures. High- and low-reactive 15-year-olds were interviewed for close to three hours in their homes by a woman who was blind to their history. High reactives smiled infrequently, offered terse answers, and showed more restless activity compared with low reactives. The interviewer asked several questions designed to discover each adolescent’s primary worries. Although all adolescents reported concerns with the quality of their performance in school and when engaged in extracurricular activities, more high than low reactives confessed to worrying about crowds, strangers, unfamiliar situations, or the future. Two thirds of high reactives, but only 20% of low reactives, confessed to one or more of these less realistic worries, which could be sustained by tonic excitability of a circuit from the amygdala to the bed nucleus via the stria terminalis (Tovote et al., 2015). Verbatim excerpts illustrate the concern with unpredictable challenges among high reactives: “In a crowd I feel isolated and left out, I don’t know what to pay attention to because it is also ambiguous”; “I worry about the future, over not knowing what will happen next”; “I wanted to be a doctor but decided against it because I felt it would be too much of a strain”; “I like being alone and, therefore, horses are my hobby, I don’t have to worry about fitting in with others when I am with my horses”; “I get nervous before every vacation because I don’t know what will

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happen.” Similar statements were rare among low reactives. High reactives were also less likely, on a Q-sort procedure, to rank the statement, “Most of the time I’m happy” as a salient characteristic of their personality, and more likely to describe themselves as serious and tense. Adolescents who report these traits are at a higher risk for a later mental illness (Colman, Wadsworth, Croudace, & Jones, 2007). Youth in the contemporary United States are trying to establish a personal philosophy at a time when there is little if any consensus on the meaning of life, or the moral values that all must honor. This condition creates high levels of uncertainty in high-reactive adolescents. As a result, these youth might be expected to seek a means of muting their angst. A religious commitment is one effective strategy because it provides a partial answer to these questions and assures each believer of his or her essential virtue when disappointments, failures, or frustrations occur. Forty-five percent of adolescents who were high-reactive infants said they were very religious, compared with only 25% of the low reactives, despite no difference in the religious commitment of their parents. Age 18 Assessments. Select features of brain anatomy and function were evaluated in 135 high- and low-reactive 18-year-olds. In addition, a clinician who was blind to each youth’s history administered a standard psychiatric interview and assigned DSM-IV-TR (2000) diagnoses to those who met relevant criteria. High- and low-reactives differed significantly on three biological measures that imply a more excitable amygdala. High reactives had a thicker cortex in a small region in the anterior ventromedial PFC of the right hemisphere (Schwartz et al., 2010). Very impulsive boys, a trait rarely seen among high reactives, had a smaller volume in the right ventromedial PFC (Boes et al., 2009). This area is connected reciprocally with the amygdala and projects to sites in the central gray that are responsible for back arching displayed by high-reactive infants. Adults with lesions in this area are less troubled by moral errors (Moretto, Ladavas, Mattioli, & di Pelligrino, 2010) (see also Hill, Tessner, Wang, Carter, & McDermott, 2010; Welborn et al., 2009; Young et al., 2010). High reactives are especially vulnerable to anxiety and guilt following violations of a moral belief. Furthermore, adolescent males who are high in a trait called surgency, which is infrequent among high reactive boys, show less activity in this area when, in the presence of friends, they make an error in a game (Segalowitz et al., 2012). The thicker cortex observed in the right vmPFC of high reactives seems, on the surface, to be inconsistent with many reports suggesting that this area modulates the amygdala and mutes its excitability. This paradox might be resolved by noting that the high reactives do not possess a thicker cortex in the left vmPFC. It is possible that projections from this area in the left hemisphere are more effective in silencing the amygdala. This suggestion is supported by the observation that adults with a thicker left vmPFC show less activation of the amygdala when judging emotional faces (Foland-Ross et al., 2010).

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 225 High reactives showed a larger surge of blood flow (BOLD signal activation) in the right amygdala to the initial presentation of an angry face (the episode contained four presentations of angry, fear, and neutral faces appearing in random order). High reactives also showed a shallower slope of habituation of the BOLD signal in the left amygdala to repeated presentations of ecologically invalid scenes (for example, an infant’s head on an animal’s body) and in the right amygdala to repeated presentations of unfamiliar faces with neutral expressions (Schwartz, Kunwar, Greve, Kagan, & Snidman, 2012; see also Blackford, Allen, Cowan, & Avery, 2010; Eley, 2011; Williams et al., 2015). Clinician’s diagnoses revealed a significantly higher prevalence of depression, social phobia, and/or general anxiety disorder among the 18-year-old high reactives compared with the low reactives (42% vs. 26%; see Frenkel et al., 2015 for a similar result). One reason these diagnoses are high is that they are based on current feelings rather than recall of the past. Moreover, projections from the PFC to limbic sites, which modulate the latter, are not yet fully mature at age 18 (Brendgen, Wanner, Morin, & Vitaro, 2005; Eley, 2011; Gladstone & Parker; 2006; Mick & Telch, 1998). French adults with a combination of social anxiety disorder and depression had the highest scores on a questionnaire that asked them to recall how shy, timid, and fearful they were as children (Rotge et al., 2011). Low-reactive males who were least fearful on every childhood assessment had the lowest rates of depression or anxiety (13%), and were free of drug and alcohol problems, conduct disorder, and ADHD. The combination of a low-reactive temperament, male sex, and growing up in a secure middle-class family generates an unusually relaxed, worry-free adolescent. The biological measures distinguished between high reactives, mainly girls, who reported a bout of depression or anxiety, and low reactives with the same symptoms. More of the former showed a thick vmPFC in the right hemisphere, a large surge of blood flow to the initial appearance of an angry face, and/or a shallow habituation of the BOLD signal to the invalid scenes. Seventy-one percent of anxious or depressed high reactives, but only 23% of depressed or anxious low reactives, had their maximal BOLD signal to the first presentations of angry faces. More high reactives with one of the diagnoses showed very frequent back arches at 4 months and/or high fear scores in the laboratory at 14 months, compared with high reactives who were free of anxiety or depression, and low reactives with either one or both diagnoses. Ninety percent of high reactives with social anxiety or depression had either frequent arches or a high fear score or both, whereas not one low reactive with these same symptoms met either criterion (these data reveal a sensitivity of 90% and a specificity of 100%). Finally, a pattern that combined the display of many fears at 14 months with a thick vmPFC in the right hemisphere at 18 years also separated the high and low reactives given exactly the same diagnoses. Two thirds of the high reactives who were fearful at 14 months

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and had a thick vmPFC received diagnoses of depression, general anxiety, or social anxiety disorder. Not one low reactive diagnosed with depression or anxiety possessed both features. Recall that high reactive children were more likely to suffer from hay fever or eczema due to an allergy. Australian adults diagnosed with depression are also more likely to suffer from an allergy (Sanna et al., 2014). Because female sex hormones activate mast cells of the immune system, which release histamine after binding to immunoglobulin E to produce allergic symptoms, it is of interest that the onset of puberty is marked by more cases of depression in females than in males (Zierau, Zenclussen, & Jensen, 2012). These results imply that DSM-5 diagnostic categories for anxiety and depression can be the product of different physiologies and life histories (i.e., equifinality; Cohen et al., 2015). Hence, biological and behavioral evidence should be gathered on all patients in order to parse those with the same symptoms into theoretically more fruitful categories that might profit from different therapeutic regimens.

SYNTHESIS The evidence contains several implications for those who study or treat psychopathology. First, investigators should be sensitive to the context of observation, which includes the procedure that is a source of evidence. Children who were high-reactive infants are cautious and subdued in unfamiliar settings, but not in familiar contexts (Buss, 2011). Second, investigators should base their inferences about causal cascades that lead to psychological outcomes on patterns of several variables rather than single measures, since most outcomes result from more than one cascade (Kagan, 2011). Too many investigators gather a single outcome variable, whether a score on a questionnaire, salivary cortisol, skin conductance, potentiated startle, a cardiovascular measure, or blood flow to a brain site. A combination of seven biological variables separated high- and low-reactive 11-year-olds far better than any single measure (odds ratio of 7.0). Every DSM-5 category is the product of more than one equifinal cascade. More high- than low-reactive 11-year-olds combined a narrow face with blue eyes (Kagan & Snidman, 2004). Coat color is often linked with behavior in mice, rats, dogs, and ungulates (Ducrest, Keller, & Roulin, 2008). It is worth noting that, among Caucasians, blue-eyed adults have a slightly higher probability of developing a dependence on alcohol and/or social anxiety disorder (Sulovari, Kranzler, Farrer, Gelernter, & Li, 2015). Clinicians who wonder about the origins of social anxiety in a Caucasian patient might note whether she possesses these features. If so, her symptoms might have a temperamental origin. If the patient has dark eyes and a broad face it is more likely that experience, rather than a high-reactive temperament, is the origin of her symptoms. The high frequency of unrealistic worries about future encounters with unfamiliar settings reported by high reactives provides a clue to processes responsible for their

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 227 profile. These adolescents are chronically uncertain over judgments by others as to how they should behave in unfamiliar settings, and often detect a change in heart rate, blood pressure, or muscle tension, which they interpret as signs of anxiety. Self-reported unrealistic fears of monozygotic twins and their spouses show higher heritabilities than realistic fears of illness, a car accident, or criticism for a mistake (Sundet, Skre, Okkenhaug, & Tambs, 2003). High reactives appear to possess a greater susceptibility to unexpected visceral feedback from the gut, muscle, and autonomic nervous systems. High-reactive 11- and 15-year-olds showed larger increases in heart rate to cognitive stressors. When the sensations that are the products of these events pierce consciousness, they create uncertainty because they are unexpected and their origin is ambiguous. High-reactive youth in our culture are biased to interpret this feeling as a sign that they are worried about an encounter with a stranger or an unexpected challenge because these events are the most frequent novelties in their lives. Members of other cultures might impose different interpretations on the same visceral feedback. Cambodian refugees who live in Massachusetts, for example, interpret an unexpected bout of tachycardia as a weak heart, produced by a loss of energy following lack of sleep or diminished appetite (Hinton, Pich, Safren, Pollack, & McNally, 2005; Hinton, Pich, Chean, Pollack, & McNally, 2005). Saulteaux Indians of Manitoba worry about contracting a serious disease because illness is a sign that they violated an ethical norm on sexual, aggressive, or sharing behavior with others (Hallowell, 1941). Social anxiety, often combined with depression, are the most likely symptoms for high reactives brought up in the United States or Europe because strangers and new places are common events and social acceptance is an important motive in these cultures. The fact that biological parents or siblings of patients with social anxiety disorder are more likely to suffer from the same symptoms than second- or third-degree relatives points to a genetic contribution to a vulnerability to visceral reactions to uncertainty (Isomura et al., 2015). A temperamental bias, therefore, renders individuals vulnerable to brain and body reactions that can generate a feeling that is often interpreted as uncertainty. A person’s life history and culture supply the target of uncertainty. Young Chinese women worry more than Americans over losing their virginity, and many Chinese 20-year-olds respond to billboards advertising hymen repair of this tissue (Steinmuller & Tan, 2015). In the contemporary United States, social failure has been added to the seven traditional sins of pride, anger, envy, avarice, sloth, gluttony, and lust as a basis for anxiety, shame, or guilt. Although White, middle-class, high-reactive infants who grow up in the United States are at higher than normal risk for becoming socially anxious, introverted adolescents, most will neither meet criteria for social anxiety disorder nor be unusually shy. About half of a sample of social phobics did not remember being excessively shy as young children, although they may distort their recollections of their childhood personality (Cox, MacPherson, & Enns, 2005).

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Many high-reactive infants did not develop an anxiety disorder during adolescence because they acquired effective coping defenses. One high-reactive boy who was fearful at 14 and 21 months learned to control his timidity after school entrance. At age 12 he wrote an essay for a class assignment that explained how he overcame his anxious feelings. He ended the essay by writing, “Because I now understand my predisposition to anxiety, I can talk myself out of simple fears.” The belief that one can control public signs of anxiety is accompanied by increased functional connectivity between the ventrolateral PFC and the amygdala and reduced activity in the amygdala (Salomons, Nusslock, Detloff, Johnstone, & Davidson, 2015). However, this boy, like more than 90 percent of high reactives, was not a consistently ebullient, relaxed, sociable, bold, risk taker because his temperament was an obstacle to acquiring this pattern. A temperamental bias makes it difficult to acquire certain traits, but does not guarantee any particular property. The same is true for experiences. The probability that children who are reared by economically secure, well-educated parents will not become prostitutes or homeless drug addicts is very high, but the probability that they will pursue a specific vocation, marry late, prefer solitary hobbies, or acquire any of a hundred different properties is much lower. Temperaments and life histories constrain a small number of extreme outcomes, while leaving the future open for a broad set of possibilities. A temperament can be likened to the basic form of song sung by a bird species. The bird’s genome imposes a serious constraint on its basic form, but does not determine variations on that form, which are dependent on which songs of conspecifics the young bird hears. Knowing that a bird is a finch rather than a meadowlark allows one to predict with confidence the many songs it will not sing, but not the specific song it will sing. So too with temperaments.

Anhedonia The brain chemistry of high reactives could interfere with the intensity as well as the frequency of subjective feelings of pleasure that occur when a person receives an unexpected or larger-than-anticipated desirable experience. Perhaps one reason why high-reactive adolescents do not like new activities (whether risky or not), even though they promise excitement, is that these youth fail to experience a great deal of pleasure when they anticipate visiting a new city, meeting a new person, or engaging in a novel activity (Netter, 2006). This argument is supported by a study of 111 college students who initially filled out a questionnaire measuring social anxiety, then rated their mood on each of 21 consecutive days. Students with high scores on the social anxiety scale were least likely to report pleasurable experiences, and more likely to confess to a melancholic mood across the 3 weeks (Kashdan & Steger, 2006).

Hyping Biology American and European scientists prefer materialistic explanations of natural phenomena. Genes, neurons, transmitters, and circuits are material entities whose

High-Reactive Temperament, Behavioral Inhibition, and Vulnerability 229 forms can be observed and imagined. Neither feelings nor thoughts, which the Greeks assigned to the soul, possess this quality. Indifference to a person’s subjective interpretation of an experience means that investigators typically ignore the pride or shame that accompanies identification with family pedigree, ethnic group, or religion, which can render a person vulnerable to psychopathology. If 12-year-old Rainer Hoess had not been told for the first time that his grandfather was Rudolf Hoess, the commandant at Auschwitz from 1941 to 1943, he may not have developed the deep depression that followed his learning this fact about his family pedigree (Grieshaber, 2011). It is rare to read a paper on the contribution of genes or brain states to pathology in which the influence of identifications are considered, even though these psychological states affect the brain’s response to incentives (Derks & Stedehouser, 2015). Ethnic identification can also influence blood cytokine levels. Among African-American 20-year-olds who report being frequent victims of discrimination, those who are proud of being Black have lower cytokine levels than those with a weaker identification with their ethnicity (Brody, Yu. Miller, & Chen, 2015). Although the study of genetic and neural correlates of psychopathology has value, it has a serious disadvantage. It fails to raise public consciousness over the substantial contributions of social experiences. Genes cannot explain why rates of teenage pregnancy in European and North American nations is highest in the United States and England, and lowest in Switzerland and Germany (Sedgh, Finer, Bankole, Eilers, & Singh, 2015). A single-minded approach to finding vulnerability genes tempts clinicians to ignore therapeutic strategies that alter the patient’s life circumstances, and instead persuade the public that genes are the major determinant of criminal behavior, marital infidelity, and symptoms of anxiety, depression, and restlessness in the classroom. Every river is capable of becoming polluted and losing its capacity to sustain life. However, ecologists do not attribute an inherent flaw to a river that has become polluted. Rather, they urge changes in the practices of industry and agriculture that are the root causes of the pollution. Psychiatrists and psychologists should adopt a similar strategy with mood and character disorders.

REFERENCES Adamec, R. E., Blundell, J., & Burton, P. (2005). Neural circuit changes mediating a lasting bias and behavioral response to predator stress. Neuroscience and Biobehavioral Reviews, 29, 1225–1241. Antonakis, J., & Dalgas, O. (2009). Predicting elections: Child’s play! Science, 323, 1183. Arcus, D., & Kagan, J. (1995). Temperament and craniofacial variation in the first two years. Child Development, 66, 1529–1540. Asendorpf, J. B. (1989). Shyness as a final pathway for two different kinds of inhibition. Journal of Personality and Social Psychology, 57, 481–492.

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CHAPTER 8

The Adaptive Calibration Model of Stress Responsivity Concepts, Findings, and Implications for Developmental Psychopathology BRUCE J. ELLIS, MARCO DEL GIUDICE, AND ELIZABETH A. SHIRTCLIFF

HISTORICAL CONTEXT

T

he stress response system (SRS) has a central role in orchestrating physical and psychosocial development of both humans and nonhuman species (Ellis, Jackson, & Boyce, 2006; Korte, Koolhaas, Wingfield, & McEwen, 2005). For many organisms, the SRS contributes crucially to responding flexibly to challenges and opportunities in the environment. One of the most remarkable features of the SRS is the wide range of individual variation in its physiological parameters. Some individuals respond quickly and strongly even to minor events, whereas others show flat response profiles across most situations. Furthermore, the balance of activation among primary SRS subsystems—the parasympathetic nervous system (PNS), the sympathetic nervous system (SNS), and limbic-hypothalamic-pituitary-adrenal (LHPA) axis—can vary considerably across individuals. It is difficult to overstate the real-world relevance of such individual variability. Decades of research demonstrate not only that physiological patterns of stress responsivity constitute a primary integrative pathway through which psychosocial environmental factors are transmuted into the behavioral, autonomic, and immunologic manifestations of human pathology (reviewed in Boyce & Ellis, 2005), but also that patterns of stress responsivity regulate variation in a wide range of adaptive processes and behaviors including (but not limited to) growth and metabolism, reproductive status and fertility, aggression and risk taking, pair bonding and caregiving, and memory and learning (reviewed in Del Giudice, Ellis, & Shirtcliff, 2011; 237

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Ellis & Del Giudice, 2014). Clearly, understanding the causes of such individual differences and their development over the life course has important implications for medicine, psychology, and psychiatry, among other disciplines. One approach has been to view individual differences in stress reactivity through a pathology lens. Indeed, a common assumption in the stress literature is that there is an optimal level of stress responsivity and that overly heightened or dampened SRS reactivity is dysfunctional and tends to undermine emotional and behavioral regulation (e.g., Evans & English, 2002). This purported biological dysregulation of the SRS is typically interpreted in an allostatic load framework (e.g., Juster, McEwen, & Lupien, 2010), whereby the wear and tear of chronic stress is presumed to impair SRS functioning (see extended discussion below). Although the allostatic load model (ALM) has proven useful in predicting health-related endpoints, it is not consilient with current theory and research from evolutionary biology. The key limitation of the ALM and related theories (e.g., toxic stress, Shonkoff & Bales, 2011) that employ a pathology lens is that they do not provide a theory of adaptive individual differences in physiological mediators and related patterns of social and physical development. As an alternative approach, we present the adaptive calibration model of stress responsivity (ACM; Del Giudice et al., 2011; Ellis & Del Giudice, 2014). We begin by reviewing concepts of developmental programming and adaptive calibration more generally. We then summarize key ACM concepts, including the theory of biological sensitivity to context, upon which the ACM builds, and discuss implications for developmental psychopathology. At this juncture, 5 years after the original publication, we review the current empirical status of the ACM and highlight potential updates and revisions that may be needed going forward. We conclude by comparing the ACM and ALM explicitly, arguing that the field needs to expand beyond allostatic load to incorporate an adaptive calibration framework that addresses the functional role of stress response systems in regulating alternative developmental pathways. Central to the ACM is the assumption that gaining a better understanding of the functional developmental changes that occur under stressful conditions will enable us to gain a better understanding of the costs of these changes (e.g., allostatic load and its consequences) and thus develop more effective interventions for the crucial goals of risk prevention and management.

CONDITIONAL ADAPTATION AND MALADAPTATION Developmental exposures to stress have always been part of the human experience. For example, almost half of children in hunter-gatherer societies—the best model for human demographics before the agricultural revolution—died before reaching adulthood (Kaplan & Lancaster, 2003). Thus, from an evolutionary-developmental perspective, stressful rearing conditions, even if those conditions engender sustained stress responses that must be maintained over time, should not so much impair SRS functioning (“dysregulation” in the ALM) as direct or regulate it toward response patterns that are biologically adaptive (i.e., tend to increase an

The Adaptive Calibration Model of Stress Responsivity 239 individual’s fitness) under stressful conditions, even if those patterns are harmful in terms of the long-term welfare of the individual or society as a whole (e.g., Ellis, Boyce, Belsky, Bakermans-Kranenburg, & van IJzendoorn, 2011; Mead, Beauchaine, & Shannon, 2010). From an evolutionary perspective, there is no optimal level of stress responsivity; adaptation is context-specific. Consider the extensive experimental work conducted by Michael Meaney and colleagues, which shows that low-quality maternal care in rats (i.e., low levels of maternal licking and grooming) alters pups’ stress physiology and brain morphology. Although such changes seem disadvantageous (as indicated, for example, by higher corticosterone levels, shorter dendritic branch lengths, and lower spine density in hippocampal neurons), they actually enhance learning and memory processes under stressful conditions (e.g., Champagne et al., 2008; Oomen et al., 2010). Moreover, such physiological and morphological changes mediate the effects of maternal behavior on central features of defensive and reproductive strategies: behavior under threat, open-field exploration, play behavior, pubertal development, sexual behavior, and parenting (Cameron et al., 2005; Cameron et al., 2008; Franks, Champagne, & Curley, 2015). In total, enhanced learning under stressful conditions, increased fearful and defensive behaviors, accelerated sexual maturation, increased sexual behavior, and reduced parental investment in offspring apparently represent functional ways of developing when the young organism is neglected. In such contexts, neglect itself may be regarded as a behavioral mechanism through which rats guide their offspring’s development toward optimal survival and reproductive strategies under conditions of adversity. It would seem mistaken, therefore, to simply view diminished licking and grooming as “poor maternal care” or the development induced by such care as “disturbed,” even though this is how they are often characterized. From an evolutionary perspective, altered care provided by parents may (at least in part) function to prepare offspring to survive and reproduce under harsh ecological conditions.

Conditional Adaptation The evolutionary perspective thus emphasizes conditional adaptation: “evolved mechanisms that detect and respond to specific features of childhood environments, features that have proven reliable over evolutionary time in predicting the nature of the social and physical world into which children will mature, and entrain developmental pathways that reliably matched those features during a species’ natural selective history” (Boyce & Ellis, 2005, p. 290; for a comprehensive treatment of conditional adaptation, see West-Eberhard, 2003). From this perspective, variation in SRS functioning results largely from individuals tracking different environmental conditions and altering their SRS profiles to match those conditions in ways that are likely to enhance survival and reproductive success. For conditional adaptations to evolve, the fitness of the alternative phenotypes must be predictable on the basis of reliable cues that can be observed by the

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individual (Pigliucci, 2001). Reliable cues to adversity are especially relevant because they may signal the need to develop a secondary (alternative) phenotype that is adversity-adapted, with resulting benefits and costs that are often reflected in health trade-offs. For example, tadpoles (Rana sylvatica) alter their size and shape based on the presence of dragonfly larvae in their rearing environment (Van Buskirk & Relyea, 1998). These alterations involve development of smaller and shorter bodies and deep tail fins. Although tadpoles that do not undergo these morphological changes are highly vulnerable to predation by dragonflies, those that do but end up inhabiting environments that are not shared with dragonflies have relatively poor developmental and survival outcomes. In short, the predator-induced phenotype is only conditionally adaptive. This process highlights that, in many cases, natural selection favors a primary phenotype that yields high payoffs under favorable circumstances and a secondary phenotype that “makes the best of a bad situation” (West-Eberhard, 2003). Developmentally, conditional adaptation is often implemented through physiological and neurobiological “switches,” or mechanisms that integrate environmental and genetic information to steer developmental trajectories along alternative trajectories. Developmental switch points are junctures during which those mechanisms become activated; they are typically located at the transition between different life stages and are regulated by hormonal signals (see Del Giudice, 2014; Ellis, 2013; West-Eberhard, 2003). For example, puberty is a critical switch point in the development of sexual, reproductive, and social behavior, including individual and sex differences in risk taking (Ellis et al., 2012).

The Meaning of Adaptive The foregoing discussion highlights that the term adaptive has different meaning when viewed from an evolutionary perspective (with its functional lens) versus a public health or standard psychological perspective (with its pathology lens; see also Ellis et al., 2012; Mead et al., 2010). Because evolution by natural selection is driven by differences among individuals in reproductive success, the evolutionary significance of any behavior, or its “adaptive value,” depends ultimately on its costs and benefits with respect to the organism’s fitness (i.e., the contribution of offspring to future generations). Even high-risk behaviors that result in net harm in terms of a person’s own well-being or long-term survival (e.g., producing miserable feelings or a shortened life), the welfare of others around them, or the society as a whole can still be adaptive in an evolutionary sense. Consider, for example, risky behaviors that expose adolescents to danger and/or inflict harm on others but increase dominance in social hierarchies and leverage access to mates (Ellis et al., 2012). Yet from a public health perspective, different patterns of behavior are regarded as “adaptive versus maladaptive” depending on the extent to which they promote versus threaten people’s health, development, and safety. Adaptive developmental outcomes are thus equated with “desirable” outcomes (as defined by dominant Western values;

The Adaptive Calibration Model of Stress Responsivity 241 e.g., health, happiness, secure attachment, high self-esteem, emotion regulation, educational and professional success, stable marriage), whereas maladaptive developmental outcomes are equated with “undesirable” outcomes constituting the opposite poles of these traits and variables. For the remainder of this paper, we use “adaptive” only in the evolutionary sense of the term. In contrast, the word “desirable” is used to connote outcomes that are typically viewed as “adaptive” from a public health perspective. That adaptive is not equivalent to desirable is an important distinction: It clarifies that hypotheses rooted in evolutionary biology do not, by default, imply that adaptations are “good” or should never be targeted for intervention. The use of evolutionary models, however, allows for adaptations to be precisely targeted based on environmental inputs and their developmental consequences.

Maladaptation The converse of adaptation is maladaptation. Biological maladaptation can occur for many reasons. Sometimes, an evolved mechanism ceases to perform its intended function because of, for example, harmful genetic mutations, accidents, or manipulation by other organisms (e.g., pathogens). Even when biological mechanisms perform normally, an organism may develop a phenotype that is poorly suited for its environment and as a consequence experiences a diminution in fitness (often accompanied by other “undesirable” outcomes). Thus, maladaptation is closely connected to the concept of developmental miscalibration or mismatch (see Frankenhuis & Del Giudice, 2012, for an extended discussion). There are a number of causes of such developmental miscalibration or mismatch. First, an individual may experience novel environments that are outside the range recurrently encountered over evolutionary history. In this case, all developmental bets are off and the person may experience abnormal outcomes. For example, Romanian or Ukrainian orphanages (Dobrova-Krol, Van IJzendoorn, Bakermans-Kranenburg, & Juffer, 2010; Nelson et al., 2007) constitute genuinely substandard, novel environments that are beyond the normative range of conditions encountered over human evolution. Children’s brains and bodies simply could not have been selected to respond adaptively to collective rearing by paid, custodial, non-kin caregivers providing minimal human contact (Hrdy, 1999). Exposures to such challenging and (evolutionarily) unprecedented conditions are likely to induce pathological development rather than evolutionarily adaptive strategies. Second, individuals may become maladapted to their environments because of a lack of behavioral plasticity. For example, one of the responsivity profiles highlighted by the ACM is the unemotional pattern (described in detail below); this pattern is characterized by low susceptibility to environmental influence (i.e., dampened physiological stress reactivity), which generally inhibits social learning and sensitivity to social feedback. One hypothesized pathway here is a genetic disposition toward SRS hypoarousal. Such a disposition could translate into a wide

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distribution of unemotional phenotypes across a range of familial and ecological conditions, including supportive and well-resourced rearing environments (Del Giudice, Hinnant, Ellis, & El-Sheikh, 2012). Maladaptation may occur in this context because unemotional phenotypes are relatively unsusceptible to environmental influence and thus may not adjust their behavioral strategies to match the high levels of support and resources that might be available to them (e.g., they may not adequately detect positive opportunities and learn to capitalize on them, such as seeing a teacher as a prospective mentor or taking advice from a loving parent; and/or they may develop a manipulative, antagonistic social strategy when trust and cooperation would better fit their social context). In total, increased probability of mismatch is a clear cost of low developmental plasticity. Third, mismatch can occur because the validity of environmental cues that guide conditional adaptation is limited spatially, so such cues become invalid in other contexts. For example, according to developmental models based on life history theory (LHT), children’s brains and bodies tend to respond to dangerous or unpredictable environments by growing up fast and “living for the here and now” (e.g., Belsky, Steinberg, & Draper, 1991; Ellis, Figueredo, Brumbach, & Schlomer, 2009). This “get it while you can” strategy often translates into high-risk activities such as early initiation of sexual behavior, greater numbers of sexual partners, violence, and, in contemporary societies, behaviors such as substance use and risky driving. These high-risk strategies may only be locally adaptive, however. Research by Gibbons et al. (2012) on African American males is instructive in this context. Youth who are exposed to greater stress while growing up (e.g., more dangerous neighborhoods, lower quality parental investment, greater racial discrimination) develop “fast” life history strategies that may be adaptive in their local context (e.g., participation in risky behaviors that leverage positions in dominance hierarchies, increased access to mates) but clearly undesirable—and possibly biologically maladaptive—in wider American society (e.g., dropping out of school, high rates of arrest and incarceration). A similar logic may apply to effects of early stress on cognitive processes (Frankenhuis & de Weerth, 2013). Fourth, mismatch can occur because the validity of environmental cues that guide conditional adaptation is temporally limited, so that those cues may become invalid at later times. One hypothesis is that individuals calibrate to environmental parameters early in life, even prenatally. When these values differ from those experienced later in life, normative processes of developmental plasticity can become maladaptive, resulting in a mismatched phenotype with increased likelihood of physical health problems (e.g., Gluckman, Low, Buklijas, Hanson, & Beedle, 2011). For instance, prenatal exposure to undernutrition may result in development of metabolic processes designed to retain and store insulin and fatty acids (Barker, 1994). However, if resources are plentiful in the postnatal environment, the individual may be at increased risk for obesity and metabolic syndrome throughout life. This hypothesis is supported by data showing that detrimental effects are often absent when the postnatal environment continues to be lacking in resources

The Adaptive Calibration Model of Stress Responsivity 243 (Stanner & Yudkin, 2001), suggesting that mismatch (rather than undernutrition per se) may be the root cause. Finally, mismatch can occur due to a restricted range of niches that undermine the ability of organisms to choose environments that match their phenotypes. For example, in a study of semi–free ranging rhesus macaques (Boyce, O’Neill-Wagner, Price, Haines, & Suomi, 1998), the troop lived in a 5-acre wooded habitat in rural Maryland, on the grounds of the National Institutes of Health Primate Center. In 1993, the troop encountered a 6-month period of protective confinement to a small, 1,000-square-foot building, during a construction project on the habitat grounds. The confinement proved highly stressful, however, and the incidence of violent injuries increased fivefold during the 6-month period. During this period, when behavioral strategies available to troop members were severely curtailed, monkeys previously characterized as high in biobehavioral reactivity to stress suffered dramatically higher rates of violent injuries than their less reactive peers. In the free-ranging wooded habitat, however, where a wide range of behavioral strategies could be used, including escape from conflict, highly reactive monkeys suffered comparatively low rates of violent injury. In summary, processes of conditional adaptation and phenotype-environment matching are fallible, and a number of circumstances can lead to maladaptation. Understanding this set of circumstances can be critical to understanding the developmental origins of psychopathology. More importantly, these forms of maladaptation are comparatively rare; the organism most commonly responds to environmental conditions by adapting to its local circumstances, regardless of whether this adaptive process is desirable for the individual or society.

FUNCTIONS OF THE STRESS RESPONSE SYSTEM Environmental events that signal threats to survival or well-being produce a set of complex, highly orchestrated responses within the neural circuitry of the brain and peripheral neuroendocrine pathways that regulate metabolic, immunologic, and other physiological functions. The SRS comprises primarily three anatomically distinct systems: the PNS and SNS branches of the autonomic nervous system, and the LHPA axis. Activity of these systems is integrated and cross-regulated, so they can be considered as partially independent yet interrelated components of a coordinated functional system, despite being anatomically distinct and physiologically diverse (e.g., Boyce & Ellis, 2005; Porges, 1995; Schlotz et al., 2008). All the components of the SRS are regulated by top-down cognitive and affective processes; conversely, SRS activation modulates brain activity at multiple levels (through direct neural connections and indirect hormonal effects) in a neuroendocrine feedback loop. Additional components of the SRS may be discovered as neurobiological methods become more sophisticated, as long as—following the ACM—the component (a) coordinates the organism’s allostatic response to physical and psychosocial

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challenges; (b) encodes and filters information from the environment, thus mediating the organism’s openness to environmental inputs; and (c) functions to shift physical or behavioral endpoints. Epigenetic, cellular, immune, or neuropeptiderelated processes may, for example, emerge as SRS components. This does not imply that all new neurobiological measures index components of the SRS. Without these three criteria, new measures may instead be better conceptualized as moderators of the SRS or substrates of other systems. Here we focus on the most common SRS components. In the absence of stress, the PNS promotes vegetative functions (i.e., rest and restorative behavior), inhibits cardiac activity and cardiac output, and enables sustained attention as a consequence of regulatory mechanisms that occur in the prefrontal cortex (see Beauchaine & Thayer, 2015; Del Giudice et al., 2011; Porges, 2007). When a stressor is encountered, the PNS responds quickly by withdrawing this inhibitory influence (i.e., vagal withdrawal), allowing the excitatory SNS to operate unopposed, which results in rapid increases in cardiac output to cope with the stressor (Lovallo & Sollers, 2007). PNS withdrawal promotes rapid, flexible responding to stress and coping with mild to moderate stressors (such as solving a difficult puzzle). More extreme defense reactions associated with freezing and fainting also involve changes in PNS activity, albeit via different brainstem nuclei and efferent fibers (Porges, 2007). In most stressful situations, ranging from mild to severe, increases in cardiac output are effected via coupled PNS withdrawal and SNS activation. However, SNS effects are delayed by a few seconds because they are mediated through a second messenger system. PNS withdrawal and SNS activation also facilitate fight/flight responses via noradrenergic innervation of visceral organs and a slower, hormonal pathway through innervation of the adrenal medulla (e.g., Goldstein & Kopin, 2008; Gunnar & Vazquez, 2006). Following SNS activation, the adrenal medulla secretes epinephrine (E) and norepinephrine (NE) to increase heart rate, respiration, blood supply to skeletal muscles, and glucose release in the bloodstream. The third component of the SRS is the LHPA axis, which mounts more delayed, long-term responses to environmental challenge (although traditional distinctions between rapid and delayed responding have become increasingly blurred; Joëls & Baram, 2009). The endpoint of the LHPA response is cortisol release by the adrenal cortex, typically within 5 minutes after the triggering event, with a cortisol peak between 10 and 30 minutes (Sapolsky, Romero, & Munck, 2000). The main effects of cortisol release are to (a) mobilize physiological and psychological resources (e.g., energy release, alertness and vigilance, memory sensitization; e.g., Flinn, 2006; van Marle, Hermans, Qin, & Fernández, 2009), and (b) counterregulate physiological effects of SNS activation, thereby facilitating stress recovery (Munck, Guyre, & Holbrook, 1984). Joint effects of the SNS and LHPA axis are complex (Hastings et al., 2011) and they can be synergistic (especially in the short term) or antagonistic (especially at later phases of responding).

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Biological Sensitivity to Context The foregoing summary of the SRS provides a brief description of how peripheral neuroendocrine responses prepare the organism for challenge or threat. However, according to the theory of Biological Sensitivity to Context (BSC; Boyce & Ellis, 2005), these “stress response” systems also function to increase susceptibility to resources and support in the ambient environment (e.g., positive social opportunities, cooperative information; see also Porges, 1995, 2007). This dual function signifies a need to conceptualize stress reactivity more broadly as biological sensitivity to context, which Boyce and Ellis (2005) defined as neurobiological susceptibility to both cost-inflicting and benefit-conferring features of the environment—operationalized biologically by heightened reactivity in one or more components of the stress response system (PNS, SNS, LHPA). Depending on levels of nurturance and support versus harshness and unpredictability in their developmental environments, highly reactive children experience either the best or the worst of psychiatric and biomedical outcomes within the populations from which they are drawn (reviewed in Ellis et al., 2011). BSC theory therefore posits that individual differences in the magnitude of biological stress responses function to regulate openness or susceptibility to environmental influences, ranging from harmful to protective (see Sijtsema et al., 2013, for a review and critical analysis of BSC assumptions). Given past evidence that early trauma increases stress reactivity and newer evidence that high reactivity may enhance developmental functioning in highly supportive settings, Boyce and Ellis (2005) postulated a curvilinear, U-shaped relation between levels of early support-adversity and the magnitude of biological response dispositions. They hypothesized that (a) exposure to acutely stressful childhood environments upregulates BSC, increasing the capacity and tendency of individuals to detect and respond to environmental dangers and threats; (b) exposure to especially supportive childhood environments also upregulates BSC, increasing susceptibility to social resources and support; and (c) by contrast, and typical of the majority of children, exposure to childhood environments that are not extreme in either direction downregulates BSC, buffering individuals against chronic stressors in a world that is neither highly threatening nor consistently safe. Exploratory analyses in two studies offered confirmatory evidence that the lowest rates of high-reactivity phenotypes are found in conditions of moderate stress, and that both tails of the support-adversity distribution are associated with higher proportions of reactive children (Ellis et al., 2005; see also Bush, Obradovic, Adler, & Boyce, 2011; Gunnar, Frenn, Wewerka, & Van Ryzin, 2009). Although BSC theory has helped move the field toward a new conceptualization of stress responsivity, it has a number of significant limitations. First, BSC theory does not systematically link different stress reactivity patterns to functional variation in behavior, such as individual differences in social and reproductive behaviors that are specified by LHT. Second, although BSC theory advances a general developmental prediction (the U-shaped curve), it does not model the developmental

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trajectories leading to individual differences in a more fine-grained way (e.g., by discussing the development of stress responsivity at different life stages and identifying “switch points” when plasticity is preferentially expressed). Third, BSC does not address the adaptive meaning and developmental origins of sex differences in responsivity. Fourth, BSC focuses on explaining heightened reactivity to stress and does not afford a theory of hypoarousal (or dampened reactivity), in terms of its development or functional significance. Fifth, BSC does not address the development or functions of basal (tonic) levels of activity of the SRS. Finally, BSC theory does not advance discriminative predictions regarding PNS, SNS, and LHPA. The ACM, an extension and refinement of BSC, was formulated to address these issues.

The Adaptive Calibration Model of Stress Responsivity Goals of the ACM are to provide (a) a coherent, systematic account of the biological functions of the SRS; (b) a theory of individual differences capable of explaining adaptation of stress physiology and behavior to local environmental conditions; and (c) a functionally valid taxonomy of stress response profiles, including neurobiological correlates (e.g., serotonergic function), behavioral correlates (e.g., aggression, self-regulation), and developmental trajectories, integrating across baseline activity and responsivity measures of the SRS (Del Giudice et al., 2011). Achieving these goals would enable scientists to move beyond the inductive theory building that now dominates the field and increase their ability to advance targeted hypotheses about individual differences and their development. The ACM has its main theoretical foundations in LHT, an evolutionary biological framework for describing developmental “decisions” of organisms and their allocation of resources over the life course (Ellis et al., 2009; Del Giudice, Gangestad, & Kaplan, 2015), as well as the theory of adaptive developmental plasticity (West-Eberhard, 2003). In the ACM, individual differences in SRS functioning are thought to result, at least in part, from the operation of evolved mechanisms that match the individual’s physiology and behavior to local environmental conditions (i.e., calibration to the environment). Thus, patterns of stress responsivity are seen as generally adaptive in the biological sense, as they function in a way that ultimately tends to maximize the individual’s survival and reproduction in specific environmental contexts. The ACM can be summarized in seven points (see Del Giudice et al., 2011, for complete explication of the model, and Ellis & Del Giudice [2014] for extended discussion of its theoretical background). The first three points make broad statements about the functions of the SRS which constitute the backbone of the model: 1. The SRS has three main biological functions: to coordinate the organism’s allostatic response to physical and psychosocial challenges; to encode and filter information from the environment, thus mediating the organism’s openness to environmental inputs; and to regulate a range of life history-relevant traits and behaviors.

The Adaptive Calibration Model of Stress Responsivity 247 2. The SRS works as a mechanism of conditional adaptation, regulating development of alternative life history strategies (i.e., suites of reproductively relevant traits such as sexual maturation, intrasexual competitive behaviors and risk taking, and patterns of mating and parenting). Different patterns of baseline activity and responsivity in early development modulate differential susceptibility to environmental influence and shift susceptible children on alternative pathways, leading to individual differences in life history strategies. 3. Activation of autonomic, neuroendocrine, metabolic, and immune system responses during the first years of life (including the prenatal phase) provides crucial information about life history–relevant dimensions of the child’s environment, especially danger and unpredictability (see Ellis et al., 2009). This information is used to adaptively regulate stress responsivity and associated development of life history strategies. The following four points rely on additional assumptions about the behavioral correlates of SRS functioning to make specific predictions about the development of individual differences: 4. At a general level, a nonlinear relation exists between exposures to environmental stress during development and optimal levels of stress responsivity (see Figure 8.1). This nonlinear relation gives rise to four prototypical responsivity patterns (labeled sensitive [I], buffered [II], vigilant [III], and unemotional [IV]). The four patterns constitute combinations of physiological parameters indexing functioning of the PNS, SNS, and LHPA axis and include neurobiological indicators, behavioral outcomes, and developmental trajectories. 5. Sensitive and vigilant individuals display relatively high responsivity to the environment, whereas buffered and unemotional individuals display relatively low responsivity. Although comparisons between the two patterns of high responsivity (sensitive vs. vigilant) and the two patterns of low responsivity (buffered vs. unemotional) show substantial convergence in SRS baseline activity and responsivity (Figure 8.1), there is marked divergence in both antecedent environmental conditions and behavioral outcomes. 6. Because of sex differences in optimal life history strategies, sex differences are expected in the distribution of responsivity patterns and in their specific behavioral correlates. Sex differences should become more pronounced at increasing levels of environmental stress; in particular, contexts characterized by severe/traumatic stress should favor the emergence of a male-biased pattern of low responsivity (the unemotional pattern) and a female-biased pattern of high responsivity (the vigilant-withdrawn pattern). 7. Prenatal and early postnatal development, the transition from early to middle childhood, and puberty are likely “switch points” for calibration of stress responsivity. Individual and sex differences in SRS functioning emerge according to the evolutionary functions of each developmental stage.

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Vulnerabilities and Risk Factors for Psychopathology Life history strategy Slower

Faster

High

III Vigilant

agonistic ♂ withdrawn ♀

♀

Responsivity

I Sensitive

II Buffered IV Unemotional ♂

Low Low stress, safe environment

Moderate stress

Dangerous/unpredictable environment

Severe/traumatic stress

Developmental Context

Figure 8.1 Predicted curvilinear relation between developmental context and optimal levels of stress responsivity. Developmental context refers to variation in rearing experiences (i.e., individual differences in developmental exposures to key dimensions of environmental stress and support). The figure does not imply that all components of the SRS will show identical responsivity profiles, nor that they will activate at the same time or over the same time course. Male/female symbols indicate sex-typical patterns of responsivity, but the model also predicts substantial within-sex variation. Adapted from Del Giudice, Ellis, and Shirtcliff, 2011, p. 1577.

ENVIRONMENTAL INFORMATION A crucial function of the SRS is to collect and integrate information about changing states in the environment—including presence of threats, dangers, and opportunities—to adjust the state of the whole organism accordingly. This information can be encoded by the SRS in its functional parameters and, in the long run, provides the organism with a “statistical summary” of key dimensions of the environment. In the ongoing process of physiological adjustment, the system’s level of responsivity acts as an amplifier (when highly responsive) or filter (when unresponsive) of various types of contextual information. In this section we consider this function of the SRS

The Adaptive Calibration Model of Stress Responsivity 249 in more detail, and take a closer look to ecological information that can be encoded through repeated SRS activation.

Key Dimensions of the Environment The conceptualization of key dimensions of environmental influence in the ACM is based on LHT—a general framework for understanding biological trade-offs involved in development, such as those between growth and reproduction, current and future reproduction, and quality and quantity of one’s offspring. According to LHT (Charnov, 1993; Stearns, 1992), variation in life history traits results from trade-offs in allocation of resources to competing life functions: bodily maintenance, growth, and reproduction. Because of structural and resource limitations, organisms cannot maximize all components of fitness simultaneously and instead are selected to make trade-offs that prioritize resource expenditures, so that greater investment in one domain occurs at the expense of investment in competing domains. For example, resources spent on an inflammatory host response to fight infection cannot be spent on reproduction. Thus, the benefits of an inflammatory host response are traded off against the costs of lower fertility. Each trade-off constitutes a decision node in allocation of resources, and each decision node influences the next decision node (opening up some options, foreclosing others), in an unending chain over the life course (Ellis et al., 2009). At the broadest level, these trade-offs result in covarying sets of traits (i.e., life history strategies) that generally fall along a dimension of “slow” versus “fast.” Fast life history strategies are comparatively high risk and present oriented (taking benefits opportunistically with little regard for long-term consequences) and prioritize mating effort (e.g., competitive risk taking, aggression); they are also characterized by earlier ages of sexual development and reproduction, and focus on producing a greater number of offspring with less investment of resources, time, and energy in each. In contrast, slow life history strategies are comparatively long-term oriented and low risk (e.g., longer time horizons, more delay of gratification, better self-regulation and behavioral control), characterized by later timing of sexual development and reproduction, and focus on producing a smaller number of offspring and investing heavily in each of them. As discussed below, trade-offs incurred by the fast strategy include reduced health, vitality, and longevity—of self and offspring. Most important for the present discussion, LHT can be used to predict how organisms adjust their life history strategies according to ecological conditions. Key dimensions of the environment relevant to life history development are availability of resources, extrinsic morbidity-mortality (i.e., external sources of disability and death that are relatively insensitive to the adaptive decisions of the organism), and predictability of environmental change. Energetic resources—caloric intake, energy expenditures, and related health conditions—set the baseline for development, slowing growth and delaying sexual maturation and reproduction under energetic stress (i.e., favoring a slow life history strategy). When bioenergetic resources

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are adequate to support growth and development, however, cues to extrinsic morbidity-mortality and unpredictability gain importance (Ellis et al., 2009). In this context, LHT predicts that individuals will respond to extrinsic morbidity-mortality cues (e.g., exposures to violence, premature disability, death of people around you) and unpredictability (e.g., stochastic changes in ecological context, resource availability, family composition) by entraining faster life history strategies (see Belsky, Schlomer, & Ellis, 2012; Simpson, Griskevicius, Kuo, Sung, & Collins, 2012, for supporting longitudinal data). Developmental models based on LHT emphasize that these ecological factors tend to operate indirectly on children through more proximal processes, including those mediated by family characteristics (e.g., harsh parental discipline vs. warm and supportive parenting behaviors, family chaos vs. routines). For example, parental investment can buffer the impact of extrinsic mortality cues and heighten controllability or predictability by providing a stable, caregiving environment. Indeed, much of the effect of “toxic stress” on children’s development works through the mechanism of both exposure and failure of supportive parental relationships to buffer the child from stress exposure (Shonkoff & Bales, 2011). The SRS is attuned exquisitely to life history–relevant features of the environment. Of particular interest, the level of extrinsic morbidity-mortality is conveyed both by frequent SNS activation (signaling a potentially dangerous ecology) and by repeated LHPA activation. Because it responds strongly to uncontrollable challenges and novel situations, the LHPA axis also encodes information about environmental unpredictability/uncontrollability, thus giving LHPA functioning a central role in regulating life history strategies (see Del Giudice, et al., 2011). Across development, environmental information collected by the SRS (in interaction with the child’s genotype) canalizes physiological and behavioral phenotypes to match local ecological contexts.

The SRS as an Information Filter/Amplifier If the SRS encodes environmental information as an aggregation of repeated responses to challenge, it follows that SRS responsivity can function as an information filter. Low SRS responsivity results in a number of potential costs (e.g., reduced alertness, reduced sensitivity to social feedback) and potential benefits (e.g., resource economization, avoidance of immune suppression). In contrast, a highly responsive SRS amplifies signals coming from the environment and maximizes the chances that the organism will be modified by current experience. Potential costs of a highly responsive SRS include adverse physiological events, hypersensitivity to social feedback, and exposure to psychological manipulation. In addition, the organism’s action plans can get interrupted easily by minor challenges, and the ability to deal with future events may be reduced if physiological resources are already overwhelmed. On the other hand, a highly responsive system facilitates social learning and social bonding, enhances mental activities in localized domains,

The Adaptive Calibration Model of Stress Responsivity 251 focuses attention, and primes memory storage, thus tuning cognitive processes to opportunities and threats in the environment. Empirical studies (e.g., Pruessner et al., 2010) illustrate how SRS thresholds for responding to environmental stimuli differ dramatically from one person to another. It is also intriguing that such thresholds may show domain specificity, as when challenges related to competition or achievement are more salient for males but challenges related to social exclusion or rejection are more salient for females (Stroud, Salavey, & Epel, 2002; Stroud et al., 2009). Moreover, sex is not the only individual difference factor capable of influencing which domain shows a low threshold for activation (e.g., Wobber et al., 2010). Close social relationships can also filter/amplify more distal environmental factors, such as when cortisol reactivity is buffered by the presence of a warm, supportive caregiver (Hostinar & Gunnar, 2013a; Hostinar, Johnson, & Gunnar, 2015). Although ACM terminology tends to emphasize the role of responsivity, components of the SRS operate at both state (situation-specific) and trait (basal) levels. Basal functioning indicates a level of physiological preparedness or anticipation of the individual’s context (Pruessner et al., 2010), exerting a permissive effect on the individual’s ability to respond to novel events and encode environmental information (e.g., Gunnar & Quevedo, 2007). It may also provide a rough index of physiological accumulation of prior stressful events. High basal SRS activity is expected when the individual anticipates or needs to be engaged, aroused, or active in that context. High basal activation of the PNS, which reflects upstream regulation from prefrontal areas, promotes calm, concentration, and self-regulation (e.g., Beauchaine & Thayer, 2015; Fabes and Eisenberg, 1997; Porges, 2007), whereas high SNS baseline relates to anxiety (El-Sheikh, Erath, Buckhalt, Granger, & Mize, 2008), and baseline cortisol secretion regulates energy mobilization and engagement with the physical and social environment (Booth, Granger, & Shirtcliff, 2008). This role of the SRS in relation to anticipation is emphasized, for example, in an extensive literature demonstrating high cortisol reactivity in contexts characterized by unpredictability (Dickerson & Kemeny, 2004). Empirical findings that cortisol levels elevate prior to laboratory arrival (e.g., Ellis, Essex, & Boyce, 2005; Hastings et al., 2011) or in anticipation of challenges of the day (e.g., Fries, Dettenborn, & Kirschbaum, 2009; Schmidt-Reinwald et al., 1999) bolster the interpretation that basal SRS activity serves an anticipatory or preparatory function. Over time, repeated SRS responses to environmental challenges may accumulate, so that state-specific activity patterns become biologically embedded as part of the individual’s trait-like functional parameters (Shirtcliff, Granger, Booth, & Johnson, 2005). Basal functioning of the SRS achieves set-points that calibrate the individual’s physiology with the expected environmental demands, but as the environment changes, so too may the optimal set-point (McEwen & Wingfield, 2003). This process implicates one of the most important functions of the SRS: to change according to anticipated or current context, using those changes to optimize physiological functioning for the expected future conditions.

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Implications for Developmental Psychopathology Looking at the SRS through the lens of information filtering and encoding provides useful insights into the developmental processes that ultimately lead to psychopathological outcomes. First, and foremost, this reconceptualization of SRS functioning as a mechanism of susceptibility to environment influence (Boyce & Ellis, 2005) helps to explain bivalent effects of stress responsivity on mental and physical health, whereby highly reactive children experience either the best or the worst of psychiatric and biomedical outcomes depending on levels of stress and support encountered over development (see above, BSC). A radical implication of this theory is that the very children whose heightened responsivity appears to make them vulnerable to developing psychopathology may also benefit most from positive, supportive environments and interventions. Thus, the very qualities that appear to increase children’s frailties may also constitute their strength given supportive contexts, thus inspiring the metaphor of “orchid children” (Boyce & Ellis, 2005). In addition, LHT delineates basic dimensions of environmental stress and support—underscoring resource availability, morbidity/mortality risk, and unpredictability as key dimensions of the environment that regulate development of SRS responsivity patterns and their behavioral correlates (see the next section). This has already proved a valuable tool in empirical research (e.g., Belsky et al., 2012; Simpson et al., 2012), given the confusing abundance of environmental/contextual variables that might be measured and correlated with developmental outcomes. Furthermore, LHT provides organizing principles needed to understand the broad network of interactions between the SRS and other physiological response systems, such as the immune system (see Miller, Chen, & Parker, 2011). Another important implication of the concepts reviewed in this section is that both high and low SRS responsivity can be adaptive precisely because they modulate the organism’s openness to environmental information. As discussed earlier, there is no optimal level of responsivity; rather, the value of high versus low informational openness varies depending on local ecologies, and in some cases an unresponsive system can be highly functional in the context of an individual’s life history strategy. This idea will be developed in the next section.

PATTERNS OF RESPONSIVITY The ACM builds on the theoretical principles outlined in the previous sections to derive a taxonomy of four prototypical responsivity patterns. Each pattern describes an integrated mode of SRS functioning, life history–relevant behavioral tendencies, and plausible neurobiological correlates. Three of the patterns correspond to regions on the U-shaped curve of the BSC theory; the fourth pattern is a novel addition, and accounts for the development of hypoarousal in severely stressful conditions.

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The Logic of Hypoarousal Some individuals show a persistent pattern of markedly reduced SRS basal activity and responsivity, even following stimuli that elicit strong physiological reactions in most people. So-called hypoarousal or hyporesponsivity is reliably associated with externalizing behaviors, conduct disorders, and psychopathic traits (especially from middle childhood on; e.g., Ortiz, & Raine, 2004), which makes it especially interesting from the perspective of developmental psychopathology. Hypoarousal is usually treated as a sign of physiological dysregulation (e.g., Lupien et al., 2006); interestingly, chronic early adversity can lead to both hyper- and hyporesponsivity of the SRS (e.g., Gustafsson, Anckarsäter, Lichtenstein, Nelson, & Gustafsson, 2010; De Bellis et al., 1999; Tarullo & Gunnar, 2006; Yehuda, 2002). The ACM suggests that dampened responsivity may actually follow an adaptive logic, as a way to maximize fitness benefit/cost ratios in severely dangerous and unpredictable environments. When danger becomes severe, engaging in high levels of risk taking (e.g., antagonistic competition, impulsivity, and extreme discounting of the future) can become the optimal response from an evolutionary perspective (see Ellis et al., 2012). Note that such strategies require outright insensitivity to threats, dangers, and social feedback. An unresponsive SRS has a higher threshold for letting environmental signals in: many potential threats will not be encoded as such, and many potentially relevant events will fail to affect physiology to a significant degree. For an extreme risk-taker, however, informational insulation from environmental signals of threat can be an asset, not a weakness (see also Korte et al., 2005). In particular, adopting an exploitative/antisocial interpersonal style requires one to be shielded from social rejection, disapproval, and feelings of shame (all amplified by heightened LHPA responsivity). In summary, generalized low responsivity can be evolutionarily adaptive (i.e., fitness maximizing) at the high-risk end of the environmental spectrum, despite possible negative consequences for the social group and for the individual’s subjective well-being. This type of chronic low responsivity should be carefully distinguished from temporary “exhaustion” periods, usually arising after prolonged SRS activation in highly responsive individuals exposed to enduring stressors (Miller, Chen, & Zhou, 2007).

The Logic of Sex Differences In sexually reproducing species, the two sexes differ predictably on life historyrelated dimensions. They are thus expected to use different strategies in response to the same environmental cues (e.g., Geary, 2002; James, Ellis, Schlomer, & Garber, 2012). In mammals, including humans, males tend to engage in higher mating effort and lower parental effort than females (Geary, 2002; Kokko & Jennions, 2008; Trivers, 1972). In addition, males usually undergo stronger sexual selection, i.e., their reproductive success is more variable than that of females,

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leading to higher risk propensity (Trivers, 1972; see also Frankenhuis & Del Giudice, 2012). The extent of sex differences in life history–related behavior, however, is not fixed but depends in part on the local environment. At the slow end of the life history continuum, both sexes tend to engage in high parental investment, and male and female interests largely converge on long-term, committed pair bonds. Thus, sex differences in behavior are thus expected to be relatively small. As environmental danger and unpredictability increase, males benefit by shifting to low-investment, high-mating strategies; females, however, do not have the same flexibility as they benefit much less from mating with multiple partners and incur higher fixed costs through childbearing. Thus, male and female strategies should diverge increasingly at moderate to high levels of danger/ unpredictability. In addition, sexual competition takes different forms in males and females, with males engaging in more physical aggression and substantially higher levels of risk-taking behavior. As life history strategies become faster, sexual competition becomes stronger, and sex differences in competitive strategies become more apparent. For these reasons, sex differences in responsivity patterns and/or in the associated behavioral phenotypes should be relatively small at low to moderate levels of environmental stress, and increase as the environment becomes more dangerous and unpredictable. In particular, we predicted that males should be more likely to develop unresponsive phenotypes in highly stressful contexts. Another possibility is that the behavioral correlates of high and low responsivity in dangerous environments may differ between the sexes. Finally, we do not expect sex differences in responsivity to be present from birth, but rather to emerge gradually during development, as social and mating competition become more biologically salient (see Del Giudice, 2014; Ellis, 2013).

The Four ACM Patterns It is now possible to present a brief outline of the four ACM patterns (see Del Giudice et al., 2011 for a detailed description). Each pattern represents a stable configuration of SRS activity. Sensitive Pattern (Type I). Sensitive patterns are hypothesized to develop in safe, predictable conditions and warm family environments. High stress responsivity among sensitive individuals increases their openness to social and physical environments. Physiological profiles of those with this pattern (high LHPA and PNS responsivity, moderate SNS responsivity) favor sustained but flexible attention and sensitivity to social feedback. Sensitive individuals are reflective, self- and other-conscious, and engaged with the environment. They are high in inhibitory control, delay of gratification, and executive function. These traits promote

The Adaptive Calibration Model of Stress Responsivity 255 sustained learning and cooperation. Other plausible correlates are high serotonergic function and slow sexual maturation (for details see Del Giudice et al., 2011). Buffered Pattern (Type II). Buffered patterns (marked by moderate to low SRS responsivity across the board) are predicted to develop preferentially in conditions of moderate environmental stress, where they strike a balance between costs and benefits of responsivity. Compared to Type III and IV patterns, buffered individuals should be lower in anxiety, aggression, and risk taking. Vigilant Pattern (Type III). Highly responsive vigilant patterns develop in stressful contexts, where they enable people to cope effectively with dangers and threats in the physical and social environment. Their SNS-dominated physiological profile mediates heightened attention to threats and high trait anxiety. Increased SRS responsivity in dangerous environments can be expected to co-occur with increased responsivity in other neurobiological systems. For example, hyperdopaminergic function may contribute to the vigilant phenotype by boosting attention to threat-related cues and fast associative learning (Gatzke-Kopp, 2011). In the ACM, vigilance is not associated with a single behavioral pattern but rather with a distribution of patterns involving different mixtures of aggressive/externalizing (“fight”) and withdrawn/internalizing (“flight”) behaviors. In males, vigilant responsivity should be associated more often with increased risk taking, impulsivity, agonistic social competition, and reactive aggression (the vigilant-agonistic subtype). In females, the typical pattern should involve social anxiety and fearful/withdrawn behavior (the vigilant-withdrawn subtype). Vigilant children who display high levels of both agonistic and withdrawn behaviors (typically females; Zahn-Waxler, Crick, Shirtcliff, & Woods, 2006) may be best described as belonging to a third subtype, the vigilant-agonistic/withdrawn pattern. Unemotional Pattern (Type IV). Unemotional patterns are marked by a profile of low stress responsivity across systems, with the possible exception of strong autonomic responses when facing immediate physical threats. Generalized unresponsivity inhibits social learning and sensitivity to social feedback; it can also increase risk taking by blocking information about dangers and threats in the environments. Predicted correlates of this pattern are low empathy and cooperation, impulsivity, competitive risk taking, and antisocial behavior, including high levels of proactive/instrumental aggression, especially in males. As explained above, we predicted the distribution of Type IV to be male-biased; moreover, we anticipated that behavioral correlates of this pattern would differ between sexes. For example, one key feature of unemotional responsivity among females may be a generalized pattern of aloof social relationships with parents, siblings, and peers. Low serotonergic activity is a likely neurobiological correlate of Type IV.

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Empirical Investigations of the ACM Responsivity Patterns Many empirical studies have attempted to link child and adolescent physiological profiles with the kinds of contextual factors and behavioral outcomes theorized in ACM (reviewed in Del Giudice et al., 2011; Obradovi´c, 2012; see also below, Implications for Developmental Psychopathology). Although not explicitly designed to test the ACM, some of these studies have used the model as a framework for interpreting their results (e.g., Essex et al., 2011; Peckins, Susman, Negriff, Noll, & Trickett, 2015). Most relevant, a small number of studies have attempted to test for the ACM responsivity profiles across multiple SRS subsystems. Del Giudice, Hinnant, Ellis, and El-Sheikh (2012) was the first explicit attempt to empirically validate the four-pattern classification of the ACM. In this study, which examined stress responsivity patterns in an at-risk sample of 8- to 10-year-old children, we identified four classes of autonomic nervous system activity during resting conditions and in response to a stressful task. SNS activity was indexed by skin conductance level and PNS activity was indexed by respiratory sinus arrhythmia. Physiological differences between the classes were dominated by SNS activity and (to a lesser extent) PNS basal activity. Furthermore, the four patterns were associated with different levels of family stress. Two components of environmental stress emerged as significant predictors of class membership: (1) negative family relationships and (2) family warmth/predictability. As predicted, high-responsivity and low-responsivity patterns were found under both low-stress and high-stress conditions. Although the study by Del Giudice et al. (2012) provided a first step toward testing the ACM responsivity patterns, it had several limitations. First, measures of physiological activity were limited to the autonomic nervous system; however, LHPA axis functioning is central to the ACM and needs to be taken into account when determining responsivity patterns. Second, Del Giudice et al. (2012) used the star-tracing task (a cognitive challenge) to elicit stress responsivity. Although the star-tracing task is a valid procedure, social-evaluative threats—and particularly exposure to challenging conditions that reliably elicit LHPA-axis activation—are necessary to obtain all of the responsivity data needed to classify individuals into the four responsivity patterns of the ACM. Finally, and most critically, Del Giudice et al. (2012) did not examine links between the identified responsivity patterns and indicators of life history strategy. Testing for these links is necessary to evaluate the theory, and especially to distinguish sensitive from vigilant phenotypes and buffered from unemotional phenotypes, which are hypothesized to display overlapping patterns of stress physiology but different life history strategies. Two studies since Del Giudice et al. (2012) have attempted to address some of these limitations. Both Quas et al. (2014; Peers and Wellness Study [PAWS]) and Ellis,

The Adaptive Calibration Model of Stress Responsivity 257 Oldehinkel, and Nederhof (in press; Tracking Adolescent Lives Study [TRAILS]) conducted a latent profile analysis (LPA) that incorporated all three SRS subsystems, assessed relations with environmental conditions, and elicited stress responses based on socio-emotional or social-evaluative threat. TRAILS also included indicators of life history strategy. These studies further differed from Del Giudice et al. (2012), and from each other, regarding age of the participants at the time of the stress physiology assessments (PAWS: 5 years of age; TRAILS: 16 years of age) as well as levels of environmental risk that characterized the samples (PAWS: moderate risk; TRAILS: low risk), and sex (TRAILS included only boys in the analyses). Given that the ACM is a developmental theory that posits changes in responsivity over child and adolescent development, differences in responsivity patterns under different levels of environmental stress and support, and sex differences, these three studies are inherently difficult to compare. Nonetheless, a discussion of similarities and differences between the results of these studies should provide a useful first step toward evaluating the ACM. The ACM predicts that a buffered pattern (without either hyperresponsivity or hyporesponsivity) emerges among most children who develop in normative environments that are not characterized by extremes of either nurturance and support or adversity and trauma. In all three empirical studies, the largest number of participants fit the buffered response pattern, displaying roughly average levels of psychosocial stress, stress responsivity, and (in TRAILS) behavioral indicators of life history strategy. TRAILS constituted a relatively low-risk sample, and the LPA placed about three quarters of its participants into the buffered profile (74%). In contrast, PAWS had more diverse socio-demographic and ethnic characteristics than TRAILS, and the LPA resulted in a buffered group with 52% of participants. Finally, the at-risk U.S. sample studied by Del Giudice et al. (2012) had a buffered group with only 45% of participants. Thus, there is emerging empirical support for the high prevalence of the buffered pattern among low-risk samples, as well as variation across samples in the relative proportion of this pattern depending on background stressor exposures. Consistent with the ACM, the LPAs of both the TRAILS and PAWS data sets resulted in two patterns of high stress responsivity: one profile characterized by heightened multisystem reactivity across PNS, SNS, and LHPA axis parameters and the other characterized by PNS-specific reactivity (i.e., strong vagal withdrawal). In the TRAILS analyses, the pattern of multisystem reactivity was labeled sensitive because it was characterized by significantly elevated scores on quality of family environment (i.e., more warmth/support and less stress/rejection in the family environment) and the lowest levels of aggressive/rule-breaking behavior (indicating a slow life history strategy), whereas the PNS-dominated responsivity pattern was labeled vigilant because it was characterized by the highest levels of prenatal/perinatal risk factors and childhood stress,

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the highest levels of depressed/withdrawn behavior, and moderately elevated levels of aggressive/rule-breaking behavior. In contrast, in the PAWS analyses, multisystem reactivity was labeled vigilant because it was associated with high family adversity. These opposing findings regarding the environmental contexts of multisystem reactivity will need to be addressed in future research. However, the two studies were markedly different, especially in the age of the participants (early childhood vs. adolescence) when stress responsivity was assessed. The substantial reorganization of neurobiological stress responses at puberty (Dahl & Gunnar, 2009; Forbes & Dahl, 2010), as emphasized in the ACM, may be especially relevant to explaining these divergent results. More generally, these initial findings call into question some of the original predictions of the ACM concerning the prevalence of SNS reactivity in vigilant pattern versus PNS reactivity in sensitive patterns (Del Giudice et al., 2011). Those predictions were based on the assumption that high PNS reactivity is a consistent marker of self-control and positive social engagement. However, this assumption may need some revision in light of later research showing that strong PNS withdrawal in response to emotional stimuli correlates with indices of behavioral problems (see Beauchaine, 2015; Beauchaine & Thayer, 2015). Moreover, tonic activity and reactivity of the PNS increase markedly with age from infancy to adulthood; PNS parameters in child and adolescent samples may be confounded with the rate of physical and sexual maturation (Beauchaine & Webb, in press; Graziano & Derefinko, 2013), which is especially problematic from the standpoint of evaluating life history models. Consistent with the ACM, the LPA of TRAILS data resulted in two responsivity patterns that were characterized by relatively high levels of environmental stress and faster life history strategies, with opposing patterns of stress responsivity. One of these profiles was characterized by high stress responsivity (the vigilant pattern, as described above) and the other by low stress responsivity (labeled unemotional); both were PNS dominated. The unemotional profile was associated with strong vagal augmentation, as well as low LHPA axis reactivity. This profile was clearly linked to a fast life history strategy (highest scores on aggressive/rule-breaking behavior; lowest scores on effortful control) and to low scores on withdrawn/depressed behavior. In addition, membership in this profile was predicted by low scores on quality of family environment and associated with elevated scores on various childhood adversity measures. Again, the centrality of the PNS in distinguishing these two higher-risk profiles highlights the need, as the ACM is further developed and revised, to more fully delineate the role of PNS activity, especially in relation to the vigilant profile. The contrast between vigilant and unemotional profiles converges with past developmental research showing that a pattern of strong vagal withdrawal in response to social or cognitive challenges (as in the TRAILS vigilant responsivity pattern) is associated with internalizing symptoms or co-occurring internalizing-externalizing behavior problems whereas weak vagal withdrawal or

The Adaptive Calibration Model of Stress Responsivity 259 vagal augmentation (as in the TRAILS unemotional responsivity pattern) is associated with externalizing behavior problems (Boyce et al., 2001; Calkins & Keane, 2004; Calkins, Graziano, & Keane, 2007; El-Sheikh, Hinnant, & Erath, 2011; Gazelle & Druhen, 2009; Hinnant & El-Sheikh, 2009). These relations are complex, however, and not always consistent, particularly when comparing clinical and normative samples (see Zisner & Beauchaine, in press). For example, Hinnant and El-Sheikh (2013) found that vagal augmentation in boys was associated with co-occurring high internalizing and high externalizing trajectories across middle to late childhood, and Pang and Beauchaine (2013) documented excessive vagal withdrawal specifically in response to an emotionally evocative video in 8- to 12-year-old children who were extremely high in conduct problems. In the ACM, both vigilant and unemotional patterns are associated with higher rates of externalizing behavior, but they reflect different patterns of SRS activity. One of the developmental hypotheses of the ACM is that boys who grow up under highly stressful conditions will initially display a vigilant profile of heightened stress responsivity—but following chronic severe stress, shift toward a male-biased pattern of low responsivity (the unemotional pattern) under the influence of adrenal androgens in middle childhood. This pattern is then expected to further intensify in adolescence in relation to the pubertal transition. Consistent with this prediction, a clear unemotional profile did not emerge in the PAWS analysis of 5-year-olds but did emerge in Del Giudice et al.’s (2012) analyses (8- to 10-year-olds) and in the TRAILS analysis (16-year-olds). This age trend converges with past research showing that, over the course of development from childhood to young adulthood, females with histories of child sexual abuse shift from initially upregulated to downregulated morning cortisol levels (Trickett, Noll, Susman, Shenk, & Putnam, 2010), and to blunted feedback of the HPA axis (see e.g., Beauchaine, Crowell, & Hsiao, 2015). On the other hand, Del Giudice et al. (2012) failed to find the predicted malebiased distribution in unemotional patterns. Most of the sample was still prepubertal, however, so caution is warranted interpreting any sex differences or lack thereof. Nonetheless, many studies have shown that both men and women become hyporesponsive under conditions of severe stress (e.g., Bruce et al., 2009; Gustafsson et al., 2010; Miller, Chen, & Zhou, 2007; Tarullo & Gunnar, 2006; Vigil et al., 2010). A possibility to be explored in future research is that, even if unemotional patterns are equally frequent in males and females, similar physiological profiles may have different manifestations in behavior in the two sexes, as discussed above (see Del Giudice et al., 2011 for a more in-depth treatment of sex differences across behavioral domains). In conclusion, theoretical models such as the ACM are useful insofar as they explain known facts and make novel, testable predictions. The ACM is a complex model, and it can be used to derive dozens of predictions at different levels of analysis, including hypotheses about relations between childhood stress and stress responsivity, stress responsivity and behavior, individual differences in neuromodulation, Gene × Environment interactions, sex differences in life history strategies,

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and responses to domain-specific stressors (such as agonistic confrontations). Clearly, no single study can address all of these predictions, and multiple studies will be required even to evaluate the more basic ones. This first set of three studies (Del Giudice et al., 2012; Ellis et al., in press; Quas et al., 2014) provide initial empirical tests of the four responsivity patterns of the ACM. To varying degrees, these investigations provide support for the ACM by documenting complex, nonlinear relations between (a) childhood indices of familial and ecological conditions and life stress, (b) multisystem/multiphasic patterns of stress responsivity in adolescence, and (c) behavioral indicators of life history strategy in adolescence. Since its original publication, many other studies have also used the ACM as a guiding framework to explain biobehavioral links, yielding results that are consistent with the theory even within studies not originally designed to tease apart complexities of the ACM. Thus, the ACM has emerged as a useful theory in the field, helping us to move toward a more coherent “big picture” of the biosocial processes involved in developmental adaptation to the environment. At the same time, each of the three empirical studies that have specifically tested for the ACM responsivity patterns displays substantial limitations; their results both show support for and deviations from the ACM predictions, highlighting important theoretical challenges and empirical issues for future research.

Implications for Developmental Psychopathology The logic sketched in this section has several implications for developmental psychopathology. First, it provides a functional account of hypoarousal that goes beyond “dysregulation,” begins to explain why early adversity can have divergent outcomes (hyper- versus hypoarousal), and suggests that sex-related factors (such as sex hormones) may play an important role in determining the behavioral and physiological outcomes of early stress. For example, the hypothesis that some children shift from vigilant to unemotional patterns across middle childhood and adolescence may explain the puzzling finding that externalizing and aggressive behavior are associated with high cortisol levels in preschoolers but low cortisol levels from middle childhood on (Alink et al., 2008; Shirtcliff, Granger, Booth, & Johnson, 2005). Second, an evolutionary focus permits a better understanding of comorbidity patterns. For example, many superficially different traits and behaviors (e.g., aggression, early and promiscuous sexuality, substance abuse, reduced empathy) can be seen as manifestations of high-risk life history strategies that discount the future and increase mating effort. Consistent with this perspective, externalizing problems and precocious sexual behaviors in children not only co-vary but also share many etiological factors (see Lévesque, Bigras, & Pauzé, 2010). Finally, the ACM helps clarify complex relations between psychosocial environmental factors and stress responsivity patterns. Although the theory is rooted in biology and evolution, in practice the ACM emphasizes the importance of the

The Adaptive Calibration Model of Stress Responsivity 261 environment for shaping children’s biosocial development. Specific predictions can be made about the effects of key dimensions of the environment—resource availability, extrinsic morbidity-mortality, and unpredictability—rather than catchall concepts such as “cumulative stress” or “lifecourse adversity.” Moreover, moderators such as supportive caregiving can be specified within each key dimension (i.e., Does the moderator change environmental unpredictability? Does it shield the child from external morbidity or mortality threats? Does it provide necessary bioenergetic resources?), rather than tautologically defining a buffer as something that acts as a buffer. Making sense of these key dimensions of environmental risk and support has tremendous implications for treatment and prevention efforts, as the theory helps sort through the wide range of possible stressors to focus on the most likely targets for successful intervention. The ACM can be criticized for suggesting that stress and adversity over development can either upregulate or downregulate levels of SNS, PNS, and LHPA responsivity and thus that “any outcome” can be consistent with the ACM. Nonetheless, this situation is reflective of the state of the empirical literature on this topic: For every study linking stressful rearing experiences to hyperarousal (e.g., De Bellis et al., 1999; Essex, Klein, Cho, & Kalin, 2002; Yehuda, 2002) another study links such experiences to hypoarousal (as reviewed above). The ACM potentially explains both hyperarousal and hypoarousal by specifying nonlinear relations between environmental conditions and development of stress responsivity (Figure 8.1). According to the theory, developmental exposures to low to moderate levels of stress either upregulate (in the sensitive pattern) or downregulate (in the buffered pattern) responsivity. Likewise, developmental exposures to high levels of stress either upregulate (in the vigilant pattern) or downregulate (in the unemotional pattern) responsivity. Thus, if one considers the environment-responsivity curves shown in Figure 8.1, it is apparent that results of any single study that examines linear statistical relationships can range from positive to null to negative, depending on the portion of the curve sampled in each case (Boyce & Ellis, 2005; Ellis et al., 2005). Many inconsistent results in the stress literature may depend, at least in part, on failures to consider nonlinear relationships between environmental factors and SRS parameters, the tendency to view SRS functioning as divergent from some optimal base-point rather than a wide range of starting points, or difficulties with assessing the full range of environmental variance necessary to capture all four patterns of responsivity and associated behavioral strategies specified by the ACM. Of equal importance, the ACM predicts that it will be difficult to discriminate between functionally different profiles of responsivity without including information about life history–relevant traits such as risk taking, self-regulation, sexual maturation, and so on. What distinguishes the sensitive and vigilant patterns in the model is not LHPA reactivity per se, but rather the constellation of traits that go with it and clarify its functional meaning (increased social sensitivity in one case versus readiness to face social or physical danger in the other). If the model is correct, attempts to discriminate between meaningful individual types based

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exclusively on SRS parameters will yield weak and inconsistent findings, as SRS profiles are only one component of the predicted responsivity patterns.

ADAPTIVE CALIBRATION AND THE ALLOSTATIC LOAD MODEL With the ACM, we are seeking an integrative theoretical framework for the study of stress, stress responsivity, and health across development. The ACM both complements and provides a counterpoint to the ALM, which has become quite popular in developmental psychopathology (e.g., Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011; Lupien et al., 2006). In recent years, researchers have started adopting the ALM as a foundation for interdisciplinary integration (e.g., Ganzel, Morris, & Wethington, 2010; Juster et al., 2011). As Hostinar and Gunnar (2013b, p. 400) conclude: “The field has two major theories for talking about stress and health: the Allostatic Load Model, which grew out of biological and neuroscience approaches to understanding health and disease, and the Adaptive Calibration Model, which developed out of an explicitly evolutionary-developmental framework.” In this section, we review key points of convergence and divergence between the models, in order to clarify some of the advantages of the ACM (for a more detailed exposition, see Ellis & Del Giudice, 2014).

The Allostatic Load Model The process by which the regulatory parameters of the SRS (as well as other neurobiological systems) are modified in the face of challenge is termed allostasis (i.e., “stability through change”; Sterling & Eyer, 1988). Allostasis is a key concept of the ALM; it refers to the moment-to-moment process of increasing or decreasing vital functions (i.e., adaptively adjusting physiological parameters within the organism’s operating range) to new steady states in response to the demands of the environment and the organism’s resources (McEwen & Stellar, 1993; see also Lupien et al., 2006). Allostasis functions to help the organism cope with challenging events or “stressors,” enabling short-term adaptation to environmental perturbations. However, the term allostasis is not always used consistently; for example, some authors (e.g., Beauchaine et al., 2011) restrict the meaning of allostasis to long-term, potentially permanent changes in the system’s parameters in contexts of protracted stress (what McEwen and Wingfield [2003] labeled allostatic states and is now more commonly referred to as biological embedding). The SRS is a crucial mediator of allostasis, though many other central and peripheral structures initiate and sustain allostatic responses (see Ganzel et al., 2010). Allostatic load is a label for the long-term costs of allostasis; it is often described as “wear and tear” that results from repeated allostatic adjustments (i.e., adaptation to stressors), exposing the organism to adverse health consequences. The ALM emphasizes that biological responses to threat, while essential for survival, have negative

The Adaptive Calibration Model of Stress Responsivity 263 long-term effects that promote illness. Thus, short-term benefits of mounting biological responses to threat are traded off against long-term costs to mental and physical health, and these costs (allostatic load) increase as the organism ages. Among other adverse outcomes, allostatic load is thought to cause SRS dysregulation, resulting for example in excessive or insufficient responses to stressors and increasing vulnerability to mental and physical health problems (e.g., Juster et al., 2010; Juster et al., 2011). The idea of physiological dysregulation is integral to the ALM, which assumes that there is an optimal level of biological responsivity to social and environmental challenges. Accordingly, both “hyperarousal” and “hypoarousal”—recurring overactivity or underactivity of physiological mediators—are routinely described as dysfunctional deviations from the norm (e.g., Adam, 2012; Juster et al., 2011; Lupien et al., 2006), usually caused by a combination of excessive stress exposure and genetic or epigenetic vulnerability. Sometimes, models based on allostatic load assume that these response patterns evolved to meet the demands of more dangerous ancestral environments, but are mismatched to less perilous modern environments, thus setting in motion pathogenic processes that eventuate in mental and physical illness (e.g., Miller et al., 2011).

ACM Versus ALM We note at the outset that there are significant points of convergence between the ACM and the ALM. First, the ACM explicitly embraces the concept of allostasis and describes the coordination of allostatic responses as one of the main biological functions of the SRS. The ACM also acknowledges that chronic SRS activation does carry substantial costs, in terms of biological fitness as well as subjective well-being. Finally, whereas the ACM focuses on conditional adaptation, it leaves open the possibility that, for a number of reasons, some developmental outcomes are biologically maladaptive (see earlier discussion). From an evolutionary standpoint, the biggest limitation of the ALM is that no distinction is made between the two meanings of “adaptive” (and maladaptive) described above: positive versus negative biological fitness outcomes, on the one hand, and desirable versus undesirable mental and physical health outcomes, on the other. Maladaptation is inferred whenever there are costs for the organism. For example, if elevated cortisol levels among children are associated with a negative outcome, such as reduced working memory, then elevated cortisol is classified as a marker of allostatic load (Juster et al., 2011). This reasoning ignores the crucial fact that biological processes are adaptive when their fitness benefits outweigh their costs, not when they are cost-free. As discussed above, even large costs can be offset by large enough expected benefits. For example, in dangerous and unpredictable environments, organisms often accept the risk of severe damage in exchange for a chance of improving their condition (see Ellis et al., 2012; Frankenhuis & Del Giudice, 2012), as illustrated by the high levels of risk taking and aggression

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that characterize the adversity-adapted unemotional responsivity pattern. Similarly, when health and reproductive success conflict, natural selection favors the latter at the expense of the former (see Nesse, 2001). Because of persistent confusion underlying the distinction between (mal)adaptive and (un)desirable outcomes, most applications of the ALM do not address the trade-offs involved in the development of physiological and behavioral phenotypes; as a consequence, the ALM literature often lacks a theory of adaptive individual variation in stress responsivity (but see Korte et al., 2005, for a notable exception). Although the ALM is sophisticated in explaining the costs of allostasis, it only captures the short-term benefits of allostasis and does not consider the long-term benefits in terms of regulating conditional adaptation to varying environmental conditions. As a result, the development of enduring individual differences is usually traced to pathogenic processes. In contrast, the ACM is built on the notion of inherent trade-offs in the life cycle of organisms; explicit consideration of these trade-offs is at the heart of the ACM taxonomy of responsivity patterns. For example, consider heightened SRS responsivity in vigilant patterns (Type III). In the ACM, it is hypothesized that the costs of repeated SRS activation are offset by improved management of danger. Although the system is on a hair trigger, with the resulting burden of anxiety and/or aggression, few instances of actual danger will be missed. In addition, engaging in a “fast,” present-oriented life history strategy discounts the long-term health costs of chronic SRS activation if the immediate benefits are large enough (for in-depth discussion, see Del Giudice et al., 2011). In the manner in which the ALM framework is often applied, the same pattern of responsivity is treated as dysfunctional, because the stress response is deployed even in absence of true dangers (“excessive” response, “unnecessary” triggering; see Beauchaine et al., 2011; Lupien et al., 2006) and because of the associated unpleasant states and health risks. This approach, however, fails to consider that natural defenses are usually designed by natural selection to accept a high rate of false positives (the so-called “smoke detector principle”; Nesse, 2005). Moreover, adaptive defenses, from environmentally triggered surges in catecholamines and glucocorticoids to development of fever in response to an infection, are often aversive, disabling, and occasionally harmful (or even fatal); but mistaking them for diseases because of these features is a fallacy, though one that is exceedingly common in the psychopathology literature (see Nesse & Jackson, 2006). A related point of divergence between the ACM and the ALM concerns responses to acute versus chronic stress. In the ALM, adaptive responses to acute stress are contrasted with the biological “wear and tear” caused by chronic stress and resulting long-term modifications of SRS regulatory parameters. In the ACM, responses to both acute and chronic stress can be adaptive (though not cost-free); and, as a rule, long-term adjustment of SRS parameters (as in the development of different responsivity profiles) is seen as adaptive calibration rather than maladaptive dysregulation. Indeed, we anticipate that many of the allegedly “toxic” effects of chronic

The Adaptive Calibration Model of Stress Responsivity 265 stress (e.g., its effects on immune function, brain physiology, memory, learning, and so forth) will ultimately find a better explanation as mediators of biological fitness trade-offs (such as the well-documented trade-offs between faster life history strategies and health; reviewed in Ellis & Del Giudice, 2014). In total, the ALM, relative to the ACM, overemphasizes the costs of allostasis and underappreciates its benefits. A comparison and contrast between the core assumptions of the two theories is presented in Table 8.1. In conclusion, we are not arguing that the ALM is wrong per se, nor that the extensive body of research documenting negative effects of allostatic load on health is incorrect, but rather that the overemphasis of the ALM on the costs of allostasis weakens its conceptual power. The ALM does not address the adaptive role of allostasis in regulating developmental plasticity, which is the main objective—and strength—of the ACM. Be that as it may, conceptual differences between the ACM and ALM should not be irreconcilable, and greater integration of the two models in the future could potentially strengthen both approaches. Most relevant to the current volume, the ACM and ALM have rather different implications for understanding the development of psychopathology and, consequently, may support different intervention strategies (Ellis & Del Giudice, 2014).

Implications for Developmental Psychopathology The ALM and the concept of allostatic load have become remarkably popular in developmental psychopathology. Here we argue that the ALM has substantive limitations, especially regarding the current manner in which it uses a pathology lens to explain influences on human development. In practice, this focus has moved the field away from the roots of the ALM, which began in evolutionary biology and an exploration of allostasis and allostatic states, toward a context-free view about optimal health outcomes or pathological deviations from normative SRS profiles. The ACM attempts to swing the pendulum back to be more consilient with theory and research in evolutionary biology, providing researchers with a broader theory of stress responsivity that acknowledges the central importance of calibration to local environmental conditions. We recognize that the ALM is attractive because it conforms to implicit assumptions of the standard mental health approach, particularly regarding stress-disease relationships, and therefore does not require a fundamental shift in thinking and logic. However, it also fails to deliver the insight and heuristic power of a modern evolutionary-developmental framework. In the long run, the field of developmental psychopathology may be better served by a model that is informed by life history theory, modeling of strategic trade-offs, and a more explicit consideration of the relations between adaptation, health, and well-being. In total, we believe that the ACM embodies the main insights of the ALM while addressing some of its key limitations. Even more importantly, most of the work that is presently carried out under the ALM umbrella could be reframed in the perspective of the ACM. For example,

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Responses to Psychosocial Stress/ Unpredictability

Examples of Response

• Acute SNS and LHPA responses Activation of mobilize energy reserves, protect autonomic, against septic shock and nutrient neuroendocrine, deprivation, permit fight or flight metabolic, and responses that are normally immune systems protective against danger. • Inflammation accelerates the healing of wounds. • Increased inflammatory tone Changes in • Elevated cortisol and allostatic catecholamines mechanisms • Muted cardiovascular responses to stress • Reduced scores on standard tests Cognitive, of intelligence, language, memory, behavioral, and and other abilities emotional • Early onset and increased impairments in prevalence of psychopathology children • Tailoring of emotion systems, Cognitive, arousal responses, and perceptual behavioral, and abilities to the detection and emotional monitoring of danger adaptations to stress in children • Development of insecure attachments, mistrustful internal working models, opportunistic interpersonal orientations, oppositional-aggressive behavior • Cognitive and physical impairments Long-term • Depression deleterious • Increased risk of cardiovascular outcomes disease and all-cause mortality Long-term adaptive • Adaptive calibration of autonomic, neuroendocrine, metabolic, and changes in immunological systems biobehavioral • Regulation of alternative life history systems strategies to match ecological conditions

ACM

ALM

Central to theory

Central to theory

Central to theory

Central to theory

Not inconsis- Central to tent with theory theory

Central to theory

Not inconsistent with theory

Not inconsis- Central to tent with theory theory Central to theory

Beyond the scope of the theory

Note. Light shading indicates a difference in emphasis between the ACM and ALM. Dark shading indicates a qualitative divergence between the two theories. SNS: sympathetic nervous system; LHPA: limbic-hypothalamic-pituitary-adrenal axis. Adapted from Ellis and Del Giudice, 2014.

The Adaptive Calibration Model of Stress Responsivity 267 the theory of developmental stages and switch points embodied in the ACM might serve as a detailed, biologically grounded foundation for the analysis of the effects of stress exposure at different points in the life cycle (Ganzel & Morris, 2011). Finally, the ACM addresses major anomalies in the field regarding complex relations between psychosocial environmental factors, stress responsivity, life-history relevant traits and behaviors, and health. In the ALM, both hyperarousal and hypoarousal are considered indicators of stress dysregulation resulting from allostatic load, and the developmental pathways leading to systematic upregulation versus downregulation of SRS parameters are not theoretically modeled (rather, hyperarousal and hypoarousal are grouped together as dysfunctional deviations from an optimal setpoint). Valid explanatory models of developmental pathways leading to both hyper- and hyporesponsivity are critical to explaining the development of psychopathology because both heightened and dampened responsivity can appear either good or bad in terms of behavioral adjustment and health. Such bivalent effects of the SRS have been documented in PNS, SNS, and LHPA studies focusing on both baseline arousal and responsivity (e.g., Bauer, Quas, & Boyce, 2002; Burke, Davis, Otte, & Mohr, 2005; Evans & English, 2002). The ACM potentially explains these anomalous findings by specifying two patterns of heightened stress reactivity (sensitive and vigilant phenotypes) and two patterns of dampened stress reactivity (buffered and unemotional phenotypes). Most importantly, each phenotype is characterized by different developmental histories and behavioral and health trajectories. Accordingly, heightened reactivity may appear to be a protective factor in sensitive phenotypes and a risk factor in vigilant phenotypes, whereas dampened reactivity may appear to be a protective factor in buffered phenotypes and a risk factor in unemotional phenotypes. This contrast highlights the critical importance of examining larger responsivity patterns in the context of environmental antecedents and life history outcomes.

CONCLUSION In this chapter, we presented and elaborated an evolutionary-developmental theory of individual differences in stress responsivity—the ACM—that reorganizes many empirical findings from different research fields, weaves them together in a theoretically coherent manner, and advances novel and testable predictions about behavior, development, and neurobiology. Built explicitly on the foundation of modern evolutionary biology, the ACM provides a framework for research on stress and development that takes us beyond the ALM; it delineates coherent, functional responses to stress, including regulation of alternative life history strategies, which reliably emerge in given developmental contexts. These responses have to be taken into account to more fully and accurately capture child and adolescent development under conditions of psychosocial stress and unpredictability. Ultimately, our ability to translate research on stress-health relationships into effective interventions for the

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crucial goals of risk prevention and management depend on understanding when and how adaptations to stress emerge and can be changed.

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CHAPTER 9

Exposure to Teratogens as a Risk Factor for Psychopathology LAUREN R. DOYLE, NICOLE A. CROCKER, SUSANNA L. FRYER, AND SARAH N. MATTSON

INTRODUCTION AND ETIOLOGICAL FORMULATIONS

A

teratogen is an agent that causes birth defects by altering the course of typical development. Examples of human teratogens exist in several classes of substances including drugs of abuse (e.g., alcohol, cocaine, nicotine), prescription medications (e.g., retinoic acid, valproic acid, thalidomide), environmental contaminants (e.g., pesticides, lead, methylmercury), and diseases (e.g., varicella, herpes simplex virus, rubella). Pregnant women are exposed to teratogens for a variety of reasons. Some women may be unaware of the teratogenic nature of certain substances. Or, in the case of viruses such as varicella, even if awareness exists, prevention of exposure may not be possible. A recent outbreak of Zika virus linked with an increased rate of microcephaly (small head circumference) in Brazil has prompted international alarm. The connection between in utero exposure to Zika virus and congenital defects has yet to be established, but preliminary investigations strongly suggest a causal relationship between the two (Mlakar et al., 2016; Victora et al., 2016). Similarly, with medical conditions such as seizure disorder or severe depression, termination of pharmacologic treatment during pregnancy may not be advisable. Furthermore, given that about half of pregnancies

Acknowledgments: Preparation of this chapter was supported in part by National Institute on Alcohol Abuse and Alcoholism Grant numbers U01 AA014834, R01 AA019605, R01 AA010417, F31 AA020142, and T32 AA013525. We gratefully acknowledge the assistance and support of the Center for Behavioral Teratology, San Diego State University.

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in the United States are unplanned, and given that pregnancy detection may not occur until fetal development is well underway, many teratogenic exposures occur prior to pregnancy recognition (Henshaw, 1998). For example, more than 130,000 pregnant women per year in the United States consume alcoholic beverages at levels believed to pose teratogenic risk to their fetuses (Lupton, Burd, & Harwood, 2004), and 10% of women who know they are pregnant report drinking alcohol during pregnancy (Centers for Disease Control and Prevention, 2004). These rates exist despite more than four decades of research on the effects of alcohol-induced birth defects and the presence of government-mandated labels on alcoholic beverages that warn of the association between drinking during pregnancy and harmful fetal effects. Thus, teratogenic exposures are common, and birth defects that result from prenatal exposures constitute a major public health concern. Behavioral teratogens are agents that alter central nervous system functions that subserve cognitive, affective, sensorimotor, and/or social behaviors following exposure during gestation (Vorhees, 1986). Behavioral teratogens can cause damage to fetuses even in the absence of gross physical or structural abnormalities. Effects of behavioral teratogens may be subtle and may not be recognizable at birth. The purpose of research aimed at identifying and characterizing effects of behavioral teratogens is to determine the degree and nature of behavioral dysfunctions attributable to fetal exposure to drugs and other agents that cause birth defects. The hope is that by identifying behavioral teratogens and increasing public awareness, we will reduce teratogen exposures and resulting fetal damage. As already noted, effects of behavioral teratogenic exposures are diverse and include structural damage to the developing brain, which may result in cognitive impairments, behavior dysregulation, and emotional dysfunction. In this chapter, we focus on associations between teratogenic exposures and the development of mental illnesses. The etiology of psychopathology is complicated by gene-environment interactions (including epigenetic effects) through which only some genotypes may be sensitive to certain environmental risks—including behavioral teratogens (cf. Rutter, 2005; Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Gene-environment correlation is also possible. For example, genes that predispose to maternal substance abuse may co-occur with maladaptive childrearing environments. Vulnerability to psychopathology is also multifactorial, with many genes interacting with both one another and with environmental risk factors to eventuate in mental illness. Thus, complex behaviors that comprise psychopathology manifest in an emergent fashion from continuous interplay between an individual’s genetic predispositions and his/her environmental risk exposures. Factors such as family placement (e.g., being raised in a biological, foster, or adoptive home; Viner & Taylor, 2005), socioeconomic status (SES; Rutter, 2003), and general intelligence (Dykens, 2000) are potential sources of variance in mental health outcomes and may be of particular concern in evaluating the mental health status of individuals with teratogenic exposures.

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Alcohol is the main focus of this chapter, as it is both an archetypal and widely studied behavioral teratogen. Associations between psychopathology and fetal exposure to nicotine, stimulant drugs, methylmercury, lead, and antidepressant medication are discussed briefly, as effects of these exposures on mental health outcome are less well studied.

HISTORICAL CONTEXT Knowledge of birth defects and their associations with teratogenic exposures has evolved over the course of history. Early depictions, including carvings and drawings, indicate knowledge of birth defects as early as 6500 B.C., and early written records indicate beliefs that birth defects were caused by various factors including witchcraft. Birth defects were also thought to portend adverse events. By the 20th century, it was thought that the fetus was afforded significant protection by the uterus, and it wasn’t until 1941 that an association between prenatal exposure to the rubella virus and subsequent birth defects was reported in the scientific literature. Even so, it took the experience with thalidomide in the late 1950s to confirm association between teratogens and resulting birth defects (Vorhees, 1986). An association between gestational alcohol exposure and adverse fetal effects was also described anecdotally for centuries. Some contend that the association was documented in Greek and Roman mythology and in the Bible. Yet throughout most of the 20th century, alcohol was not recognized as a human teratogen. In fact, physicians used alcohol to treat premature labor in a procedure referred to as an ethanol drip. Perhaps in part due to this medical use, the first descriptions in the scientific literature of alcohol as a human teratogen were met with considerable resistance. Instead, it was posed that the constellation of symptoms later identified as fetal alcohol syndrome (FAS) was due to other factors such as inadequate prenatal nutrition or genetic effects (“Effect of alcoholism at time of conception,” 1946). Because of their ability to control confounding factors, preclinical animal models were crucial in establishing the causal role of alcohol in bringing about fetal alcohol spectrum disorders (FASD). After more than 40 years of research on alcohol teratogenesis, prenatal alcohol exposure is now recognized as a major public health concern. As an example of this increased public awareness, in 1989 the U.S. government passed the Alcoholic Beverage Warning Label Act, which mandates that alcoholic beverages contain labels that warn of alcohol’s harmful effects on the developing fetus. In addition, in February 2005, the U.S. surgeon general issued an updated “Advisory on Alcohol Use and Pregnancy,” which recommended that (a) pregnant women not drink alcohol, (b) pregnant women who have already consumed alcohol during pregnancy stop drinking to minimize further risk, and (c) women who are considering pregnancy or who might become pregnant abstain from alcohol (http://www.hhs.gov/ surgeongeneral/pressreleases/sg02222005.html) (Warren & Hewitt, 2009). Despite this progress, women continue to drink in pregnancy (see above).

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TERMINOLOGICAL AND CONCEPTUAL ISSUES Since the first descriptions of FAS in the literature ( Jones & Smith, 1973; Jones, Smith, Ulleland, & Streissguth, 1973; Lemoine, Harousseau, Borteyru, & Menuet, 1968), patterns of birth defects associated with maternal alcohol consumption have been studied extensively. FAS is characterized by a triad of presenting symptoms, including (1) pre- and/or postnatal growth deficiency, (2) dysmorphic facial features (short palpebral fissures, indistinct philtrum, and a thin upper lip), and (3) central nervous system (CNS) dysfunction. Although CNS dysfunction is required for the diagnosis of FAS, cognitive deficits and behavioral abnormalities are commonly observed following prenatal alcohol exposure even in the absence of growth deficiency and facial stigmata required for clinical recognition of FAS (e.g., Mattson, Riley, Gramling, Delis, & Jones, 1997, 1998). FASD encompasses the entire range of effects attributable to prenatal alcohol exposure (Bertrand, Floyd, & Weber, 2005). These effects may range from full manifestation of FAS, to subtle neurobehavioral or physical defects. FASD is a diagnostic umbrella term under which both dysmorphic (i.e., FAS) and nondysmorphic cases of prenatal alcohol exposure fall. FASD encompasses historical terms such as fetal alcohol effects (FAE), and current diagnoses of partial FAS (pFAS), alcohol-related birth defects (ARBD), alcohol-related neurodevelopmental disorder (ARND), and the new term neurobehavioral disorder associated with prenatal alcohol exposure (ND-PAE). Incidence rates of FAS average about 1 per 1,000 live births (Bertrand et al., 2005), making FAS the leading preventable cause of intellectual disability (Pulsifer, 1996). More subtle birth defects related to prenatal alcohol exposure occur more frequently. The combined rate for dysmorphic (i.e., FAS) and nondysmorphic FASD cases is estimated conservatively to be 9.1 cases per 1,000 live births (Sampson et al., 1997), although recent studies in North America find rates as high at 48 per 1,000 (May et al., 2014; May et al., 2015). The costs of fetal alcohol effects pose a heavy burden on society, ranging between $4 billion and $9.7 billion annually (Lupton et al., 2004; Thanh, Jonsson, Dennett, & Jacobs, 2011). Costs related to FASD are considerably higher. With the recent transition to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association [APA], 2013), new terminology has been introduced to identify effects of prenatal alcohol exposure. ND-PAE is included under “conditions for further study,” indicating a need for future research to validate proposed criteria. In addition to a confirmed history of more than minimal prenatal alcohol exposure (>13 drinks per month), ND-PAE criteria require significant impairment in three domains of neurobehavioral functioning: neurocognition, self-regulation, and adaptive functioning (APA, 2013). The effects of prenatal alcohol exposure on neurocognitive function have been studied extensively (Mattson, Crocker, & Nguyen, 2011), although describing such findings in full detail is beyond the scope of this chapter. However, it should be noted that combined effects of dysfunction in these three domains, and their interaction with

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environmental factors, contributes to the adverse consequences of prenatal alcohol exposure (Kable et al., 2015). Deficits in self-regulation can manifest as impaired mood/arousal regulation and/or impaired attention/impulse control (APA, 2013). As described below, impairments in adaptive function persist throughout life, and may become more pronounced with age (Kable et al., 2015). Criteria for impairment in adaptive function include deficits in communication, social interaction, daily living skills, and/or motor skills (APA, 2013). Impairment in self-regulation and adaptive function can contribute to psychopathology in FASD (Kable et al., 2015), as outlined in this chapter. When establishing an ND-PAE diagnosis, certain issues must be considered. For example, individuals who are exposed to alcohol prenatally have high rates of co-occurring mental health disorders (Fryer, McGee, Matt, & Mattson, 2007). Thus, it is essential to determine whether symptoms associated with a possible secondary diagnosis, such as oppositional defiant disorder (ODD), are merely a manifestation of issues attributable to prenatal alcohol exposure (Kable et al., 2015). Because of such overlapping symptoms between FASD and other mental health disorders, there are high rates of missed diagnoses and misdiagnosis (Chasnoff, Wells, & King, 2015). Missed diagnoses may represent ignorance or denial of etiological factors in individual cases, and therefore missed opportunities for intervention. Specific criteria for ND-PAE, and empirical evidence supporting those criteria, are essential to aid in differential diagnosis. Finally, current substance use or misuse (e.g., prescription medication, alcohol, drugs) must be ruled out, along with any general medical condition(s) that may better account for symptoms (Kable et al., 2015). Thus, comprehensive, multidisciplinary assessment is imperative (Doyle & Mattson, 2015; Kable et al., 2015).

MENTAL HEALTH OUTCOMES IN FASD Although not as well studied as cognitive deficits associated with prenatal alcohol exposure, there is a sizeable literature on mental health outcomes. Studies of affected individuals consistently demonstrate deficits in both parent and self-reported behaviors (Coles, Platzman, Brown, Smith, & Falek, 1997; Coles, Platzman, & Lynch, 1999; Mattson & Riley, 2000; Nash et al., 2006; O’Leary et al., 2009; Sayal et al., 2009; Sayal, Heron, Golding, & Emond, 2007; Steinhausen, Willms, Metzke, & Spohr, 2003). In one early longitudinal investigation of children with FAS, a large portion of whom were intellectually disabled (Steinhausen, Willms, & Spohr, 1994; Steinhausen, Nestler, & Spohr, 1982; Steinhausen, Willms, & Spohr, 1993), increased rates of many maladaptive behaviors were observed, including stereotypies, sleeping problems, tics, head and body rocking, peer relationship difficulties, and phobic behaviors. Moreover, an index of psychopathological behavior, created from the sum of symptom scores, correlated with the degree of dysmorphology (Steinhausen et al., 1982). A follow-up report demonstrated persistence of psychopathological symptoms through late childhood, including

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hyperkinetic (i.e., overactive) behaviors, sleep disturbances, abnormal habits, stereotyped behaviors, and emotional disorders (Steinhausen & Spohr, 1998). In another investigation that used IQ-matched controls, alcohol-exposed children exhibited significantly more parent-report behavioral and emotional disturbances on 5 of 8 subscales on the Child Behavior Checklist (Mattson & Riley, 2000). As a group, children with prenatal alcohol exposure demonstrated clinically significant scores in several externalizing behavior domains, including social problems, attention problems, and aggressive behavior. Children with FASD also demonstrated elevated internalizing behaviors, but differences on these scales were not as large as on externalizing behaviors. Studies that have used criteria from the fourth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; APA, 1994, 2000) show that rates of clinical diagnoses are also elevated among children with FASD. In one sample of 23 children with histories of heavy prenatal alcohol exposure, ages 5 to 13 (O’Connor et al., 2002), 87% met criteria for at least one psychiatric disorder, with mood disorders (major depression and bipolar disorders) most common. Somewhat surprisingly, these data revealed comparable mental health outcomes regardless of the severity of fetal alcohol effects. Indeed, nondysmorphic individuals were just as likely to have clinically significant psychopathology as children with hallmark facial features of FAS. In an effort to focus on the development of psychiatric illness in children with FASD independent of intellectual disability, children with an intelligence quotient (IQ) below 70 were excluded from this study. The authors noted that because the sample was clinical-referred, high rates of observed psychopathology may not generalize to the entire alcohol-exposed population (O’Connor et al., 2002). In a later study, also of a clinical-referred FASD sample, Fryer, McGee, Matt, Riley, and Mattson (2007) found that 97% of alcohol-exposed children met criteria for at least one DSM-IV disorder, compared with 40% of control children. Among children with FASD, 28% met criteria for a mood and/or anxiety disorder, and 59% met criteria for externalizing disorders such as attention-deficit/hyperactivity disorder (ADHD), conduct disorder (CD), and/or ODD. These rates were higher than those observed among children in the control group and in the general population. A more recent study compared rates of general anxiety disorder, major depressive disorder, ODD, and CD among children with FASD to those of nonexposed children with ADHD (Ware et al., 2013). Both groups displayed elevated rates of all diagnoses. However, participants with ADHD and histories of prenatal alcohol exposure had higher rates of CD than those with ADHD without prenatal alcohol exposure. As already suggested above, because these studies included participants who were identified based on clinically significant behavioral problems or recognition of fetal alcohol effects, mental health outcomes may not generalize to the entire alcohol-exposed population. Such clinical samples exclude individuals who were exposed to alcohol prenatally but experience few or no symptoms. Moreover, although retrospective studies are important in characterizing individuals most in need of clinical services, studies that identify participants prospectively (i.e., at or

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near the time of teratogenic exposure) can increase the external validity of research findings. Also, prospective studies typically enable better control of confounding factors, because environmental and demographic information can be collected more accurately and closer to the time of exposure. The Seattle Study on Alcohol and Pregnancy, a large-scale population-based study of alcohol’s behavioral teratogenicity, used a prospective design. Streissguth and colleagues identified 1,529 pregnant women in the mid-1970s, and collected information about their use of alcohol, cigarettes, caffeine, and other recreational and prescription drugs (Barr et al., 2006). Importantly, these pregnancies were not considered high risk, and all women received prenatal care. A cohort of 500 mother-infant pairs was selected—oversampling for alcohol use—and followed through adulthood. Of this birth cohort, 400 young adults, including individuals with and without prenatal alcohol exposure, were interviewed at about age 25 using Structured Clinical Interviews for DSM-IV. The purpose of this study was to determine whether high rates of psychiatric illness observed in clinical samples of individuals with FASD would replicate in a nonclinical, community sample. The odds of developing somatoform and substance use disorders, and paranoid, passive-aggressive, and antisocial traits, were at least doubled in individuals who were exposed to one or more binge drinking episodes versus those who were not. Substance use disorders and both passive-aggressive and antisocial personality traits remained at least a twofold risk among alcohol-exposed individuals, even after adjusting for confounding factors including prenatal nicotine and marijuana exposure, family placement, low SES, poor maternal nutrition, breastfeeding, and family history of psychiatric problems and alcoholism. The authors noted that given the epidemiological focus of their study, including thorough covariation of many other factors that predict mental health, prenatal alcohol exposure is likely to play a causal role in increased rates of the disorders noted. Taken together, these mental health outcome studies suggest that individuals with fetal alcohol exposure histories suffer from substantial psychiatric illness. Moreover, the diversity of study methodologies (e.g., both prospective and retrospective subject ascertainment, longitudinal versus cross-sectional design, different portions of the age span) supports generalizability of the association between FASD and psychopathology. To date, empirical studies that examine psychiatric illness among individuals with FASD have relied on DSM-IV criteria. Thus, future research is needed to determine rates of psychopathology based on new DSM-5 criteria.

Disruptive Behavior Disorders The available literature suggests that certain types of psychopathology are more likely than others following gestational alcohol exposure. Among these are disorders on the disruptive behavior spectrum (e.g., ADHD, ODD, CD). As stated earlier, findings from the Fryer et al. (2007) study indicate group differences between alcohol-exposed and typically developing peers in rates of ADHD, ODD,

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CD, depressive disorders, and specific phobias. The largest group difference pertains to ADHD behaviors. This finding is consistent with research suggesting that increased attention difficulties and ADHD are among the most notable psychopathological outcomes within the FASD population (Burd, Klug, Martsolf, & Kerbeshian, 2003; Coles, Platzman, Lynch, & Freides, 2002; Mattson & Riley, 1998; Steinhausen & Spohr, 1998; Steinhausen et al., 1993; Streissguth, Barr, Kogan, & Bookstein, 1996). Given the high rates of ADHD in FASD, an entire body of research comparing these two clinical groups has formed, with the goal of differentiating them on cognitive and behavioral function to aid in better identification of alcohol exposed individuals (Burden et al., 2010; Coffin, Baroody, Schneider, & O’Neill, 2005; Coles, Platzman, Raskind-Hood, et al., 1997; Crocker, Vaurio, Riley, & Mattson, 2009, 2011; Greenbaum, Stevens, Nash, Koren, & Rovet, 2009; Jacobson, Dodge, Burden, Klorman, & Jacobson, 2011; Kooistra, Crawford, Gibbard, Kaplan, & Fan, 2011; Kooistra, Crawford, Gibbard, Ramage, & Kaplan, 2010; Kooistra et al., 2009; Nanson & Hiscock, 1990; Nash et al., 2006; Vaurio, Riley, & Mattson, 2008; Glass et al., 2014; Ware et al., 2013). In one such investigation, parent-report items reflecting hyperactivity, inattention, lying and cheating, lack of guilt, and disobedience were particularly useful at discriminating children with FASD from children with ADHD (Nash et al., 2006). Additionally, children with prenatal alcohol exposure appear to show different profiles of hyperactivity and inattention than children with ADHD. In one recent study, both children with ADHD and children with prenatal alcohol exposure showed similar patterns of inattention throughout a sustained attention task, but children who were exposed prenatally to alcohol showed activity levels more similar to those of a typically developing control group (Glass et al., 2014). Several studies have examined the interaction between prenatal alcohol exposure and ADHD on behavioral and psychiatric outcomes. These studies suggest differential behavioral outcomes in alcohol-exposed children with and without ADHD, particularly on externalizing behavior domains (Graham et al., 2013; Ware et al., 2013). Interestingly, the same does not appear to be true for neuropsychological abilities; alcohol-exposed children with and without ADHD seem to have similar neuropsychological profiles (Glass et al., 2013). Increased delinquent behavior and deficient moral decision making have also been reported in alcohol-exposed youth (Alvik, Aalen, & Lindemann, 2013; Roebuck, Mattson, & Riley, 1999; Sayal et al., 2014; Schonfeld, Mattson, & Riley, 2005; Streissguth et al., 1996), and rates of ODD and CD are high among these individuals (Disney, Iacono, McGue, Tully, & Legrand, 2008; Fryer et al., 2007; Hill, Lowers, Locke-Wellman, & Shen, 2000). In one population study of 626 adolescent twin pairs, prenatal alcohol exposure was associated with high rates of CD symptoms even after covarying parental externalizing disorders, prenatal nicotine exposure, monozygosity, gestational age, and birth weight (Disney et al., 2008). In another study, children with low IQs and prenatal alcohol exposure exhibited less moral maturity than typically developing children. In addition, children with

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FASD displayed a specific deficit in moral value judgments in their relationships with others (Schonfeld et al., 2005). Other studies demonstrate that children with prenatal alcohol exposure are more likely to lie about their behavior, and are more skilled liars at younger ages than their nonexposed peers (Rasmussen, Talwar, Loomes, & Andrew, 2008). Given the increase in delinquent behavior demonstrated by individuals with prenatal alcohol exposure, it is not surprising that alcohol-exposed youth are overrepresented in the criminal justice system (Boland, Burrill, Duwyn, & Karp, 1998; Fast & Conry, 2009, 2004). Interestingly, corrections staff are largely unaware of this phenomenon (Burd, Selfridge, Klug, & Bakko, 2004). One of the few systematic FASD screens of a delinquent group undertaken by a forensic psychiatric facility in Canada revealed that 23% of juvenile detainees were exposed to significant amounts of alcohol prenatally (Fast, Conry, & Loock, 1999). Of the 67 individuals who were identified as having birth defects related to alcohol exposure, only 3 had been given an alcohol-related diagnosis prior to the screen. Overall, the association between prenatal alcohol exposure and disruptive behavior appears to be reliable and persistent, and it is evident at relatively low exposure levels. For example, when researchers in the Seattle project conducted psychosocial assessments of 14-year-old exposed offspring, misbehaviors were among the outcomes most strongly associated with alcohol exposure (Carmichael Olson et al., 1997). However, whether the association between prenatal alcohol exposure and delinquency is direct—or is mediated by a more proximal linkage between FASD and early-appearing attention/impulse control problems and/or learning difficulties, which themselves predict later conduct problems—is indeterminate, yet both are mediated by similar central nervous system processes (e.g., Gatzke-Kopp, 2011; Hinshaw, 1992).

Mood Disorders Psychopathology associated with alcohol teratogenesis is not limited to disruptive behaviors. Elevated rates of depressive features and depressive disorders are also found among children with FASD based on parent interviews and questionnaires (Fryer et al., 2007; Mattson & Riley, 2000; O’Connor et al., 2002; Roebuck et al., 1999). Furthermore, as in the general population (Angold, Costello, & Erkanli, 1999), internalizing-externalizing comorbidities are common among children with FASD. Thus, some degree of overlap among those needing services for disruptive behaviors and mood disorders is expected.

Potential Mediating and Moderating Factors As discussed previously, factors such as IQ, SES, and family placement are important sources of variance in mental health outcomes, and several authors have attempted to tease apart effects of environmental risk factors from prenatal alcohol exposure when evaluating behavioral difficulties among affected children (D’Onofrio et al.,

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2007; Hill et al., 2000; Rodriguez et al., 2009; Staroselsky et al., 2009). Some findings suggest that maternal psychopathology is a better predictor of internalizing problems among children with FASD, whereas alcohol exposure is related more directly to externalizing problems (Staroselsky et al., 2009). One longitudinal study examined the relation between prenatal alcohol exposure, negative infant affect, and subsequent symptoms of childhood depression (O’Connor, 2001). Results indicated that gestational alcohol exposure was a significant risk factor for depressive features at age 6 years, both as a direct effect and as an indirect effect mediated through negative infant affectivity. Interestingly, associations between alcohol exposure and depressive symptoms may be moderated by factors such as sex and maternal depression, as girls whose mothers had high levels of depression were among those most affected (O’Connor & Kasari, 2000). More recent work indicates that prenatal alcohol exposure is a possible etiological factor in increased negative affect and depressive symptoms (O’Connor & Paley, 2006). However, this association appears to be mediated by the quality and nature of mother-child interactions. Other studies fail to find strong associations between prenatal alcohol exposure and externalizing difficulties once environmental factors are taken into account (D’Onofrio et al., 2007). For example, regarding delinquent behaviors noted in cases of FASD, factors such as amount of exposure (Lynch, Coles, Corley, & Falek, 2003) and home placement (biological, foster, or adoptive; Schonfeld et al., 2005) are likely to exacerbate the relation between prenatal alcohol exposure and delinquency. In one investigation of a low-SES community sample, investigators did not find increased delinquency when alcohol-exposed youth were compared to either nonexposed peers (also low SES) or a special education comparison group (Lynch et al., 2003). Rather, delinquency was related to environmental and behavioral variables such as low parental supervision, adolescent life stress, and self-reported drug use. In addition, higher rates of delinquent behavior were endorsed by alcohol-exposed adolescents in biological and foster homes versus those in adoptive homes (Schonfeld et al., 2005). However, these findings are to be expected with the interaction of genes and environment (e.g., genes may be expressed differently in different environments, or environmental effects may have varying impacts with different genetic makeup). Thus, findings do not suggest prenatal exposure history is unimportant. Several studies have examined moderating effects of genes and impact of prenatal alcohol exposure on the fetus. For example, one recent study demonstrated that children born to mothers with at least one ADH1B*3 allele showed no adverse effects of alcohol exposure, whereas children of mothers without this allele showed impairments associated with prenatal alcohol exposure, such as externalizing behaviors and attention deficits (Dodge, Jacobson, & Jacobson, 2014). The mechanism of this protective effect is unknown. It is hypothesized that peak blood alcohol concentrations are reduced in mothers with the ADH1B*3 allele, and as such less alcohol reaches the fetus (Dodge et al., 2014; McCarver, Thomasson, Martier, Sokol, & Li, 1997). Others have suggested those with the ADH1B*3 allele present with a stronger

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physiological response to alcohol consumption, thus resulting in a protective factor against alcohol dependence among those individuals (McCarthy, Pederson, Lobos, Todd, & Wall, 2010). More studies are needed to determine the exact role of genes in moderating/mediating effects of prenatal alcohol exposure.

Adaptive Dysfunction As might be expected in a population characterized by cognitive impairments and increased rates of mental illness, adaptive dysfunction is well documented among individuals with prenatal alcohol exposure (Carr, Agnihotri, & Keightley, 2010; Crocker et al., 2009; Jirikowic, Carmichael Olson, & Kartin, 2008; Streissguth et al., 1991; Thomas, Kelly, Mattson, & Riley, 1998; Ware et al., 2014; Whaley, O’Connor, & Gunderson, 2001). The Seattle study found that as alcohol-exposed individuals reached adulthood, their overall adaptive abilities were equivalent to those of typically developing 7-year-olds, with social skills showing the most severe detriment (Streissguth et al., 1991). More recent studies confirm that socialization of children with FASD is the most affected domain of adaptive function (Crocker et al., 2009; McGee, Bjorkquist, Price, Mattson, & Riley, 2009; McGee, Fryer, Bjorkquist, Mattson, & Riley, 2008; Thomas et al., 1998; Whaley et al., 2001). Based on these studies, adaptive dysfunction is one of the domains included in the DSM-5 criteria for ND-PAE (APA, 2013; Kable et al., 2015). Furthermore, these abilities often fail to improve with increasing age, suggesting an arrest in development rather than a delay (Crocker et al., 2009; Thomas et al., 1998; Whaley et al., 2001). A similar arrest in development in communication skills was documented in an investigation comparing children with FASD to children with ADHD and controls (Crocker et al., 2009). Thus, children with prenatal alcohol exposure are likely to have increasing difficulty meeting greater demands in social and communication function as they become teenagers and adults.

Psychopathology Among Adults With FASD Evidence suggests that behavioral difficulties and psychopathology among children with FASD persist into adulthood (Barr et al., 2006; Famy, Streissguth, & Unis, 1998; Spohr, Willms, & Steinhausen, 2007; Streissguth, 2007), and correlate with adverse outcomes such as substance abuse problems (Alati et al., 2006; Alati et al., 2008; Baer, Barr, Bookstein, Sampson, & Streissguth, 1998; Baer, Sampson, Barr, Connor, & Streissguth, 2003) and trouble with the law (Fast et al., 1999; Streissguth et al., 2004). In the Seattle cohort, prenatal alcohol exposure was associated with alcohol problems at age 21 years, an effect that remained after covarying family history of alcohol use disorders, other prenatal exposures, and other environmental factors such as postnatal parental use of other drugs (Baer et al., 2003). These findings are supported by more recent studies (Alati et al., 2006; Alati et al., 2008) and demonstrate the persistent

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nature of the behavioral effects of prenatal alcohol exposure. Furthermore the wide range of clinical difficulties associated with prenatal alcohol exposure, including impulsivity, mood disorder, and substance abuse, place affected individuals at high risk for suicide. Indeed, individuals with FASD have an increase in lifetime suicide attempts relative to the general population (Baldwin, 2007; O’Malley & Huggins, 2005; Streissguth et al., 1996). In one account, 43% of adults with FASD reported suicide threats and 23% reported a history of suicide attempts throughout their lifetime (Streissguth et al., 1996).

Possible Mechanisms of Action Because of the infeasibility of controlling for confounding factors such as maternal nutrition and timing and dose of alcohol exposure in humans, research focused on identifying mechanisms of alcohol teratogenesis is typically derived from preclinical animal models of FASD and in vitro tissue culture studies. It is unlikely that the variable and wide-ranging effects associated with prenatal alcohol exposure are produced via a single process or pathway. Rather, a multitude of possible pathophysiological mechanisms associated with FASD have been identified, including oxidative stress, changes in glucose metabolism, mitochondrial damage, abnormal growth factor activity, dysregulation of developmental gene expression, anomalous cell adhesion, and abnormalities in the development and regulation of neurotransmitter systems (e.g., excitotoxicity; Goodlett & Horn, 2001; Uban et al., 2011). The majority of these potential mechanisms may result in CNS damage by inducing either necrotic or apoptotic cell death, although disturbance to normal cell division and maturation could also be operative. Unfortunately, pinpointing exact mechanisms through which alcohol exerts teratogenic effects in any given individual is complicated by a host of factors, including variations in timing, dose, and pattern of exposure, maternal characteristics, and genetic factors. Further complicating matters, mechanisms of damage are likely to vary by brain region and cell type (Goodlett, Horn, & Zhou, 2005). Despite these complexities, mechanistic studies have been invaluable in clarifying alcohol’s negative effects on the developing fetus and will continue to be of great utility in the future, particularly in development of prevention and treatment efforts, which are lacking for this population. With regard to treating the psychopathology associated with FASD, preclinical studies can inform intervention efforts by refining our understanding of the structural and functional CNS deficits that contribute to mental illness in this population. Such translational research is crucial to developing effective, evidence-based treatments. In summary, prenatal alcohol exposure is associated with clinically significant psychopathology that is often severe. Moreover, certain psychiatric sequelae, including disruptive behavior disorders, delinquency, substance use disorders, and depressive disorders, are more prevalent among individuals with FASD than comparison populations. As discussed above, etiologic pathways are likely to be complex (i.e., equifinality, see Chapter 1 [Hinshaw]). Indeed, it is not always possible to disentangle direct effects of prenatal alcohol exposure from important

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correlates. Nevertheless, we now know that alcohol teratogenesis is an etiological factor for diverse forms of mental illness and that many individuals with histories of prenatal alcohol exposure may need to be referred for psychiatric evaluation.

PSYCHOPATHOLOGY RELATED TO OTHER PRENATAL EXPOSURES In comparison to alcohol, less is known about effects of other potential teratogens on behavioral and psychopathological outcomes, although the research base is expanding. In this section, we describe findings related to prenatal nicotine, stimulant drugs, methylmercury, lead, and antidepressant medication exposures.

Nicotine Perhaps because of the high frequency at which fetuses are exposed to cigarette smoke, effects of gestational nicotine exposure have been studied fairly extensively, although less is known about behavioral and psychopathological outcomes. It is estimated that about 15% of pregnant women in the United States continue to smoke during pregnancy (Substance Abuse and Mental Health Services Administration, 2014). The most commonly reported effects include increases in ADHD, delinquency, and antisocial behavior. Several studies have focused on the effects of nicotine exposure on antisocial and/or delinquent behavior. Converging data from criminal records (Brennan, Grekin, & Mednick, 1999; Gibson, Piquero, & Tibbetts, 2000; Piquero, Gibson, Tibbetts, Turner, & Katz, 2002; Rantakallio, Läärä, Isohanni, & Moilanen, 1992; Räsänen et al., 1999), parental reports of child behavior (Gatzke-Kopp & Beauchaine, 2007a; Maughan, Taylor, Taylor, Butler, & Bynner, 2001; Ruckinger et al., 2010; Wasserman, Liu, Pine, & Graziano, 2001), and structured psychiatric clinical interviews (Langley, Holmans, van den Bree, & Thapar, 2007; Nigg & Breslau, 2007; Wakschlag et al., 1997; Wakschlag, Pickett, Cook, Benowitz, & Leventhal, 2002; Wakschlag, Pickett, Kasza, & Loeber, 2006; Weissman, Warner, Wickramaratne, & Kandel, 1999) support a relation between prenatal nicotine exposure and increased delinquency. Importantly, the relation between conduct problems among offspring and fetal nicotine exposure remains after covarying potential confounds (Ruckinger et al., 2010), such as genetic factors (Maughan, Taylor, Caspi, & Moffitt, 2004), parental antisocial behavior (Gatzke-Kopp & Beauchaine, 2007a; Maughan et al., 2004), income, prematurity, birth weight, and poor parenting practices (Gatzke-Kopp & Beauchaine, 2007a). The relation between prenatal nicotine exposure and ADHD is also supported by several studies. Offspring exposed to nicotine during gestation are at increased risk for ADHD symptoms (Batstra, Hadders-Algra, & Neeleman, 2003; Fried, Watkinson, & Gray, 1992; Holz et al., 2014; Langley et al., 2007; Mick, Biederman, Faraone, Sayer, & Kleinman, 2002; Naeye & Peters, 1984; Rodriguez & Bohlin, 2005;

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Romano, Tremblay, Farhat, & Côté, 2006), and effects of fetal nicotine exposure on attention may be independent of those associated with antisocial behaviors (Button, Thapar, & McGuffin, 2005; Langley et al., 2007) and genetic transmission (Thapar et al., 2003). In a study comparing a large sample of boys with ADHD to their peers, increased rates of maternal smoking were documented retrospectively in the ADHD group (Milberger, Biederman, Faraone, Chen, & Jones, 1996). Importantly, the relation remained significant after adjusting for SES, parental IQ, and parental ADHD diagnosis. Similarly, a case-control study estimated that maternal smoking was associated with a threefold increase in developing a hyperkinetic disorder, although other predictive factors such as SES and family psychiatric history weakened this association (Linnet et al., 2005). Despite this evidence, the relation between nicotine exposure and disruptive psychopathology is not universally accepted, and some researchers have contested the degree of risk once confounding factors are controlled (D’Onofrio et al., 2010; Hill et al., 2000; Knopik et al., 2005; Nigg & Breslau, 2007; Roza et al., 2009; Silberg et al., 2003). In one study, researchers evaluated offspring of women who were conceived using assisted reproduction technologies in an attempt to remove the confound of inherited genetic risk for ADHD, as these children are genetically unrelated to the women who carry them during pregnancy. This study demonstrated that the association between prenatal smoking exposure and ADHD was significantly higher in genetically related mother-child pairs than in genetically unrelated pairs, suggesting ADHD is linked to inherited genetic effects rather than prenatal smoking exposure per se (Thapar et al., 2009). This study and others highlight the need to test causal hypotheses regarding behavioral teratogenesis with careful consideration for confounding factors. Prenatal nicotine exposure is also associated with other indicators of disruptive behavior, such as increases in dimensional measures of externalizing behavior, delinquency, and ADHD-like symptoms (Cornelius et al., 2011; Fergusson, 1999; Griesler, Kandel, & Davies, 1998; Indredavik, Brubakk, Romundstad, & Vik, 2007; Obel et al., 2009; Orlebeke, Knol, & Verhulst, 1997; Piper, Gray, & Birkett, 2012; Stene-Larsen, Borge, & Vollrath, 2009; Williams et al., 1998). In some samples, these effects survive statistical covariation of potentially confounding influences including child variables (e.g., sex, ethnicity), maternal variables (e.g., education, age, emotional responsiveness), SES, and parental histories of substance use and criminality (Cornelius et al., 2011; Fergusson, Horwood, & Lynskey, 1993; Indredavik et al., 2007; Obel et al., 2009). There is some evidence that teratogenic exposure may interact with genetic factors to produce psychological outcomes. In one study, for example, a polymorphism in the dopamine transporter (DAT1) gene was associated with increases in hyperactive/impulsive and oppositional behaviors, but only in children who were prenatally exposed to nicotine (Kahn, Khoury, Nichols, & Lanphear, 2003). Another study demonstrated an interaction between prenatal exposure to smoking and variations in the DAT1 and DRD4 loci among children with ADHD. Children who inherited the DAT1 440 allele or the DRD 7-repeat allele and were exposed were almost 3 times more likely than nonexposed children

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to be diagnosed with ADHD (Neuman et al., 2007). Some research suggests that the interaction between DAT1 genotype and prenatal smoke exposure increases risk for hyperactivity and impulsivity only among males (Becker, El-Faddagh, Schmidt, Esser, & Laucht, 2008), but more recent prospective investigations reveal predictive associations in females (Gard, Owens, & Hinshaw, 2015). Although the etiology of such behaviors is clearly multifaceted, these studies identify potential mechanisms through which gene-environment interactions increase vulnerability to psychopathology. The association between disruptive, externalizing behaviors and prenatal nicotine exposure appears to manifest among offspring at a young age. Assessments of toddlers whose mothers smoked during pregnancy reveal higher rates of negative conduct, including aggressive, oppositional behaviors, and/or hyperactive behaviors, even after controlling for socioeconomic and child-rearing variables (Brook, Brook, & Whiteman, 2000; Day, Richardson, Goldschmidt, & Cornelius, 2000; Linnet et al., 2006; Stene-Larsen et al., 2009). Poor peer relations and increased tantrums are also observed among toddlers exposed to nicotine, even when covarying effects of other drug exposures such as alcohol, marijuana, and cocaine (Faden & Graubard, 2000). Finally, maternal smoking during pregnancy is a risk factor for persisting generalized behavioral problems from ages 3 to 8 years (Gray, Indurkhya, & McCormick, 2004). Although disruptive disorders are the most commonly studied, a smaller body of literature suggests that higher rates of substance use problems, depression, and other internalizing symptoms are associated with nicotine exposure (Ashford, van Lier, Timmermans, Cuijpers, & Koot, 2008; Brennan, Grekin, Mortensen, & Mednick, 2002; Ekblad, Gissler, Lehtonen, & Korkeila, 2010; Fergusson, Woodward, & Horwood, 1998; Indredavik et al., 2007; Weissman et al., 1999). In summary, prenatal nicotine exposure increases vulnerability to psychiatric symptoms, particularly those on the externalizing spectrum, although other important explanatory variables, such as concurrent prenatal exposures and family history, are likely to contribute to the association. Future research with greater adjustment for confounding variables will be useful in further defining the role that prenatal nicotine exposure plays in the development of psychiatric symptoms.

Other Stimulant Drugs Teratogenic effects of other drugs of abuse are less studied than those of alcohol and nicotine, but there is evidence that prenatal exposure to stimulants may be associated with certain neurobehavioral alterations. Although early depictions of fetal cocaine exposure in the popular press were somewhat exaggerated, more recent research has helped to clarify this issue. Increased levels of aggressive behavior have been reported in cocaine-exposed children (Bada et al., 2007; Bada et al., 2011; Bendersky, Bennett, & Lewis, 2006; Griffith, Azuma, & Chasnoff, 1994; Linares et al., 2006; Minnes et al., 2010; Richardson, Goldschmidt, Leech, & Willford, 2011; Singer, Minnes, Min, Lewis, & Short, 2015; Sood et al., 2005), although moderating effects of sex and comorbid alcohol exposure are important to consider (Nordstrom

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Bailey et al., 2005). Nevertheless, several studies demonstrate that group differences remain even after covarying potential confounds (Bada et al., 2007; Bada et al., 2010; Minnes et al., 2010; Richardson et al., 2011)—and that behavior problems manifest in boys more than girls (Bendersky et al., 2006; Bennett, Bendersky, & Lewis, 2007; Delaney-Black et al., 2004; Dennis, Bendersky, Ramsay, & Lewis, 2006). However, a few more recent investigations have found the opposite, with cocaine-exposed girls being at greater risk for delinquent behaviors (McLaughlin et al., 2011; Minnes et al., 2010; Sood et al., 2005). Prenatal cocaine exposure may also relate to increased infant and toddler irritability and mood lability (Behnke, Eyler, Garvan, Wobie, & Hou, 2002; Chaplin, Fahy, Sinha, & Mayes, 2009; Richardson, Goldschmidt, & Willford, 2008; Richardson, 1998). Additionally, cocaine exposure may result in impaired self-regulation, heightened excitability, more passive-withdrawn negative affect, and decreased adaptability in infants (Lambert & Bauer, 2012). Still, it is not clear whether these behaviors observed in infancy correlate directly with increased psychopathology later in life. One follow-up study of 6-year-olds did not find effects of prenatal cocaine exposure on teacher ratings of child behavior after adjusting for the influences of race, child IQ, school grade, and fetal exposure to alcohol, marijuana, and tobacco (Richardson, Conroy, & Day, 1996).1 However, another investigation that evaluated prenatal cocaine exposure during the first trimester of pregnancy versus exposure throughout pregnancy demonstrated that school-aged children of mothers who used cocaine through the third trimester showed increased levels of externalizing behaviors after addressing confounding variables (Richardson et al., 2011). Furthermore, a recent study indicated that prenatal cocaine exposure was related to teen use of cocaine at age 14 (Delaney-Black et al., 2011). These findings suggest that the neurobehavioral effects of cocaine may manifest differently as a factor of the exposed child’s age, as well as the dose and the timing of maternal cocaine use, all of which may interact with a host of other risk factors. Interestingly, cognitive deficits, particularly deficits in attention, are associated with prenatal cocaine exposure (Ackerman, Riggins, & Black, 2008; Bandstra, Morrow, Anthony, Accornero, & Fried, 2001; Heffelfinger, Craft, White, & Shyken, 2002; Noland et al., 2005; Savage, Brodsky, Malmud, Giannetta, & Hurt, 2005); however, the relation of these deficits to the development of ADHD remains unclear. Despite effects noted above, many studies have failed to find a significant association between prenatal cocaine exposure and the development of psychopathology such as behavior problems (Accornero, Morrow, Bandstra, Johnson, & Anthony, 1. Concerns arise whenever one covaries influences that are highly correlated with a primary independent variable from relations between that independent variable and a focal dependent variable. This practice creates statistical circumstances that do not exist in reality (see Miller & Chapman, 2001). In this instance, since cocaine use and alcohol use are highly correlated, covarying alcohol use from the relation between cocaine use and child psychopathology creates a situation that is rare in practice (cocaine use without concurrent alcohol use). Because considerable shared variance between cocaine use and alcohol use was removed from the prediction equation, this study and similar studies likely underestimate the effects of prenatal cocaine exposure on children’s behavioral outcomes.

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2002; Azuma & Chasnoff, 1993; Bennett, Bendersky, & Lewis, 2002; Frank, Augustyn, Knight, Pell, & Zuckerman, 2001; Messinger et al., 2004; Nair, Black, Ackerman, Schuler, & Keane, 2008; Phelps, Wallace, & Bontrager, 1997; Warner et al., 2006), depressive symptoms (Lambert & Bauer, 2012; O’Leary et al., 2006), and poor impulse control (Bendersky & Lewis, 1998). Rather, research suggests that postnatal variables such as the mother’s continued drug use, level of mental functioning, and depressive symptoms are better predictors of mental health status in cocaine-exposed children than exposure-related variables per se. In one previously discussed study that focused on alcohol exposure and development of depressive features among children, exposure to cocaine was associated with negative infant affect but not with subsequent development of depressive features (O’Connor & Paley, 2006). Nicotine, marijuana, and caffeine were also examined in this sample. They were not associated with childhood depression, although it is unclear whether exposure to these other drugs occurred at rates high enough to afford adequate statistical power to detect effects, were they to exist.

Possible Mechanisms of Action As with the study of FASD, preclinical animal models of gestational stimulant drug exposure have been invaluable in elucidating the role of drugs of abuse on the developing CNS. In particular, monoaminergic systems (dopamine, serotonin, norepinephrine) are affected by such exposure (cf. Mayes, 2002; Middaugh, 1989), although factors such as age and sex may be important moderators of outcome (Glatt, Bolaños, Trksak, & Jackson, 2000). Atypical development of monoamine neurotransmission may help to explain attention and arousal dysfunctions observed in prenatal exposure to strong stimulants. For example, one possible causal model of arousal dysregulation following prenatal cocaine exposure is impairment in the ability to switch between executively versus automatically driven arousal (Mayes, 2002), functions subserved by the prefrontal cortex that rely on intact dopamine and norepinephrine neurotransmission. Because of effects of cocaine and methamphetamine on developing monoaminergic systems (e.g., uncoupling of the D1 receptor, increased D2 receptor binding), (a) midbrain dopamine responding, which is crucial for healthy hedonic capacity and associative learning (see Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007b), is altered into adulthood (e.g., Bubenikova-Valesova et al., 2009); and (b) the normal balance between dopiminergically mediated and noradrenergically mediated arousal regulatory systems is disrupted (e.g., Mayes, 2002). Similar to human studies, animal models have shown an increased response to stimulants and limited alterations in behavioral functioning among rats exposed to cocaine in utero (Glatt, Bolaños, Trksak, Crowder-Dupont, & Jackson, 2000; Peris, Coleman-Hardee, & Millard, 1992). As compared to nonexposed rats, rats exposed to cocaine in utero displayed heightened responses to amphetamine exposure as adults. It is hypothesized that dopaminergic pathways are largely unaffected

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by prenatal cocaine exposure, as behavioral functioning appears to be intact in exposed rats. Instead, observed differences in sensitivity to amphetamine use in adulthood appear to be mediated by changes specifically in striatal dopaminergic functioning (Glatt, Bolaños, Trksak, Crowder-Dupont et al., 2000). Additionally, the observed change in sensitivity to stimulant use in adulthood appears to be mediated by sex (Peris et al., 1992), with prenatally exposed males and females displaying differential neurochemical responses to stimulant exposure. Findings on other aspects of behavioral functioning have been mixed. One study showed that in utero exposure to cocaine increased rates of anxiety in adult male and female rats (Salas-Ramirez, Frankfurt, Alexander, Luine, & Friedman, 2010), while other incidental findings suggest similar levels of anxiety between cocaine exposed and control rabbits (Thompson, Levitt, & Standwood, 2005). In sum, behavioral effects of prenatal stimulant exposure may be less pronounced than those associated with alcohol and nicotine teratogenesis, although problems with statistical covariation of confounds may have caused a literature-wide underestimation of such effects. In addition, environmental factors related to caregiving may be especially important to consider in stimulant exposure cases. Ultimately, more research is needed to clarify the extent to which exposure to stimulants increases vulnerability to psychopathology.

Methylmercury and Lead Methylmercury toxicity is also associated with neurobehavioral deficits following both pre- and postnatal exposures (Debes, Budtz-Jorgensen, Weihe, White, & Grandjean, 2006; Julvez, Debes, Weihe, Choi, & Grandjean, 2010; Mendola, Selevan, Gutter, & Rice, 2002). However, there is little existing evidence that low-level exposures are associated with marked alterations in typical behavioral development (Davidson et al., 2011; Davidson, Myers, Shamlaye, Cox, & Wilding, 2004; Myers et al., 2003). Much of the research on methylmercury derives from one longitudinal study of relatively low levels of exposure resulting from fish consumption (for review, see Davidson, Myers, Weiss, Shamlaye, & Cox, 2006). Findings do not indicate an association between prenatal methylmercury exposure and later adverse developmental outcomes. The most recent study evaluated the main cohort at age 17 years and found improved performance or no association between prenatal methylmercury exposure on 26 of 27 cognitive and behavioral measures (Davidson et al., 2011). However, in another cohort exposed to methylmercury through maternal consumption of whale meat, mercury-related cognitive deficits were found (Grandjean et al., 1997; Julvez et al., 2010). Although developmental outcome studies of prenatal exposure to methylmercury yield inconsistent findings (e.g., Spurgeon, 2006), only a few have examined behavior. Of the studies in which a behavioral measure was used, prenatal exposure to methylmercury was not related to negative outcomes (reviewed in Davidson et al., 2011; Myers & Davidson, 1998). Thus, although existing data do not suggest a link between methylmercury teratogenesis and psychopathology, more research is needed to confirm this conclusion, particularly in cases with higher exposure levels.

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In considering teratogenic exposure to lead, it is often difficult to differentiate between prenatal and postnatal exposure, given the likelihood of continued environmental exposure after birth (Burns, Baghurst, Sawyer, McMichael, & Tong, 1999; Needleman, McFarland, Ness, Fienberg, & Tobin, 2002; Thapar, Cooper, Eyre, & Langley, 2013; Wasserman, Staghezza-Jaramillo, Shrout, Popovac, & Graziano, 1998). Lead exposure in childhood gained considerable attention in 2014 when it was discovered that blood lead levels of children in Flint, Michigan were notably higher than accepted standards (5 μg/dL). After switching the city’s water supply from the Detroit water system to the Flint River, the percentage of children with elevated blood lead levels (>5 μg/dL) in affected zip codes increased from 2.1 to 4.0, a statistically significant change (Roy, 2015). With almost 45,000 homes affected by increased water lead levels, the mayor of Flint declared a state of emergency (Lew, 2015). Currently, no studies have been conducted examining impacts on prenatal health or effects of the elevated water lead levels on child development in Flint. However, previous studies investigating effects of elevated lead exposure have shown an increased rate of fetal deaths (Edwards, 2014) and risk of serious health impacts among sensitive populations (e.g., formula-fed infants) even at low levels of exposure (Triantafyllidou, Gallagher, & Edwards, 2014). Future research will be necessary to determine the exact consequences of elevated water lead levels among children and pregnant women in Flint, but public health implications are immense. Research aimed at disambiguating effects of timing of lead exposure suggests that postnatal lead exposure may be more influential than prenatal exposure (Bellinger, 1994; Leviton et al., 1993). However, there is some evidence for increased rates of delinquency among children exposed prenatally to lead. The Cincinnati Lead Study, which identified a cohort of pregnant women prospectively in order to examine effects of lead toxicity on child development, found increased rates of both selfand parent-reported delinquency and antisocial behavior associated with prenatal lead exposure (Dietrich, Ris, Succop, Berger, & Bornschein, 2001). This relation was independent of birth weight, parental IQ, quality of home environment, and SES. In a later investigation conducted on the same cohort, prenatal lead exposure was related to high numbers of arrests in early adulthood (Wright et al., 2008). Thus, although developmental lead exposure has received the most attention regarding cognitive and behavioral outcomes in children, the potential importance of prenatal lead exposure should not be underestimated as there is some evidence that it can lead to deleterious consequences throughout the lifespan. Regardless of time of exposure, the seemingly irreversible deleterious effects associated with lead exposure have prompted increased focus on primary prevention (Betts, 2012).

Antidepressant Medication Research findings on effects of prenatal exposure to selective serotonin reuptake inhibitors (SSRIs) are inconsistent. Limited evidence shows delayed motor control and motor development (Casper et al., 2003) and higher levels of internalizing behaviors (Hanley, Brain, & Oberlander, 2015) among young children exposed

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to SSRIs. However, some studies found no evidence to suggest that exposure in utero to antidepressant medications adversely affects cognitive or behavioral development (Grzeskowiak et al., 2015; Nulman et al., 1997). Instead, maternal illness and attachment/interaction with infant may provide a stronger predictor of adverse behavioral outcomes among those children (Grzeskowiak et al., 2015; Misri et al., 2006). Several studies that have investigated the relation between autism spectrum disorder (ASD) and prenatal exposure to antidepressant medications show increased vulnerability (Gidaya et al., 2014; Rai et al., 2013), with exposure during the first trimester being most dangerous (Harrington, Lee, Crum, Zimmerman, & Hertz-Picciotto, 2014; Croen, Grether, Yoshida, Odouli, & Hendrick, 2011). Others, however, have found no increase in vulnerability to development of ASD (Sørensen et al., 2013; Hviid, Melbye, & Pasternak, 2013). Similarly, findings on perinatal complications (e.g., preterm birth, low birth weight, small gestational age) due to prenatal exposure to SSRIs have been mixed (see Ellfolk & Malm, 2010). One study found that both exposure to SSRIs or untreated maternal depression increased the chance of preterm birth (Wisner et al., 2009). Since untreated maternal depression also imparts vulnerability to the fetus, the importance of weighing costs and benefits of medication use is imperative (Gidaya et al., 2014; Harrington et al., 2014). Further investigation is warranted to disentangle the effects of maternal illness and medication exposure and the impact of each on prenatal and postnatal health of the fetus (Misri et al., 2006).

CONCLUSIONS Available data underscore the need for clinicians to take thorough prenatal exposure histories and consider possible influences of teratogens when assessing psychiatric symptoms. Examples discussed in this chapter demonstrate that teratogenic exposure increases risk for several common psychiatric disorders. However, effects of potential mediating and moderating factors underscore a common theme: Fetal exposures to teratogenic agents are not necessarily the sole or direct cause of mental illness. Rather, it seems that teratogenic exposures act in concert with other risk factors, and a combination of interacting determinants is likely necessary to lead to the development of psychopathological behavior, exemplifying multifinality (see Chapter 1 [Hinshaw]). Importantly, individuals with teratogenic exposures, such as those to alcohol, may not respond in the same manner as other mental health patients to psychotherapeutic and/or pharmacological treatments (Doig, McLennan, & Gibbard, 2008; O’Connor et al., 2002). Thus, taking an accurate prenatal history could be important for determining the most effective treatment.

RISK AND PROTECTIVE FACTORS Although complete prevention of teratogenic exposure is clearly ideal, this may not always be possible or practical. In addition, given the multifactorial nature

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of the etiology of psychiatric illness, it is important to identify factors that may prevent or limit the development of mental health problems in the face of teratogenic exposure. Such protective factors can form the cornerstone of effective mental health intervention and prevention efforts. An equally important task is to identify variables that increase the likelihood of developing psychopathology in cases of teratogenic exposures. Hopefully, once identified, exposure to such risk factors can be minimized. For example, in the case of alcohol, potential aspects that may protect individuals against a negative mental health outcome status include disability service eligibility, a nurturing and stable home (Streissguth et al., 1996), and early identification and treatment of children (Streissguth et al., 2004). Based on caregiver interviews, children who are reared in more stable home settings are three- to fourfold less likely to experience the majority of adverse life events examined (i.e., disrupted schooling, legal trouble, substance abuse, inappropriate sexual behaviors; Streissguth et al., 2004). In a more recent study, behavior problems in alcohol-exposed children were related to the length of time spent in out-of-home placements (Fagerlund, Autti-Rämö, Hoyme, Mattson, & Korkman, 2011). This is an important point to underscore, as it highlights the interactive nature of biology-environment relationships that drive the development of psychopathology. Thus, a stable and nurturing home is one potential and salient environmentally mediated pathway to protect children with prenatal alcohol exposure from developing psychopathological behavior.

SYNTHESIS AND FUTURE DIRECTIONS Insufficient data exist to determine conclusively whether associations exist between some known teratogens and psychopathology. Furthermore, the behavioral teratogenicity of many additional compounds, such as common prescription medications, remains virtually unknown. However, effects of prenatal alcohol exposure reviewed above provide clear evidence that teratogenic exposures confer vulnerability to psychopathology. More research is needed to provide pregnant women and their health care providers with adequate information to promote the health of both the mother and her child. In particular, future studies might focus on developing a profile of potential mental health problems for exposed individuals, while also distilling factors that may prevent development of mental health problems in these children. To promote factors that protect against mental illness and to deliver interventions effectively, valid early detection methods and increased awareness of teratogenic exposures, especially among pediatric healthcare providers, are necessary.

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Wakschlag, L. S., Pickett, K. E., Kasza, K. E., & Loeber, R. (2006). Is prenatal smoking associated with a developmental pattern of conduct problems in young boys? Journal of the American Academy of Child and Adolescent Psychiatry, 45, 461–467. Ware, A. L., Glass, L., Crocker, N., Deweese, B. N., Coles, C. D., Kable, J. A., . . . the CIFASD. (2014). Effects of prenatal alcohol exposure and attention-deficit/ hyperactivity disorder on adaptive functioning. Alcoholism: Clinical and Experimental Research, 38, 1439–1447. Ware, A. L., O’Brien, J. W., Crocker, N., Deweese, B. N., Roesch, S. C., Coles, C. D., . . . the CIFASD. (2013). The effects of prenatal alcohol exposure and attention-deficit/hyperactivity disorder on psychopathology and behavior. Alcoholism: Clinical and Experimental Research, 37, 507–516. Warner, T. D., Behnke, M., Hou, W., Garvan, C. W., Wobie, K., & Eyler, F. D. (2006). Predicting caregiver-reported behavior problems in cocaine-exposed children at 3 years. Journal of Developmental and Behavioral Pediatrics, 27, 83–92. Warren, K. R., & Hewitt, B. G. (2009). Fetal alcohol spectrum disorders: When science, medicine, public policy, and laws collide. Developmental Disabilities Research Reviews, 15, 170–175. Wasserman, G. A., Liu, X., Pine, D. S., & Graziano, J. H. (2001). Contribution of maternal smoking during pregnancy and lead exposure to early child behavior problems. Neurotoxicology and Teratology, 23, 13–21. Wasserman, G. A., Staghezza-Jaramillo, B., Shrout, P., Popovac, D., & Graziano, J. (1998). The effect of lead exposure on behavior problems in preschool children. American Journal of Public Health, 88, 481–486. Weissman, M. M., Warner, V., Wickramaratne, P. J., & Kandel, D. B. (1999). Maternal smoking during pregnancy and psychopathology in offspring followed to adulthood. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 892–899. Whaley, S. E., O’Connor, M. J., & Gunderson, B. (2001). Comparison of the adaptive functioning of children prenatally exposed to alcohol to a nonexposed clinical sample. Alcoholism: Clinical and Experimental Research, 25, 1018–1024. Williams, G. M., O’Callaghan, M., Najman, J. M., Bor, W., Andersen, M. J., Richards, D., & Chinlyn, U. (1998). Maternal cigarette smoking and child psychiatric morbidity: A longitudinal study. Pediatrics, 102, e11. Wisner, K. L., Sit, D. K. Y., Hanusa, B. H., Moses-Kolko, E. L., Bogen, D. L., Hunker, D. F., . . . Singer, L. T. (2009). Major depression and antidepressant treatment: Impact on pregnancy and neonatal outcomes. American Journal of Psychiatry, 166, 557–566. Wright, J. P., Dietrich, K. N., Ris, M. D., Hornung, R. W., Wessel, S. D., Lanphear, B. P., . . . Rae, M. N. (2008). Association of prenatal and childhood blood lead concentrations with criminal arrests in early adulthood. PLoS Med, 5, e101.

C H A P T E R 10

Brain Injury and Vulnerability to Psychopathology PETER ARNETT, JESSICA E. MEYER, VICTORIA C. MERRITT, LISA GATZKE-KOPP, AND KATHERINE E. SHANNON BOWEN

HISTORICAL CONTEXT

T

he 1848 accident incurred by railroad worker Phineas Gage is legendary in psychology and neuroscience, and is described commonly in introductory textbooks. Gage attained fame after surviving an extraordinary accident in which an explosion propelled a 3-foot-long iron rod through the frontal portion of his skull and brain. Merely surviving such an accident is uncommon, but more remarkable was his apparent recovery of memory, communication, and most other basic mental functions. However, reports from those close to Gage indicate that the injury conferred permanent changes to his personality, resulting in self-destructive and socially inappropriate behaviors stemming from poor judgment. As indicated by his friends, he was “no longer Gage” (see Kotowicz, 2007, p. 117). Continued fascination with this story over the past 150 years follows from its demonstration that the brain is responsible for fundamental aspects of our individuality. This story illustrates the importance of brain function for psychological health, and the brain’s sensitivity to acute trauma.

TERMINOLOGICAL AND CONCEPTUAL ISSUES Gage’s story describes an instance of open head trauma. More recently, scientists have gained increased understanding of the consequences of traumatic force that occurs without skull penetration—referred to as closed head injury. Closed head injuries and their sequelae continue to be a prominent focus of medical research. This is especially the case for mild head injuries, commonly known as concussions. A concussion is usually defined as neurological impairment caused 316

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by biomechanical strain on central nervous system (CNS) tissue. As McCrory et al. (2009) note, a concussion occurs as a result of “a complex pathophysiological process affecting the brain, induced by traumatic biomechanical forces” (p. 37). However, the term concussion is often used inconsistently among clinicians to refer to varying severities of brain injury. When children are injured, terms such as concussion may be used to ease parental concern, with the implication of a lack of lasting consequences (Dematteo et al., 2010). Despite the lack of formal definition, concussions are usually diagnosed when symptoms are observed in one or more of the following domains: (a) cognitive— including confusion, poor concentration, inability to follow directions or answer questions, amnesia, and/or loss of consciousness; (b) medical—including headaches, nausea and/or vomiting; (c) sensory—including dizziness, poor coordination, and/or loss of balance, alterations in vision or hearing (e.g., seeing stars or hearing ringing); and (d) psychological—including irritability, changes in personality, and/or context-inappropriate emotions (McCrory et al., 2005). Merritt and Arnett (2014) found that symptoms from the commonly used Postconcussion Symptom Scale (PCSS) load on four distinct factors involving cognitive, affective, physical, and sleep symptoms. Interestingly, one of the most common and often debilitating postconcussion symptoms, headache, does not load clearly on any factor. Concussions were long believed to be transient physical states with complete resolution of symptoms expected within three months. Thus, it was believed that no permanent changes in brain structure, function, or behavior were incurred by concussion victims (Gaetz, Goodman, & Weinberg, 2000). However, more recent research indicates that detrimental effects can persist for many individuals for extended periods of time, even in cases classified as mild (see Slobounov, Sebastianelli, & Hallett, 2012; Yeates, 2010). Some evidence also suggests that impairment can increase rather than decrease in weeks following injury (Scherwath et al., 2011). Some research also shows that EEG abnormalities can persist weeks after clinical symptoms subside (Slobounov et al., 2012). Thus, full brain recovery may take longer than indicated by self-reported symptoms. Importantly, detrimental effects of mild head injuries are extended and exacerbated when such injuries are experienced repeatedly. Repetitive head injuries are common among both amateur and professional athletes, from childhood through adulthood. High-contact sports such as football, soccer, and hockey, where head-to-head contact occurs between athletes, and where the head may hit the ground or strike a ball, are associated with high concussion rates (Delaney, Puni, & Rouah, 2006). Consequently, organized sports have become a focus of both research and policy developments with regard to brain injury. In 2009, the Zackery Lystedt Law (2009) (Federal House Bill 1824) was passed, which prohibits young athletes from returning to play after a suspected concussion without approval from a medical professional. This law follows in part from evidence that concussions result in metabolic changes that temporarily enhance susceptibility of the brain to further

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damage. Unfortunately, recent neuroimaging research suggests that resolution of these metabolic changes may not coincide with remission of cognitive symptoms or recovery time (Slobounov et al., 2012; Vagnozzi et al., 2008). Considerably more work is needed before more accurate decisions can be made about when vulnerability subsides. In addition to traumatic head injury, the brain is susceptible to insults from other sources, most notably teratogenic substances (i.e., substances ingested by children or pregnant mothers, which affect the developing brain; see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]), and insufficient supply of oxygen (hypoxia) or blood flow (ischemia). The brain may be especially vulnerable to these influences prenatally. In particular, hypoxia and ischemia result in extensive cell death (see Ment, Hirtz, & Huppi, 2009; Vannucci, 2000), although behavioral and psychological consequences are not specific or well understood. In this chapter, we review basic brain injury mechanisms, discuss specific developmental aspects of brain injury, and consider how injury contributes to the development of psychopathology.

PREVALENCE Brain injuries occur most often among children between ages 0 and 4 years, and among adolescents between ages 15 and 19 years (Faul, Xu, Wald, & Coronado, 2010). Children between ages 5 and 9 years are less likely to sustain injury (Toledo et al., 2012). Each year an estimated half million children are brought to emergency rooms for treatment of traumatic brain injury (TBI), of whom less than 1% die. An unknown number of additional individuals sustain injuries that are unreported and receive no medical attention (Faul et al., 2010). Abuse is a common cause of head injuries among infants and toddlers, representing an estimated 22% of all TBIs among children between ages 0 and 3 years (Leventhal, Martin, & Asnes, 2010). Factors that result in even mild levels of oxygen desaturation—including medical conditions such as congenital heart disease, sleep-disordered breathing, and severe or poorly treated asthma, as well as accidents such as near drownings or carbon monoxide poisoning—can also result in significant cell death (Bass et al., 2004; Hori, 1985). However, such injuries are difficult to quantify and may go unrecognized in mild cases, making occurrence rates difficult to estimate. In addition to age, other individual differences are also associated with susceptibility to brain injury. Rates of occurrence are higher among males than among females, and among those of low socioeconomic status (Bruns & Hauser, 2003; Faul et al., 2010; Toledo et al., 2012). Researchers who reviewed medical charts across more than 70 hospitals found that children who are impulsive, including those with attention-deficit/hyperactivity disorder (ADHD), are more likely to sustain injuries to all areas of the body, with the head being no exception. In this study, children with ADHD were also more likely to sustain severe injuries (DiScala, Lescohier, Barthel, & Li, 1998). Some have suggested that the apparent link between ADHD and head injury is due in large part to poor parental supervision commonly experienced by externalizing children (Schwebel, Hodgens, & Sterling, 2006).

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Furthermore, although impulsivity is highly heritable (see Chapter 6 [Neuhaus & Beauchaine]), child-specific environmental factors are better predictors of head injury than either genetic or family environmental factors, suggesting little support for a heritable “injury/accident proneness” trait among children (Ordoñana, Caspi, & Moffitt, 2008). Adeyemo et al. (2014) conducted a recent meta-analysis of the mild Traumatic Brain Injury (mTBI)-ADHD relation among over 3,000 mTBI patients and almost 10,000 controls. They found an association between ADHD and mTBI, with a relative risk ratio of 2.0, which indicates that an individual with ADHD has two times the risk of mTBI compared with controls. For studies in which ADHD was present prior to mTBI, there was no association between the two variables. In contrast, for studies in which ADHD emerged after mTBI, a significant relation was found, with a pooled relative risk of 2.2. This finding supports the contentious suggestion that ADHD does not confer vulnerability to mTBI; rather, it is sometimes an adverse sequela of mTBI. It is important to note that other studies have failed to report such findings (see Davidson, 1987; Olsson, Le Brocque, Kenardy, Anderson, & Spence, 2008). Debate continues regarding impulsivity as a risk factor for head injuries, including whether head injuries and externalizing behaviors are multifinal consequences of other environmental risks.

ETIOLOGICAL FORMULATIONS As noted above, causes of brain injury include accidental trauma (e.g., falls, car accidents, bicycle accidents, sports collisions), nonaccidental trauma (e.g., child abuse), and hypoxic-ischemic events (e.g., pregnancy and birth complications, infections, damage secondary to trauma). Research over the past several decades highlights that brain injuries can occur at any time during development and that multiple causes of injury can result in similar types of brain damage (signifying equifinality). Animal studies and postmortem studies with humans, along with advanced neuroimaging techniques, have helped elucidate mechanisms through which brain injuries and related impairments are effected. In sections to follow, we focus on the most common and most basic factors that result in brain cell death—trauma and hypoxia—and we describe neuroimaging methods that can detect various types of brain injury, and identify causes of cell death.

Mechanisms of Brain Injury In this section, we discuss two key mechanisms of brain injury, trauma and hypoxia. Trauma. Traumatic brain injury is defined as a change in brain function that manifests as confusion, altered level of consciousness, coma, seizure, acute sensory or motor neurological deficit, neuropsychological deficit, or behavioral change, resulting from any blunt or penetrating force to the head (Bruns & Hauser,

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2003). TBI occurs when rapid deceleration of the brain against the bony inner surface of the skull produces tissue compression, resulting in neuronal and vascular damage (Finnie & Blumbergs, 2002). The nature of mechanical forces applied to the head produces different types of tissue damage, which are often classified as focal or diffuse (Gennarelli & Meaney, 1996). Focal tissue damage occurs most often in injuries that result from translational forces applied along the linear axis of the brain (Yeates, 2000). Under conditions insufficient to penetrate the skull, such force results in a localized deformation of the bone and compression of underlying tissue (Gennarelli & Meaney, 1996). When the brain compresses against the skull, small hemorrhages develop on its gyral surfaces, which cause a contusion or focal tissue damage (Finnie & Blumbergs, 2002). Such injuries also result in contrecoup contusions, defined as compressive tissue damage at regions remote from the initial contact point. This occurs when a force applied to the head causes the brain to rebound and contact the skull a second time at a point opposite the initial injury (Gennarelli & Meaney, 1996). These types of injuries can result in significant tissue damage, most commonly without loss of consciousness (Gennarelli & Meaney, 1996). Given the degree of tissue damage that can occur without loss of consciousness, unconsciousness is a poor surrogate for radiological and/or neuropsychological assessments (Schutzman & Greenes, 2001). This consideration is reflected in recent updates to sports concussion grading systems, in which postconcussion self-reported symptoms have taken on an increasingly prominent role in defining severity, above and beyond issues relating to loss of consciousness per se (Arnett et al., 2014). In contrast to focal damage caused by translational injuries, diffuse damage results from rotational forces, producing angular movement around the brain’s center of gravity. This damage occurs when the head strikes against a broad object, such as the interior of a car, diffusing the force across the surface of the skull (Gennarelli & Meaney, 1996). Rotational force produces a shearing strain on the brain, tearing axonal tissue. By destroying axons, both afferent and efferent activity may be interrupted in any brain region. Destruction of axonal communication between and across regions can produce functionally similar impairments as those associated with direct focal damage to the disrupted region. For instance, a disruption in the connection between the frontal cortex and subcortical structures can produce frontally mediated impairment without observable damage to the frontal lobe (Schnider & Gutbrod, 1999). In fact, axonal damage is frequently undetectable by standard neuroimaging protocols and thus requires advanced imaging techniques such as volumetric analysis and diffusion tensor imaging (DTI) (Ashwal, Holshouser, & Tong, 2006; Van Boven et al., 2009). Wäljas et al. (2015) recently reported that a high proportion (about 50%) of their mTBI sample showed microstructural abnormalities in the brain, as detected by DTI, within about three weeks postinjury, compared with only about 12% of controls. Because of disrupted

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connections between brain areas, these types of injuries often lead to widespread damage and can affect deeper anatomical structures than those related to focal contusions. Regardless of the form of injury, TBI severity is most commonly classified into categories of mild, moderate, or severe based on acute neurological impairment using the Glasgow Coma Scale (GCS; Teasdale & Jennett, 1974). Past estimates of hospitalized brain injured patients indicate that as many as 80% suffer injuries classified as mild based on GCS ratings (Kraus & Nourjah, 1988). Mild injuries can include loss of consciousness, concussive symptoms, and need for short-term hospitalization, but they may also present with sequelae mild enough to be dismissed by the patient (Gabriel & Turner, 1996; Rimel, Giordani, Barth, Boll, & Jane, 1981). Although clinical neuroimages may appear normal, suggesting no lasting damage, diffusion imaging studies with children and adolescents reveal microscopic damage (see Ashwal, Wycliffe, & Holshouser, 2010; Chu et al., 2010). Interestingly, Wilde et al. (2008) found that even among adolescents with normal GCS scores (i.e., 15) and normal CT scans, microstructural brain abnormalities were detected by DTI within 6 days postinjury. Acquisition of small lesions resulting from mild injuries may be especially dangerous if they accumulate over time through repeated injury (Collins et al., 2003; Prins, Hales, Reger, Giza, & Hovda, 2010). In addition to primary effects of damage in response to biomechanical strain placed on tissue, secondary injuries frequently evolve from brain trauma. Edema, or swelling, often occurs at the site of focal injuries, increasing intracranial pressure and restricting blood flow, which leads to metabolic failures, resulting in cell death (Bigler, 2001b). This can lead to apoptosis, or signaling of one cell to induce death in neighboring cells. Secondary brain injury in response to trauma develops over time and can occur among those whose injuries are initially classified as mild and whose clinical evaluations in the immediate aftermath of the injury appear normal (Schutzman & Greenes, 2001). Because of the extent of secondary injuries, tissue damage is often more global than local. Studies of both children and adults indicate that reductions in total gray and white matter follow even mild injuries, and they appear to increase linearly with injury severity (Bigler, 2001a; Wilde et al., 2005). Hypoxia. As noted above, hypoxia refers to a reduction in the supply of oxygen necessary for normal cellular function, and can occur through both respiratory and circulatory failures (Nyakas, Buwalda, & Luiten, 1996). Hypoxia leads to brain damage through both acute and protracted pathways. Acute reduction in oxygen inhibits metabolic processes in cells and results in release of neurotransmitters with excitotoxic effects (Golan & Huleihel, 2006). This cytotoxic process then induces a stress response that propagates chemical signaling of the self-destructive process known as apoptosis. Extended activation of

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programmed cell death can occur up to several weeks beyond the original hypoxic insult. Accumulation of cell loss over these several weeks is often what leads to behavioral deficits (Golan & Huleihel, 2006). Although research has focused on medical interventions that may arrest this process and alleviate damage induced by acute hypoxic events, such procedures vary widely in their use, often with uncertain clinical utility, particularly for pediatric patients (see Morrow & Pearson, 2010). Hypoxia that occurs in conjunction with a variety of medical conditions can cause adverse neurological effects (Bass et al., 2004). However, the majority of hypoxic events occur pre- and perinatally. Consequently, pre- and perinatal effects have dominated the study of hypoxia, with far less attention paid to effects of hypoxic events later in life. A common correlate of compromised pregnancies, hypoxia can result from a variety of causes including premature birth and placental insufficiency (Vannucci, 2000). Hypoxia can also follow from restricted blood flow to the umbilical artery, which occurs during episodes of maternal alcohol consumption (Mukherjee & Hodgen, 1982) and smoking (Socol, Manning, Murata, & Druzin, 1982). In cases of prenatal hypoxia, infants are often of low birth weight for their gestational age, a gross indication of maldevelopment (McClure, Peiffer, Rosen, & Fitch, 2005). In addition to prenatal damage, hypoxia can also occur during the birthing process from restricted oxygen flow to the fetus during a prolonged or complicated delivery, resulting in respiratory difficulties requiring resuscitation. Hypoxic damage ranks among the top 10 causes of death among neonates (Martin, Kochanek, Strobino, Guyer, & MacDorman, 2005), and is a common complication for babies born preterm. The incidence of preterm birth was 12.3% in the United States in 2003 (Martin et al., 2005). Fortunately, in recent years, survival rates have been increasing, leading to decreases in medical complications, negative neurological sequelae, and adverse cognitive effects (Baron & Rey-Casserly, 2010). Regions of tissue damage and resultant behavioral implications following hypoxia depend on a wide range of factors, which complicates clinical efforts to generate prognoses (Golan & Huleihel, 2006). Factors such as developmental maturation of neural tissue, duration and degree of hypoxic exposure, and degree of neuroprotective factors intrinsic to an individual are difficult to identify and quantify in clinical practice. Thus, sequelae of hypoxia are variable and range from mild impairments in cognition and behavior to deficits in motor coordination and development of cerebral palsy. If ischemia also occurs, more severe atrophy of brain regions including the motor cortex, hippocampus, and striatum may occur (Decker & Rye, 2002). When extreme and overt compromise is evident—resulting in such conditions as motor disabilities, cerebral palsy, and epilepsy—the extent of damage may be revealed with neuroimaging techniques. Using magnetic resonance imaging, white matter damage is the most commonly identified pathology among infants who suffer hypoxia prenatally, with additional reductions in overall cortical gray matter (Robinson, 2005; see Ment et al., 2009).

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However, more subtle variations in neurochemical functions that affect cellular communication also occur in response to hypoxia. These may be insufficient to produce gross structural damage. For instance, researchers have found decrements in dopamine receptors in the striatum following experimental induction of hypoxia/ischemia, despite normal structural appearance (Zouakia, Guilloteau, Zimmer, Besnard, & Chalon, 1997). In fact, striatal cells are the most vulnerable to cell death incurred by mild hypoxia (Rothstein & Levison, 2005). Such insults may result in psychological and behavioral disturbances, including ADHD (Gatzke-Kopp, 2011), even in the absence of marked neurological dysfunction (Nyakas et al., 1996). These findings are consistent with theories identifying mesolimbic, striatal dopamine deficiency as a primary etiological contribution to the development of ADHD-related symptoms (Beauchaine & McNulty, 2013; Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007; Sagvolden, Johansen, Aase, & Russell, 2005). Low-grade hypoxia may also contribute directly to development of psychopathology. In animal experiments, intermittent hypoxia results in attenuation of extracellular dopamine in nigrostriatal regions, which is implicated in behavioral hyperactivity and increased responding to novelty (Decker, Jones, Solomon, Keating, & Rye, 2005). Interestingly, evidence suggests that male and female brains differ in the degree of vulnerability to ischemia/hypoxia induced damage, with females showing less severe pathological outcomes (Hurn, Vannucci, & Hagberg, 2004; see Anderson, Spencer-Smith, & Wood, 2011).

Advances in Neuroimaging of Pediatric TBI The most common clinical imaging techniques include computed tomography (CT) and magnetic resonance imaging (MRI). Because MRI does not require radiation exposure, it is advantageous when repeated scans are necessary. MRI volumetric analysis identifies both gray and white matter total and regional volume loss, which correlate with injury severity (Levine et al., 2008; Van Boven et al., 2009). However, findings from the past 5 to 10 years, in which the use of advanced imaging techniques has become increasingly common, suggest that volumetric MRI may be insufficiently sensitive to neuronal damage associated with mild head injuries. Structural measures such as susceptibility-weighed imaging (SWI) and diffusion tensor imaging (DTI) allow for increased sensitivity to hemorrhagic and axonal injury, respectively (Van Boven et al., 2009). SWI capitalizes on different magnetic susceptibilities of discrete tissue types, and can be calibrated to preferentially enhance sensitivity to detection of blood (Van Boven et al., 2009). SWI can identify 4 to 6 times as many microhemorrages as standard clinical imaging protocols and is useful in predicting neurologic and neuropsychiatric outcomes (Ashwal et al., 2010). DTI measures diffusion of water molecules and is thought to index integrity of white matter tracts. DTI is sensitive to microstructural abnormalities, and is especially useful in mild TBI, for which structural abnormalities may not be detected with standard imaging protocols. However, this method is nonspecific,

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and abnormalities may represent a variety of conditions, including axonal sheering, demyelination, inflammation, and edema (Van Boven et al., 2009). Regardless of etiology, changes in diffusivity identified by DTI predict working memory and executive function deficits among children (Wozniak et al., 2007). In fact, in assessing diffuse prefrontal injury, DTI may be more predictive of neurological outcomes than traditional MRI techniques (Oni et al., 2010). Key indices for DTI include fractional anisotropy (FA), an index of diffusion restriction, and apparent diffusion coefficient (ADC), a measure of magnitude of diffusion. Higher FA and lower ADC are typically associated with greater brain integrity. This relation can reverse during acute stages of mTBI, a pattern that may relate to acute cytotoxic edema or swelling of the brain, as indicated in a small sample of 12 adolescent mTBI patients (Wu et al., 2010). Compared with 11 controls, mTBI patients in an acute phase following injury (mean ∼3 days, range = 1–6 days) exhibited higher FA and lower ADC. Also of interest, FA was correlated negatively with verbal memory performance in the mTBI group, but positively in the control group. Thus, in the acute phase following injury, the meaning of DTI metrics can be different than among healthy controls and among those who are in the more chronic phase following injury. Other measures, such as magnetic resonance spectroscopy (MRS), may be better suited for detecting metabolic changes in cell function related to brain injury and vulnerability. MRS allows for assessment of metabolites that mark injury, even in clinical scans that are deemed “normal.” Among children, altered metabolite ratios (i.e., lower N-acetylaspartate (NAA)/creatine (CR), lower NAA/choline (Cho), higher Cho/Cr) are related to poorer neurological and neurobehavioral outcomes (see Ashwal et al., 2010). Advances in statistical analyses have also provided better understanding of the sequelae of damage, but only recently have these methods been used with children. Functional connectivity analyses provide information about interrelations between brain regions rather than simple independent levels of activation within given regions. Functional connectivity can refer to any correlational measure of regional activation but is most often used to refer to correlations in blood-oxygen level dependent (BOLD) activation either during task or resting-states. It provides an indirect measure of coordination between brain regions without assuming anatomical connectivity (Fox & Raichle, 2007). To date, functional connectivity studies with children are sparse. Most existing investigations include adolescents, presumably because they are easier to scan. However, in one study, task-related functional connectivity between Wernicke’s area and other bilateral language areas during passive listening was stronger for children born preterm than for controls, suggesting a broader and less specialized functional brain network for language processing among preterm children (Gozzo et al., 2009). Functional neuroimaging studies are limited in the child/adolescent literature on mTBI. In one study, Slobounov et al. (2010) found that, compared with matched controls, concussed athletes showed increased activation on fMRI in the right parietal,

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right dorsolateral prefrontal, and right hippocampal regions. Such findings have sometimes been replicated in adult samples (Mayer, Bellgow, & Hanlona, 2015). This pattern of increased brain activation following injury may suggest compensation. However, across a broader literature that includes both children and adults, some studies show evidence for hypoactivation in certain brain regions following mTBI (Mayer et al., 2015). At this stage of knowledge, there is no clearly integrated theory that can account for apparently contradictory findings in which some studies show increased brain activation in mTBI and others show decreased activation. In the section below on “Brain Injury and The Frontal Lobes,” we discuss some issues relating to the possible importance of site of injury in functional outcome. Resting-state patterns in brain functional integration, or “default mode” networks, also change across development (Fair et al., 2009). Abnormalities in resting-state connectivity have been identified among children born preterm (Damaraju et al., 2010), and among adults who sustain TBI (Johnson, Zhang et al., 2012). Advanced imaging techniques have allowed for greater detection of injury and predictive utility in pediatric populations. These measures are not only more sensitive to changes that result from both primary and secondary injury, but also, in conjunction with traditional imaging modalities, hold promise for better detection of pediatric brain injury (Ashwal et al., 2006).

DEVELOPMENTAL CONSIDERATIONS Injuries sustained by children may confer different vulnerabilities than similar injuries sustained by adults. Rodent models demonstrate that the same dopaminedepleting lesions that produce severe motor impairment in mature rats may result in motor hyperactivity when induced in juvenile rats (Davids, Zhang, Tarazi, & Baldessarini, 2003). Among humans, children who experience frontal lobe damage exhibit greater loss of psychosocial function than adults who sustain similar injuries (Anderson, Bechara, Damasio, Tranel, & Damasio, 1999). Developmental factors affect the nature and degree of injury sustained and the degree of functional recovery likely to follow. Greater neck strength can mitigate kinematic responses to head impact across all planes of motion in both pediatric and adult athletes (Eckner, Oh, Joshi, Richardson, & Ashton-Miller, 2014). Children’s relatively large heads and weaker neck muscles therefore increase their vulnerability to rotational movements implicated in diffuse axonal injuries. Furthermore, greater flexibility of their skulls allows force to be distributed over a greater surface area, favoring diffuse over focal injuries (Anderson, Catroppa, Morse, Haritou, & Rosenfeld, 2005). The developmental state of tissue is also implicated in the extent of damage that mechanical forces have on the brain. More than any other organ in the human body, brain development is far from complete at birth, with developmental changes continuing well into the postnatal period, through adolescence and early adulthood (Johnson, 1999; Nowakowski & Hayes, 2002; Sowell, Thompson, Holmes,

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Jernigan, & Toga, 1999). Developmental changes in brain maturation also differ across tissue types. White matter develops its characteristic appearance after birth, as axons connecting cells across anatomical regions become myelinated (Andersen, 2003). Myelination occurs rapidly in the first few years of life (McKinstry, 2011), but continues throughout childhood and adolescence (Giedd et al., 1999). Lower levels of axonal myelination among children increase susceptibility to shearing strain, rendering children vulnerable to diffuse injuries (Lea & Faden, 2001). Furthermore, hypoxia can induce failure of myelination (see Ment et al., 2009). In contrast to white matter development, gray matter development includes processes that refine synaptic relationships between neurons. Immature brains contain excess neurons. Based on an individual’s experience, neurons that are used regularly form connections with other neurons to develop efficient circuits, whereas neurons that are not used are eliminated. Despite pruning, the brain continues to grow through early childhood. This growth is due in part to arborization, or branching of neurons to increase the number of neighboring cells with which they communicate. Gray matter develops at different rates across each of the four lobes (Giedd, 2004), with regions of the frontal lobe continuing to develop well into adulthood (Diamond, 2002). Gray matter in children is more susceptible to secondary injuries following trauma, such as edema (Aldrich et al., 1992). This susceptibility is likely to be related to immaturity of neurochemical receptors in young brains, increasing vulnerability to excitotoxic damage associated with hypoxia and contributing to extensive apoptotic cell death (Lea & Faden, 2001). The relative immaturity of the brain at birth is also an asset in human development because the brain remains plastic. Structure of neural tissue is not determined entirely by genetic or chemical signals that take place during development. Experience-dependent specialization also emerges (Johnson, 1999). Thus, when structure is compromised through injury prior to specialization of cortical tissue, alternate brain regions may assume functions of lost tissue. For example, portions of the auditory cortex may respond to visual stimuli when the visual cortex is damaged prior to neuronal specialization (Johnson, 1999). However, despite the remarkable compensatory ability of younger brains, there are clear limits to plasticity, and functional recovery is often far from absolute. The diffuse nature of damage in TBI may limit healthy tissue available for organizational compensation. Furthermore, when recovery occurs for some functions, it may be at the expense of acquiring other abilities (Luciana, 2003). Among rodents, early brain tissue damage results in neural organizational compensations that allow for recovery of motor control not seen in animals damaged in adulthood, yet diminished cognitive functioning is also observed (Kolb & Gibb, 2001). Brain plasticity in childhood therefore may not predict full recovery. Rather, extensive brain damage may prevent acquisition of new skills necessary to traverse developmental landscapes effectively (see Anderson et al., 2011). Timing of TBI has significant implications for vulnerability to poor outcome. Early damage often carries a substantial cost over the course of development.

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Damage to the brain that results in an inability to acquire basic functions may affect wide-ranging higher-order processes that depend on that initial component (Bachevalier & Loveland, 2003; Black, Jones, Nelson, & Greenough, 1998). For example, children who sustain brain injuries prior to age 4 years exhibit worse cognitive and social outcomes than children who sustain injuries just 2 years later (Sonnenberg, Dupuis, & Rumney, 2010). Although younger age at injury is cited consistently as a vulnerability to poor outcome (Anderson et al., 2005), research does not support a linear relationship between age at time of injury and outcome (Crowe, Catroppa, Babl, Rosenfeld, & Anderson, 2012). Rather, the relation between age and outcome is better characterized by a stepwise pattern, with several critical periods of development marking times of increased vulnerability. Developmentally, the brain is characterized by sensitive periods. In general, sensitive periods refers to any developmental epoch during which plasticity is heightened to facilitate skill acquisition across certain brain regions. Damage sustained during peak periods of developmental sensitivity may be most likely to induce long-term deficits (Ewing-Cobbs, Prasad, Landry, Kramer, & DeLeon, 2004). Indeed, damage incurred prior to periods of developmental sensitivity allows time for alternative brain regions to be recruited, whereas damage incurred later allows for preservation of skills that were acquired prior to the injury. A 2012 study comparing intellectual outcome following TBI among four age-at-injury groups (infant, preschool, middle childhood, and late childhood) demonstrated that middle childhood injuries were associated with lower IQ scores across domains and injury severity (Crowe et al., 2012). Thus, middle childhood may be a particularly sensitive period of neural development. Contradictory predictions offered by increased plasticity versus increased vulnerability are complex; they cannot be accounted for fully by severity or age at injury alone. Anderson, Spencer-Smith, and Wood (2011) proposed a hierarchical model to account for the high degree of variability in outcomes and considerable clinical challenges in prognosis. They suggest that functional and neural recovery from early brain injury is influenced by independent and interacting effects of developmental, constitutional, and environmental factors. Individual differences in biological susceptibility and resilience to injury are also being identified (see below). Furthermore, individual difference factors such as cognitive ability and sex may moderate outcomes. For example, cognitive ability, measured within 3 weeks of injury among children with mild TBI, moderates postconcussive symptoms 3 months later (Fay et al., 2010). In addition, females are at greater risk for postconcussive symptoms after mild TBI, but at the same time may be protected from social skills and processing speed deficits postinjury (see Stavinoha, Butcher, & Spurgin, 2011). Animal research suggests that the less-lateralized female brain may have a greater potential for plasticity and transfer of function between hemispheres after injury. However, other research indicates that male animals show greater neural and behavioral recovery after injury in response to enriched environments (Anderson et al., 2011; Kolb, Gibb, & Gorny, 2000). Finally, environmental factors

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including both interpersonal support and medical intervention also affect prognosis (Anderson et al., 2011).

BRAIN INJURY AND THE FRONTAL LOBES Regionally, the temporal and frontal lobes are especially vulnerable to damage (Mendelsohn et al., 1992; Wilde et al., 2005). Susceptibility of these regions is a consequence of their proximal location to the jagged inner surface of the skull (Schnider & Gutbrod, 1999). In addition, these regions readily sustain contrecoup contusions regardless of the initial site of impact (Gennarelli & Meaney, 1996). Although many brain regions are developmentally stable at adult levels by adolescence, maturational changes in frontal regions continue through adolescence and into early adulthood, supporting continuing emotional and cognitive development during this age range (Sowell et al., 1999). Given such protracted maturation, prefrontal structures may be vulnerable to effects of injury longer than other anatomical sites. Functions performed by the frontal lobes are critical to mental health, and their compromise is of substantial clinical importance. This anatomical region is frequently divided into dorsal and orbital cortical subregions, which have unique yet interactive psychological functions (Duncan & Owen, 2000). The orbital frontal cortex (OFC) is the ventral-most region of the frontal cortex, whereas the dorsolateral prefrontal cortex (DLPFC) occupies the lateral region above the OFC. These regions maintain extensive reciprocal connectivity with limbic structures.

Dorsolateral Prefrontal Cortex The DLPFC and mid-dorsal cortices respond to a variety of cognitive demands that require problem solving and executive functioning (Duncan & Owen, 2000). The DLPFC operates through a network of interconnected structures including the dorsal caudate, global pallidus, dorsomedial thalamic nucleus, and cerebellum (Heyder, Suchan, & Daum, 2004). Integrity of this network is essential for future planning toward attaining distal goals (Anderson & Catroppa, 2005; Levin & Hanten, 2005). This region is implicated in inhibitory control and the ability to integrate environmental feedback into ongoing behavior to make rapid behavioral changes. These skills are often deficient among individuals who incur frontal brain injuries (Ornstein et al., 2009), with potential long-term consequences including long-term neurodegenerative changes. Keightley et al. (2014) reviewed evidence for volume loss in several brain regions (hippocampus, amygdala, globus pallidus, thalamus, etc.), as well as reduced whole brain volumes and increased cerebral spinal fluid and ventricular space, following TBI in children (Keightley et al., 2014). Because executive functions are crucial for adapting to changing developmental and environmental demands, early damage to this region may establish cascading effects of initial decrements across multiple domains of function. Such skills begin

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to emerge in preschool and undergo rapid development thereafter (Diamond, 2002). Because frontal regions are not well developed among young children, damage is less likely to reveal immediate behavioral deficits, whereas such damage would be detected readily among adults. In adults who sustain TBIs during childhood, executive functioning difficulties are found (Papoutsis, Stargatt, & Catroppa, 2014). In the immediate aftermath of injury among children, executive deficits may be minimal, but may become evident later in development (Eslinger, Biddle, & Grattan, 1997; Bachevalier & Loveland, 2003). In a case study of two individuals who sustained significant orbitofrontal damage before age 16 months, recovery and function appeared very positive in the immediate aftermath of the lesions, and cognitive and motoric development proceeded normally. However, many years later these individuals were brought to medical attention because of significant psychopathological behaviors. Both appeared to be insensitive to punishment, unresponsive to future consequences, and showed extensive impairment in moral and social reasoning (Anderson et al., 1999). DTI assessments with children who incur OFC damage show disruption of the uncinate fasciculus, which connects the orbital frontal cortex to temporal regions and correlates with poor social/behavioral outcomes (Johnson, Juranek, et al., 2011). Increased deficits in comparison to adult-onset lesions indicate impairment in the acquisition of normal social behavior leading to more global dysfunction.

Orbitofrontal Cortex In contrast to executive function deficits, damage to orbitofrontal regions is associated with deficits in social/emotional functions that are important in interpersonal relationships, such as the ability to read social and emotional cues and the ability to use this information for self-regulatory purposes (Bachevalier & Loveland, 2003). In a recent study, adult survivors of pediatric TBI showed significantly poorer emotion perception than controls (Ryan et al., 2014). Damage in this region is also associated with inability to develop and/or use internal cues of potential punishment to guide behavior (Damasio, Tranel, & Damasio, 1990). Interestingly, behavior and personality deficits associated with damage to this region frequently exist in the absence of overt neuropsychological deficits (Schnider & Gutbrod, 1999). Hemispheric localization of orbitofrontal lesions is influential in the clinical presentation of symptoms. Lesions localized to the left hemisphere are associated with depressive symptoms, apathy, emotional blunting, and poor planning, whereas right-hemisphere lesions are associated with hyperactivity, disinhibition, socially inappropriate behavior, irritability, and lack of empathy (Schnider & Gutbrod, 1999). When damage extends across both hemispheres, characteristics of both syndromes coexist (Schnider & Gutbrod, 1999). A number of investigations have also evaluated long-term effects of childhood TBI, which is associated with psychosocial difficulties in adulthood

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(Ryan et al., 2015; Scott, McKinlay, McLellan, Britt, & Grace, 2015). In a study of theory of mind among patients who were 6 months post-TBI, those who sustained more severe injuries were more seriously affected than children with mild to moderate TBIs or healthy control participants (Ryan, Catroppa et al., 2015).

Genetics and Heritability In addition to factors such as developmental phase, sex, injury location, and injury severity, individual differences in functional and structural deficits following brain injury may be influenced by genetic factors (Blackman, Worley, & Strittmatter, 2005; see McAllister, 2010, for review). Research addressing genetically mediated differences in susceptibility to postinjury outcome has expanded rapidly over the past decade. Allelic variants of genes associated with cognitive function, and variants of genes that enhance or impede postinjury cellular recovery, moderate outcomes following neurological insult (Dardiotis et al., 2010; Jordan, 2007; McAllister et al., 2008; McAllister, 2010). The latter category has received the majority of attention to date, with a significant emphasis on the apolipoprotein E (ApoE) gene, which has at least three well-characterized allelic variants. Extensive research on the function of the ApoE proteins indicates a role in neurologic repair, with variability between alleles implicated in the degree of neural damage suffered from oxidative, circulatory, and traumatic type injuries over the lifespan (Blackman et al., 2005; Laskowitz et al., 2010). In contrast to the 𝜀2 and possibly the 𝜀3 allele, the 𝜀4 allele appears less effective in conferring neuroprotection and leads to increased damage due to postinjury inflammation, edema, and excitotoxicity (Aono et al., 2002; Lee, Aono, Laskowitz, Warner, & Pearlstein, 2004; Lynch et al., 2002). Thus, potential for important Gene × Environment (G × E) interactions applicable to neuropsychological function exists. However, given differences between children’s developing brains and adult brains, genes may have varying degrees of effects on outcome depending on when injury is sustained (Kurowski, Martin, & Wade, 2012). Although G × E interactions have begun to be examined in adult TBI samples, research examining ApoE 𝜀4 in children is relatively sparse. Among investigations that have examined ApoE 𝜀4 in adolescent samples, inconsistent findings have resulted. Some indicate a neuroprotective function of 𝜀4 (Blackman et al., 2005; Oria et al., 2005), whereas others suggest that having an 𝜀4 allele may confer risk for poor outcome following brain injury (Brichtova & Kozak, 2008; Teasdale, Murray, & Nicoll, 2005). Another concluded that the 𝜀4 allele appears to have little effect on overall outcome (Moran et al., 2009). However, two significant findings were reported: (1) 𝜀4 allele carriers are more likely to have worse injury severity scores (as indicated by Glasgow Coma Scale) than non-𝜀4 carriers, and (2) 𝜀4 allele carriers display better performance on a visual-motor task than non-𝜀4 allele carriers (Moran et al., 2009). Finally, a study that examined cerebral perfusion pressure identified a marked discrepancy between brain

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swelling postinjury and severity of outcome for children with the 𝜀4 allele (Lo et al., 2009). Despite the lowest degree of cerebral perfusion, children with the 𝜀4 allele evidenced far worse outcomes, whereas the opposite held for children with the 𝜀3 allele. Some of these discrepancies may well pertain to small sample sizes. In a recent study of collegiate athletes who were mostly within the first week following concussion, Merritt and Arnett (2016) found that those with the 𝜀4 allele reported significantly more symptoms overall than concussed athletes without the 𝜀4 allele. In addition, 𝜀4 allele carriers were more likely to report physical and cognitive postconcussion symptoms compared to the non-𝜀4 allele group. It will be useful for investigators to replicate such findings in younger-aged samples, tested in the acute phase postconcussion. Research also indicates the potential for genotypes to interact with environmental trauma exposure in ways that produce specific psychiatric outcomes (see, e.g., Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011). For example, a range of perinatal traumatic factors, many of which may contribute to hypoxic damage in neonates, are associated with later development of schizophrenia (Rosso & Cannon, 2003). Cannon and colleagues (2002) found that a history of fetal hypoxia was associated with a distinct pattern of brain abnormalities visible on MRI in patients with schizophrenia, but not in a control sample. One component of genetic risk for schizophrenia might be heightened sensitivity to hypoxic events. Thus, onset of illness is potentiated particularly for genetically vulnerable individuals who experience hypoxia during neural development (Cannon et al., 2002). In fact, as many as 50% of reported schizophrenia-related genes may be regulated in part by hypoxia/ischemia (Schmidt-Kastner, van Os, Steinbusch, & Schmitz, 2006). Animal models indicate that these genes are likely to be expressed during development and contribute to vulnerability to schizophrenia. Vulnerability genes that respond to oxidative stress may confer risk by producing defective gene products that would normally subserve neuroprotective functions. Other lines of research have shown that the relation between genetic risk for depression and offspring externalizing behavior was moderated through pregnancy risk (Pemberton et al., 2010). Thus, one mechanism through which genetic risk confers vulnerability to various forms of psychopathology is through susceptibility to injurious influences on neural development. Dopamine functioning is also highly sensitive to environmental insults such as hypoxia (Gatzke-Kopp, 2011). Changes in dopamine function following hypoxic insults may be especially detrimental for individuals whose dopaminergic function is genetically compromised (McAllister et al., 2005). Although not yet explored in a pediatric samples, in adult TBI, dopamine-related genes (e.g., CatecholO-methyltransferase (COMT) Val158Met, ANKK1 and the dopamine D2) may play an important role in neuropsychological functioning postinjury (Lipsky, Sparling, Ryan, Xu, & Salazar, 2005; McAllister et al., 2005; McAllister et al., 2008).

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CLINICAL CONSIDERATIONS As the Phineas Gage example makes clear, brain injury can play a causal role in the pathogenesis of psychological disorders by compromising neural systems directly. Changes in behavior and personality are common in response to brain injury as a consequence of the high prevalence of orbitofrontal damage. Children with a history of mild TBI prior to age 5 years are more likely to evidence clinical impairment in adolescence, with a 4.2-fold increase in ADHD, a 6.2-fold increase in conduct and oppositional defiant disorders, a 3.6-fold increase in the development of substance abuse, and a 3.1-fold increase in prevalence of mood disorders (McKinlay, Grace, Horwood, Fergusson, & MacFarlane, 2009). In addition, TBI often compromises social functioning. Research aimed at delineating the nature of such social impairments indicates problems in emotion perception, theory of mind, and identification of irony and empathy (Dennis et al., 2013; Robinson et al., 2014; Ryan et al., 2014). Although psychological symptoms may develop as a direct result of lesions to a damaged area, brain injury may also contribute to psychopathology indirectly, through exacerbation of preexisting pathologies, or via development of traumatic stress disorders following injury (Middleton, 2001). This observation is especially salient given that factors such as low socioeconomic status and poor family functioning increase risk of sustaining brain injuries (Bruce, 1996). Some research also suggests that brain-injured patients show higher levels of premorbid psychological and behavioral disturbances (Cattelani, Lombardi, Brianti, & Mazzucchi, 1998). Premorbid functioning also contributes significantly to development of adverse outcomes postinjury (Donders & Strom, 2000). Brain injury may also increase stress in family systems, leading to the display of further contextual risk factors for suboptimal recovery and development. Greater family-level distress and caregiver burden are observed among families of children who sustain a brain injury, compared to other injuries that require hospitalization (Stancin, Wade, Walz, Yeates, & Taylor, 2010). High family functioning moderates the relationship between injury and long-term functioning (Gerrard-Morris et al., 2010; Yeates et al., 1997). Young children, ages 3 to 6 years, who sustain mild to moderate head injuries, demonstrate lower social competence postinjury than matched controls who sustain orthopedic injuries (Yeates, Taylor, Walz, Stancin, & Wade, 2010). Individuals with severe brain injury evidence the worst outcomes regardless of parenting practices, whereas parents are an important influence in children’s coping with and compensating for functional impairments resulting from milder brain injuries (Yeates et al., 2010). These findings highlight the importance of postinjury clinical support for parents and the injured child in order to maximize recovery and prepare for behavioral challenges. Brain injuries establish vulnerability, and when such vulnerability is met with environmental risk, the likelihood of developing psychopathology may well be increased. Identifying effects of brain injury on psychopathological development may also have important implications for treatment. For instance, research suggests that

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methylphenidate is less effective when ADHD emerges after traumatic brain injury than when ADHD follows a traditional developmental course (Jin & Schachar, 2004). When brain injury is identified, treatment should focus not only on the child’s level of functioning but also on quality of family environment. Treatment programs targeting behavioral symptoms of TBI through a focus on problem solving in the family environment have yielded promising results (Cohen, Heaton, Ginn, & Eyberg, 2012; Wade et al., 2015). Family problem-solving programs are particularly effective for families of lower socioeconomic status (Wade et al., 2015). Because dysfunctional family systems may already be in place, the potential effectiveness of the family to cope with the injury and contribute to successful recovery is already limited. These factors are especially important given that head injuries may result from abuse or neglect. Unfortunately, assessing the role that head injuries play in the development of psychopathology is extremely challenging because brain injury can be difficult to detect in cases in which it exists primarily at a microscopic or neurochemical level. Furthermore, a long interval between acquisition of injury and onset of psychopathology may obscure causal relations between injury and later behavior. As many as 75% of infants who survive acute perinatal asphyxia are classified as nonimpaired because they fail to show neurological indicators of encephalopathic damage in weeks after injury. As noted above, however, impairments in cognitive, memory, and socioemotional behavior are often not evident until later in life, when children fail to meet increasing developmental demands (de Haan et al., 2006). Even mild insults may produce lasting alterations in development, which may take years to recognize (Gronwall, Wrightson, & McGinn, 1997). In addition, even mild brain damage, which can be caused by low-grade hypoxia associated with snoring, may result in reductions in attention and intelligence, even when children score within normal ranges when tested, and are thus overlooked medically (Blunden, Lushington, Kennedy, Martin, & Dawson, 2000). Therefore, careful consideration of potential contributions of brain injury to presenting psychological symptoms should be undertaken so that appropriate comprehensive treatment plans can be developed.

SUMMARY AND CONCLUSIONS Although children who incur acute brain injuries present and are treated in medical settings, effects of their injuries may be lifelong and include psychopathology. Severe injuries affect multiple domains of functioning and present serious challenges to both children and their caretakers. However, brain injuries can also be subtle, as in mild TBI or hypoxia. Such injuries may be difficult to detect even when they potentiate psychopathology. In addition to environmental and genetic factors that are becoming increasingly well characterized in the development of psychopathology, early brain injury should not be overlooked, particularly as an environmental potentiator of genetic susceptibility (Gatzke-Kopp, 2011). Because injuries can be difficult to detect and their sequelae may take years to manifest, associations between

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injury and psychopathological outcomes may be overlooked in clinical practice. However, information about brain injury may be important in informing treatment strategies, and therefore should be assessed. Research aimed at addressing these challenges will improve our ability to assess brain damage resulting from concussions. In the past decade, advances in neuroimaging have allowed for increased detection of microscopic injuries that may cause lasting effects, but these methods have not been readily adopted in clinical practice. Standard neuroimaging protocols and acute neuropsychological testing continue to dominate current postinjury assessments and are used in recommendations for return to play for athletes, even though both yield limited sensitivity in quantifying extent of neurological damage (Ellemberg, Henry, Macciocchi, Guskiewicz, & Broglio, 2009.) Further research on genetics of brain injury may also assist in (a) identifying individuals who are especially vulnerable, (b) characterizing biological processes involved in injury, and (c) developing appropriate pharmaceutical approaches to arresting neurodegenerative processes. The next steps in understanding pediatric brain injury should focus on multidisciplinary, translational research, which capitalizes on recent advances in neuroimaging, behavioral research, and clinical practice (Anderson et al., 2011).

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C H A P T E R 11

Emotion Dysregulation as a Vulnerability to Psychopathology PAMELA M. COLE, SARAH E. HALL, AND NASTASSIA J. HAJAL

T

he idea that people can become dysregulated emotionally is of keen interest in the mental health sciences despite challenges in (a) defining emotion, emotion regulation, and emotion dysregulation, and (b) knowing whether emotion dysregulation is a causal factor, a correlate, or a consequence of psychopathology. In grappling with these thorny issues, we have continued to find that emotion dysregulation is a valuable concept in clinical practice and therefore worthy of study. We adopt a developmental psychopathology framework to conceptualize the role of emotion dysregulation in clinically meaningful trajectories of development as well as the view that emotions are fundamentally adaptive processes. Emotions allow people to adjust to diverse circumstances in which they find themselves, and to modulate their reactions and behavior to fit those circumstances. Over the course of development, individual differences can conspire with life circumstances to lead to maladaptive patterns of emotional functioning. In this chapter, we discuss these issues and offer four characteristics that differentiate emotion dysregulation from emotion regulation.

HISTORICAL CONTEXT Emotions, their regulation, and their capacity to interfere with adaptive behavior have been a focus of scholarly thought in both the humanities and the sciences for centuries. For example, Aristotle, like Plato before him, noted the importance of emotion in persuasion (Rhetoric, 335–322 B.C.E.) and morality (Nicomachean Ethics, 350 B.C.E.). He concluded that emotions serve a functional purpose but are also irrational, and that children must learn to manage their emotions through the logic of deliberative cognition. Aristotle defined emotion, which he conceptualized 346

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in terms of physiological changes, as “affectations of the soul” that were “enmattered formulable essences” (De Anima, ca. 359 B.C.E.). These Western philosophical views have been echoed through the centuries in scientific treatments of emotion. Darwin (1872) noted phylogenic patterns in emotion expression that led him to argue for evolutionary consistencies across species. But it was James (1884) who posed the classic question “What is an emotion?” Emphasizing its centrality in human functioning, James concluded that physiological changes constituted emotion (see also Lange’s [1922] view in Ellsworth, 1994) and that the brain was the seat of emotion. In the next 100 years, debate about the nature of emotion was contentious, particularly regarding the roles of physiological changes and emotion expressions. James thought physiological changes comprise emotion, Cannon (1927) that those changes were caused by emotions, and Schacter and Singer (1962) that emotions were the interpretation of physiological change. Emotion research moved to the background as behaviorism and the cognitive revolution ascended in prominence, but by 1980 interest in emotion research was revived. Zajonc (1980), on receiving the 1979 Distinguished Scientific Contribution Award of the American Psychological Association, asserted that emotion and cognition were separate processes and that emotion took precedence over cognition. In addition to philosophical and scientific debate about the nature of emotion, religious writings have also contributed to the view that emotions must be controlled. Jewish scriptures, for example, discuss the moral imperative of bringing emotions under control of reason as guided by Jewish law (Bokser, 1981). Christian scriptures conceptualize emotions as desires of the flesh (Galatians 5:16–24) and state that rule over emotion is empowering (Proverbs 16:32). Buddhist texts regard emotions as indices of attachments to illusory, temporary realities and encourage the mental fortitude to be emotionally detached (Goleman, 2002). Yet no one more than Freud (1901) brought the public to see emotions as subconscious influences that interfere with human functioning. Most contemporary views now acknowledge that emotions have been conserved over time because of their functional, adaptive value in survival and maintenance of well-being. Furthermore, there is general consensus that emotions often operate out of conscious awareness, only occasionally becoming evident to the self in the form of feelings or to others in the form of emotional expression. And, in regard to emotion dysregulation, there is a general view that a steady pattern of poorly managed emotional reactions compromises behavioral functioning and psychological development. Advances in our understanding of behavior, cognition, and neurophysiology have not resolved the complicated issues about the nature of emotion, yet they have enlightened the discussion in ways that are of value to understanding the role of emotion in the development and maintenance of psychopathology.

TERMINOLOGICAL AND CONCEPTUAL ISSUES In the following sections we discuss the concepts of emotion, emotion regulation, and emotion dysregulation. There have been many debates about the nature of

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these constructs and their scientific utility, debates we do not resolve. However, these issues are critical to guiding our understanding of the available research on emotion and psychopathology and our future research endeavors.

Emotion Emotions comprise two integrated processes: appraisal and action readiness, which constitute a kind of psychological radar and response readiness system (e.g., Arnold, 1960; Frijda, 1986; Lazarus, 1991). Appraising is the means by which we evaluate the significance of circumstances vis-a-vis our goals for well-being. Action readiness is biological or learned preparedness to respond in a particular way that enables us to regain or maintain well-being. Emotions therefore do not deter reasonable action, but instead organize responses to perceived circumstances (Frijda, 1986; Lazarus, 1991). They organize the processes that allow us to relate to changing environments (both actual and perceived) in terms of their significance for well-being (e.g., Barrett & Campos, 1987). They are adaptive because they permit rapid detection of threats and obstacles to well-being and prepare us to act on our own behalf without delay. At the same time, most human beings are well equipped to regulate emotions such that appraisals can be modified and readiness to act does not dictate action. Before discussing the regulation of emotion from a clinical perspective, it must be acknowledged that emotions are inherently regulatory. The dynamic flow of emotion over the course of moments and hours and days organizes how we relate to the changing environment (Frijda, 1986); emotions focus attention, facilitate or limit memory processes, and facilitate specific motor activities (e.g., Hajcak et al., 2007; Hamann, 2001; Isen, Shalker, Clark, & Karp, 1978; Ochsner & Schacter, 2000). Advances in affective neuroscience also indicate that emotional functioning involves dynamic, ongoing organismic adjustments to situational changes (Davidson, 2000) that usually operate out of conscious awareness (e.g., LeDoux, 1986). Relevant neural transmission occurs in feed-forward and feedback networks involving limbic structures and prefrontal and orbitofrontal regions, which support regulatory processes such as deliberate reallocation of attention and inhibiting action. Studies using methods such as functional magnetic resonance imaging (fMRI), eye blink startle, positron emission tomography (PET), and electrophysiology (EEG and ERP) to study these neural processes suggest that individuals with psychopathology regulate their emotions differently than nonpsychiatric controls, but these techniques are limited in their ability to inform our knowledge of psychopathology and its development. EEG and ERP, for example, measure the summation of electrical activity at the scalp, and are not well suited for precise localization of activation, particularly in subcortical regions. Although fMRI can assess spatial distributions of neural activity, including subcortical connections, it requires constrained experimental conditions that are many steps removed from ecologies in which emotion dysregulation occurs. Nevertheless, fMRI research is helping

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us understand brain function, which will ultimately allow specific hypotheses for testing the respective roles of different neural networks in emotion regulation and dysregulation (Ochsner & Gross, 2007). In short, affective neuroscience underscores the systemic, inherently regulatory nature of the central nervous system and holds promise for our ability to better understand how emotions are regulated, and the brain’s role in the development of emotion dysregulation. The autonomic nervous system (ANS), which plays an important role in action readiness, is also a self-regulatory system. When emotions change (e.g., a person feels angry), neural information directs the heart to increase output, pumping blood to other muscles and bringing increased oxygen to them, thereby fortifying their readiness to contract. This increased cardiac output, facilitated by reduced parasympathetic and increased sympathetic ANS activation, readies a person for action. The vagus nerve of the parasympathetic nervous system regulates much of the cardiovascular response. As we breathe, heart rate fluctuates in response to cyclic activation and deactivation of parasympathetic input, under the control of the vagus nerve. Parasympathetic efference to the heart, often referred to as vagal tone, is indexed by measures of heart rate variability (e.g., respiratory sinus arrhythmia). Individuals with greater heart rate variability are better regulated emotionally (Porges, 2001), perhaps due to greater autonomic control over responding. For instance, children with higher heart rate variability are less negative in response to lab-based frustrations (Calkins & Dedmon, 2000; Calkins & Keane, 2004) and appear to be buffered from the negative sequelae of family adversity (El-Sheikh, Harger, & Whitson, 2001; Katz & Gottman, 1995, 1997; Shannon, Beauchaine, Brenner, Neuhaus, & Gatzke-Kopp, 2007). In sum, being emotional entails continual processes of evaluating the meaning of, and readying responses to, ever-changing circumstances, a set of processes that regulate other psychological systems and are inherently self-regulatory.

Emotion Regulation Defining emotion as a dynamic process that has regulatory influences creates a challenge for defining emotion regulation (Cole, Martin, & Dennis, 2004). How—if at all—does emotion regulation differ from emotion (Cicchetti, Ackerman, & Izard, 1995; Thompson, 2011)? This is a particular challenge for the study of the role of emotion in psychopathology, which often involves judgments about how adaptively an individual is functioning. Importantly, emotion regulation as a psychological activity is a construct that is inferred. At the behavioral level, the mental operations that underlie emotion cannot be observed; rather we observe behavior that is a product of both a response to changing circumstances and the inherent regulation of the response at levels that are beyond the individual’s awareness and ordinary observation. If an emotion is an appraisal and readiness to act to the changing environment, then emotion regulation refers to the modulation of that appraisal/action readiness response (Cole et al., 2004).

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As an emotional response unfolds, psychological processes can be recruited to modulate it—we can shift attention, recall memories that intensify or reduce the emotion, reappraise the situation, or take instrumental action. These processes can alter the valence or temporal dynamics of the emotion (Davidson, 2000; Thompson, 1994). Given that the adaptive nature of emotion is derived from its permitting quick, intense, and sustained emotion, it is the ability to regulate emotional responses that allows us to vary and modulate a response to fit the often complex circumstances that we face. Appropriate inferences about emotion regulation can be gleaned from meticulous observations of behavior, including examination of emotion-behavior-emotion sequences, convergence of data from multiple levels of measurement (behavioral, self-report, and physiological), and strategic manipulations of situational context (Cole et al., 2004). Ultimately, evidence from studies of neurophysiological processes, along with both field and lab research of children’s emotion-related behavior in situational context, can provide a fuller picture of emotional development and of trajectories that lead to emotional competence and disorder. This is particularly important for studies of very young children, during age periods when patterns of emotion regulation are being established (e.g., Tarullo & Gunnar, 2006). In sum, despite the complex nature of emotion, emotion regulation can be conceptualized as changes in initial appraisal/action readiness responses to circumstances that can be modulated by other processes (attentional, cognitive, social, and behavioral; Cole et al., 2004). Mental health and emotional competence require both emotional responsiveness and regulation of responses, in order for behavior to accord to social standards (Saarni, 1999).

Emotion Dysregulation We all have times when our emotions get the better of us. Within a developmental psychopathology framework, such instances of feeling emotionally “out of control” contrast with atypical functioning. The contrast is important, because these common instances do not disrupt and compromise lives in persistent ways. Emotion dysregulation occurs when patterns of emotion regulation compromise longer term functioning even as they serve the goal of achieving an immediate sense of well-being. For instance, a person with borderline personality disorder may fend off feeling rejected by being hostile to others. In this and other clinical examples, emotions are not unregulated but instead are dysregulated (Cole et al., 1994). They are regulated in that they diminish psychological discomfort, but they do so at a cost: they jeopardize relationships, productivity, and future achievements. These patterns of emotion dysregulation develop when biology and circumstances conspire to compromise the development of, or override, patterns of emotion regulation that achieve well-being and promote the longer-term goals of becoming a competent, healthy person. Emotion dysregulation thus refers to dysfunctional patterns of emotion regulation. But how do we distinguish these from adaptive instances of emotion regulation?

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EMOTION DYSREGULATION FROM A CLINICAL PERSPECTIVE We regard emotion dysregulation as a general feature of psychopathology (Cicchetti, Ackerman, & Izard, 1995; Cole, Michel, & Teti, 1994; Gross & Muñoz, 1995). It is treated as central to many different disorders (e.g., Barkley, 1997; Beauchaine, 2015; Beauchaine, Gatzke-Kopp, & Mead, 2007; Gotlib, Joormann, Minor, & Cooney, 2006; Kovacs et al., 2006; Leibenluft, Charney, & Pine, 2003; Linehan, 1993; Mennin, Heimberg, Turk, & Fresco, 2002; Suveg, Morelen, Brewer, & Thomassin, 2010), and featured in descriptions of several disorders in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). More recently, the National Institute of Mental Health has moved away from science that relies on DSM classifications of psychiatric disorder; it has instead instituted research domain criteria (RDoC) in which emotion (positive and negative valence systems) and behavioral and biological regulatory systems (e.g., executive function, affiliation and attachment, arousal modulation) figure prominently as the future direction of understanding the development of both mental health and psychopathology (Cuthbert & Insel, 2013). The centrality of emotion dysregulation in psychopathology has led to studies of the developmental links between early emotional functioning and later symptoms (e.g., Calkins, Dedmon, Gill, Lomax, & Johnson, 2002; Cole, Zahn-Waxler, Fox, Usher, & Welsh, 1996; Gilliom, Shaw, Beck, Schonberg, & Lukon, 2002). In general, higher levels of negative emotion earlier in life are associated with both concurrent and later symptoms and disorders, suggesting emotion dysregulation as a risk factor for rather than a consequence of mental health problems (McLaughlin, Hatzenbuehler, Mennin, & Nolen-Hoeksema, 2011). Thus, there is urgent need to understand emotional competence and the ways it develops and is derailed. Emotional competence is defined by being able to experience a full range of emotions, to be responsive to others’ emotional states, to value one’s own and others’ emotions, to appreciate the need to regulate emotion and the ability to do so in ways that fit situational constraints, and to achieve a sense of emotional self-efficacy (Halberstadt, Denham, & Dunsmore, 2001; Saarni, 1999). Emotion dysregulation can be distinguished from emotionally competent patterns of regulation in the following ways: Emotions endure and regulatory attempts are ineffective. Emotions interfere with appropriate behavior. • Emotions are context inappropriate. • Emotions change too abruptly or too slowly. • •

These qualities are not mutually exclusive. As a group, they share qualities that are unpredictable, inappropriate, and maladaptive. Thus, dysregulation stems not from strong anger or shame or other specific emotions per se, but rather from deviations in how emotion is regulated (see Cole, Dennis, Martin, & Hall, 2008,

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for a clinical example) and from the failure of regulatory efforts to meet goals appropriately (Thompson, 2011).

Emotions Are Enduring and Regulatory Attempts Are Ineffective Intense or persisting emotions are not necessarily dysfunctional if they are regulated well. One partner in a couple may be very angry for days due to something the other partner did; he may even actively work to maintain anger in order to emphasize the perceived seriousness of the problem. If the couple has a good relationship, the other partner will come to see how great the concern is; then through both mutual and self-regulatory behavior, the anger is resolved. In other instances, sustained emotion such as prolonged irritability and generalized anxiety interfere with rather than foster effective problem solving and healthy relationship maintenance (Carthy, Horesh, Apter, & Gross, 2010; Sheeber et al., 2009; Suveg, Hoffman, Zeman, & Thomassin, 2009). In the case of dysregulation, the sustained and/or intense emotion loses any initial value and begins to interfere with functioning, both intra- and interpersonal. Different patterns of ineffective self-regulation are revealed by dynamic modeling of behavioral time series data. Three-year-olds instructed by their mothers to wait to open a gift engaged in both prepotent responding, such as staying focused on the gift and expressing frustration about waiting, and executive control efforts, such as calm information seeking about the wait and distracting themselves with play activities (Cole, Bendezú, Ram, & Chow, under review). For 3-year-olds with elevated externalizing behavior problems, their prepotent responses had the effect of eventually damping their executive control efforts; that is, their desire for the gift and frustration about waiting to open the gift diminished their strategy use over the course of the task. For 3-year-olds who were in temperamental reactivity, however, there was a different pattern of dynamic relations between prepotent and executive processes. Specifically, their strategy use had the effect of amplifying their desire and frustration. These are two different forms of ineffective strategy use that help to explain individual differences in typically developing young children. In the case of psychopathology, dysfunctional sustained emotion is often unresponsive to regulatory efforts because the person has an insufficient repertoire of regulatory strategies, lacks skill at executing strategies, is undermined by faulty biological systems that ordinarily support emotion and coping, or receives secondary or partial gains for sustaining the emotion. Sustained but dysfunctional emotion occurs if strategies to modify the emotion are either ineffective (e.g., ruminating about a failure) or are used ineffectively (e.g., distraction is used when action is needed). A variety of regulatory strategies are known to modify the temporal and intensive dynamics of emotional responses—delaying a reaction, reducing intensity, shortening duration, or shifting from one emotion to another (Davidson, 2000;

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Thompson, 1994). Active problem solving, cognitive reappraisal, exercise, information seeking, and support seeking are generally adaptive strategies; avoidance, denial, emotion suppression, rumination, substance use, aggression, and venting are less optimal (e.g., Aldao, Nolen-Hoeksema, & Schweizer, 2010; Connor-Smith, Compas, Wadsworth, Thomsen, & Saltzman, 2000; Grolnick, Bridges, & Connell, 1996; Hayes, Wilson, Gifford, Follette, & Strosahl, 1996). Mentally healthy, emotionally competent children have a repertoire of strategies from which to choose, are able to select and deploy them effectively and flexibly to fit personal needs and situational constraints, and respond to the regulatory efforts of others (Halberstadt et al., 2001; Saarni, 1999). Individual Differences. In early and middle childhood, children with externalizing problems do not regulate frustration as well as asymptomatic children (e.g., Bar-Haim, Bar-Av, & Sadeh, 2011; Calkins & Dedmon, 2000; Cole, Zahn-Waxler, & Smith, 1994; Eisenberg et al., 2001; Gilliom et al., 2002), suggesting they have less adaptive or less effective strategies. They are also less likely to use effective strategies (e.g., Calkins & Dedmon, 2000; Calkins et al, 2002; Eisenberg et al., 2001; Melnick & Hinshaw, 2000). Internalizing symptoms in children and adolescents are also linked with use of ineffective strategies (Hughes, Gullone, & Watson, 2011). This work, however, did not evaluate whether children attempted effective strategies without success. Particularly compelling evidence for the clinical importance of effective strategies in the face of high levels of negative emotion comes from a study in which 3-year-old boys with oppositional defiant symptoms who used effective strategies to immediately reduce anger were better adjusted at later ages than those who could not do this (Gilliom et al., 2002). That is, attention shifting at age 3 predicted more cooperativeness and fewer externalizing symptoms at age 6, and information seeking predicted later assertiveness. In a different study, 4- to 7-year-old daughters of depressed mothers, a group of children at risk for later psychopathology, did engage in strategies but did so less actively than children whose mothers were asymptomatic (Silk, Shaw, Skuban, Oland, & Kovacs, 2006). Finally, highly anxious, sad, and angry children and adolescents use less effective strategies and are less confident in their strategy use (Burwell & Shirk, 2007; Carthy et al., 2010; Suveg & Zeman, 2004). We do not know the degree to which children with specific symptoms or disorders deploy different strategies, have fewer strategies available, use less mature or appropriate strategies, or use strategies less effectively or flexibly. However, recent evidence suggests that, among typically developing children, temperamental negative affectivity is associated with the effect of strategy use amplifying negative affect rather than damping it, an example of ineffective strategy engagement (Cole et al., under review). Nevertheless, evidence suggests that (a) attention-shifting,

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problem-focused information seeking, and appropriate, instrumental actions regulate children’s emotions, at least in the short term; (b) greater reliance on these strategies is associated with better adjustment from infancy to adolescence; and (c) children with symptoms of anxiety, depression, and oppositionality are both less likely to use these strategies and lack confidence that they can use them effectively. With a repertoire of effective strategies that are deployed flexibly to match situational demands, a person should experience enduring or intense emotion only as needed and without being disabled by it.

Emotions Interfere With Appropriate Behavior Emotions are defined in part by readiness to act in a particular way—to approach a blocked goal with force (anger), approach a goal with eagerness and openness (joy), withdraw from perceived threat (fear), and relinquish a lost goal (sadness), to name a few. Again, emotions are inherently adaptive; they quickly (without requiring awareness) ready us to act on the environment to maintain and regain well-being. Indeed, young children can stay focused on a task despite being frustrated. For example, when typically developing 3- and 4-year-olds’ anger is modulated, it is followed by appropriate action, such as persistently and flexibly solving a problem (Dennis, Cole, Wiggins, Cohen, & Zalewski, 2009). The world, however, is complex, and many situations involve multiple and sometimes conflicting goals and social constraints. Although emotion makes certain actions more likely, those actions can be appropriate or inappropriate. Emotionally competent, mentally healthy children who feel strong emotions have learned to behave in ways that take into account these constraints. Actions that are understandable and effective in the short run (a child hits another child to get a toy) compromise functioning if they become a stable pattern that interferes with friendship formation or self-control. A poignant illustration is that of incest victims who may dissociate from their overwhelming emotions. At first, this strategy is not linked to symptoms, but if it becomes a generalized coping strategy there are detrimental effects on long-term mental health (e.g., Marx & Sloan, 2002). In sum, emotion dysregulation occurs when emotions lead to behaviors that violate social standards or compromise developmental goals. Individual Differences. This discussion implies that emotion-behavior sequences differentiate typically developing children from those at risk for psychopathology. Research, however, that examines correlations between emotions and behaviors rather than behavior sequences cannot address whether emotions organize or disorganize behavior (Cole et al., under review; Dagne & Snyder, 2011). Anger followed by inappropriate acts is seen in school-aged children with behavior problems, whereas this sequence is not observed in asymptomatic children (Casey, 1996). Specifically, after exposure to background anger, 86.7% of children with

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oppositional defiant disorder either stopped playing (i.e., shut down) or became disruptive, but 60% of asymptomatic children continued to behave appropriately. Adolescent females with difficulty regulating emotion engage in self-injury (Adrian, Zeman, Erdley, Lisa, & Sim, 2011), which may be a maladaptive strategy borne from inability to tolerate negative emotion (Klonsky, 2007). Emotions About Emotions. One way that emotion can lead to inappropriate action is when a person has an emotional reaction to an initial emotional response. It is natural and essential for humans to reflect on their inner lives, although the aspect of this reflexivity involving emotions about emotions is little studied (Mendonça, 2013). Feeling embarrassed for being angry, for example, can contribute to inhibiting hurtful behavior. Emotions about emotions, however, can also contribute to maladaptive functioning, as clinicians often note. For example, clients may feel overwhelming guilt when they feel angry, further contributing to depression, or they may feel heightened anxiety when feeling a vulnerable emotion, such as sadness. Consider youth who engage in serious misconduct. Some of them will have grown up in highly stressed families, experiencing acute vulnerability as young children and without the adult support they need to help them cope with their emotions (see Cole, Hall, & Radzioch, 2009). Their parents may have had poorly regulated emotions, modeling inadequate emotion regulation as well as their emotions distressing their children. Clinically, we form the view that these children regulated their feelings of vulnerability as best they could but could not resolve them. Yet when the clinician tries to help them experience, understand, and resolve those feelings, they encounter the youth’s avoidance of feeling vulnerable, as well as anger and even hostility when feelings of sadness, rejection, and/or anxiety are stirred. We know little about emotions about emotions in either typical or atypical development. However, parents who have negative attitudes and emotions about feelings have children who are symptomatic (Hunter et al., 2011; Katz & Hunter, 2007). Saarni (1999) points out that the emotionally competent person is aware of all emotions (i.e., does not selectively attend to certain emotions), appreciates that mixed emotions often occur, and realizes one can be unaware of emotions (see also Halberstadt et al., 2001). A body of developmental work on understanding mixed emotions provides a point of departure for a deeper understanding of this aspect of the development of emotion dysregulation. By about age 8 to 9 years, typically developing children have a fairly differentiated understanding of multiple emotions, including mixed and conflicting emotions (Harter & Buddin, 1987; Harter & Whitesell, 1989; Pons, Harris, & de Rosnay, 2004; Wintre & Vallance, 1994). Appreciation that one emotion influences another emotion, however, appears to be more sophisticated, not appearing until the preadolescent period (Donaldson & Westerman, 1986). Adolescents who disavow having mixed emotions endorse repressive coping styles (Sincoff, 1992). Moreover, youth with externalizing problems, compared to internalizing and asymptomatic youth,

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have less complex, differentiated understanding of their emotional responses, but internalizing youth are less differentiated in understanding their reactions to perceived threat and are more confused about their emotions (O’Kearney & Dadds, 2005). In sum, the study of emotion-behavior sequences and emotion-emotion sequences helps us understand an important aspect of emotion dysregulation. In considering how the unfolding of an emotional response influences behavior, we need both basic and applied developmental work on emotions about emotions.

Emotions That Are Context-Inappropriate A third feature of emotion dysregulation involves the goodness of fit between an emotional response and the situation in which it occurs. In contrast to inappropriate action that results from an emotional response, affect—experienced or expressed— may deviate from the typical feeling in a given context. There are a number of considerations to determining context appropriateness. First, positive as well as negative emotions can be contextually inappropriate, as when a person enjoys something that would distress most people. Second, situations alone do not dictate emotional responses. The functional perspective contends that emotions reflect the personal meaning ascribed to a situation; emotional responses to the same situation can and do vary among individuals. Nonetheless, many contexts generally elicit emotions from a particular emotion family (Ekman, 1994), which allows a means of studying atypical emotional responses. Third, a major source of individual differences in appraising situations is a child’s developmental level. A child’s ability to understand a situation and appreciate its complexity will influence whether the child finds a specific situation frightening, funny, frustrating, or inconsequential. Socially Inappropriate Emotion Expression. An expressed emotion is inappropriate when it violates social or cultural norms for the situation, such as laughing when someone is hurt. Although it may be understandable to feel like laughing, doing so is inconsiderate or disrespectful. The expression of socially inappropriate emotion is linked to a variety of psychological risk factors and problems (Casey, 1996; Cole, Zahn-Waxler, & Smith, 1994; Shields & Cicchetti, 1998; Suveg & Zeman, 2004; Weisbrot, Gadow, DeVincent, & Pomeroy, 2005). It can include poor expressive control of emotions that are inappropriate in a situation but can also include overcontrol of emotions that may be important to the maintenance and repair of relationships (Saarni, 1999). Cole et al. (1994) found that oppositional preschool-aged girls, in contrast to asymptomatic girls, suppressed disappointment even without the social pressure to mask disappointment. In contrast, the ability to modulate emotion expression according to social standards is linked to social competence. As early as age 3, typically developing children

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spontaneously attempt to modulate the expression of disappointment (anger and sadness) according to social standards (Cole, 1986). Preschoolers (Garner & Power, 1996; Liew, Eisenberg, & Reiser, 2004) and school-aged-children (Hudson & Jacques, 2014; McDowell, O’Neil, & Parke, 2000) who try to smile while disappointed have better emotion knowledge and are more socially competent. A consistent pattern of socially inappropriate emotion expression interferes with relationships (Halberstadt et al., 2001; Saarni, 1999), but the specific dynamics of this link are yet to be well elaborated. Might inappropriately expressed fear predict a different developmental outcome than inappropriately expressed anger? Are certain ways of modulating emotion preferable? The abilities to take another’s perspective, to understand social display rules for emotions, and to inhibit prepotent responses have been linked to the ability to mask socially inappropriate disappointment (Hudson & Jacques, 2014). Therefore, inappropriate emotion expression may involve poor social awareness, disregard for social display rules, or an inability to regulate expression even when the individual wishes to do so. Atypical Emotional Responses to Specific Contexts. A second form of contextinappropriate emotion involves atypical emotional responses, such as feeling sad or afraid in situations that most children enjoy. Excessive distrust, pleasure at another’s distress, and emotional reactions for no apparent reason embody the idea of emotion-context mismatch. An example is hostile attribution bias, in which malevolent intent attributed to an ambiguous interpersonal situation leads to context-inappropriate anger (Crick & Dodge, 1994). Few studies address the role of context-inappropriate emotion in child psychopathology, but a link is suggested. Aggressive children, who typically have difficulty regulating anger, may respond with positive emotions to situations in which most children feel angry, anxious, or subdued. For example, oppositional preschoolers laughed at their mothers’ anger more than asymptomatic children did (Cole, Teti, & Zahn-Waxler, 2003). They did not seem anxious about the harm to the relationship or of negative consequences; they may have derived a sense of mastery or power from their misconduct (Cole et al, 2009). Indeed, aggressive children identify more positive and fewer negative consequences of aggression (Boldizar, Perry, & Perry, 1989; Slaby & Guerra, 1988). In addition, as early as 36 months (Székely et al., 2014), they are less accurate in reading peers’ negative emotions, including sadness, anger, and fear (Blair & Coles, 2000; Bowen & Dixon, 2010; Bowen, Morgan, Moore, & van Goozen, 2014; Casey, 1996), as well as detecting others’ facial expressions of pain (Wolf & Centifanti, 2014); these difficulties may contribute to the lack of appropriate empathy or concern for others. Context-inappropriate emotion has also been linked with anxiety symptoms. Toddlers who react fearfully to situations that other children enjoy may be exhibiting dysregulated fear (Buss et al., 2013; Kiel & Buss, 2014), which may forecast risk for the development of anxiety symptoms (see also Chapter 7 [Kagan]). Similarly, emotion-context mismatch

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has been reported in depressed adults, who report high levels of sadness while watching films that evoke happiness in control participants (Rottenberg, Gross, & Gotlib, 2005). Unknown is the timing and conditions under which these atypical emotional reactions develop and how they relate to the lack of remorse and empathy. Developmental studies of children who are exposed to abusive relationships suggest that children can develop patterns of context-inappropriate emotional responding as an adaptive strategy for coping (Bonanno et al., 2007; Cole et al., 2009). Over time, an initially adaptive response to an adverse situation becomes a form of emotion dysregulation when that pattern becomes inflexible or overgeneralized, that is, becomes a response to conflict in relationships that are not abusive (see e.g., Mead, Beauchaine, & Shannon, 2010). Moreover, there may be mismatches between observed expressions and physiological responding. For example, externalizing males may appear sad or empathic in regard to another’s distress but may not show the autonomic responses that typically developing children do (Marsh, Beauchaine, & Williams, 2008). Dyssynchrony between expressive and physiological aspects of emotional responding may reveal information about emotion regulation and mark risk for psychopathology.

Emotional Unresponsiveness A third form of context-inappropriate emotion is emotional unresponsiveness to situations that usually evoke emotion. The appearance of flattened affect has been observed in individuals with schizophrenia, depression, and posttraumatic stress disorder, although inexpressiveness does not necessarily indicate a lack of emotional responsiveness (Kring, Kerr, Smith, & Neale, 1993). In children, inexpressivity has been associated with both child externalizing and internalizing symptoms (Cole et al., 1996; Folk, Zeman, Poon, & Dallaire, 2014; Frick, Lilienfeld, Ellis, Loney, & Silverthorn, 1999; Hayden, Klein, & Durbin, 2005). Emotional unresponsiveness in situations that anger or frustrate most children has been observed in children with disruptive behavior or general difficulties with emotion regulation. These include the absence of sympathy, empathy, or guilt when another is distressed (Cole et al., 1996; Eisenberg et al., 1996; Frick et al., 1999; Garner, 2012; Liew et al., 2003). Such unresponsiveness may have a biological basis, as these children often lack the physiological responsiveness to emotional stimuli that typical children have (Fung et al., 2005; Marsh et al., 2008; Van Hulle et al., 2013) and have morphological differences in their brains linked with emotional deficits (Wallace et al; 2014; Zhang et al., 2014). Emotional unresponsiveness to situations that elicit positive emotions in most children is linked with internalizing problems. Preschoolers who express low levels of positive emotion during usually enjoyable tasks appear depressed or disruptive as preschoolers and later develop

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helplessness and negative self-views (Hayden, Klein, Durbin, & Olino, 2006; Luby et al., 2006). Emotional inexpressivity and avoidance (in conjunction with other risk factors) also predict self-harm among college students (Anderson & Crowther, 2012; Gratz, 2006), consistent with the idea that nonsuicidal self-injury is likely in individuals whose ability to experience emotion is impaired (Crowell, Beauchaine, & Linehan, 2009; Chapter 18 [Klein, Goldstein, & Finsaas]). Distressed individuals may also actively limit the experience of negative emotion (Hayes et al., 1996). Although healthy individuals occasionally avoid negative emotions (e.g., avoid discussing a sad event), generalized avoidance of negative affect interferes with relationships and paradoxically increases stress (Hayes et al., 1996; Marx & Sloan, 2002). Experiential avoidance is linked to a variety of disorders, including depression, anxiety, obsessive-compulsive disorder, and borderline personality disorder (Hayes et al., 1996; Hayes et al., 2004). In sum, context-inappropriate emotion is a form of emotion dysregulation that includes socially inappropriate emotional expressions, mismatches between emotional reactions and situational context, and unresponsiveness to particular or a range of situations. Research on context-inappropriate emotion and the development of psychopathology is limited.

Emotions Change Too Abruptly or Too Slowly Finally, emotional reactions may deviate in how they ebb and flow. Second-bysecond emotion coding reveals considerable variation across individuals and situations. Emotional expressions can appear and fade in a matter of seconds, but even those of short duration follow a typical pattern: beginning at a low level of intensity, reaching a peak, and then steadily decreasing. Moreover, there are typically longer periods of neutral (nonemotional) expressions between codable emotion expressions. This pattern contrasts with a dysregulated pattern of emotions that linger (do not recover quickly) or that change abruptly or frequently. Slow emotional recovery (e.g., unremitting dysphoria or anxiety) and lability (i.e., affective instability) are symptomatic of psychopathology. Individual Differences. Rapid changes in emotion are common in infancy (Camras, 1994) but in children are linked to ADHD (Anastopoulos et al., 2011; Sobanski et al., 2010), internalizing symptoms (Kim-Spoon, Cicchetti, & Rogosch, 2013), externalizing symptoms (Martin, Boekamp, McConville, & Wheeler, 2010), bullying (Garner & Hinton, 2010), and social difficulties (Jacob, Suveg, & Whitehead, 2014). Emotional lability is especially common among youth with comorbid internalizing and externalizing disorders and hospitalized adolescents (Gerson et al., 1996; Stringaris & Goodman, 2009). Self-reported lability is associated with adolescent depression,

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aggression, and anxiety and with adult ADHD and borderline personality disorder (Koenigsburg et al., 2002; Larsen, Raffaelli, Richards, & Ham, 1990; Maciejewski et al., 2014; Neumann, van Lier, Frijns, Meeus, & Koot, 2011; Silk, Steinberg, & Morris, 2003; Skirrow & Asherson, 2013). Finally, extreme childhood lability in some cases may be an early sign of later bipolar disorder (Correll et al., 2014; Egeland et al, 2012; Fergus et al., 2003; Kochman et al., 2005). Emotional Responses That Resist Change. Emotional lability is usually not contrasted with emotions that resist change. We place them together to underscore that the temporal dynamics of emotions are at least as important as their valence. Emotional responses normally develop and resolve in short periods of time (Cole et al., 2011), and the ability to recover from negative emotion is one hallmark of emotional health (Davidson, 2000). Individual Differences. Aggressive kindergartners have difficulty shifting to positive emotion states after a difficult task (Wilson, 2003), and adolescents with high levels of trait anger, which includes slower fading of anger episodes, experience more health problems and social isolation (Quinn, Rollock, & Vrana, 2014). Yet little is known about the development of the ability to recover emotionally and the significance of individual differences in latency to regain a calm or content state after being upset or to enjoy activities after being distressed. Regulating negative emotion may deplete psychological resources (Baumeister, Bratslavsky, Murven, & Tice, 1998), but in adults the experience of positive emotion after being frustrated seems to restore the ability to self-regulate (Tice, Baumeister, & Zhang, 2004). Resistance to emotional change is a feature of mood and anxiety disorders. Prolonged sadness or irritability is the central symptom of adult and childhood depression. Furthermore, inability to resolve anger and sadness is a main concern of parents who try, unsuccessfully, to soothe distress of depressed children or encourage them to feel better (Cole, Luby, & Sullivan, 2008). Enduring negative emotion and anhedonia may be related. Prolonged anxiety, even when immediate threats to well-being have subsided, is a central symptom of anxiety disorders. Oppositional children endure in being angry even when asymptomatic children’s anger is resolved (Cole et al., 1994), and depressed adolescents who ruminate (a maladaptive strategy) have cortisol levels that are slower to decrease following a stressor (Stewart, Mazurka, Bond, Wynne-Edwards, & Harkness, 2013). Thus, emotions that resist change may signal dysregulation.

ETIOLOGICAL FORMULATIONS From the perspective of developmental psychopathology, it is important to understand typical development of emotional competencies as a broader context for understanding pathways to atypical emotional functioning. In this section, typical

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development is first discussed, followed by three sources of risk that are associated with the development of atypical emotional functioning: parental psychopathology, exposure to violence and abuse, and genetic factors.

Normal Development Initially, negative infant emotional expressions appear undifferentiated (Bridges, 1931; Lewis & Haviland-Jones, 2000), but by the end of the first year anger, sadness, and fear are discernible (Bennett, Bendersky, & Lewis, 2002; Camras, Oster, Campos, & Bakeman, 2003; Izard, 2002). At first, infant emotions switch quickly (Camras, 1994)—a crying infant can quickly smile at a parent’s intervention—yet by 6 months facial expressions change less rapidly or frequently (Malatesta & Haviland, 1982). Emotional lability appears to follow a normative decline across development (Gerson et al., 1996). Caregivers help infants maintain and regain calm and pleasant states, which fosters the development of more autonomous emotion regulation (Diener & Mangelsdorf, 1999; Kopp, 1989; Thompson, 1994). Infants can spontaneously engage in behaviors that immediately reduce negative emotions, but with limited effectiveness. Infant self-comforting and attention redirection reduce distress (Crockenberg & Leerkes, 2004; Stifter & Braungart, 1995), but very young children resume being distressed if the situation is unchanged (Buss & Goldsmith, 1998). With age and experience, the variety and effectiveness of regulatory strategies increase (Grolnick et al., 1996; Mangelsdorf, Shapiro, & Marzolf, 1995; Stansbury & Sigman, 2000). By age 3, low-level anger appears to motivate task persistence and flexible problem solving (Dennis et al., 2009), and children can delay and modulate frustration and disappointment (Cole, 1986; Cole et al., 2011). By the time children enter school, most deal with ordinary, familiar frustrations and disappointments without becoming dysregulated. They use distraction and cognitive reappraisal effectively (Reijntjes, Stegge, Meerum Terwogt, Kamphuis, & Telch, 2006; Silk et al., 2003). Being fear- or anger-prone or experiencing family adversity appears to compromise the development of emotion regulation (Calkins & Dollar, 2014; Morris, Silk, Steinberg, Meyers, & Robinson, 2007).

Temperament Temperament, defined as early-appearing, biologically influenced predispositions involving reactivity and regulation (Derryberry & Rothbart, 1988; Rothbart & Bates, 2006), predicts difficulties with emotion regulation even from early childhood (Calkins & Fox, 2002). Six-month-olds disposed to both high activity and low attention control are more readily frustrated and use less effective strategies than other infants (Calkins et al., 2002). In preschoolers, high-negative affectivity is linked to the use of fewer constructive and more maladaptive regulation strategies (Blair, Denham, Kochanoff, & Whipple, 2004; Santucci et al., 2008). Links have

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also been found between inhibitory control and emotion regulation in preschoolers (Carlson & Wang, 2007), and emotion dysregulation is associated with low temperamental reactivity and low persistence (Yagmurlu & Altan, 2010). Temperamental characteristics also predict school age children’s regulatory strategies (Jaffe, Gullone, & Hughes, 2010). Overall, biologically based dispositions seem to influence the development of emotion regulation and dysregulation (Rothbart & Sheese, 2007). In addition, children are influenced by their interactions with their caregivers. Crucially, temperament may also render a child more susceptible to caregiving quality (Ellis, Boyce, Belsky, Bakermans-Kranenburg, & Van IJzendoorn, 2011).

Parenting and Parent-Child Relationships Attachment quality has been linked with emotion dysregulation. Securely attached children are better able to self-distract and to rely on their mothers as well as use objects as regulatory aids when they are stressed, whereas insecurely attached children exhibit poorer emotion regulation (e.g., Brody & Flor, 1998; Brumariu, Kerns, & Siebert, 2012; Contreras, Kerns, Weimer, Gentzler, & Tomich, 2000; Crugnola et al., 2011; Kidwell et al., 2010; Kim, Stifter, Philbrook, & Teti, 2014). Secure attachment is linked to parent-child discussion and validation of children’s emotions when they are upset (Waters et al., 2010). In addition, warm, supportive parental responses, and firm discipline when needed, appear to support the development of healthy emotion regulation (Bocknek, Brophy-Herb, & Banerjee, 2009; Chang, Schwartz, Dodge, & McBrideChang, 2003). Although parental sensitivity as early as the first few months of life predicts children’s later emotion regulation skills (Halligan et al., 2013), less is known about the specific practices that foster self-regulation of emotion; parental emotion expressions, reactions to children’s emotions, and teaching opportunities may all play a role (Eisenberg, Cumberland, & Spinrad, 1998; Morris et al., 2007). The evidence is mixed as to whether it is the maternal expression of mainly positive emotion (Nelson et al., 2012) or both positive and negative emotions (Eisenberg et al., 2003) that promotes better self-regulation in children. It is clear, however, that maternal emotional reactions to child emotion predict the quality of child emotion regulation strategies (Garner, 2006) and exacerbate or remediate child anger control problems (Cole et al., 2003). Whereas mothers are more commonly studied, fathers’ expression of positive emotions in response to child positivity may have unique effects on emotion regulation and psychopathology (Thomassin & Suveg, 2014). In addition, unsupportive parental reactions such as minimization, rejection, and suppression are linked to poorer emotion regulation across childhood and adolescence (Remmes & Ehrenreich-May, 2014; Shaffer, Suveg, Thomassin, & Bradbury, 2012; Tonyan, 2005); in contrast, parents who are validating and supportive of their children’s emotional experience and who use distraction and cognitive reframing strategies have children who self-regulate more effectively (Hurrell,

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Hudson, & Schniering, 2015; Morelen & Suveg, 2012; Morris et al., 2011). Sensitive and supportive parental responses to children’s emotions appear to be undergirded by parental beliefs about emotions and its regulation (Meyer, Raikes, Virmani, Waters, & Thompson, 2014). Most of this work, however, has not examined the effects of parenting qualities and practices as a function of child disposition. Certain children may benefit from active and early intervention, but for others such parenting may be distressing and interfere with the development of self-reliance in emotion regulation (Grolnick, Kurowski, McMenamy, Rivkin, & Bridges, 1998; Mirabile, Scaramella, Sohr-Preston, & Robison, 2009).

Parental Psychopathology Parents’ ability to foster healthy emotion regulation in a child depends on their ability to regulate their own emotions (Teti & Cole, 2011), serving as a link between parental mental health problems and child emotion dysregulation. Evidence comes from work on maternal depression (Goodman, Rouse, Connell, Broth, Hall, & Hayward, 2011). Maternal depression is not always associated with poorer child emotion regulation (Silk, Shaw, Forbes, Lane, & Kovacs, 2006), but emotion dysregulation is more common among children of depressed mothers, even as early as infancy (Dagne & Snyder, 2011; Feldman et al., 2009; Goodman et al., 2011; Hoffman, Crnic, & Baker, 2006; Wang & Dix, 2013). Child characteristics, such as temperamental proclivity for negative reactivity, may heighten effects (Blandon, Calkins, Keane, & O’Brien, 2008; Dix, Moed, & Anderson, 2014; Dix & Yan, 2014; Tronick & Weinberg, 2000). The negative impact of parental depression on child emotion regulation appears to be conferred along both biological (e.g., prenatal environment, genetic) and environmental pathways (e.g., parenting, modeling) (Kerr et al., 2013). In understanding the mechanisms of risk from parental depression to child emotional health, the child’s developmental status must be considered (Feng et al., 2008; Maughan, Cicchetti, Toth, & Rogosch, 2007; Silk et al., 2006). Maternal prenatal depression is associated with infant negative affectivity, which in turn predicts outcomes associated with poor emotion regulation in the toddler (Putnam & Stifter, 2005), preschooler (Gartstein, Putnam, & Rothbart, 2012), and adolescent years (Mezulis, Salk, Hyde, Priess-Groben, & Simonson, 2014). Second-trimester maternal depressive symptoms appear to have specific associations with infant negative affectivity, consistent with the increased development during that gestational period of limbic and cortical systems relevant to emotion regulation (Rouse & Goodman, 2014). During infancy, maternal depression influences how mothers and infants respond to one another (Gianino & Tronick, 1988; Tronick & Cohn, 1989). Depressed mothers misread infant emotions, increasing the chance of emotional mismatches and insensitive responses by the mother, which may cause infant emotion dysregulation, such as disengaging from the mother or being hard to soothe (Tronick & Reck, 2009). Decades of research show that depressed mothers of infants and young children

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are prone to engage in either irritable, intrusive parenting or withdrawn, disengaged parenting (see meta-analysis by Lovejoy, Graczyk, O’Hare, & Neuman, 2000), and patterns of either primarily intrusive or mixed intrusive/withdrawn patterns are stable across 6, 15, and 24 months postpartum (Wang & Dix, 2013). Parenting that is highly negative, disengaged, or mismatched with infants’ signals disrupts the development of a parent-child relationship in which the caregiver can serve as a source of external emotion regulation for the child and in which secure attachment is fostered. Social learning may also play a role in risk for emotion dysregulation in children of depressed parents. A recent study of depressed mothers of 5- to 11-year-olds suggested that aversion sensitivity may account for alternating patterns of intrusive and withdrawn parenting in depression: In the context of a challenging parent-child interaction, mothers were low in reactivity to child behavior when it was low in aversiveness, but highly reactive when child behavior became high in aversiveness (Dix et al., 2014). Thus, depressed parents may have a tendency to model maladaptive interpersonal and emotion regulation strategies (i.e., conflict avoidance followed by eruption of negative affect when tension builds beyond a certain set-point) during the preschool and school-age years, a critical developmental stage for children to obtain mastery in their ability to self-regulate. This risk may be particularly strong when maladaptive interpersonal processes are more ingrained for the depressed parent; a history of maternal childhood-onset depression affects her parenting above and beyond effects of her current depression status (Shaw et al., 2006), and 4- to 7-year-olds with mothers with childhood-onset depression used less effective emotion regulation strategies during a frustrating task (Silk, Shaw, Skuban et al., 2006). Changes in maternal behavior, in turn, change the way children engage emotionally with a depressed parent. Children whose mothers have depressive symptoms sometimes display more negative affect during parent-child interactions but may also show diminished negative affect (Cole, Barrett, & Zahn-Waxler, 1992; Dagne & Snyder, 2011; Dix, Meunier, Lusk, & Perfect, 2012; NICHD Early Child Care Network, 2004), and less engagement than children of nondepressed mothers (Apter-Levy, Feldman, Vakart, Ebstein, & Feldman, 2013; Yan & Dix, 2014). At least some children may even try to care for the withdrawn or irritable parent (Radke-Yarrow, Zahn-Waxler, Richardson, Susman, & Martinez, 1994; Van Parys, Bonnewyn, Hooghe, De Mol, & Rober, 2014), which could restrict their own emotions and impede their learning to regulate them in a healthy manner.

Maltreatment and Violence Exposure Maltreatment is associated with child emotion dysregulation as early as the first three years of life and into young adulthood (Burns, Jackson, & Harding, 2010; Kim & Cicchetti, 2010; Maughan & Cicchetti, 2002; Robinson et al., 2009; Shields & Cicchetti, 1997, 1998; Shipman, Zeman, Penza, & Champion, 2000). Maltreatment may interfere with the development of healthy emotion regulation in several ways. First, the trauma of maltreatment affects children neurologically

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(Wilson, Hansen, & Li, 2011). Neural regions involved in processing emotion are altered by neurochemical responses to prolonged stress. Second, maltreating parents are less sensitive to their children’s needs and may lack the skills to both teach and model effective emotion regulation (Shipman et al., 2007). Third, maltreatment affects child emotion processing. Children who are maltreated are quicker to detect anger in others (Pollak, Messner, Kistler, & Cohn, 2009). Their negative internal representations of their parents may interfere with how well they regulate emotion in any interpersonal situation (Shields, Ryan, & Cicchetti, 2001). Finally, the way a child copes with maltreatment may require emotion dysregulation (Cole et al., 1994; Maughan & Cicchetti, 2002). Overregulating one’s negative affect may avoid parental anger, which promotes safety in the short term but can lead to a style of emotional functioning that is dysregulated in other relationships. Exposure to unresolved marital conflict, particularly domestic violence, affects child emotion regulation (Koss et al., 2011). Exposure to adult anger distresses children, and they respond in varying ways. Exposure may threaten a child’s sense of security (Cummings & Davies, 2010), which interferes with the ability to emotionally engage fully and appropriately with activities and relationships (El-Sheikh, Cummings, Kouros, Elmore-Staton, & Buckhalt, 2008). Maltreatment and interadult violence often co-occur; some evidence suggests that effects of marital conflict on child emotion regulation can be accounted for by maltreatment (Maughan & Cicchetti, 2002).

HERITABILITY OF EMOTION DYSREGULATION Behavioral (e.g., adoption, twin, and sibling studies) and molecular genetics studies suggest that there are genetic effects on emotion dysregulation. Genes influence the psychological processes that support or hinder adaptive emotion regulation, both directly (i.e., are heritable) and indirectly (e.g., via gene-environment correlation; Scarr & McCartney, 1983). Genes directly influence physiology, which enables emotional responsiveness and regulation. Furthermore, genetic influences interact with environmental influences at multiple levels (e.g., gene-environment correlations). One way that child development researchers have approached genetic influences on emotion regulation and dysregulation is through the construct of temperament, defined above as biologically based individual differences in reactivity and regulation. Although temperament is influenced by pre- and postnatal environments (DiPietro, Ghera, & Costigan, 2008; DiLalla & Jones, 2000), it is partly to largely heritable, depending on the facet being assessed. Heritabilities range from 20% to 60%, depending on the temperamental construct and the age of participants (Auerbach et al., 1999; Goldsmith, Buss, & Lemery, 1997; Goldsmith, Lemery, Buss, & Campos, 1999; Lakatos et al., 2003; see Saudino, 2005, for a review). Some basic cognitive processes implicated in temperament (and potentially supportive of emotion regulation) are also heritable, such as (a) attention control (Holmboe et al., 2010; Sheese, Voelker, Posner, & Rothbart, 2009), with implications for regulating attention

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when distressed (e.g., averting gaze from aversive stimuli; Soussignan et al., 2009); (b) verbal and nonverbal communication (Hardy-Brown & Plomin, 1985), which contribute to young children’s ability to reflect on their circumstances, communicate needs, and guide their own behavior (Cole, Armstrong, & Pemberton, 2010); and (c) the capacity to control prepotent responses (inhibitory control) and to activate subdominant responses (effortful control; Gagne & Saudino, 2010; Goldsmith et al., 1997; Leve et al., 2013). Temperament may confer vulnerability to unhealthy emotion regulation if it (a) predisposes a child to strong, intense emotional reactions that are difficult to modulate, potentially overwhelming inchoate regulatory strategy development or (b) limits a child’s development of attention control, communicative skills, or the ability to inhibit or activate behavior. Psychophysiological research has also examined genetic influences on physiological indicators of emotional reactivity and regulation. For example, specific gene polymorphisms are associated with hormonal responses to stress (e.g., cortisol; Armbruster et al., 2009), respiratory sinus arrhythmia (Kupper et al., 2005; Propper et al., 2008; and see meta-analysis by Bridgett, Burt, Edwards, & Deater-Deckard, 2015), heart rate variability (see Bridgett et al., 2015), and neural measures of recovery from unpleasant stimuli (Larson, Taubitz, & Robinson, 2010). In the executive functioning literature, many investigators have found moderate to high heritability estimates (60–90%) on EF (Bridgett et al., 2015), which provides complex cognitive support for emotion regulation. Genetic influences on the etiology of emotion dysregulation may also operate indirectly through relational processes that are central to the development of emotion regulation. These associations may arise because (a) caregiver genetic characteristics influence their interactions with their children, (b) children’s genetic characteristics elicit specific types of responses from adults (i.e., evocative gene-environment correlation, or, rGE), and (c) certain types of child-parent interactions influence the expression of genetic risk under certain genetic conditions (e.g., gene-environment interaction). One indirect way that genes operate in the etiology of emotion dysregulation is through the influence of parents’ genes on how they behave with their children, and thus socialize or undermine the development of emotion regulation. A recent meta-analysis on genetic and environmental effects on parenting behavior showed that parent genetic effects account for 28–37% of the variance in parental negativity and warmth (Klahr & Burt, 2014). For example, we know that depression is modestly to moderately heritable (Kendler, Gatz, Gardner, & Pedersen, 2006; Kendler & Prescott, 1999; Philibert et al., 2003) and is related to parenting behaviors that undermine children’s emotional development (Goodman, 2007; Lovejoy et al., 2000). In addition, children’s genes may influence parents’ behaviors via evocative rGE; a recent meta-analysis (Klahr & Burt, 2014) showed that child genetic effects account for 23–40% of the variance in parental negativity, warmth, and control. For example, adoption studies suggest that adoptive parents’ harsh parenting is associated with higher birth mother psychopathology (Fearon et al., 2014;

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Ge et al., 1996) and lower levels of birth mother sociability (Hajal et al., 2015). Finally, parent-child relational processes influence the development of emotion regulation through gene-environment interaction. For instance, the short allele of the serotonin transporter gene interacts with attachment security to predict infant and adolescent self-regulation (Kochanska, Philibert, & Barry, 2009; Zimmermann, Mohr, & Spangler, 2009). Additionally, there is growing evidence that associations between parenting quality and temperamentally based cognitive processes that support emotion regulation depend on specific gene polymorphisms and haplotypes. Specifically, parenting quality was related to inhibitory control only for children with the COMT Val158-Met polymorphism (results also varied depending on child sex; Sulik et al., 2015) and to effortful control only for children with the 7-repeat allele of the DRD4 (Sheese, Rothbart, Voelker, & Posner, 2012) or with certain haplotypes of SLC6A3 (Li et al., 2016). In terms of emotion dysregulation, child temperament and parental depression, both of which are influenced by genetic characteristics, interact to compromise normal growth in child emotion regulation (Blandon et al., 2008). Thus, in addition to the direct effects of genes on the etiology of emotion dysregulation, parents’ and children’s genes may also operate indirectly on the development of emotion dysregulation through parent-child interactions.

SUMMARY AND CONCLUSIONS Emotion dysregulation is a general feature of psychopathology. Dysregulation is not simply a matter of a person’s being emotionally negative. Four characteristics of emotional functioning that occur in the presence of psychopathology define dysregulation: (1) emotions that endure due to ineffective strategies, (2) emotions that lead to inappropriate behavior, (3) emotions that are contextually inappropriate, and (4) aberrations in how emotions change. These are not exclusive categories, yet each is associated with specific types of symptoms. The empirical evidence is limited, especially from a developmental viewpoint, and does not address when emotion dysregulation as defined is a precursor, a correlate, or an outcome of psychopathology. It is critical to have a clinically informed, developmental approach to conceptualizing and studying emotion regulation and dysregulation. Therefore, there is an acute need for studies that examine how skillful emotion regulation develops typically, how children with and without specific clinical problems differ in this development, how emotion regulation and dysregulation patterns relate to symptoms, and which conditions lead to the skillful emotion regulation and emotion dysregulation outcomes. Such research not only has promise for understanding childhood disorders but may be crucial for understanding the emergence of adult disorders that are thought to have their origins in early childhood, such as personality disorders (Linehan, 1993; Rogosch & Cicchetti, 2005). In addition, this approach to research will inform early identification of risk and both early and crisis intervention (Tolan & Dodge, 2005).

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C H A P T E R 12

Neighborhood Effects on the Development of Delinquency WESLEY G. JENNINGS AND NICHOLAS M. PEREZ

HISTORICAL CONTEXT

C

riminologists have long acknowledged that crime is more highly concentrated in socially disadvantaged neighborhoods. Accordingly, community-level factors meaningfully affect an individual’s propensity for criminal and delinquent behavior (Ingoldsby & Shaw, 2002). At the same time, developmental psychologists and psychological criminologists also recognize that certain individual traits and characteristics, such as impulsivity, callous and unemotional demeanor, negative emotionality, cognitive deficits, and poor conditionability are also robust predictors of delinquent behavior (Meier, Slutske, Arndt, & Cadoret, 2008). Criminal behaviors have often been explained or studied using only one of two major theoretical paradigms—an individual-level or a community-level perspective. Individual-level theories are the primary focus of psychologists and psychiatrists who examine the onset and continuity of criminal behavior (Lynam, Caspi, Moffitt, Wikstrom, Loeber, & Novak, 2000). On the other hand, neighborhood- and community-level theories tend to be advocated for and tested by sociological criminologists who focus on social processes and structural factors as mechanisms that affect variation in crime rates (Lynam et al., 2000). Although these two perspectives have received a great deal of support in their respective fields, they have rarely been evaluated or integrated into a unified theoretical framework (Lynam et al., 2000). However, prior research evaluating these two risk factor domains simultaneously shows support for a theoretically integrated, multilevel model for explaining crime and antisocial behavior. Accordingly, there is growing interest in integrating individual- and neighborhood-level factors when examining deviant and delinquent behavior. In this chapter, we (a) briefly review 387

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prior literature that focuses on only individual- or community-level factors and their relation to crime and delinquency; (b) examine results of prior studies that combine the two perspectives; and (c) suggest paths for subsequent and integrated theoretical and empirical research incorporating individual-level and community-level factors when seeking to explain the development of delinquency. At the outset, we highlight that integrative perspectives are highly likely to provide greater explanatory power than investigations examining either level of analysis alone.

ETIOLOGY In order to discuss the salient risk factors for delinquent behavior, one must examine two distinct areas of empirical research: individual-level vulnerabilities and neighborhood-level risk factors. Individual-level factors include differences found among people that may impact their respective behaviors, while neighborhood-level factors pertain to characteristics found in certain communities that can influence the behavior of the people who reside in the area. Although these are two different schools of thought, both are valuable to the understanding of risk for delinquency and will be discussed below.

Individual-Level Vulnerabilities and Delinquent Behavior Theories that examine individual-level characteristics or traits as causes of delinquent and criminal behavior are rooted in early 19th-century psychology (Binder, 1987). In these models, psychologists and psychologically oriented criminologists recognized several key individual differences that consistently predict criminal behavior. These individual traits include poor conditionability (Eysenck, 1977), cognitive defects (Lau, Pihl, & Peterson, 1995; Lynam, Moffitt, Stouthamer-Lober, 1993; Moffitt, 1993a), negative emotionality (Caspi, 2000; Chess & Thomas, 1984; Farrington, Biron, & LeBlanc, 1982; Wilson & Hernstein, 1985), callous/unemotional traits (Frick, Cornell, Barry, Bodin, & Dane, 2003), and impulsivity (Cleckley, 1941; Gottfredson & Hirschi, 1990; Lipsey & Derson, 1998; Moffitt, Caspi, Rutter, & Silva, 2001). It is noteworthy, in terms of cognitive deficits, that a range of variables have been considered in the empirical literature: verbal (e.g., Moffitt, 1990, 1993a), perceptual (e.g., Raine, et al., 1994; Raine, et al., 2006), and executive (e.g., Giancola, Mezzich, Ada, Tarter, 1998; Morgan & Lilienfeld, 2000; Stevens, Kaplan, Hesselbrock, 2003). Among these traits, impulsivity is often suggested to be a key correlate in many different psychological and criminological perspectives on delinquent behavior (see e.g., Cleckley, 1941; Gottfredson & Hirschi, 1990; Hare, 1980; Moffitt, 1993b). In this vein, hyperactivity and impulsivity predict both chronic offending and psychopathy (Lynam, 1998) as well as aggressive behavior, violence, and delinquency in general (Gottfredson & Hirschi, 1990). Even so, researchers have also suggested that there are “a bewildering number of constructs referring to poor ability to

Neighborhood Effects on the Development of Delinquency 389 control behavior” (i.e. impulsivity, restlessness, risk taking, sensation-seeking, etc.; Farrington, 2005, p. 179), and have implied that this variety of constructs leads to an assortment of different underlying hypotheses, some of which are complementary and some of which are competing. For example, hypotheses may examine the construct of impulsivity using a cognitive approach, a behavioral approach, or a personality approach (White et al., 1994). These methods often have different assumptions about the origin (inherited or learned) and mechanism (causal or otherwise) of impulsivity in affecting behavior. In addition, a large proportion of variance in delinquency still remains unexplained, after accounting for impulsivity and related individual-level vulnerabilities (Farrington, 1993; Tonry, Ohlin, & Farrington, 1991). This concern leads to the assumption that community-level or structural factors may also be integral to understanding the development of delinquency.

Neighborhood-Level Risk and Delinquent Behavior The majority of researchers who study neighborhood effects (e.g. Bursik & Grasmick, 1993) associate the concentration of socioeconomic disadvantage in an area with social disorganization of neighborhoods and communities. This neighborhood disorganization is presumed to negatively affect individuals and families who reside in the area. This association may occur as a result of a variety of different mechanisms, including the concepts of social disorganization (e.g., Sampson & Groves, 1989; Shaw & McKay, 1969), broken windows (e.g., Wilson & Kelling, 1982), higher levels of trauma in disorganized or impoverished neighborhoods (Klest, 2012), early interpretations of anomie (Durkheim, 1893), or a number of other processes unique to these neighborhoods and communities. Yet other researchers suggest that concentrated poverty cannot be explained fully without also accounting for family- and individual-level factors and processes (Gephart, 1997). Although the original conception of social disorganization theory sought to explain delinquent and criminal involvement at the community level, there are empirical reasons to anticipate that social disorganization may also affect an individual’s behaviors. In fact, Shaw and McKay (1969, p. 14), who are generally attributed to be the founders of the social disorganization perspective, even acknowledged this perspective when they stated the following: While these maps and statistical data are useful in . . . differentiating the areas where the rates of delinquency are high from areas where rates are low . . . they do not furnish an explanation of delinquent conduct. This explanation . . . must be sought . . . in the field of the more subtle human relationships and social values which comprise the social world of the child in the family and the community. Simcha-Fagan and Schwartz (1986, p. 671) went on to argue that structural characteristics may negatively affect an individual’s ability to develop and maintain

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informal social ties to his or her community. For example, renters as opposed to property owners have less investment in their community, as they are often temporary residents or those who do not have resources to purchase their own property. Other scholars have suggested that this lack of investment in the community affects neighborhood civility and, in turn, leads to increased social disorganization (Roncek, 1981; Sampson, 1995; Sampson & Groves, 1989; Wilson & Kelling, 1982). Indeed, Felson (1987, p. 917) has also argued: The street belongs to everyone, hence is supervised by no one, except for an occasional policeman who does not know who belongs there anyway. The very system that fosters easy movement and vast opportunity for good experiences also interferes with informal social control of youths and protection of person and property from intruders. In a review of the effects of social compositional measures (e.g., poverty) on developmental outcomes (e.g., educational attainment, cognitive skills, criminal activity, and economic success), Jencks and Mayer (1989) found that the literature is generally mixed regarding the influence of socioeconomic status (SES) on an individual’s educational success and likelihood of criminal behavior. In addition, Klebanov, Brooks-Gunn, Chase-Landsdale, and Gordon (1997) demonstrated positive effects of living in a higher-SES community on children’s levels of academic achievement and verbal abilities, and negative effects on subsequent conduct problems. Their results also indicated negative effects of living in more heterogeneous neighborhoods on youths’ verbal ability scores. This relation was mediated by the quality of the learning environment in the home. Consequently, research indicates that neighborhood-level effects can be both direct and indirect, with indirect effects largely operating through, or interacting with, individual- and family-level factors (Brooks-Gunn, Duncan, Klebanov, & Sealand, 1993). In short, the limitations of both individual and neighborhood variables in terms of explaining the wide variance in the degrees of antisocial behavior and delinquency have motivated efforts to integrate and span these levels of analysis.

Interactions Among Individual-Level and Neighborhood-Level Factors As demonstrated by prior empirical research, individual-level and community-level characteristics are supported regularly as predictors of delinquent and criminal behavior. Unfortunately, empirical studies using one perspective or the other are incomplete. This limitation is exemplified by Tonry, Ohlin, and Farrington’s (1991) statement that “most individual-level research is inadequate because it neglects variation in community characteristics, while community-level research fails to take account of individual differences” (p. 42). Despite this realization over 25 years ago, investigations that include both individual-level and community-level factors

Neighborhood Effects on the Development of Delinquency 391 are still quite rare (for further discussion, see Farrington, Sampson, & Wikstrom, 1993; LeBlanc, 1997; Reiss, 1986; Tonry et al., 1991; Wikstrom & Loeber, 2000; Wilcox, Sullivan, Jones, & van Gelder, 2014). We now examine the limited literature to date on this essential perspective. Initial inquiries testing the effects of both individual- and community-level factors have generally examined how neighborhood-level conditions are related to an individual’s demographic characteristics and family background. One such study, conducted by Kupersmidt and colleagues (1995), evaluated levels of aggression among children. Their results showed that, while neighborhood factors are related to individual rates of delinquency, the correlation is weakened when controlling for family-level factors. They also found that children who grow up in both single-parent households within lower-SES neighborhoods are more aggressive than children who grow up in single-parent households in higher-SES neighborhoods. Additionally, Lindstrom (1995) found that, whereas positive family interactions decreased an individual’s level of delinquency, this relationship was influenced by community factors and context. Essentially, these findings suggest that a positive neighborhood environment can be more important in disadvantaged neighborhoods, as it may act as a safety net for parents who are not providing the necessary protection and supervision for their children. Despite such findings indicating a combined influence of individual- and neighborhood-level risk factors, early studies did not include key individual-level characteristics, such as personality traits or developmental features, or more nuanced neighborhood contexts, such as opportunities to engage in crime and other important situational factors (Bohman, 1996; Lynam et al., 2000; Leventhal & Brooks-Gunn, 2000; Mednick, Gabrielli, & Hutchings, 1984; White, Moffitt, & Silva, 1989). Similarly, early research examined neighborhood-level effects in overly simplistic terms (e.g. SES, or general indicators of disorganization) and often excluded complex aspects of criminal opportunities that may be present in a community, affecting the prevalence of delinquency and crime in the area (Cohen, Felson, & Land, 1980). The first prospective examination of interactive effects of impulsivity and neighborhood context on delinquency was conducted by Lynam and colleagues (2000) in a high-risk sample of boys from the Pittsburgh Youth Study. Their results showed that the relation between impulsivity and self-reported delinquent behavior was stronger for boys who lived in lower-SES neighborhoods, which led the authors to conclude that impulsive boys are more likely to take advantage of criminal opportunities that are present in lower-SES communities (Lynam et al., 2000). In short, the findings revealed a moderational effect: The effects of impulsivity on future delinquent behavior were influenced by neighborhood context (and, vice versa, that neighborhood effects were moderated by the individual-difference factor of impulsivity). In a second study relying on Pittsburgh Youth Study data, Wikström and Loeber (2000) found that youth with very few risk factors and multiple protective factors

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were influenced most by neighborhood context, whereas neighborhood context had no effect for boys who exhibited multiple risk factors and few protective factors. Together, these results suggest that an interactive effect exists between individual-level vulnerabilities and neighborhood context. Given such results, it is far too limiting to rely on either “level” alone in the attempt to explain the development of delinquent behavior.

DEVELOPMENTAL PROGRESSION In light of these findings, theory has progressed toward more integrated and transactional developmental perspectives. For example, some researchers contend that key socialization processes that take place in communities affect the development of an individual’s self-control (Leventhal & Brooks-Gunn, 2000). According to this view, individuals who reside in neighborhoods with higher levels of informal social control (e.g., community residents invested in their community, higher concern for children, etc.) tend to develop stronger self-control than those who reside in neighborhoods with lower levels of informal social control and less concern for local children. Others have suggested that community-level socialization and collective efficacy (the level of social cohesion among community members along with their willingness to intercede on behalf of the good of the neighborhood) affect children’s self-control as well as the availability of criminal opportunities (Wikström & Sampson, 2003)—the latter of which, in turn, increases youths’ likelihood for involvement in delinquent behavior (Sampson, Raudenbush, & Earls, 1997). Subsequent research has revealed supporting evidence for the importance of the community on the development of self-control. For instance, Pratt and colleagues (2004) tested the effect of neighborhood characteristics, such as collective efficacy, on self-control, above and beyond effects of children’s parents’ ability to monitor and discipline. Their results showed that both parental and community effects have nearly equal influence on the development of children’s self-control. This finding was in stark contrast to Gottfredson and Hirschi’s (1990) original suggestion that parents were far more influential in this process. Another line of empirical investigation addresses whether the effects of individual traits, such as impulsivity, differ according to community factors and neighborhood context. For example, Vazsonyi, Harrington Cleveland, and Wiebe (2006) showed that whereas impulsivity exerts a direct influence on antisocial behavior, neighborhood context also independently influences levels of impulsivity and antisocial behavior. In addition, neighborhood disadvantage was associated with lower levels of nonviolent delinquency, contrary to expectations and prior research (Lynam et al., 2000; Wikström & Sampson, 2003). According to Vazsonyi and colleagues (2006), this may be the result of middle-class neighborhoods being more tolerant of nonviolent delinquency, but more vigilant toward preventing violent delinquency. As such, the levels of violent delinquency may be significantly

Neighborhood Effects on the Development of Delinquency 393 higher in disorganized neighborhoods, but levels of nonviolent delinquency may still be relatively comparable. In response to a concern that Vazsonyi and colleagues’ (2006) impulsivity index may have been imprecise and problematic since it did not assess integral behavioral processes associated with low self-control or the inability to delay gratification or control conduct, but instead only assessed an individual’s problem-solving and decision-making techniques, Gibson, Sullivan, Jones, and Piquero (2009) examined the effects of neighborhood factors on the development of self-control using data from the Project on Human Development in Chicago Neighborhoods (PHDCN). Results indicated that neighborhood factors affect individual youth’s levels of self-control, although the effect is rendered insignificant once individual-level and selection effects are controlled for. This may be the result of “neighborhood influences operating through parenting styles and nurturing to influence the children’s self-control” (Gibson et al., 2009, p. 18). In a more current study, Jennings and colleagues (2011) analyzed data from over 5,000 middle school youth who participated in Project Northland Chicago (PNC) (Komro, et al., 2004; Komro, et al., 2008) to assess linkages between individual-level and neighborhood-level factors and processes on physical aggression. A scaled measure representing neighborhood problems was derived from parents’ responses to questions such as: (1) “How much of a problem is drug dealing on your block?”; (2) “How much of a problem is unsupervised youth on your block?”; (3) “How much of a problem are people drinking alcohol on the street in your block?”; (4) “How much of a problem is too many stores that sell alcohol on your block?”; (5) “How much of a problem is the lack of supervised activities for youth on your block?”; (6) “How much of a problem is too many alcohol ads on your block?”; and (7) “How much of a problem is poor police response on your block?” Using multilevel, hierarchical regression models, Jennings et al. reported that neighborhood-level risk was significant even when controlling for a number of individual-level vulnerabilities (e.g., alcohol use, peer alcohol use, lack of adult supervision, depression) and demographics (e.g., age, sex, race, being born in the United States, and coming from a two-parent household). These findings lend credence to an integrated theoretical model for explaining youth aggression and antisocial behavior. In one of the most recent examinations of an interactive individual- and neighborhood-level theoretical model, Jennings and colleagues (2013) used a cohort of 411 South London males, ages 10–50, from the Cambridge Study in Delinquent Development to evaluate effects of individual-level vulnerabilities in boys at ages 8/10 (e.g., low junior school attainment, daring disposition, small height, low nonverbal intelligence, nervous/withdrawn boy, high extraversion of boy, high neuroticism of boy, psychomotor impulsivity, dishonesty, unpopular, troublesome, and lacking concentration/restless) and environmental risk factors at ages 8–10 (e.g., harsh attitude/discipline of parents, teen mother at birth of her first child, behavior problems of siblings, criminal record of a parent, delinquent older sibling, large family size, poor housing, low family income, parental disharmony,

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neurotic/depressed father, neurotic/depressed mother, low socioeconomic status, separated parents, poor supervision, and high delinquency rate school) on the frequency of offending and involvement in violence. The key analyses revealed support for an integrated perspective, as both the individual-level vulnerability index and the environmental-level risk factor index remained significant predictors of offending, controlling for myriad other relevant variables such as low resting heart rate, participation in team sports measured at age 16, smoking, binge drinking, impulsivity, and body mass index (BMI) measured at age 18.

SEX DIFFERENCES The observed relations between individual- and community-level factors and delinquency may be influenced by sex. For example, Chapple and Johnson (2007) used data from the National Longitudinal Survey of Youth (NLSY79) to examine whether a number of predictors of impulsivity differed depending on the sex of youth. Results showed that relations between parental discipline and impulsivity, and parental attachment and impulsivity, were different for males and females. That is, the importance of these parental characteristics in the development of impulsivity was much stronger for boys than for girls. Additionally, results from a meta-analysis of 277 studies and 741 effect sizes examining impulsive behavior by sex indicate that females are more punishment sensitive, whereas males have a higher preference for sensation-seeking and risk-taking behaviors (Cross, Copping & Campbell, 2011). These results and others indicate that associations between delinquent and antisocial behaviors and individual-level traits are influenced by sex (see also Eme, 2015; LaGrange & Silverman, 1999). Beyond the effects on individual traits, the relation between neighborhood-level contextual factors and delinquency may also be moderated by sex. According to Kroneman, Loeber, and Hipwell (2004), the majority of early research on neighborhood factors and delinquency was conducted with adolescent male samples, ignoring any influence of neighborhood factors on female delinquency. Some studies, however, have considered sex differences in parenting and family functioning as a moderator for the relation between neighborhood disadvantage and risk of delinquency (Plybon & Kliewer, 2001; Stern & Smith, 1995). Greenberg and colleagues (1999) found that the relation between neighborhood characteristics and externalizing behavior was significant for male youth, but not female youth. Similarly, more recent studies have found differential effects on delinquency of neighborhood poverty (Kling, Ludwig, & Katz, 2005) and neighborhood disadvantage (Zimmerman & Messner, 2010) for each sex. Collectively, interactive relations between individual- and neighborhood-level traits and the development of delinquent behavior appear to be influenced by sex. Accordingly, empirical research has begun to consider effects of sex on the two types of predictors concurrently. For instance, recent research by Meier and colleagues (2008) yielded a moderating effect of neighborhood collective efficacy

Neighborhood Effects on the Development of Delinquency 395 on the relation between impulsivity and delinquency. The effect was much stronger for those who reside in neighborhoods characterized by lower collective efficacy than those with higher collective efficacy. Furthermore, although males experienced a stronger effect for impulsivity and had a higher likelihood of experiencing neighborhood risk factors, the neighborhood context affected the relation between impulsivity and delinquency more strongly for females than males. Additional multilevel analyses have revealed that levels of impulsivity are more predictive of offending in less risky neighborhoods for females than for males (Zimmerman, 2010). Taken together, the literature suggests that sex influences the relationship between both individual- and community-level factors and delinquent behavior among youth.

CULTURAL CONSIDERATIONS Research also finds that neighborhood effects on individual outcomes in early childhood and middle childhood vary across different race/ethnicities and cultures (Peeples & Loeber, 1994). For example, Brooks-Gunn and colleagues (1993) found that the benefit of affluent neighbors (vs. middle-SES neighbors) on children’s IQ scores was stronger for White children than African-American children at age 3. Similar findings have been found for children at older ages for IQ scores, verbal ability, and reading achievement scores (Chase-Lansdale, Gordon, Brooks-Gunn, & Klebanov, 1997; Duncan, Brooks-Gunn, & Klebanov, 1994). Each of these studies also showed that having lower-SES neighbors was associated with behavior problems during early childhood. In another important study, Ludwig, Duncan, and Hirschfeld (2001) conducted a randomized experiment in which poor, minority families were assigned to one of three conditions: (1) a group who received Section 8 vouchers and were mandated to move from the housing projects to low-poverty neighborhoods (experimental condition); (2) a group who received Section 8 vouchers to move to any available housing; and (3) a group who did not receive any vouchers and remained in public housing (control condition). Results indicated that the boys whose families stayed in public housing were arrested for twice as many violent crimes as their counterparts whose families moved to low-poverty/middle-class neighborhoods. These findings suggest that the ability to move a family from a more disorganized and impoverished neighborhood to less disorganized and lower-poverty area may substantially decrease the likelihood of juvenile arrests (by 30%–50%).

SUMMARY AND CONCLUSIONS The collection of findings regarding the influence of individual vulnerabilities and neighborhood risk factors produces numerous policy implications and prevention-related recommendations. For starters, as suggested by the multitude of studies included in this chapter, neighborhoods with higher levels of informal

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social control, social cohesion, and collective efficacy exert protective effects on the development of individual self-control. Increased self-control helps to prevent the onset and continuance of delinquent and criminal behavior. Additionally, even youth who demonstrate individual vulnerabilities related to delinquency, such as impulsivity, are less likely to commit criminal acts in neighborhoods with strong protective factors (Meier et al., 2008). On the other hand, more disorganized and risky neighborhoods often do not have the informal social control to protect youth who live there. These individuals are therefore at an increased risk of engaging in impulsive behavior, and of encountering opportunities to engage in deviant and delinquent behavior. Although individual vulnerabilities were once believed to be entirely learned or inherited, research has more recently suggested that those traits that produce a vulnerability to delinquent behavior may be affected by contextual, environmental, and neighborhood factors (e.g., Katz, Kling, & Liebman, 2001; Leventhal & Brooks-Gunn, 2003; Ludwig et al., 2001; Meier et al., 2008). Accordingly, public policies should be developed to enhance the individual-level protective factors, as well as improve the neighborhood-level conditions to prevent youth from engaging in delinquent behavior. Costs of such programs may be large and deter policy-makers from implementation, but benefits of crime prevention considerably outweigh costs. Warner, Beck, and Ohmer (2010, p. 366) summarized several of the main features that should be included in policies and programs that aim to reduce delinquency from an integrated individual- and community-level approach: [Money should be devoted to] programs to educate community residents about shared responsibility for creating safe neighborhoods and create education programs that (1) help residents identify and establish community norms that support pro-social behavior and mutual trust; (2) facilitate residents’ abilities to intervene in inappropriate neighborhood behavior in a respectful, supportive manner using the principles of restorative justice; and (3) develop social capital [the collective value or benefits of the social relationships or networks that one possesses] among residents using community organizing strategies. It is clear that delinquency is not the product of either individual vulnerabilities or neighborhood risk factors alone. Instead, delinquent behavior should be considered as an outcome of interplay between certain individual differences (both genetic and developed) and the social contexts of certain community characteristics. As this truism becomes increasingly more obvious to researchers, support for this integrated perspective has become more widespread in the criminological literature. Three key examples of this line of theoretical and empirical inquiry have been proposed to further progress our understanding of interactive effects. The first is David Farrington’s Integrated Cognitive Antisocial Potential (ICAP) theory (2003). This theory combines aspects of several different criminological theories, such as

Neighborhood Effects on the Development of Delinquency 397 learning, strain, social control, labeling, and rational choice to explain deviant and criminal behavior. The main tenet of the ICAP theory is that criminal behavior results from an individual’s antisocial potential as it interacts with a criminal opportunity affected by certain situational and contextual community-level factors. Initial empirical tests have revealed preliminary support for this integrated theory (see Sullivan, 2006; van der Laan, Blom, & Kleemans, 2009). The second example is P.-O. Wikström’s (2004) Situational Action Theory. This perspective proposes that individual differences (e.g., morality, emotion) interact with environmental provocations (e.g., opportunity) to alter behaviors that an individual perceives as available in certain circumstances. If an individual perceives alternative options for an action, the decision to engage in a certain behavior, including crime, is based on traits of the individual (e.g., self-control) and characteristics of the environment (e.g., deterrent factors) (Wikström, 2004). Preliminary tests have yielded encouraging results for this perspective as well (Wikström & Treiber, 2009). The third and most recent example is Pamela Wilcox and colleagues’ (2014) integrated approach, which examines offending and victimization while considering individual personality traits, as well as criminal opportunity. This approach proposes that an individual’s personality traits affect their criminal-victim propensity and have direct effects on offending and victimization, and indirect effects when combined with situational criminal opportunity (Wilcox et al., 2014). Wilcox et al.’s preliminary test of their perspective demonstrated that individuals with certain “high risk” personality traits were more likely to pursue opportunities with other individuals who “fit” their personality, causing them to engage in more criminal behavior (Wilcox et al., 2014). In conclusion, recent development of these three integrated conceptualizations of criminal behavior is quite encouraging for subsequent explanations of delinquent behavior using more interdisciplinary tests of both individual vulnerabilities and neighborhood risk factors. Future tests of the aforementioned perspectives, as well as development of new integrated theories, should improve our understanding of the complex and interactive effects of individual differences and community factors on delinquent behavior. Additional attention and focus in this area of interdisciplinary research should shed brighter light on the more probabilistic and interactive nature of vulnerability and risk, and their joint effects on the development of delinquency and antisocial behavior.

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Neighborhood Effects on the Development of Delinquency 403 Wikström, P.-O., & Loeber, R. (2000). Do disadvantaged neighborhoods cause well-adjusted children to become adolescent delinquents? Criminology, 38, 1109–1142. Wikström, P.-O., & Sampson, R. J. (2003). Social mechanisms of community influences on crime and pathways in criminality. In B. B. Lahey, T. E. Moffitt, & A. Caspi (Eds.), The causes of conduct disorder and serious juvenile delinquency (pp. 118–48). New York, NY: Guilford Press. Wikström, P.-O., & Treiber, K. (2009). Violence as situational action. International Journal of Conflict and Violence, 3, 75–96. Wilcox, P., Sullivan, C. J., Jones, S., & van Gelder, J.-L. (2014). Personality and opportunity: An integrated approach to offending and victimization. Criminal Justice and Behavior, 41, 880–901. Wilson, J. Q., & Hernstein, R. J. (1985). Crime and human nature. New York, NY: Simon and Schuster. Wilson, J. Q., & Kelling, G. L. (March, 1982). Broken windows: The police and neighborhood safety. Atlantic Monthly, 29–38. Zimmerman, G. M. (2010). Impulsivity, offending, and the neighborhood: Investigating the person–context nexus. Journal of Quantitative Criminology, 26, 301–332. Zimmerman, G. M., & Messner, S. F. (2010). Neighborhood context and the gender gap in adolescent violent crime. American Sociological Review, 75, 958–980.

P A R T III

EXTERNALIZING DISORDERS

C H A P T E R 13

Attention-Deficit/Hyperactivity Disorder JOEL NIGG

HISTORICAL CONTEXT

F

ew child difficulties generate as much controversy and concern in our society as problems with attention and impulse control, especially the syndrome of attention-deficit/hyperactivity disorder ADHD (American Psychiatric Association [APA], 1994, 2013). Such concern is fueled in part by rates of medication treatment for children in the United States, which rose dramatically in the 1990s (Robison, Sclar, Skaer, & Galin, 1999), and which have continued to climb (Setlik, Bond, & Ho, 2009). This concern is often combined with inadequate mental health and educational services for children with special needs in this country, raising worries that medication is the lowest cost but not the best treatment. The debate is important: ADHD is a highly impairing syndrome that affects a large number of children for much of their lives. Although probably no definitive conclusion is possible about whether there is a true secular trend of rising incidence or prevalence of ADHD in the United States, diagnostic prevalence is clearly rising (Boyle et al., 2011; MMWR, 2010). The disorder, by different names, was first mentioned in the late 1700’s and early 1800’s (e.g., Rush, 1812/1962), but only occasionally noted for the next 100 years (for review see Taylor, 2011). By the early 20th century, the medical literature referenced children with “minimal brain damage,” followed by references to “hyperkinetic reaction of childhood,” “hyperkinesis,” “minimal brain dysfunction” (not to be confused with minimal brain damage), “attention deficit disorder” (ADD), and

Author Note. Work on this chapter was supported by NIMH grant R37-MH59105.

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“attention-deficit/hyperactivity disorder” (ADHD). Each of these refers to largely the same group of children. In the 1930s, it was discovered that Benzedrine (an amphetamine-like stimulant) seemed to “calm” hyperkinetic children (Bradley, 1937), and by the 1950s, stimulants were coming into regular use to treat hyperactivity (FDA approval was granted for Ritalin in the early 1960s). By the 1970s, treating inattentive and hyperactive children with stimulants began to spark controversy, a theme that continues in Western societies to the present time. Treatment rates rose markedly again from 1990 to the present, attributable in part to changes in educational policy that facilitated identification of children with ADHD in the United States. This rise has also been influenced by a widening definition of the ADHD phenotype. In the nomenclature of the American Psychiatric Association in the DSM-III (APA, 1980), the condition was labeled attention deficit disorder (ADD with two types: with and without hyperactivity). These subtypes were eliminated and the condition was renamed as ADHD in the DSM-III-R (APA, 1987). In the DSM-IV (APA, 1994), ADHD was retained, but subtypes were reintroduced in modified form: a predominantly inattentive type (ADHD-PI, similar to DSM-III ADD without hyperactivity), a predominantly hyperactive-impulsive type (ADHD-PH, unprecedented in previous nomenclatures), and a combined type (ADHD-C). Subsequent work suggested that these types were not stable, leading the DSM-5 to downgrade them to indicators of current course called presentations, but not to eliminate them due to a continued lack of genetic and brain imaging work on these designations. For example, the first large brain imaging study to use contemporary connectomics modeling differentiated neuroimaging findings for children with inattentive versus combined presentations (Fair, Nigg, et al., 2012). However, it remains to be seen whether such an effect is “carried” by a subgroup with a refined inattentive presentation without hyperactivity as discussed elsewhere in this review. As this abbreviated history suggests, appropriate breadth and delineation of the ADHD phenotype has been a persistent concern. For instance, historically, motor control problems were a part of the overinclusive term “minimal brain dysfunction” used in the midcentury. They were removed from DSM-III-R and subsequent editions, to the diagnostic category developmental coordination disorder. However, data continue to appear on motor control problems, clumsiness, and motor output in ADHD (Piek, Pitcher, & Hay, 1999). Traditionally considered a disorder of childhood, by the end of the 20th century it became clear that ADHD often persists into adolescence and adulthood (Mannuzza & Klein, 2000). Accordingly, data on adults with ADHD have accumulated in the past 20 years. The DSM-5 introduced slight changes in wording and examples for adults, but questions remain about the appropriateness of DSM-5 criteria for adults with ADHD. One particular concern is that among adults, problems with advanced executive functioning may be more central than other kinds of attention problems (such as not seeming to listen), and these items may not be well represented (Kessler et al., 2010). Another concern is that the three items intended

Attention-Deficit/Hyperactivity Disorder 409 to cover impulsivity do not adequately capture differentiated forms of impulsivity. Work in the past two decades shows that impulsivity is a multicomponent construct and its differentiated elements may be quite important to clinical formulations (Sharma, Markon, & Clark, 2014). In this chapter, I emphasize mechanistic theories about within-child psychological and/or cognitive dysfunction in ADHD, and a multilevel perspective on etiology. I conclude by emphasizing that ADHD is not a unitary syndrome but reflects important heterogeneity among affected children. For more extended discussions of ADHD see Barkley, (2006), Nigg (2006b), and Nigg, Hinshaw, and Huang-Pollock (2006).

TERMINOLOGICAL AND CONCEPTUAL ISSUES Despite periodic public controversy, there is substantial evidence for validity of the ADHD syndrome with regard to factor structure, impairment, and family patterns (Faraone et al., 2005; Willcutt et al., 2012). It is important to note that symptom domains are divided into distinct dimensions in the DSM-5: (a) inattentive-disorganized, and (b) hyperactive-impulsive. The field has debated for decades whether impulsivity and hyperactivity should count as one dimension or as separate subdimensions. This is because in children, some studies show a slightly better fit for a three-factor than a two-factor solution. However, fit is usually so close between two- and three-factor structural models that the more parsimonious two-factor solution is preferred. The picture is murkier among adults, for whom there is more support for the three-factor solution. However, having different criteria structure for children versus adults would introduce enormous complications for clinical practice. The two-factor structure does have substantial external validation, at least among children. Inattentive behaviors are most strongly associated with academic problems and a range of other impairments, whereas hyperactive/impulsive behaviors aggregate with peer rejection and disruptive tendencies in school and at home (Willcutt et al., 2012). Distinct molecular genetic influences also accrue for inattention/disorganization versus hyperactivity/impulsivity (Nikolas & Burt, 2010). Thus, ADHD is best understood not as unitary, but as a two-dimensional syndrome. Less clear is whether, from a conceptual standpoint, it is more accurate to view ADHD as a discrete syndrome, or as reflecting extreme standing on a normalvarying trait. Although this is a difficult question to answer in terms of underlying causal factors, evidence to date indicates fairly clearly that the syndrome usually reflects extreme standing on a continuously varying trait in the population (Willcutt, Pennington, & DeFries, 2000). Yet clinicians still need to make diagnostic decisions, and cut points in the DSM have empirical support as efficiently identifying impaired children who need services. However, when we consider etiology, a dimensional model is likely to be most useful.

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As noted earlier, the three presentations in the DSM-5 have limited empirical support. In part this is due to a shortage of research on children who present as primarily inattentive, particularly if they have no evidence of symptoms of hyperactivity. Some data raise the tantalizing possibility that if a child has two or fewer symptoms of hyperactivity-impulsivity, but otherwise meets DSM criteria for ADHD, this could be a meaningful type. This group emerges in latent class analyses (Volk, Todorov, Hay, & Todd, 2009), and has worse problems with some types of attention than more symptomatic children with both inattentive and hyperactive impulsive symptoms (Carr, Henderson, & Nigg, 2010; Goth-Owens, Martinez-Torteya, Martel, & Nigg, 2010). This literature is still in its infancy and it may turn out that this group of children lacks a stable “type” presentation over time. This will be an interesting area for future study. I discuss newer and alternative subtyping strategies below. Note that studies of ADHD sometimes used the DSM-IV or DSM-5 criteria, sometimes the International Classification of Disease–10th Edition (ICD-10) criteria for hyperkinetic disorder, and sometimes merely extreme rating scale scores. Herein, I use the term ADHD throughout to avoid tedious cataloguing of differences in phenotype definition across studies.

DIAGNOSTIC ISSUES AND DSM CRITERIA DSM-5 criteria (APA, 2013) likely will differ somewhat from the forthcoming ICD-11 (due out in 2017). Students should find their comparison interesting when ICD-11 appears. Although the symptom lists are similar, several distinctions can be seen between DSM-5 and ICD-10 hyperkinetic disorder, which may well remain in ICD-11. The most important difference is that the two systems historically have different rules about comorbid disorders as rule-outs, with ICD-10 being more restrictive and DSM-5 being more inclusive. Additional criteria include onset by age 12 years, cross-situational display, and impairment (a general requirement for most mental disorders in the DSM system). Although guidelines about age of onset have remained controversial, the shift to an age 12 onset requirement in DSM-5 was reasonably supported (Kieling et al., 2010). On the other hand, guidelines requiring cross-situational problems and impairment have strong empirical support. Failure to assess impairment, in particular, is quite likely to inflate prevalence estimates (Gordan et al., 2005) and inclusion of parent and teacher standardized ratings greatly enhances assessment validity (Pelham, Fabiano, & Massetti, 2005). Thus, appropriate assessment requires a careful history, data from multiple adult informants with well-normed rating instruments, distinguishing ADHD from either normal developmental variation or any of several medical and psychiatric conditions that feature inattention and impulse control problems (e.g., anxiety and mood disorders, sleep and other health-related disorders, and some types of learning disorders), and, when possible, direct observation. Careful consideration of functional adjustment in multiple domains can further assist with treatment tailoring (Pelham et al., 2005). Assessing these issues can be very difficult among preschool

Attention-Deficit/Hyperactivity Disorder 411 children due to their high base rate of impulsive and hyperactive behaviors. However, ADHD can be identified reliably and validly in research settings as early as age 3–4 years, which has prompted many clinicians to attempt to do the same. Medical guidelines now exist for assessment and treatment among 4- to 5-year-old children (Wolraich et al., 2011). Assessment is more difficult among adults, because retrospective history is difficult to obtain reliably, and because some of the extant DSM-5 symptoms are rarely endorsed by adults.

PREVALENCE In the past decade, systematic population-based national surveys were conducted for the first time. Because different method were used across surveys, these did not yield identical results, but they still yield a consistent picture of ADHD as a very common condition. In one national survey, a 1-year prevalence rate for children and adolescents of 8.5% was reported (Merikangas et al., 2010). Among U.S. adults, the prevalence of ADHD is 4.4% (Kessler et al., 2006). Worldwide, or when data from different countries are pooled, meta-analytic reviews suggest a 1-year prevalence rate of ADHD among both children and adolescents of around 5.3% (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007), among adults of around 2.5% (Simon, Czobor, Balint, Meszaros, & Bitter, 2009). A more stringent Bayesian meta-analysis, which included surveys that used structured interviews and multi-informant ratings, yielded a lower prevalence of 2.5–3.5% around the world, and suggested no change in true incidence of the disorder in the past two decades (Erskine et al., 2013). Although improving, these data are still very limited. Most developed nations have been studied, but almost no attempt has been made to study a syndrome like ADHD among original aboriginal peoples (i.e., untouched by modern technology or disease) to address the common lay speculation that ADHD is a contemporary ailment. However, ADHD-like problems are seen among Native American and Inuit peoples, though often confounded with other health conditions. Practitioner surveys by the Centers for Disease Control from the 1990s to the late 2000s (Boyle et al., 2011) and Centers for Disease Control surveys of parents both indicate steadily increasing rates of case identification. Why identification rates appear to be so much higher than true incidence rates, and rising if incidence rates are flat, is open to speculation. One can point to limited services for children or speculate that DSM criteria are in fact too conservative. But which of the many possible explanations tell the appropriate story is limited to just that, speculation.

RISK FACTORS AND ETIOLOGICAL FORMULATIONS I now turn to etiological approaches, emphasizing both (a) within-child correlates that may elucidate etiology and help explain observed behavioral problems, and (b) risk factors that may contribute to the disorder, perhaps via these internal

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mechanisms. I bypass a range of psychological mechanisms such as self-esteem and locus of control and instead focus primarily on neurally mediated models.

Genetic Influences on Liability to ADHD Perhaps the most critical domain for nuanced understanding of ADHD etiology concerns the much discussed, and much misunderstood, role of genetics. This section traces the fast evolving and fascinating research story in that area. The Past. Family studies established long ago that ADHD “breeds true,” with a two- to fourfold increased risk among first-degree relatives. How much of this familial similarity is due to heritability versus common family experiences? Numerous twin studies, along with a few adoption studies have established that for parent ratings, a substantial portion of liability for ADHD is carried by heritability, with a heritability coefficient, averaged across many studies, exceeding .8 (Burt, 2009; Willcutt, in press). Heritability estimates are somewhat lower, however, when teacher ratings are examined, although the heritability of a latent variable for shared parent and teacher agreement was .78 in a large Dutch sample (Derks, Hudziak, Van Beijsterveldt, Dolan, & Boomsma, 2006). Relatively few studies have examined twin concordance of ADHD diagnoses derived from full clinical evaluation, or the combination of parent- and teacher-reports of symptoms and impairment. Variation in results for teacher- versus parent-ratings raises questions of rater bias (known as contrast effects) as an influence on heritability estimates. Contrast bias (parents emphasizing differences more in DZ than MZ twins) is known to inflate heritability estimates of activity level among preschoolers (Saudino, 2003). Such effects in ADHD ratings appear to depend on what rating scale is used. Rietveld et al. (2004) reported on a longitudinal study of a large sample of twins in Europe, with maternal CBCL ratings at four age points (3, 7, 10, and 12 years). Even with rater contrast effects controlled, heritability was above .7 at each age. Simonoff et al. (1998) confirmed maternal contrast effects but also noted biases in teacher ratings due to twin confusion (known as correlated errors), especially for MZ twins. In other words, many twins have the same teacher, and teachers have more difficulty keeping MZ twins straight in their minds. When these effects are accounted for, heritability is between .6 and .7. In sum, the heritability of ADHD based on quantitative twin studies—at least of the dimensions of inattention and hyperactivity-impulsivity in the population—is likely to be around 0.7. Nonshared environmental effects account for the remainder of variance in ADHD behaviors. In response to these findings, researchers aggressively pursued molecular genetic studies during the period from 2000 to 2010. Since 2010, the molecular approaches have been complemented by new statistical tools (see below) and by Gene × Environment interaction studies (see below) and heritability is re-estimated using molecular studies as also discussed below.

Attention-Deficit/Hyperactivity Disorder 413 Looking back for a moment, the most common approach to studying molecular correlates in ADHD initially was to look at candidate genes—that is, selected markers on genes believed for theoretical reasons to be of interest, such as dopamine receptor genes (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer] and Chapter 6 [Neuhaus & Beauchaine]). A meta-analysis of that literature indicated that six genes have common markers that are associated reliably with ADHD to date: dopamine transporter (DAT1), dopamine D4 and D5 receptors (DRD4, DRD5), the serotonin transporter (5HTTPLR), HTR1B, and the SNAP25 gene (Gizer et al., 2008). However, these in combination accounted for only 1% of phenotypic variance in ADHD—a common problem in psychiatric genetics (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]) to which I will return shortly. A second approach is to conduct genome-wide scans (GWAS). With this approach searches are conducted across hundreds of thousands of common markers (called single nucleotide polymorphisms or SNPs). Somewhat to the surprise and disappointment of many scientists, genome-wide scans largely failed for many years to identify important new genes for ADHD (Franke et al., 2011). In part, such failures occurred because a very large number of statistical tests are required (hundreds of thousands), resulting in low statistical power. This problem also plagues psychiatric genetics more generally (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). The Present. Since this problem was identified, researchers in psychiatric genetics have begun to build worldwide collaborations that pool thousands of cases. In ADHD, as of this writing the international psychiatric genetics consortium is close to having 25,000 cases available for study. Within a few years, that number will reach 40,000. These can be compared to some 90,000 controls that are available. When these giant studies are completed, it is likely that several new genes related to ADHD will be identified, which in turn will provide clues to its pathophysiology. For example, already to date, GWA studies identified additional candidates that warrant follow up, including one that is under a genome-wide significant linkage peak in a meta-analysis, CDH13 (Lasky-Su et al., 2008). This gene is expressed in nicotinic receptors and neurite outgrowth (Poelmans, Pauls, Buitelaar, & Franke, 2011). Similar interesting findings are likely in the near future. GWA studies assume what is called a “common disease common gene variant” model. That is, they assume that gene variants that are common in the population are the most relevant; the GWA chips assay common variations in the genome. However, an alternative is to look for rare variants in the genome. (In reality, these two models will and should be integrated, so they should not be seen as competing but as complementary). Although rare variants cannot account for all of a common disorder such as ADHD, when discovered they can yield important insights into pathophysiology. The search for “rare variants” has now been productive in schizophrenia and autism spectrum disorder, as well as ADHD. Frequently, they focus on a particular structural variation called copy number variants (meaning the

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only difference in the gene is that a given nucleotide sequence is repeated too many times). This can be accomplished by reanalyzing GWAS data. One study that used this method found evidence of a rare copy number variation at locus related to ADHD on chromosome 15 at q13.3, which occurs in a little under 1% of the population and doubles risk for ADHD (Williams et al., 2011). New variants can be discovered by sequencing exomal regions of the genome (nonsynonymous variants), or by sequencing the entire genome. These types of studies are now underway in ADHD on a large scale and are likely to yield new discoveries in the coming decade. The Near Future. What about the problem of candidate genes having very small effects? How will remaining genetic variation be understood? Here, recent breakthroughs have been quite provocative, based on new statistical tools that effectively increase the information yield from GWA studies. Rather than searching for individual genes, scientists can examine aggregate signals in two basic ways. The first basic statistical approach is to organize common gene variants into their chemical and physiological groupings, which I refer to as gene sets (they are often called pathways but this terminology can be confusing if not employed carefully). This approach tests for significant association with over- or under-expressed gene sets and thus has more power than searches for individual markers. This approach is fraught with methodological challenges (Mooney, Nigg, McWeeney, & Wilmot, 2014), not least of which is lack of consensus on the best analytic tools and the best gene groupings. However, when those are carefully considered, this approach can become a promising way to gain insights and generate hypotheses for future study. As of this writing, less than a dozen studies have adopted versions of a gene-set or pathway approach to ADHD. However, preliminary findings have been thought-provoking. For example, Poelmans et al. (2011) identified a coherent network related to nicotinic receptors (already one of the biochemical theories of ADHD) and related to neural growth (relevant to newer theories of neurodevelopmental delay). In another example, Stergiakouli et al. (2011) also identified relevant biological pathways, most interestingly among metabolic systems related to CNS development and cholesterol metabolism (essential for neural development). It is likely that more gene-pathway-based approaches will be fruitful in the future, and will begin to yield enough analyses that consensus findings begin to emerge. Second, and the dominant approach in the field as of this writing, is the use of what are called polygenic scores. In simplified terms, this is a method of adding up all the variance from all the common variants in the GWAS chip (weighted by effect size in a discovery data set) to estimate heritability and total genetic contribution to a phenotype. Although this is conceptually and computationally straightforward, its implementation requires adequate discovery data sets and so was achieved only relatively recently as statistical advance (for an authoritative yet accessible introduction and tutorial suitable for graduate students and nonexperts, see Wray

Attention-Deficit/Hyperactivity Disorder 415 et al. (2014)). Findings using this approach have already had a major impact on our understanding. For example, the first major study by the international psychiatric genetics consortium suggested that about 28% of the liability for ADHD (or a little less than half of its heritability as estimated from twin studies) was attributable to common genetic variants (Lee et al., 2013). The remainder is due to rare variants, G × E interaction (below), or measurement error. A second major finding was that genetic variation is heavily shared across many psychiatric conditions, although notably there was some uniqueness for ADHD relative to “adult” disorders such as depression and schizophrenia (Gratten, Wray, Keller, & Visscher, 2014; Lee et al., 2013). Ancillary analysis in the large-scale psychiatric genetics consortium suggests that one of the shared genetic effects involves calcium channel signaling—consistent with brain-wide neurodevelopmental effects (Lancet, 2013). Expect several new analyses using this approach to yield interesting insights in coming years. Summary. In short, the molecular genetics of ADHD remains a vibrant, exciting area of research despite some surprising and disappointingly small results to date. Our inability to explain most of the variance in the ADHD phenotype has several possible explanations. Perhaps the most interesting include the possibilities of Gene × Environment interaction and/or epigenetic effects (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]).

Gene × Environment Effects and Epigenetics As insights about genetics have become more refined, the dynamic interplay of genes and environments have increasingly moved to center stage for all of psychopathology, and ADHD is no exception. Epigenetic mechanisms provide a direct biological route for linking gene by environment interplay with process understanding of development; this exciting new direction requires understanding of both developments in G × E of ADHD and the intriguing, though still very new area, of epigenetic influence on ADHD. G × E. Much of the unexplained heritable variation in ADHD is potentially due to Gene × Environment interaction. In the past decade, studies of G × E effects have become the norm in psychiatric research. Initially, most of these studies examined one or two selected genetic markers (candidates) in relation to selected measures of the environment. The hazards in such studies are many. In particular, (a) the environmental measure may itself be influenced by variation in unmeasured genes, and (b) if variables are not properly scaled, artifactual or “false positive” effects are often found. Nevertheless, initial efforts in this area are interesting. An early meta-analysis (Nigg, Nikolas, & Burt, 2010) indicated reliable and consistent interactions of psychosocial distress measures and genotype, particularly for dopamine

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transporter (DAT1) and serotonin transporter, in predicting ADHD. Recent studies have continued to echo that impression, linking psychosocial distress measures with serotonergic and dopaminergic genotypes (Elmore, Nigg, Friderici, Jernigan, & Nikolas, 2016). Epigenetics. Recent years have seen exciting developments in epigenetics, that is, processes through which experience can alter the genome—and thus the phenotype—sometimes dramatically. This occurs through methylation (modification of chromatin, the material in which DNA is “housed”), to alter gene expression. Expression of much of human variation may not depend only on DNA structure but on the regulatory markings that control whether and how a gene is expressed. Epigenetic study of psychiatric problems in humans faces daunting challenges. For one, epigenetic marks are heavily tissue specific and quite localized. It is difficult to generalize from one brain region to the next, much less from one physical system to another outside the brain. In addition, direct assay of epigenetic variation in living humans is not currently possible—it requires taking a brain biopsy, clearly out of the question in psychiatric studies of living people. It is hoped that in the near future noninvasive technology to measure epigenetic effects in living people will emerge. In the meantime, however, these problems are not insurmountable. Recent years have seen evidence of considerable cross-tissue conservation within the framework of tissue-specificity of epigenetic changes. Such cross-tissue conservation in turn has enabled the beginnings of a study of peripheral tissue epigenetic changes in psychiatric conditions. Recently, the first genome-wide methylation study of ADHD was published (Wilmot et al., 2015). Although preliminary, it suggested promising new targets for ADHD research. This work is likely to hold considerable promise in combination with other approaches. Summary. These two insights (the importance of G × E and the importance of epigenetic effects) have sparked a renaissance in studies of environmental contributions to ADHD (as well as many other psychiatric conditions), which is changing the face of research in the second decade of the 21st century and may potentially also change the face of clinical practice in the decades that follow.

Environmental Risks and Triggers When G × E and epigenetic mechanisms are recognized, many possible environmental contributors to the etiology of ADHD emerge as potentially important. A fruitful way to think about the etiology of ADHD is to consider structural DNA (the part that, as far as we know, cannot be changed except by mutations) as conveying liability or susceptibility to ADHD. Experiences then activate the condition, either by causing direct changes in the brain or physiology or via epigenetic markings that change gene expression. This model suggests that a given environmental risk will not affect all children: some are “immune” to the

Attention-Deficit/Hyperactivity Disorder 417 effect but other children are susceptible and develop ADHD in the presence of this risk. G × E empirical studies tend to support such possibilities. For example, it is known that (a) neurotoxic pesticide clearance rates from the body depend on genotype (Engel et al., 2011); (b) blood lead levels are modulated by iron uptake, in turn controlled by genotype (Hopkins et al., 2008); and (c) responses to dietary additives may be modulated by genotype (Stevenson et al., 2010). It also appears from neuroimaging studies of discordant identical twins in which one has ADHD and one does not, that major changes in the brain associated with ADHD are not accounted for genetically (Castellanos et al., 2002). Thus, it appears likely that a susceptibility-plasticity model will ultimately fit best for ADHD (and probably for other kinds of psychopathology and complex disease generally), rather than a genetic main-effect model. Sociological Factors. As for specific environments, several are notable. First, commentators have suggested that inadequate schooling, rapid societal tempo, and family stress are contributing to an alleged increase in ADHD incidence. Many of these sociological ideas are interesting but untested (or untestable) and some (like schooling) occur too late in development to account for ADHD onset. Perinatal Risk. Regarding other potential environmental potentiators of genetic liability, biological context, both pre- and postnatally, are important. For example, we have had evidence for some time that low birth weight (< 2500 grams) is a specific risk factor for inattention/hyperactivity and certain learning and motor problems, but not for other behavioral or emotional problems at age 6 (Breslau & Chilcoat, 2000). However, low birth weight is itself multiply determined by factors such as maternal health and nutrition, maternal smoking, maternal weight, low SES, stress, and other factors, making identification of specific biological mechanisms difficult. Teratogens. An extensive literature indicates that some pre- and postnatal teratogens increase risk for ADHD (see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]). For example, alcohol exposure, at least for women in the United States at moderate levels of drinking (Jacobson, Jacobson, Sokol, Chiodo, & Corobana, 2004; Mick, Biederman, Faraone, Sayer, & Kleinman, 2002), increases risk of offspring ADHD. However, alcohol-exposed children may have a somewhat distinct neuropsychological profile from typical ADHD, with particular problems in visual attention and mathematics. Prospective population studies also implicate household and outdoor pesticide exposures during critical periods in pregnancy as predictive of ADHD (Marks et al., 2010; Sagiv et al., 2010). It has been known for centuries that lead is neurotoxic and for decades that sufficiently high exposure can cause hyperactivity and other health problems. In the past decade it has been discovered that even at background level exposure—which is near-universal in the U.S. population (about 10 parts per billion in blood samples)—blood lead level is correlated with ADHD symptoms (Braun

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et al., 2006; Chiodo, Jacobson, & Jacobson, 2004; Nigg et al., 2008; Nigg, Nikolas, Knottnerus, Cavanagh, & Friderici, 2010). Diet. Dietary factors are increasingly implicated in ADHD. However, because that literature enables experimental studies rather than relying mostly on correlational designs, I return to it in the next paragraph on causality. Causality. A crucial challenge is to determine if such correlates, even though they emerge in prospective population based studies, are causal (Lewis, Relton, Zammit, & Smith, 2013). Although G × E as well as gene-environment correlation can mask environmental effects, they can also mask genetic effects. Diet, teratogens, and toxins could be proxies for genetic risk because of gene-environment correlation (see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]). Animal experiments provide circumstantial evidence that causality is possible, but human behavior is complexly determined relative to laboratory animals. Yet although causal, proof among humans is difficult to obtain, it is not impossible to gain leverage here. First, in the case of diet, direct experimental trials can be conducted among humans. These have recently begun to bear considerable fruit. Two recent metaanalyses of randomized experimental data concluded that dietary factors provide a clinically meaningful causal influence on ADHD (Nigg, Lewis, Edinger, & Falk, 2012; Sonuga-Barke et al., 2013). The most-studied agent was synthetic food additives (dyes and/or preservatives). Although these effects are real, they are small—about one sixth the size of a medication effect. We (Nigg et al. 2012) estimated that a major dietary intervention using a restriction diet had about a 30% chance of some beneficial effect in children with ADHD. Only a small fraction of this effect is likely due to synthetic additives; the rest may be food allergies. It has likewise become clear through a maturing literature that omega-3 fatty acids are related to ADHD. The most detailed meta-analysis in this arena showed both that (a) children with ADHD tend to have reduced blood levels of omega-3 relevant markers and (b) dietary supplementation with omega-3 fatty acids improves ADHD symptoms (Hawkey & Nigg, 2014). Again, the effect, although significant, is quite small. Clearly, omega-3 supplementation cannot replace medications, but it may provide a useful adjunct. Second, surrogate parent designs and sibling designs provide two different ways to use family genetic variation to draw causal inference. For example, two clever family designs, one using surrogate mothers who were related and unrelated to their offspring and one using siblings who differed in whether their mother smoked during pregnancy, both concluded that causal effects of prenatal smoking on ADHD were likely smaller than previously believed (D’Onofrio et al., 2008; Thapar et al., 2009). Third, a promising strategy to evaluate causality in human designs is in use of a design called Mendelian randomization (Lewis et al., 2013). This strategy entails

Attention-Deficit/Hyperactivity Disorder 419 identifying a gene that biochemically influences toxicant action (for example, it might change the rate of metabolism of a pesticide). When the genetic variation is randomly distributed in the population (as it usually is), then nature has created an experiment for us. If the randomized changes in physiological response to the toxin changes the clinical outcome, then this is evidence supporting causality. For example, Engel et al. (2011) examined variation in the PON1 gene and pesticide exposure. PON1 regulates speed of metabolic excretion of organophosphate pesticide. Mothers with the fast-metabolizing allele of PON1 had offspring with a weaker impact of pesticide on ADHD symptoms, consistent with a causal influence of prenatal pesticide on human offspring attention problems. Similarly, Nigg et al. (2016) examined variation in the HFE gene (on chromosome 6) on the association of blood lead with ADHD. The HFE gene regulates iron uptake and thus exerts indirect effects on lead handling. In this report, again, a significant Gene × Lead interaction on hyperactivity was observed, consistent with a causal influence. For one genotype, the association of lead with hyperactivity was weak; for the other genotype, it was twice as strong. The lead finding is notable as an exemplar case of a Susceptibility × Experience model of ADHD. Because this level of lead is nearly universal even in well-regulated countries like the United States, the potential population effect of even a small causal influence on ADHD is substantial. Many other experiential factors have been hypothesized to influence ADHD, from general sociological claims such as “faster pace of life” to more testable effects of early electronic media on brain development. In most cases data are limited or conclusions are muted. The literature on media effects on developing attention is instructive. A massive literature shows that exposure to violent media increases child aggression. However, it has been difficult to see effects on attention development per se. Only recently has this literature reached sufficient maturity to enable a meta-analysis (Nikkelen, Valkenburg, Huizinga, & Bushman, 2014). That review identified a significant association, although the aggregate effect was small after controlling for covariates (r ∼ .12).

Mechanisms I: Neuroimaging Findings Whether we discuss genetic contributors to liability or environmental triggers, these effects are presumably expressed in the brain. Thus, isolation of causal mechanisms suggests that we consider brain circuitry and associated abilities. I therefore review neuroimaging findings before turning to psychological functioning in ADHD. Table 13.1 lists some fairly well established brain imaging findings in ADHD. Structural Imaging. Perhaps the most striking findings in the brain imaging literature to date come from a large nationally representative sample of several hundred children, some of them scanned several times, undertaken by the intramural branch at NIMH from the 1990s to the present. First, that cohort revealed that

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Structure

Key Findings

Prefrontal Cortex

Reduced right>left asymmetry, with relatively smaller right side; reduced dorsolateral prefrontal cortex volume; underactivation of right medial prefrontal cortex and ventrolateral prefrontal cortex to challenge. Few structural studies of this region; functional studies indicate possible hypoactivation during challenge tasks, need replication. Reduced volume of the caudate, but not putamen; decreased volume of the pallidum, reduced size of globus pallidus in preliminary studies; hypoactivation of left caudate during executive task performance; reduced blood flow to the putamen in reflexometric MRI study. Reduced size of vermis, especially posterior-inferior lobules; overall decreased right cerebellar volumes. Smaller rostrum (anterior and inferior region); abnormalities of the posterior regions linked to temporal and parietal cortices in the splenium. Decreased volume of the parietal lobe, reduced occipital gray and white matter, significantly larger posterior lateral ventricles bilaterally. Insufficient data on subregions of key structures; insufficient control of confounds; lack of data on key subcortical regions.

Dorsal Anterior Cingulate Cortex Basal Ganglia

Cerebellum Corpus Callosum

Exploratory Findings

Caveats

Data from Castellanos et al., 2002; Giedd, Blumenthal, Molloy, and Castellanos, 2001; Seidman, Valera, and Makris, 2005.

smaller volume of key brain structures was apparent at the earliest ages studied (4–5 years old) and remained stable in relation to comparison children throughout development (Castellanos et al., 2002). Second, it revealed that ADHD was associated with altered timing of posterior-to-anterior cortical thinning, which normally happens with development due to the pruning and shaping of brain circuits (Shaw et al., 2006). Although the effects remain too small to be helpful in diagnosing individual cases, they confirm that at a group level, ADHD is a neurodevelopmental condition that is associated with reliable, persistent, and widespread alterations in brain maturation throughout development. These findings complement a host of smaller studies. Structural findings demonstrate that on average, children with ADHD evidence a 5% reduction in overall brain volume and a 12% reduction in volume of key frontal and subcortical structures, particularly the prefrontal cortex (PFC), which is crucial to complex, planned behavior, keeping goals in mind, and overriding inappropriate responses; the basal ganglia/striatum, a group of subcortical structures important in response control;

Attention-Deficit/Hyperactivity Disorder 421 the cerebellum, which is important in temporal information processing and motor control; and the corpus callosum, which is involved in integrating information for efficient responding. The most compelling evidence points to a neural circuit that links the prefrontal cortex and a subcortical region known as the striatum, a circuit thought to be important in response output control. Additionally, notably smaller structural sizes are observed in the cerebellum (especially the cerebellar vermis), a region important for temporal information processing and executive functioning, which is connected via long fiber projections with the prefrontal cortex. Task-Based Functional Studies. Task-based functional imaging studies seem to support this view. A meta-analysis of 16 functional imaging studies of ADHD revealed consistent brain activation deficits in virtually all regions of the prefrontal cortex, as well as other brain regions (Dickstein, Bannon, Castellanos, & Milham, 2006). These findings support abnormal functioning in fronto-striatal as well as frontal-parietal neural circuitry in ADHD see also (Durston et al., 2003). However, some recent reviews have extended this, pointing to apparent abnormalities also in fronto-cerebellar and possibly fronto-limbic networks (Rubia, Alegria, & Brinson, 2014). For example, a recent review of MRI studies of reward anticipation shows reduced activation in ADHD of ventral-striatum, which has important connections to prefrontal cortex involved in motivation (Plichta & Scheres, 2014). Thus, structural and functional findings tend to converge on the involvement of frontal-subcortical circuitry in ADHD but in a broad sense that includes multiple frontal-subcortical and fronto-parietal circuits. This suggests widespread involvement of multiple attention and control systems in ADHD, as well as systems involved in emotion regulation. The extent to which these effects can be understood in terms of subgroups of children within the ADHD population, and the extent to which effects are specific to ADHD, remain key next steps for the field to understand. In the past decade, newer imaging methods have begun to change the face of psychiatric neuroimaging. Most notable in relation to ADHD and other child disorders is the emergence of more robust methods for evaluating neural circuits directly (rather than via inference). DTI. The first of these methods is diffusion tensor imaging (DTI). Conducted with MRI, this method traces the directional flow of water molecules in the brain. These molecules generally “diffuse” in line with the tissue in which they are located. This principle makes it possible to trace the integrity of axonal connections (white matter tracts). The DTI signal is altered when white matter microstructure is altered (e.g., due to reduced development of glial cells, reduced axonal size, or other physiological changes). Over a dozen studies have been conducted in ADHD using DTI. All show multiple areas of altered white matter microstructure in ADHD. One of the most comprehensive analyses, conducted with the youngest group of children studied with this method to date, showed that by age 7–8 years,

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widespread (brain-wide) microstructural alterations are observable in ADHD (Nagel et al., 2011). A meta-analysis (van Ewijk, Heslenfeld, Zwiers, Buitelaar, & Oosterlaan, 2012) confirmed these findings, showing reliable effects in reduced white matter development in key regions related to frontal-posterior association circuits. However, this literature remains small. Particularly interesting was a nascent suggestion of more findings in younger children. However, that finding was heavily reliant on the Nagel et al. study, which used the youngest ADHD sample and an unusually thorough analytic approach. More age-based studies are very important in this arena and longitudinal studies will be particularly exciting when they appear. Although questions remain about reproducibility of specific DTI findings in ADHD (just as was the case for many years with other MRI studies), widespread nature of the findings being observed calls into question theories of ADHD that focus on single brain circuits or neurotransmitters. In contrast, it puts renewed emphasis on theories that address brain-wide mechanisms such as synaptic signaling, myelin formation, and the like. This direction converges strikingly with the recent developments in molecular genetic pathways analyses, which also focus on systems related to neurodevelopment, synaptic signaling, and related processes that are widespread in the brain. rsfMRI. Second, a promising new technique in recent years is known as resting state functional connectivity MRI. In this method, functional MRI is used and the key measure is the BOLD signal (a measure of blood flow, which is assumed to reflect neuronal activity; so long as that assumption can be supported, the inferences are of interest). Traditionally, fMRI studies have examined changes in the BOLD signal during different task conditions. However, that method suffers at times in reproducibility and clinical application because results are heavily dependent on specific task characteristics. It also focuses on discrete brain regions in most instances. The hope is that resting-state functional connectivity MRI (rs_fcMRI) can transcend this limitation and produce more cross-site reproducible results. It is also a method that is attractive in regard to studying large scale circuit dynamics (as opposed to particular regions). The intriguing idea behind the rs_fcMRI signal is that instead of subtracting the massive background “noise” of neural activity that goes on outside of task specific activation, such main neural action in the brain is now the focus of study. Spontaneous neural firing occurs throughout the brain when people are not engaged in any particular task (as well as when they are). However, that firing is not random—it is synchronized across different brain regions. When those synchronized signals are mapped, they create functional maps of both known and novel brain circuits. It is as if the Hebbian signal has been mapped—as if neurons are firing together to maintain their connection in case it is needed. Questions remain about the potential for artifact in this method. For example, some circuits may appear connected to participant head motion. Even so, studies using this method also show particular patterns of altered

Attention-Deficit/Hyperactivity Disorder 423 functional connectivity in ADHD and can potentially provide new tools to map subcortical circuits (Castellanos et al., 2008; Fair et al., 2010; Uddin et al., 2008). The ADHD literature here is steadily taking shape with consistent findings related to intrinsic connectivity of default and executive control networks (Posner, Park, & Wang, 2014). Recent work on ADHD has provided newer and exciting tools for visualizing the broad network-based topology of the entire brain, helping researchers understand overall efficiency of cortical organization and systems dynamics of complex network structures that organize brain function and maturation (Gates, Molenaar, Iyer, Nigg, & Fair, 2014; Miranda-Dominguez et al., 2014). Combining the results of resting and task-based studies, consensus models are emerging that suggest that ADHD may involve both a dorsal network called the frontal-parietal executive control network (including dorsolateral-prefrontal cortex, interparietal sulcus, and subcortical nodes) and a ventral network called the cingulo-opercular network (including the frontal operculum, anterior cingulate cortex, and thalamic nuclei). Both networks are well grounded in the literature (Petersen & Posner, 2012), and involve cortical-cortical circuits as well as cortical-subcortical connections.

Mechanism II: Performance Studies of Neuropsychological and Cognitive Abilities In understanding psychological mechanisms that are involved, which might correspond to what is known about neural findings, four key functional systems in the brain are implicated in ADHD: (1) nonexecutive attention and arousal, (2) executive functioning and cognitive control, (3) motivation and reinforcement, and (4) temporal information processing. Table 13.2 summarizes some common findings in this domain. However, these can be consolidated into an integrated two-process model of cognition and regulation that is useful for heuristic purposes. Two process models generally take the form of having, ironically perhaps, three elements: (1) a bottom-up or automatic process, (2) a top-down or controlled process, and (3) a resource or capacity function that governs how much energy or mental resource is available for allocating these processes and limits their application. With that in mind, I consider attention, executive functions, and arousal.

Bottom-Up Processes Attention can be defined as facilitated processing of one piece or source of information over others—in other words, the ability to focus or filter information. Usually considered a cognitive process, attention can be influenced by emotion as well (for example, when anxiety narrows attentional focus). Attentional selection (whether by location, movement, timing, or other features) is influenced both by bottom-up stimulus-driven processes that are relatively automatic and early developing, and by top-down goal-driven processes that are strategic, relatively deliberate, related to the concept of executive control, and later developing.

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Domain Attention Perceptual selection Reflexive orienting Alerting/Vigilance System Cognitive Control/Executive functioning Interference Control Working Memory Verbal Working Memory Spatial Planning Response Inhibition Set Shifting Activation Motivational Response Reactive (anxious) inhibition Reward response (approach) Motor and Temporal Response Motor control Temporal processing

Status

Meta-Analytic Effect Size

4 4 1

na d=.20 d=.75

4 2 1 2 1 2 3

d=.20 d=.45 d=1.0 d=.55 d=.60 d=.50 na

4 2

na na

2 2

na na

Note: Ratings of status: 1=replicated deficit, substantial in size (reliably larger than .50 based on number of studies, confidence interval around pooled effect size); 2=deficit probably exists, but aggregate effects are modest in size (not reliably larger than .50) or consistent results rely on a small number of studies (so pooled effect size has wide confidence interval); 3=possible deficit but findings are mixed across different indicators, and positive findings rest on small number of studies; 4=spared or effect is too trivial in size to be clinically meaningful). Na=not available or not applicable. Effect sizes are rounded off estimates. Reprinted from Nigg, (2006b), where detailed review is also available.

In contrast, a posterior network involved in reflexive orienting and perceptual filtering is apparently not involved in ADHD (Huang-Pollock & Nigg, 2003; Huang-Pollock, Nigg, & Carr, 2005; Sergeant & van der Meere, 1998). Motivation, Approach, and Reinforcement Response. Central motivational processes involved in ADHD have been approached from a temperament perspective (for reviews see Nigg 2006a; Rothbart & Bates 1998), and from an experimental perspective using a decision making framework that evaluates responses to reinforcements varied by time delay or level of probability. I begin with temperament, synthesizing several models to focus on two key traits. Avoidance of possible punishment, also known as reactive behavioral inhibition, is associated in part with limbic structures including the amygdala, the hippocampus, and their interconnections with regions in prefrontal cortex. This form of bottom-up control implements reactions of anxiety and fear that trigger spontaneous inhibition of some or all behaviors, along with alert scanning of novel

Attention-Deficit/Hyperactivity Disorder 425 stimuli, in response to novelty or possible threat. A rich literature suggests that such reactive control of behavior is related to anxiety and anxiety disorders (Kagan & Snidman, 2004). Low responses of this system (failure of fear response) appear to be related to psychopathy (Blair, Peschardt, Budhani, Mitchell, & Pine, 2006) and perhaps conduct disorder, but not specifically to ADHD. As reviewed in detail by Nigg (2001), experimental paradigms designed to elicit caution in response to potential punishment cues do not yield a reliable set of responses in relation to ADHD. This earlier conclusion has not been overturned. Approach, or willingness to approach, possible incentive or reward/ reinforcement is associated with speed of reinforcement learning. It is conceptualized as related to the appetitive, dopaminergic systems, including the nucleus accumbens and ascending limbic-frontal dopaminergic networks. At the level of the autonomic nervous system, it is linked with sympathetic activation during the performance of rewarded behaviors. One crucial index is heart rate acceleration in response to the application of effort or the appearance of incentive (Beauchaine, 2001). Goldsmith, Lemery, and Essex (2004) followed children from birth through first grade, with multisource temperament measures and parent and teacher ratings of ADHD symptoms. Observational data linked hyperactivity/ impulsivity primarily to high approach, though magnitudes of associations were modest (rs in the .2 to .3 range). Other studies of personality and temperament have yielded mixed results for this trait (Nigg, Blaskey, Huang-Pollock, & Rappley, 2002), although cross sectional ratings of children suggest it is related to hyperactivity/impulsivity but not inattention (Martel & Nigg, 2006). Another line of work has considered ADHD from the viewpoint of reinforcement response—mechanistic activation levels in the appetitive and reinforcement learning systems of the midbrain ascending DA network. These neurons appear to signal unexpected responses to the PFC and to be heavily involved in reinforcement learning, as well as in triggering cognitive control in a bottom-up process. Relying heavily on a series of elegant animal studies, Sagvolden, Johansen, Aase, & Russell (2005) suggested that ADHD may be linked to a weakened reinforcement-delay gradient—that is, as the time to wait for an outcome increases, children with ADHD lose interest in earning the reward more precipitously than do other children. The result is difficulty in learning and in unlearning behaviors that are linked to reinforcers. Additional human studies to evaluate this theory are needed. A large literature on reward response in ADHD has yielded complex findings that are difficult to link to any one theory. An earlier, but comprehensive review by Luman, Oosterlaan, & Sergeant (2005) concluded that ADHD is associated with (a) increased weighting of near-term over long-term (but larger) reward, (b) possible positive response to high-intensity reinforcement, and (c) lack of physiological response (e.g., heart rate acceleration) to potential rewards. A more recent meta-analysis (Plichta & Scheres, 2014) noted that ADHD seems to associate with hypoactivation of ventral striatum (nucleus accumbens) during anticipation of monetary reward. However, comorbid conditions, notably conduct disorder, continue to be sparsely considered in this literature.

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Top-Down Processes Cognitive control (related to the older but widely used term, executive function) refers to strategic or deliberate allocation of both attention and response. When in the service of a later goal held in mind we suppress an unwanted thought (I am anxious but I focus on the exam question), or behavior (I am eager to interrupt but I want to keep my New Year’s resolution not to), we engage in cognitive control. Children must use this ability to study first and play later, to pay attention in class even when other children are talking, to keep track of their materials when returning home from school, and to wait their turn. Such behaviors depend in large part on dopaminergic and noradrenergic circuits in dorsolateral, orbital-prefrontal, and anterior cingulate cortices and their projections to and from the basal ganglia and parietal cortex. These circuits track whether what has occurred is consistent with expectations—and adjust behavior accordingly. Yet also relevant are prefrontal-cerebellar circuits, which may be important for determining if the timing of events is consistent with what was expected and then modulating behavior (Nigg & Casey, 2005). We parse this broad domain into (a) working memory, which depends on maintaining attentional control; (b) response suppression (executive inhibition); and (c) shifting (involving parietal activity). Working memory refers to a limited capacity system for keeping something in mind while doing something else, such as remembering a phone number while completing a conversation. It is supported by simple passive storage or “short-term” memory (holding something in mind for a moment). It includes separate neural loops for handling verbal information (the left-lateralized phonological loop) and spatial information (right lateralized). Most of the 20 or so studies on working memory and ADHD have taken place in the last decade and were evaluated in two recent meta-analyses (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Both reviews estimated effect sizes for verbal working memory and storage in the small to medium range, at d = .43 (Martinussen et al., 2005) to d = .54 (Willcutt et al., 2005). In contrast to these modest effects, spatial working memory weaknesses were medium to large, in the range of d = .72 (Willcutt et al., 2005) to d = 1.06 (Martinussen et al., 2005). Effects were nearly as large for short term memory (d = .85 in (Martinussen et al., 2005). The small number of studies included (6–8) must be kept in mind, but results indicate a meaningful ADHD effect for spatial tasks. Response suppression (executive inhibition) refers to the ability to interrupt a response during dynamic moment-to-moment behavior. Although often associated conceptually with impulsivity, this ability may be equally or more related to inattention-disorganization, in that maintaining focused behavior requires continually suppressing alternative behaviors that may be activated by context. Imagine a “check-swing” in baseball as an index of being able to keep behavior immediately responsive to a rapidly changing context (Logan, 1994). Several experimental computer-based paradigms, brain imaging results, and brain-injury studies converge on links between this ability and a right-lateralized neural circuit

Attention-Deficit/Hyperactivity Disorder 427 involving the inferior frontal gyrus and, subcortically, the caudate, a structure in the basal ganglia, and possibly parts of the thalamus. Key measurement paradigms include the go/no-go task, the anti-saccade task (an eye-movement experiment), and the Logan (1994) stopping task. All converge on some ADHD-related weakness in this ability. Over 30 studies have been conducted on the stopping task alone, making it perhaps the most heavily studied paradigm in ADHD. Willcutt et al. (2005) reviewed 27 of these studies and noted a composite effect size for ADHD versus control of d =.61 (a medium effect size). Set shifting refers to shifting one’s mental focus within a task such as sorting by color versus sorting by number; whereas task switching refers to alternating tasks, such as counting objects versus naming objects. These abilities involve attentional networks in the parietal cortex (particularly for set shifting) and are likely to involve executive control and perhaps cerebellar control for task switching. Most neuropsychological studies of ADHD appear to involve set shifting, using tasks such as the Wisconsin Card Sort, and these yield only small to medium ADHD effects that do not replicate well (d =.46 across 24 studies, [Willcutt et al., 2005]). On the other hand, task-switching paradigms have only recently begun to be examined in ADHD. Overall, difficulties in cognitive control are relevant to ADHD. They appear to be right-lateralized, involving in particular spatial working memory (and the dorsolateral prefrontal cortex) and response suppression (and inferior right prefrontal cortex and projection zones). In contrast, other executive abilities, such as verbal working memory and set shifting, exhibit smaller weaknesses, suggesting that they are less likely to be core mechanisms.

Resource, Capacity, and Arousal Aspects of a system responsible for attentional alerting (immediate focus of attention on something important, related to the older concept of arousal) and vigilance (maintaining the alert state over time, also called sustained attention) are salient for ADHD. This system involves a right-lateralized network of neural structures that includes the noradrenergic system originating in the locus coeruleus, the cholinergic system of the basal forebrain, the intralaminar thalamic nuclei, and the right prefrontal cortex (Petersen & Posner, 2012; Posner & Petersen, 1990), likely supported by ascending noradrenergic fibers from the locus ceruleus. Sustained attention (vigilance) appears to be affected only under certain task conditions (such as different event rates), potentially implicating a process known as activation or response readiness (Scheres, Oosterlaan, & Sergeant, 2001). In contrast, abnormalities in the alerting function in ADHD are apparent in the form of (a) poor signal detection on continuous performance tasks (Losier, McGrath, & Klein, 1996); (b) a tendency to respond too slowly on “fast as you can” reaction time tasks (apparently due to an excess of extremely slow responses, suggesting failures of alertness); (c) excess reaction time variability on fast reaction-time tasks; and (d) excess slow-wave activity in brain EEG observations (Barry, Clarke, & Johnstone, 2003).

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Particularly interesting recently has been work on reaction time variability. It has been of interest because it seems to be one of the most robust, easily observed, and reliable measures of ADHD difference—on fast reaction time tasks, children with ADHD tend to have more variable reaction times (Kofler et al., 2013). Considerable work has gone into explaining this, suggesting either that it reflects attentional lapses or generally inefficient processing. A key effort has been to decompose reaction time variability into component psychological functions. Multiple methods exist for doing this (for an authoritative review, see Karalunas et al., 2014). One promising method separates the components into a decision bias (cautious or bold), an information processing efficiency parameter (which can be understood as alertness in simplistic terms), and a remainder considered noncognitive (e.g., motor processing). Work on that method tends to isolate reaction time variability in ADHD principally to the efficiency measure, consistent with a resource availability, energy, or arousal or alertness parameter (Karalunas, Geurts, et al., 2014).

Temporal Information Processing and Motor Control In recent years the field has focused increasingly on temporal information processing. This idea emanates both from (a) recent theories of executive functioning, which emphasize the importance of temporal integration for both behavioral control (Barkley, 1997) and learning and modulation of behavior (Nigg & Casey, 2005), and (b) imaging findings of cerebellar alterations, as noted earlier. The cerebellum is now thought to be involved not only in learning of complex motor behaviors but also in timing of behavior and temporal-dependent learning. In short, the mind’s internal “clock” may depend on the cerebellum. Implications of faulty time perception for behavioral control are extensive (Barkley, 1997). Toplak & Tannock (2005) reviewed some 20 studies and concluded that ADHD is associated with poor time estimation and poor time reproduction. Although more work in this area is needed, implications for a complete understanding of neurobiology in ADHD are substantial. Problems in cerebellar functioning and temporal information processing could contribute to poor reinforcement learning, poor executive function, and even poor motor coordination.

DEVELOPMENTAL PROGRESSION Despite questions about the appropriate age of onset criterion (if any) for diagnosing ADHD, the early school years are the modal age of case identification. It may be possible to identify ADHD reliably among children as young as age 3 years (Lahey et al., 1998; see also Campbell, 2002), although this is controversial. Even earlier in development, it is likely that consolidation of regulatory capacities in the toddler years and the influence of temperament in the first year of life may interact with the social environment to shape vulnerability to ADHD (Nigg, 2006b; see also Chapter 6

Attention-Deficit/Hyperactivity Disorder 429 [Neuhaus & Beauchaine]). However, diagnostic prediction from these early temperamental precursors remains uncertain, and many vulnerable toddlers do not develop ADHD in later childhood. Some variation in subtype status is related to normal developmental trajectories. That is, motoric hyperactivity is more pronounced in preschool, and tends to decline with time, whereas problems with inattention can become more pronounced with age as peers undergo rapid maturation of prefrontal cortical structures and accompanying cognitive abilities at the same time that school demands intensify. Many children with the hyperactive-impulsive presentation of ADHD during the preschool years develop the combined presentation ADHD (or remit) as they enter the school years. Correspondingly, the inattentive presentation becomes more common later in childhood and through adolescence (Hart, Lahey, Loeber, Applegate, & Frick, 1995). Moreover, heterotypic continuity is not well addressed in the diagnostic system. That is, most behavioral symptoms are designed to describe school-age children; corresponding criteria for adolescence or adulthood are lacking, although this issue is beginning to be addressed by empirical work by Achenbach (1991), Conners et al. (1997) and others. Adult findings are emerging, suggesting a syndrome with clinical validity in terms of impairment and cognitive deficits (Barkley, Murphy, & Fischer, 2008; Murphy, Barkley, & Bush, 2001). It seems likely that for numerous reasons, components of ADHD differentiate with development. It is difficult, in a preschooler, to isolate inattention from impulsivity or hyperactivity—or for that matter, from irritability. Factor analytic studies are able to do so with partial success, yet we know that children at this age do not well differentiate their environments, and that their neural systems are also not yet well differentiated. For example, they show widespread brain activation on tasks that show focal activation in older children and adults. Likewise, the brain continues to prune and sculpt and specialize through adolescence, suggesting more differentiated and specialized abilities by adulthood. Consistent with this formulation, a recent study examined the interrelation of ADHD symptoms using a network modeling approach across preschool to adulthood. Results suggested that ADHD is most effectively seen as a single factor syndrome in preschool, but a two-factor syndrome in childhood, with symptoms progressing into a three- or even four-factor syndrome among adults as the role of impulsivity differentiated from hyperactivity (Martel, Levinson, Langer, & Nigg, 2016). Thus, further work on developmental formulation of ADHD, in conjunction with understandings of the development of attention, self-control, and cognitive capacity, remains very important.

COMORBIDITY ADHD is highly likely to exist in concert with one or more disruptive behavior disorders (oppositional defiant disorder, conduct disorder) as well as other conditions. Among children, this rate is about 50% for ODD and about 22% for CD

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(Polanczyk, Willcutt, Salum, Kieling, & Rohde, 2014). The DSM-5 introduced a new disorder, disruptive mood dysregulation disorder, which describes children who exhibit extreme irritability (e.g., temper tantrums and anger). Such children were too frequently labeled as bipolar in recent years (see Chapter 19 [Kaufman, Crowell, & Lenzenweger]). Although research on the new disorder has yet to emerge, most affected children will likely also meet criteria for ADHD, although the prevalence of this disorder in ADHD samples is unclear. Anxiety co-occurs with ADHD, but any one anxiety disorder is seen only in a minority of cases (e.g., 10%–15% of children with ADHD will have a generalized anxiety disorder (Polanczyk et al., 2014), and only 8% of adults, (Kessler et al., 2006). However, by adulthood, when all anxiety disorders are combined, nearly half of adults with ADHD experienced at least one type of anxiety disorder or phobia in the past year (Kessler et al., 2006). In addition, about one quarter of children with ADHD meet criteria for a learning disorder (Willcutt et al., 2012), underscoring the value of cognitive evaluation in these samples. Some studies also indicate above chance associations with obsessive compulsive disorder, tic disorders, and autism spectrum disorder. Comorbid profiles may provide clues to etiology. For example, consistent with the nosology in ICD-10, when ADHD co-occurs with clinically significant aggression, or with major internalizing (anxious, depressed) features, it may constitute a substantially different condition than when it exists alone (Jensen et al., 2001). Alternatively, G × E interactions (see above) may facilitate progression from ADHD to more severe externalizing conduct for some vulnerable children. Figure 13.1 illustrates patterns of comorbidity for boys with ADHD-C in the large, multisite, multimodal treatment study of ADHD (hereafter referred to as the MTA (A 14-month randomized clinical trial, 1999). As can be seen, even after accounting for oppositional defiant disorder (the most co-occurring condition), over two thirds of the children had at least one additional DSM-IV syndrome. Thus, clinical assessment must include comorbid disorders in case formulation. Similarly, a complete nosological account of heterogeneity must account for patterns of comorbidity. It is important to recognize that many studies of etiology have not considered comorbid conditions adequately. Thus, degree of specificity of many of these effects to ADHD versus other disorders remains under dispute.

SEX DIFFERENCES As with most psychiatric/developmental disorders of childhood onset, ADHD shows a male preponderance, on the order of 2:1 or higher (Polanczyk et al., 2007), which drops somewhat, to only 1.6 to 1, by adulthood (Kessler et al., 2006), perhaps due in part to underidentification of girls in childhood. Boys are referred for treatment at much higher rates than girls. In addition, the larger sex difference in childhood may be an artifact of criteria that were developed based on

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ADHD alone 179 (31%) 15

Tic 14 5

Conduct 43 (7%)

ODD 126 (21%)

12

8 4

1

2

Anxiety+ODD 67 (12%) 11

3 Mood 5 26

Anxiety 58 (10%)

Data from Archives General Psychiatry, 1999 (n = 579) courtesy of Jim Swanson

Figure 13.1 Patterns of comorbidity in the MTA study (A 14-month randomized clinical trial, 1999). predominately male samples. Girls may be more likely to display inattentive behaviors, yet whether they show a greater number of comorbid internalizing problems is controversial. Studies of clinically referred girls and boys with ADHD indicate that they show comparable levels of impairment in academic and social functioning, but girls with the disorder may have greater intellectual deficits (Gaub & Carlson, 1997). In community samples, however, girls are less likely to have comorbid externalizing problems than boys, and they do not show greater intellectual impairment (Gaub & Carlson, 1997). With regard to cognitive and biological correlates, girls with ADHD show similar patterns of impairments in executive functioning and cognitive control as their male counterparts (Hinshaw, Carte, Sami, Treuting, & Zupan, 2002; Rucklidge & Tannock, 2001). In a major series of clinical cases, girls and boys with ADHD showed similar patterns of impairment on measures of set-shifting and interference control, and both groups performed significantly worse than sex-matched controls (Seidman, Biederman, et al., 2005). In the same sample at Massachusetts General Hospital, Doyle et al. (2005) reported patterns of neuropsychological impairment in family members of girls with ADHD similar to those in the relatives of boys with the disorder. These types of data suggest important similarities between manifestation of ADHD in boys and girls and suggest that the same construct is being captured.

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Nevertheless, key issues remain. It is unclear whether sex-specific cutoffs should be considered when diagnosing ADHD among girls (see Hinshaw & Blachman, 2005). Girls are less active and disruptive than boys overall, yet symptom counts used to diagnose ADHD are the same for both sexes. Hence, it is possible that some impaired girls are missed by current criteria. Second, girls may have greater resistance to the etiological factors that cause ADHD. In a twin study, Rhee, Waldman, Hay, and Levy (1999) found evidence consistent with this differential threshold model, suggesting that girls with ADHD need more risk genes before manifesting ADHD. Further studies that incorporate studies of hormonal and other sex-specific effects in early development will be important to a complete understanding of ADHD. Despite recent advances, ADHD in girls remains less well understood than in boys, and the apparent equalizing of prevalence in adolescence and adulthood is not well explained.

CULTURAL CONSIDERATIONS A pooled meta-regression analysis by Polanczyk et al. (2007) included data from over 170,000 participants in 102 studies on all populated continents (although the majority of studies have been conducted in North American and Europe), with a pooled worldwide prevalence rate of 5.3%, as noted earlier. Cross-nationally, apparent variation was observed: Prevalence was highest in South America (11.8%) and Africa (8.5%) and lowest in the Middle East (2.4%), though these differences were nonsignificant after adjustment for methodology differences (i.e., differences in how ADHD was assessed). Few studies were available in these regions, so confidence intervals encompassed too wide of a range to enable differentiation across these regions. Continents with enough data for narrow confidence intervals all had similar prevalences (North America, 6.3%, Europe, 4.7%, Oceania, 4.6%, Asia, 3.7%). Within-country data were not analyzed due to the reduced N of available studies. In the more recent Bayesian meta-analysis by Erskine et al (2013), variation was again seen, albeit at lower overall levels due to the more stringent definition of ADHD used in that study. Here, prevalence ranged from under 3% in southeast Asia and Australia, to over 4% in northern Africa the East Asia. Thus, although all prevalences were on the same order of magnitude, potentially important regional variation was observed. As better resolution is achieved in future studies of incidence and prevalence, clues to cultural, genetic, or other sources of variation (such as variation in exposure to early risk factors in development) may become highly informative to theories of etiology. For example, if lead exposure, anemia, or malnutrition contribute to ADHD, then prevalence should be somewhat higher in nations with higher exposures, unless these effects are countered by alternative etiologies in developed nations (e.g., higher rates of surviving children with low birth weight).

Attention-Deficit/Hyperactivity Disorder 433 True prevalence is quite distinct from identification or treatment rates. For example, although Erskine et al. (2013) estimated that rates of ADHD in the North America were right in the middle of rates around the world, rates of stimulant treatment are higher in the United States than in many nations due to differences in historical approach, laws, and professional practice (Scheffler, Hinshaw, Modrek, & Levine, 2007). Other wealthy nations are on a similar use trajectory (Lang, Scheffler, & Hu, 2010). Several additional complexities are worthy of comment. First, ADHD-related behaviors may not have the same meaning in the eyes of teachers and parents across cultural groups. For example, Mann et al. (1992) and Mueller et al. (1995) found that clinicians of different cultures rate the same child actors at significantly different symptom levels, even when faced with identical behaviors (independent of race of the child). On the other hand, Epstein et al. (2005) found that teacher’s ratings of excess ADHD symptoms among African American children were consistent with behavioral observations of the same classrooms. This main effect of race was partially due to the fact that African American children were more often in classrooms where the average child had more misbehavior. The paucity of research on these issues represents a gaping hole in our knowledge base. Second, it is unclear to what extent the ADHD syndrome has similar internal validity across ethnic or cultural groups, or under what conditions this might change. Data suggest that ADHD symptom factor structure is essentially the same across nations (Toplak et al., 2011). Yet Reid et al. (1998) examined factor loadings of ADHD symptoms in African American and Euro-American children in the United States. Although the general two-factor symptom structure was preserved across groups, the item loadings differed, suggesting that the syndrome might have a different meaning in the two groups. It is not difficult to imagine how the same behavior could have different meanings across racial groups in the United States (for instance, one might speculate that an African American child more often may be socialized to call out in groups, whereas a European American child may be socialized to remain quiet or wait his or her turn in large groups). Different meanings across nations are also plausible. Still, countering such suppositions, the major review by Rohde et al. (2005) concluded that studies in nondeveloped nations yield similar factor structures, treatment responses, prevalences, and biological correlates as studies in developed nations, supporting the cross-cultural validity of ADHD. Similarly, Yang, Schaller, and Parker (2000) found similar factor loadings in Taiwan and the United States. Such evidence raises the question of when, if at all, racially or culturally specific norms should be included in the assessment of ADHD. Again, a paucity of research signifies ripe opportunities for future investigators in this area to clarify local variation or boundary conditions, if any, on the cross-cultural validity of ADHD and thus to provide a more differentiated map of construct validity.

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Third, as noted, treatment rates vary radically across nations (Hinshaw et al., 2011), and approaches to treatment may be different across cultural groups even within the U.S. (Visser, Bitsko, Danielson, & Perou, 2010). Data are lacking as to the important issue of whether this discrepancy leads to excess poor outcomes among minority children. These differences in services may reflect reduced access to care, or distinct attitudes toward the diagnostic and treatment infrastructure. Further empirical work is needed on such issues as costs, access to care, attitudes and beliefs, and differential outcomes.

PROTECTIVE FACTORS Aside from obvious and global protections such as strong prenatal care and avoidance of early risk factors (e.g., severe trauma, low birth weight), little is known about protective factors against ADHD. However, some clues have come from recent studies of Risk × Experience interactions. For example, when children are exposed to environmental contaminants early in life (typically prenatally), effects on intellectual and ADHD outcomes are moderated by family context (those who breastfed, a proxy for more well-prepared parents, do not show associations of exposure to later problem outcome [Jacobson & Jacobson, 2003]). Similarly, Tully, Arseneault, Caspi, Moffitt, & Morgan (2004) found that parental warmth moderated the effect of low birth weight on ADHD outcomes. This result is consistent with Breslau & Chilcoat’s (2000) finding that the effect of low birth weight on ADHD was smaller in suburban than urban communities, suggesting that additional family resources and/or better health care may have prevented these risk factors from having their full effect. Second, recent findings regarding biological, genetic, and cognitive protective factors suggest that children who are exposed to multiple indicators of adversity (low SES, parental Axis I disorders, marital conflict, family stress)—but who are below clinical cutoffs for ADHD symptoms (classified as the resilient group)—are more effective in neuropsychological response inhibition and have fewer “risk” catecholamine genotypes (defined by a count of risk markers across three catecholamine genes expressed in the brain [Nigg, Nikolas, Friderici, Park, & Zucker, 2007]). Notably, higher IQ did not serve as such a protective factor. Third, what about secondary protection? That is, once a child has ADHD, what are protective factors that prevent the worst outcomes? Here, we are on firmer empirical footing, although protective factors often appear to be simply the converse of risk factors. These include (a) stronger reading ability, (b) absence of aggressive behavior, and (c) positive peer relations (for a review, see (Barkley, 2006). Additionally, effective parenting may have some effect in reducing persistence of ADHD from preschool into childhood (Campbell, 2002).

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THEORETICAL SYNTHESIS ADHD is a syndrome that reflects multiple developmental pathways and causal processes. A range of early risks during development apparently affects a minority of these children via neural injury (e.g., prenatal substance exposure)—effects that could be preventable (if society so desired) through adequate prenatal care and reduction in exposure to environmental toxins. A percentage may represent extreme temperaments interacting with a society demanding tight conformity to indoor, desk-type work in childhood. However, findings of neuropsychological weaknesses among many of these children argue against this subgroup’s constituting the modal group. Another unknown percentage is likely to reflect the confluence or interaction of vulnerable constitution (genetic liability) and environmental risks (e.g., contaminants or teratogens). Identifying such interactions is a major objective. The primary internal mechanism driving ADHD appears to involve various types of breakdown in the striatal-prefrontal neural circuitry that supports cognitive control. However, whether the primary element here is a problem in top-down control, or in bottom-up signaling of the need for control, remains in debate. One possible resolution lies in the idea that each is involved in a distinct aspect of the disorder. Sonuga-Barke (2005) suggested a dual pathway approach. Within the framework advanced here, it may be that the inattentive-disorganized symptom domain reflects breakdowns in top-down control mechanisms (anchored in a frontal-striatal neural circuit), whereas hyperactivity-impulsivity reflects breakdowns in bottom-up signaling, perhaps involving reactive control or motivational response processes (anchored in frontal-limbic circuitry). Additional pathways have been suggested (Nigg, 2006a, 2006b). Thus, more than one mechanism may lead to ADHD, exemplifying equifinality, and multiple influences may converge to create the full syndrome. For example, it was noted earlier that distinct external correlates accompany ADHD with and without aggression. What remains to be clarified is the extent to which distinct etiological influences (e.g., lead exposure, teratogens) operate independently of or interactively with genetic susceptibility. One promising model is that genes confer susceptibility to ADHD and that a range of biological and perhaps experiential stressors in the preand postnatal environment set the vulnerable child on a course toward ADHD. In other instances, ADHD may reflect an extreme temperament (e.g., high approach, activity level, or extraversion) that plays out in recursive loops in a particular socialization context to lead to the requisite set of symptoms. Defining and mapping of these distinct routes and pathways is an exciting challenge for researchers in the coming generation. In summary, ADHD is a complex syndrome, with substantial genetic influence that involves early departures from normal maturation of prefrontal-subcortical and cerebellar brain circuitry. In many instances, this pathway may reflect activating

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effects of early pre- or perinatal insult; in other instances, it may reflect an extreme genotype. These child characteristics serve as a liability or vulnerability to ADHD. Effects are then likely to be mediated through socialization (genotype-experience correlations), to culminate in breakdowns or failure in the learning of self-regulation and cognitive control, which in turn manifest as persistent problems with adaptation and regulation of cognition and motor control—that is, symptoms of ADHD.

SUMMARY AND CONCLUSIONS Key issues for future directions emerge under three broad domains. First, how will the phenotype best be defined, and in particular how will heterogeneity and specificity issues be resolved? This clarification will require a shift from variable-centered to person-centered approaches, and greater attention to mechanistic variability within ADHD samples. For example, Fair, Bathula, et al. (2012) conducted a novel community detection analysis that identified distinct subgroups of ADHD based on their neuropsychological profiles. Karalunas, Fair, et al. (2014) reported a similar analysis using indices of emotional functioning (temperament ratings) and cross-validated newly identified ADHD profiles in relation to physiology and neuroimaging. They suggested that it is more productive to think of children with ADHD in relation to irritable versus exuberant temperament profile than in relation to ADHD symptom profile. As such efforts come to fruition, it becomes possible to envision a tractable, biologically based approach to heterogeneity. Yet such efforts will need to cut across existing disorders entirely. ADHD cannot be neatly distinguished biologically from other disorders. Rather, nearly all biological and genetic findings are nonspecific—that is, similar genes, neural regions, and cognitive problems are seen in other disorders. This nonspecificity suggests that either very subtle differences in neural effects are extremely important or, more likely, that these neural or temperamental susceptibilities are shaped by particulars of the learning environment that are yet to be fully specified. The critical roles of integration of functions and of socialization in the development of regulatory control supports this supposition. Second, what are the specific etiologies of the expected subgroups currently defined as having ADHD? Most research has focused on specifying within-child mechanisms, but these are not adequately linked with causes (be they specific genotypes, specific perinatal or toxic events, or specific epigenetic processes in socialization). Thus, whereas genetic work on ADHD is in its infancy, its ultimate integration with likely experiential etiologies will be essential. These include both direct gene-experience interactions (such as effects of low level lead or low birth weight on genetically vulnerable children) and epigenetic effects in which the nature of the socialization environment or the nature of biological insult may alter or instantiate expression of genetic liability. Third, what are key moderators of the meaning and outcome of these behaviors? Here, we can point particularly to the need to understand cultural variation in the

Attention-Deficit/Hyperactivity Disorder 437 meaning of the behaviors and their external correlates, as we map validity with greater precision. At the same time, contextual moderators during development, such as changes in family process or other inputs particularly in adolescence, need better elucidation. A recent special issue of the Journal of Abnormal Psychology (in press) provides a rich array of new looks at this critical question and exemplifies the directions needed. Relatedly, it is clear that this syndrome, like many child psychopathologies, will not be fully understood without an adequate explanation of sex differences in incidence and risk (Martel, 2013). More generally, interactions with widespread societal risk factors also remain unknown. Distinct neuropsychological and temperamental pathways are emerging, and a key goal for the next generation of research is to map those to specific etiologies. Following from an emphasis on etiology, in addition to identifying prevention opportunities, there remains a need to identify long-term treatments that can alter the developmental course for these children, so that self-regulation is more easily at their command. Current treatments ameliorate symptoms, but it is not clear that they reverse the developmental course of regulatory problems. A better understanding of the early developmental origins and dynamics of etiology and mechanism may be helpful in this regard. In conclusion, ADHD is an important and fascinating syndrome, with multiple routes to its final endpoint. Despite controversies about misdiagnosis, which may emanate from inadequate mental health services, the multiple impairments and strong psychobiological underpinnings seen in these children argue against the idea that ADHD is merely a cultural construct. Furthermore, the complexity of the syndrome’s mechanisms and causes is becoming tractable. Still, these effects are likely interacting with poorly understood biological activators and, perhaps, cultural moderators. Their understanding will require describing experiential and genetic effects in integrative studies. The study and treatment of ADHD are characterized by energy and optimism on the part of researchers and practitioners, as their efforts of begin to show promise of bearing further fruit.

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Scheffler, R. M., Hinshaw, S. P., Modrek, S., & Levine, P. (2007). The global market for ADHD medications. Health Affairs, 26, 450–457. Scheres, A., Oosterlaan, J., & Sergeant, J. A. (2001). Response execution and inhibition in children with AD/HD and other disruptive disorders: The role of behavioural activation. Journal of Child Psychology and Psychiatry and Allied Disciplines, 42, 347–357. Seidman, L. J., Biederman, J., Monuteaux, M. C., Valera, E., Doyle, A. E., & Faraone, S. V. (2005). Impact of gender and age on executive functioning: Do girls and boys with and without ADHD differ neuropsychologically in preteen and teenage years? Developmental Neuropsychology, 27, 79–105. Seidman, L. J., Valera, E. M., & Makris, N. (2005). Structural brain imaging of ADHD. Biological Psychiatry, 57, 1263–1272. Sergeant, J. A., & van der Meere, J. J. (1998). What happens after an hyperactive child commits an error? Psychological Research, 24, 157–164. Setlik, J., Bond, G. R., & Ho, M. (2009). Adolescent prescription ADHD medication abuse is rising along with prescriptions for these medications. Pediatrics, 124, 875–880. Sharma, L., Markon, K. E., & Clark, L. A. (2014). Toward a theory of distinct types of “impulsive” behaviors: A meta-analysis of self-report and behavioral measures. Psychological Bulletin, 140, 374–408. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., . . . Giedd, J. (2006). Intellectual ability and cortical development in children and adolescents. Nature, 440, 676–679. Simon, V., Czobor, P., Balint, S., Meszaros, A., & Bitter, I. (2009). Prevalence and correlates of adult ADHD: Meta-analysis. British Journal of Psychiatry, 194, 204–211. Simonoff, E., Pickles, A., Hervas, A., Silberg, J. L., Rutter, M., & Eaves, L. (1998). Genetic influences on childhood hyperactivity: Contrast effects imply parental rating bias, not sibling interaction. Psychological Medicine, 28, 825–837. Sonuga-Barke, E. J. (2005). Causal models of ADHD: From common simple deficits to multiple developmental pathways. Biological Psychiatry, 57, 1231–1238. Sonuga-Barke, E. J., Brandeis, D., Cortese, S., Daley, D., Ferrin, M., Holtmann, M., . . . Sergeant, J. (2013). Nonpharmacological interventions for ADHD: Systematic review and meta-analyses of randomized controlled trials of dietary and psychological treatments. American Journal of Psychiatry, 170, 275–289. Stergiakouli, E., Hamshere, M., Holmans, P., Langley, K., Zaharieva, I., Hawi, Z., . . . Thapar, A. (2011). Investigating the contribution of common genetic variants to the risk and pathogenesis of ADHD. American Journal of Psychiatry, 169, 186–194. Stevenson, J., Sonuga-Barke, E., McCann, D., Grimshaw, K., Parker, K. M., Rose-Zerilli, M. J., . . . Warner, J. O. (2010). The role of histamine degradation gene polymorphisms in moderating the effects of food additives on children’s ADHD symptoms. American Journal of Psychiatry, 167, 1108–1115. Taylor, E. (2011). Antecedents of ADHD: A historical account of diagnostic concepts. Attention Deficit Hyperactivity Disorder, 3, 69–75.

Attention-Deficit/Hyperactivity Disorder 447 Thapar, A., Rice, F., Hay, D., Boivin, J., Langley, K., van den Bree, M., . . . Harold, G. (2009). Prenatal smoking might not cause ADHD: Evidence from a novel design. Biological Psychiatry, 66, 722–727. The MTA Cooperative Group. Multimodal Treatment Study of Children with ADHD. (1999). 14-month randomized clinical trial of treatment strategies for ADHD. Archives of General Psychiatry, 56, 1073–1086. Toplak, M. E., Sorge, G. B., Flora, D. B., Chen, W., Banaschewski, T., Buitelaar, J., . . . Faraone, S. V. (2011). The hierarchical factor model of ADHD: Invariant across age and national groupings? Journal of Child Psychology and Psychiatry and Allied Disciplines, 53, 292–303. Toplak, M. E., & Tannock, R. (2005). Time perception: Modality and duration effects in ADHD. Journal of Abnormal Child Psychology, 33, 639–654. Tully, L. A., Arseneault, L., Caspi, A., Moffitt, T. E., & Morgan, J. (2004). Does maternal warmth moderate the effects of birth weight on twins’ ADHD symptoms and low IQ? Journal of Consulting and Clinical Psychology, 72, 218–226. Uddin, L. Q., Kelly, A. M., Biswal, B. B., Margulies, D. S., Shehzad, Z., Shaw, D., . . . Milham, M. P. (2008). Network homogeneity reveals decreased integrity of default-mode network in ADHD. Journal of Neuroscience Methods, 169, 249–254. van Ewijk, H., Heslenfeld, D. J., Zwiers, M. P., Buitelaar, J. K., & Oosterlaan, J. (2012). Diffusion tensor imaging in ADHD: A systematic review and meta-analysis. Neuroscience and Biobehavioral Reviews, 36, 1093–1106. Visser, S. N., Bitsko, R. H., Danielson, M. I., & Perou, R. (2010). Increasing prevalence of parent-reports of ADHD among children—United States, 2003 and 2007. Morbidity and Mortality Weekly Report, 59, 1439–1443. Volk, H. E., Todorov, A. A., Hay, D. A., & Todd, R. D. (2009). Simple identification of complex ADHD subtypes using current symptom counts. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 441–450. Willcutt, E. G. (in press). Genetics of ADHD. In D. M. Barch (Ed.), Cognitive and affective neuroscience of psychopathology. New York, NY: Oxford University Press. Willcutt, E. G., Doyle, A. E., Nigg, J. T., Faraone, S. V., & Pennington, B. F. (2005). Validity of the executive function theory of ADHD: A meta-analytic review. Biological Psychiatry, 57, 1336–1346. Willcutt, E. G., Nigg, J. T., Pennington, B. F., Solanto, M. V., Rohde, L. A., Tannock, R., . . . Lahey, B. B. (2012). Validity of DSM-IV ADHD symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991–1010. Willcutt, E. G., Pennington, B. F., & DeFries, J. C. (2000). Etiology of inattention and hyperactivity/impulsivity in a community sample of twins with learning difficulties. Journal of Abnormal Child Psychology, 28, 149–159. Williams, N. M., Franke, B., Mick, E., Anney, R. J., Freitag, C. M., Gill, M., . . . Faraone, S. V. (2011). Genome-wide analysis of copy number variants in ADHD: The role of rare variants and duplications at 15q13.3. American Journal of Psychiatry, 169, 195–204.

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Wilmot, B., Fry, R., Smeester, L., Musser, E. D., Mill, J., & Nigg, J. T. (2015). Methylomic analysis of salivary DNA in childhood ADHD identifies altered DNA methylation in VIPR2. Journal of Child Psychology and Psychiatry, 57, 152–160. Wolraich, M., Brown, L., Brown, R. T., DuPaul, G., Earls, M., Feldman, H. M., . . . Visser, S. (2011). ADHD: Clinical practice guideline for the diagnosis, evaluation, and treatment of ADHD in children and adolescents. Pediatrics, 128, 1007–1022. Wray, N. R., Lee, S. H., Mehta, D., Vinkhuyzen, A. A., Dudbridge, F., & Middeldorp, C. M. (2014). Research review: Polygenic methods and their application to psychiatric traits. Journal of Child Psychology and Psychiatry, 55, 1068–1087. Yang, K. N., Schaller, J. L., & Parker, R. (2000). Factor structures of Taiwanese teachers’ ratings of ADHD: A comparison with U.S. studies. Journal of Learning Disabilities, 33, 72–82.

C H A P T E R 14

Oppositional Defiant Disorder, Conduct Disorder, and Juvenile Delinquency BENJAMIN B. LAHEY AND IRWIN D. WALDMAN

INTRODUCTION

A

ntisocial behaviors are among the most common behavior problems, and are significant symptoms of several psychiatric disorders in childhood and adolescence. They are also among the most refractory to treatment. Children and adolescents who persistently violate laws and important social rules are seriously impaired in their social relationships and at risk for a range of adverse sequelae, including incarceration, suicide, and violent death (e.g., Linker, Gillespie, Maes, Eaves, & Silberg, 2012; Loeber & Stouthamer-Loeber, 1998; Moffitt, Caspi, Rutter, & Silva, 2001), marital problems and divorce (Robins, 1966), underand unemployment (Robins, 1966), and various forms of substance abuse (Myers, Stewart, & Brown, 1998; Robins, 1966). Antisocial behavior also harms others in a variety of ways, from loss of property to death by homicide (Loeber et al., 2005).

TERMINOLOGICAL AND CONCEPTUAL ISSUES A number of constructs have been developed to conceptualize and label antisocial behavior in youth. The term juvenile delinquency is used in the criminal justice system to refer to children and adolescents who break laws. This is a broad term Preparation of this chapter was supported in part by grants R01 MH070025 and R01 MH53554 from the National Institute of Mental Health.

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that encompasses crimes ranging from sneaking into a movie without a ticket to homicide. In the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5; American Psychiatric Association [APA], 2013), two diagnoses are directly relevant to antisocial behavior in youth: oppositional defiant disorder (ODD) and conduct disorder (CD). Conduct disorder is diagnosed when at least 3 from a list of 15 antisocial behavior criteria are met within a 12-month period. Conduct disorder only partially overlaps with delinquency for three reasons. First, not all juvenile crimes are symptoms of CD (e.g., selling drugs, receiving stolen property). Second, some symptoms of CD do not necessarily violate laws (e.g., bullying, staying out late without permission). Third, CD describes youth who frequently engage in a variety (i.e., at least three) of antisocial behaviors in a relatively short time frame, whereas a youth could be considered to be delinquent on the basis of a single criminal act. Oppositional defiant disorder is also related to antisocial behavior in youth. It is defined as frequently engaging in at least four disruptive interpersonal behaviors, including arguing with adults, actively defying adult requests, and spiteful or vindictive behavior, for at least 6 months. ODD often severely impairs social relationships of children and adolescents (e.g., Lahey et al., 1994), and often (but not always) portends development of CD (e.g., Loeber, Burke, Lahey, Winters, & Zera, 2000). It is important to note that many researchers believe that DSM-5 diagnoses of ODD and CD reflect arbitrary dichotomizations of what are probably continua (Beauchaine & McNulty, 2013; Beauchaine, Shader, & Hinshaw, 2016; Boyle et al., 1996; Lahey et al., 1994). That is, youth do not suddenly shift from normality to abnormality when they engage in their fourth ODD symptom or their third CD symptom. Rather, the more symptoms of ODD and/or CD that a youth exhibits, the more serious the consequences for the youth and others. Another reason for caution regarding both DSM-IV (APA, 1994) and DSM-5 diagnostic definitions is a large “hole” in the diagnosis of ODD (Rowe, Maughan, Costello, & Angold, 2005). In the 10th edition of the International Classification of Diseases (ICD-10; World Health Organization [WHO], 1993), ODD is defined by the same symptoms as in DSM-IV, but in a different way. In ICD-10, if a youth does not meet diagnostic criteria for either ODD or CD, the total number of ODD plus CD symptoms is counted. If there are four such ODD + CD symptoms, the youth meets criteria for ODD. Rowe et al. (2005) found that this large group of youth was just as impaired in social functioning as youth who met DSM-IV criteria for ODD. It is not surprising that youth who exhibit three symptoms of ODD and one or two symptoms of CD (i.e., who fall short of the diagnostic criteria for either disorder) would be impaired. Similar findings were obtained in a recent study in which treating CD symptoms as ODD symptoms when diagnostic criteria for CD were not met identified more functionally impaired children than the more restrictive DSM-IV definition of ODD, thus showing the validity and virtue of plugging this diagnostic “hole” (Burke, Waldman, & Lahey, 2010).

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Although there are important differences among the constructs of juvenile delinquency, ODD, and CD, it is necessary to refer collectively to all three constructs in this chapter for the sake of brevity and clarity. For this purpose, the terms conduct problems and antisocial behavior refer collectively to juvenile delinquency, ODD, and CD. Similarly, the term youth refers collectively to both children and adolescents.

COMORBIDITY One cannot view any form of psychopathology as separate from all others. Youth with any mental disorder are considerably more likely than chance to meet criteria for other mental disorders (Angold, Costello, & Erkanli, 1999; Lahey et al., 2004; Nottelmann & Jensen, 1995). That is, co-occurrence of symptoms and diagnoses (or comorbidity) is the rule, not the exception. ODD and CD often co-occur, and both disorders often co-occur with attention-deficit/hyperactivity disorder (ADHD; see Angold et al., 1999; Beauchaine & McNulty, 2013; Beauchaine, Zisner, & Sauder, 2017; Lahey, Miller, Gordon, & Riley, 1999). In addition, ODD and CD often co-occur with depression (Angold et al., 1999; Lahey et al., 2002; Rowe, Maughan, & Eley, 2006). Some investigators view comorbidity as a problem for taxonomies of mental disorders (Rutter, 1997), whereas others view comorbidity as the inevitable result of the nearly ubiquitous correlations among symptoms of different disorders (Lahey, Rathouz et al., 2008; Lahey et al., 2004). In the latter view, comorbidity is informative rather than problematic. For example, CD is impairing and requires intervention regardless of whether it occurs alone or in the presence of symptoms of other disorders. On the other hand, a youth who meets criteria for CD and another disorder such as major depression may well need treatment for each. In addition, viewing comorbidity as informative facilitates the study of both common and distinct causal influences on different forms of psychopathology. This perspective recently has gained momentum at the National Institute of Mental Health, which is investing in novel research strategies that will examine basic biological and psychological mechanisms that cut across traditional diagnostic boundaries (Insel & Wang, 2010; see Chapter 2 [Beauchaine & Klein]). Such studies should shed new light on the phenomenon and causes of comorbidity (Beauchaine & Cicchetti, 2016a, 2016b).

CONSIDERING DEVELOPMENT AND SEX DIFFERENCES It is important in discussing any mental disorder to take a developmental perspective (see Chapter 1 [Hinshaw]). In this chapter, conduct problems are considered from four different developmental perspectives: (1) developmental trajectories of conduct problems; (2) age differences in the prevalence of conduct problems; (3) childhood characteristics that predict later conduct problems; and (4) adolescent and adult outcomes of childhood conduct problems. It also is important to consider potential differences between females and males when discussing the development of conduct problems. Although conduct

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problems are prevalent and problematic in both sexes, they are considerably more common in males (Lahey et al., 2006; Moffitt et al., 2001). Because the sex differences in the prevalence of childhood conduct problems are large, especially for aggression (see e.g., Eme, 2016), it is necessary to understand causes of sex differences to fully understand causes of conduct problems themselves. For the same reason, any theory of the origins of conduct problems that does not address sex differences is incomplete and perhaps inaccurate for one or both sexes.

PREVALENCE AND AGE OF ONSET Many have suggested that one can understand youth conduct problems only by distinguishing between different developmental trajectories of behavior (e.g., Farrington, 1991; Hinshaw, Lahey, & Hart, 1993; Loeber, 1988; Moffitt, 1993; Patterson, Reid, & Dishion, 1992; Quay, 1987). In this context, a trajectory is identifiable temporal pattern of conduct problems youth engage in from early childhood through adolescence. For example, two 17-year-olds who are arrested for shoplifting might have very different developmental trajectories. One may have exhibited no symptoms of CD as a child and never broken a law until skipping school and shoplifting for the first time at age 17, whereas the other might have continuously met criteria for CD since early childhood, shoplifted dozens of times before, and committed many other crimes since middle childhood. As described below, such differences in developmental trajectories reveal a great deal about differences in the causes of conduct problems.

Adolescent Limited versus Life Course Persistent Conduct Problems Moffitt (1993, 2003) proposed that youth who follow two different trajectories engage in delinquency for qualitatively different reasons. According to Moffitt, a relatively small number of youth follow a childhood-onset (life-course persistent) trajectory in which they exhibit symptoms of ADHD, ODD, and CD in childhood and engage in persistent conduct problems through adolescence and into adulthood. A larger group of youth follow an adolescent-onset (adolescence-limited) trajectory in which they engage in relatively few conduct problems during childhood, first break laws during adolescence, and often desist from offending in early adulthood. Adolescent delinquency is common, but the exact numbers depend on how juvenile delinquency is defined. Approximately 10% to 21% engage in what Moffitt (1993) refers to as adolescent-onset delinquency, compared with 5% to 14% of youth who exhibit childhood-onset delinquency (Lahey et al., 2006; Moffitt et al., 2001). Moffitt (1993, 2003) hypothesized that childhood-onset conduct problems are caused by neurodevelopmental deficits, inadequate parenting, and adverse social influences, whereas adolescent-onset conduct problems are caused by peer

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influences during the transition to adulthood, in the context of an ever-extended period of adolescence in modern societies. For this reason, Moffitt has argued that investigating causes of delinquency without distinguishing these trajectories may produce disinformation. In considering developmental trajectories, it also is important to note that some children who engage in high levels of childhood conduct problems do not do so in adolescence (Côté, Vaillancourt, Le Blanc, Nagin, & Tremblay, 2006; Moffitt, 2007; Moffitt, Caspi, Dickson, Silva, & Stanton, 1996; Raine et al., 2005) and that some adolescence-limited youth do not completely desist by adulthood (Moffitt, 2007).

Are There Sex Differences in Developmental Trajectories? Essentially equal numbers of females and males exhibit adolescent-onset delinquency (see below), but males outnumber females by between 3:1 and 5:1 on the childhood-onset trajectory (Lahey et al., 2006; Moffitt et al., 2001). Silverthorn and Frick (1999) suggest that females rarely follow the childhood-onset trajectory but rather follow a trajectory unique to girls. Although this hypothesis stimulated research that clarified the nature of sex differences in delinquency, it has not been supported (Côté, Zoccolillo, Tremblay, Nagin, & Vitaro, 2001; Lahey et al., 2006; Moffitt et al., 2001). Instead, it appears that girls follow both delinquency trajectories as Moffitt (1993, 2003) defines them, but there are fewer girls on a childhood-onset trajectory.

Alternatives to Qualitative Developmental Trajectory Models Lahey and Waldman (2003, 2005, 2012) suggest a different view of developmental trajectories. They agree with Moffitt (1993, 2003) that adolescent delinquents with high or low levels of childhood conduct problems tend to be antisocial for different reasons, but hypothesize a continuum of such differences rather than two qualitatively distinct trajectories. According to this view, a continuum exists, ranging from those who were well behaved as children to those who were poorly behaved from the toddler years onward, with every gradation in between regarding levels and consistency of childhood behavior problems. It appears that there are two distinct groups of adolescent delinquents only when researchers arbitrarily divide them into two such groups. Nonetheless, because the notion of two distinct developmental trajectories is a useful heuristic, Moffitt’s dichotomous terms are often used in this chapter for simplicity.

Relations Among ODD, CD, and Developmental Trajectories of Delinquency Nearly all studies of developmental trajectories have examined delinquent behavior rather than ODD or CD. Thus, there is currently not enough information to know

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how many youth in Moffitt’s two developmental trajectories of delinquency meet diagnostic criteria for ODD or CD. Because most definitions of delinquency require commission of only a single delinquent act, and because CD requires a variety of antisocial behaviors during the past 12 months, many delinquent youth do not meet criteria for CD, as noted earlier. One study of CD indicated that most clinic-referred adolescents who meet criteria for CD report that their CD behaviors began in childhood, with only a small percent reporting adolescent onset (Lahey et al., 1998). In a longitudinal study of a representative sample of girls, Côté et al. (2001) found that nearly all adolescent females who meet criteria for CD have childhood-onset CD. These studies suggest that the majority of youth who meet criteria for CD follow what Moffitt (1993, 2003) would define as a childhood-onset trajectory. On the other hand, there may be a group of youth who meet criteria for CD who have later ages of onset and who share risk factors and outcomes with adolescent-onset delinquency. Indeed, DSM-5 distinguishes between childhood- and adolescent-onset CD based on this premise. Unfortunately, the validity of these subtypes has not been studied extensively in large longitudinal studies (Lahey et al., 1998). In addition, most youth on a childhood-onset trajectory of delinquency met criteria for ODD during childhood (Lahey et al., 2006), but more remains to be learned. This issue is also complicated by the overlap of the early- versus late-onset distinction with other possible criteria for subtyping, such as aggressive versus nonaggressive CD, or high versus low levels of callous unemotional traits, issues addressed later in this chapter.

Age, Sex, and Prevalence of Conduct Problems Although it is difficult to estimate the exact prevalence of ODD and CD in the general population, there is good evidence that ODD is more prevalent than CD during early childhood, but by adolescence the number of youth who meet criteria for ODD and CD are close to equal (Lahey, Miller et al., 1999; Maughan, Rowe, Messer, Goodman, & Meltzer, 2004). This is because the prevalence of ODD either stays constant or declines somewhat from early childhood through adolescence (Lahey et al., 2000; Maughan et al., 2004), whereas the prevalence of CD increases from early childhood through adolescence. The age-related increase in the prevalence of CD is much greater in boys than girls, which means that the sex difference in CD is greatest during late adolescence (Lahey et al., 2000; Maughan et al., 2004; Moffitt et al., 2001), whereas boys appear to be somewhat more likely to meet criteria for ODD at all ages (Lahey et al., 2000; Maughan et al., 2004). Rates of delinquency increase steeply with age until they peak at 16 or 17 years, then decline with increasing age almost as steeply, a developmental pattern known as the age-crime curve (Hirschi & Gottfredson, 1983). Given the age-crime curve, more than half of all crime is committed by juveniles. This curve is consistent

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with Moffitt’s (1993, 2003) view that youth on a childhood-onset trajectory are joined by the larger number of youth on an adolescent-onset trajectory, swelling the total number of adolescents who engage in delinquency. Males are more likely to engage in delinquency than females at all ages, but like the diagnosis of CD, the sex difference in delinquency is greatest when males are at the peak of their age-crime curve at ages 16–17 years (Farrington & Painter, 2004; Lahey et al., 2006; Moffitt et al., 2001). Although current evidence is thin, the age-crime curve might be flatter for females, with an earlier peak (Farrington & Painter, 2004; Lahey et al., 2006; Moffitt et al., 2001).

Childhood Characteristics That Predict CD and Delinquency Many emotional and behavioral characteristics predict later CD and delinquency among children. In some cases, these behavioral characteristics are developmental precursors that appear to be “juvenile forms” of later conduct problems. Other childhood characteristics do not resemble later conduct problems but are still useful predictors of future serious conduct problems. Knowledge of these predictors makes it possible to study children who are likely to develop a disorder before it emerges, facilitating both studies of the early causes of conduct problems and efforts to prevent them.

Childhood Predictors The following early childhood characteristics predict serious conduct problems during later childhood and adolescence. It should be kept in mind, however, that none predicts adolescent antisocial behavior with a high degree of certainty. Temperament. Several aspects of young children’s temperamental dispositions predict later conduct problems (see Valle Krieger & Stringaris, 2016; Chapter 6 [Neuhaus & Beauchaine]). These include a tendency for young children to resist control by adults (Keily, Bates, Dodge, & Pettit, 2001), to respond to threat and frustrations with excessive negative emotions (Gilliom & Shaw, 2004; Waldman et al., 2011), to engage in daring and sensation-seeking behaviors (Gilliom & Shaw, 2004; Raine, Reynolds, Venables, Mednick, & Farrington, 1998; Waldman et al., 2011), low levels of prosocial behavior (Côté, Tremblay, Nagin, Zoccolillo, & Vitaro, 2002; Waldman et al., 2011), and impulsivity/lack of persistence (Beauchaine, Hinshaw, & Pang, 2010; Henry, Caspi, Moffitt, & Silva, 1996). ODD and ADHD. Although ODD is an important disorder in its own right (Burke, Waldman et al., 2010), it also may be a developmental precursor to CD. ODD symptoms typically emerge earlier in childhood than most but not

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all CD symptoms, and the presence of ODD in early childhood predicts emergence of CD in the future (Lahey, McBurnett, & Loeber, 2000; Rowe, Maughan, Pickles, Costello, & Angold, 2002), particularly among males (Rowe, Costello, Angold, Copeland, & Maughan, 2010). The percentage of children with ODD who go on to meet criteria for CD is not known precisely, but one estimate put it at ≥ 25% (Lahey, Loeber, Quay, Frick, & Grimm, 1992). A longitudinal study of a large representative sample indicated that symptoms of ADHD, ODD, and CD at ages 4–7 years each independently predict future conduct problems (Lahey et al., 2009). Perhaps unsurprisingly, early CD symptoms were the strongest predictor of later CD. Still, many children with ODD never meet criteria for CD but are nonetheless impaired (Burke, Waldman et al., 2010; Lahey, McBurnett, et al., 2000; Rowe et al., 2002; Rowe et al., 2010). Early Shyness and Anxiety. There is evidence that in the absence of early conduct problems, shyness and fearfulness in early childhood decrease risk for later conduct problems (Graham & Rutter, 1973; Kohlberg, Ricks, & Snarey, 1984; Mitchell & Rosa, 1981; Moffitt, Caspi, Harrington, & Milne, 2002; Sanson, Pedlow, Cann, Prior, & Oberklaid, 1996; see Chapter 7 [Kagan]). In addition, delinquent youth with higher levels of anxiety are less likely to commit future crimes (Quay & Love, 1977). They are also less physically aggressive, regarded less negatively by peers, and experience fewer police contacts than youth with CD alone (Walker et al., 1991). These findings are puzzling, as other studies show that anxiety disorders co-occur with conduct problems at greater than chance rates (Loeber & Keenan, 1994; Zoccolillo, 1992). It is possible that anxiety is heterogeneous: some aspects of anxiety (e.g., social inhibition) appear to foster conduct problems whereas other aspects (e.g., high constraint) inhibit conduct problems (Lahey & Waldman, 2003). In addition, children with conduct problems and aggression who are socially withdrawn are at increased risk for persistent and serious conduct problems (Blumstein, Farrington, & Moitra, 1985; Kerr, Tremblay, Pagani-Kurtz, & Vitaro, 1997), as well as other forms of serious psychopathology and maladjustment as adolescents or adults (Serbin et al., 1998). The construct of “socially withdrawn” in these studies may refer to a lack of interaction with other children, perhaps due to lack of interest in (i.e., asociality) or rejection by others, and not to shyness associated with social anxiety and fear (Rutter & Giller, 1983). Childhood Cognitive Skills and Language. Considerable research indicates that children with relatively low cognitive abilities are more likely to develop conduct problems (Elkins, Iacono, Doyle, & McGue, 1997; Fergusson, Horwood, & Ridder, 2005; Ge, Donnellan, & Wenk, 2001; Kratzer & Hodgins, 1999; Lynam, Moffitt, & Stouthamer-Loeber, 1993; Moffitt & Silva, 1988). This finding does not appear to be an artifact of low socioeconomic status (SES), the likelihood that more-intelligent youth avoid detection of their antisocial behavior, or low test motivation (Lynam et al., 1993; Moffitt & Silva, 1988). Still, it is not clear whether deficits in specific

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cognitive abilities, such as executive functions (e.g., Morgan & Lilienfeld, 2000), versus lower general intelligence, are associated with conduct problems. Some evidence exists, however, that a specific cluster of executive functions, memory, and language abilities are associated with early onset conduct problems and aggression, even adjusting for general intelligence (Giancola, Martin, Tarter, Pelham, & Moss, 1996; Raine et al., 2005; Séguin, Boulerice, Harden, Tremblay, & Pihl, 1999; Waldman, 1996). Notably, lower verbal intelligence is correlated with slower language development in early childhood (Sparks, Ganschow, & Thomas, 1996), and the latter is associated with the development of conduct problems (Baker & Cantwell, 1987; Beitchman et al., 2001; Cohen et al., 1998; Stattin & Klackenberg-Larsson, 1993). Results from a longitudinal quasi-experimental sibling-comparison study of a large representative sample of children found evidence that slow development of receptive vocabulary predicts children’s vulnerability to subsequent delinquency in adolescence, particularly among children without high levels of childhood conduct problems (Lahey, D’Onofrio, Van Hulle, & Rathouz, 2014). Keenan and Shaw (1997) hypothesized that slowly developing language in toddlerhood interferes with parental socialization, which is difficult and frustrating for both parent and child. Toddlers with better language skills can communicate their needs more clearly and are more likely to understand rules and requests of adults, both of which facilitate socialization. Language development is slower on average among boys, which may be one reason why boys exhibit more conduct problems from age 4 on (Keenan & Shaw, 1997).

Developmental and Child-Level Predictors of Serious Conduct Problems It is revealing to examine childhood characteristics that predict future conduct problems in the context of viewing developmental trajectories as continua (Lahey & Waldman, 2003). A large longitudinal study of offspring of a nationally representative sample of mothers indicated that adolescents who engage in high levels of delinquency vary considerably in levels of childhood characteristics that predict later delinquency (Lahey et al., 2006). Highly delinquent youth during adolescence, who exhibited increasingly higher levels of childhood conduct problems, showed increasingly lower scores on cognitive ability tests, were progressively less sociable with interviewers and less compliant with adult instructions, and displayed increasingly higher levels of ADHD and ODD symptoms. Two qualitatively distinct trajectory groups did not emerge. Instead, at each progressively higher number of childhood conduct problems, delinquent adolescents exhibited more maladaptive levels of child characteristics that predict later delinquency. A provocative finding is that preadolescent children who exhibit high levels of conduct problems during childhood exhibit similar levels of childhood precursors,

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including ADHD, ODD, temperament, and cognitive ability scores (Lahey et al., 2006; Raine et al., 2005), regardless of whether they improve (i.e., are not delinquent during adolescence) or go on to exhibit childhood-onset delinquency. Thus, much remains to be learned about factors that differentiate children with childhood conduct problems who improve from those who progress to engage in adolescent delinquency.

ADOLESCENT AND ADULT OUTCOMES OF CHILDHOOD ODD AND CD Another way to understand conduct problems from a developmental perspective is to examine later mental health outcomes. Although ODD and CD are important because they cause serious impairment during childhood, they also increase the likelihood of other serious mental disorders in adolescence and adulthood. It is crucial to remember that not all children with high levels of childhood conduct problems continue to manifest them or develop other problems (i.e., follow a life-course persistent trajectory). Rather, many children with childhood conduct problems outgrow them and do not develop serious mental disorders (Moffitt et al., 1996). ODD in childhood and adolescence is associated with maladaptive outcomes in adulthood among young men, even covarying co-occurring ADHD and CD during childhood and adolescence (Burke, Rowe, & Boylan, 2014). CD in childhood increases risk for criminal behavior and antisocial personality disorder (ASPD) in adolescence and adulthood (Fergusson et al., 2005; Kjelsberg, 2002; Lahey, Loeber, Burke, & Applegate, 2005; Maughan & Rutter, 2001). ASPD is a pernicious syndrome characterized by irresponsible behavior, persistent crime, aggression, and violence. Children with CD who exhibit greater numbers of symptoms are at particular risk for adult ASPD (Lahey et al., 2005; Le Corff & Toupin, 2014). Nonetheless, the majority of children and adolescents with CD (perhaps 60% to 70%) do not progress to ASPD (Lahey et al., 2005; Maughan & Rutter, 2001). Some studies suggest that ADHD in early childhood is an independent developmental precursor to later conduct problems (Beauchaine & McNulty, 2013; Beauchaine et al., 2017; Mannuzza et al., 1991; Mannuzza, Klein, Abikoff, & Moulton, 2004; Mannuzza, Klein, Bessler, Malloy, & LaPadula, 1993), whereas other longitudinal studies indicate that childhood ADHD does not predict future antisocial behavior when childhood CD is taken into account (Lahey, McBurnett et al., 2000; Lilienfeld & Waldman, 1990). However, it is important to note that adjusting statistically for CD when predicting mental health outcomes from ADHD may not be appropriate since the two disorders are related etiologically (see Beauchaine et al., 2010, 2017; Miller & Chapman, 2001). Childhood ADHD predicts ASPD in adulthood (Mannuzza et al., 1993; Mannuzza et al., 1991; Mannuzza et al., 1991). This is plausible, as ASPD is defined partly by impulsivity and irresponsibility—parallel to key symptoms of ADHD—but support for this hypothesis is inconsistent. One possible explanation may be that the

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combination of childhood ADHD and CD is the key developmental precursor to adult ASPD (Beauchaine et al., 2010; Hinshaw et al., 1993; Lynam, 1998). Adolescents who engage in high levels of delinquent behavior are at increased risk for criminal behavior during early adulthood (Piquero, Brame, & Moffitt, 2005), even though many such adolescents desist. Crime is not the only adverse outcome associated with CD and serious adolescent delinquency, as antisocial adolescents are also at increased risk for educational underachievement, substance dependence, early parenthood, poor work records, dependence on welfare, unsuccessful family relationships, deviant peer group affiliations, incarceration and criminal records, dangerous driving, and accidental injuries along with early death (Loeber et al., 2005; Loeber, Farrington, Stouthamer-Loeber, & Van Kammen, 1998; Moffitt et al., 2001). Moffitt et al. (1996) hypothesized that many of these outcomes “ensnare” youth in antisocial and nonproductive futures (see also Jennings & Hahn Fox, 2016). Childhood ODD is associated with vulnerability to later depressive disorders, but it has traditionally been thought that CD increases risk for depression indirectly by causing stressful life events such as expulsion from school, peer rejection, and incarceration, which in turn precipitate depression (Burke, Loeber, Lahey, & Rathouz, 2005; Little & Garber, 2005; Patterson & Stoolmiller, 1991). However, evidence is mounting that CD and depression share common neural vulnerabilities including both striatal (Zisner & Beauchaine, 2016) and orbitofrontal dysfunction (e.g., Rubia, 2011). Children with CD are also vulnerable to adolescent drug and alcohol abuse (Marshal & Molina, 2006; see also Brent et al., 2002; Fombonne, Wostear, Cooper, Harrington, & Rutter, 2001). Adolescent and adult outcomes of serious conduct problems are quite poor for both males and females (Bardone, Moffitt, Caspi, & Dickson, 1996; Bardone et al., 1998; Moffitt et al., 2001). Nonetheless, there are sex differences in the extent to which females and males are impaired specific areas of adult functioning (Moffitt et al., 2001). Males are particularly likely to exhibit criminal behavior, work problems, and substance abuse, whereas females are more likely to experience depression and suicidal behavior and have poor physical health (see also Eme, 2016).

VULNERABILITIES TO AND RISK FACTORS FOR CONDUCT PROBLEMS An important goal for developmental psychopathologists in the 21st century is to move from cataloging lists of risk factors for conduct problems to understanding their underlying causal mechanisms (Beauchaine et al., 2017; Lahey, Moffitt, & Caspi, 2003). Behavioral genetics provides one intermediary step (Rutter, 2006; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Such methods, including various types of twin and adoption studies, can distinguish heritable from environmental influences on behavior (Rutter, 2006). For example, contrasting the similarity for a trait between pairs of identical (monozygotic) twins, who share all of their segregating genes, and fraternal (dizygotic twins), who share on average 50%

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of their segregating genes, allows one to estimate the magnitude of heritable and environmental influences on a trait or disorder. When certain assumptions are met, finding greater resemblance among monozygotic than dizygotic twin pairs suggests genetic and other heritable influences on the trait (Rutter, 2006; Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). In a meta-analytic review of 51 twin and adoption studies of conduct problems, Rhee and Waldman (2002) found that heritable influences account for 41% of the variance in broadly defined antisocial behavior. For CD, the magnitude of heritable influences was slightly higher (∼50%). A small proportion (11%) of the variance in CD is attributable shared environmental influences (e.g., common parenting practices, family’s financial resources), with the remainder (39%) attributable to aspects of the environment that siblings experience uniquely and make them different (e.g., only one sibling being abused; different peer groups for different siblings), along with measurement error. Early childhood characteristics that predict later serious conduct problems, including difficult temperament, ADHD, and ODD, are influenced predominantly by genes (Saudino, 2005; Simonoff, 2001; Waldman, Rhee, Levy, & Hay, 2001). Interestingly, Meier, Slutske, Heath, and Martin (2011) suggest that although the magnitude of heritable, shared environmental, and nonshared environmental influences on childhood CD are similar for boys and girls, specific heritable or shared environmental risk factors may differ somewhat. Thus, heritable influences account for considerable population variance in conduct problems. However, genes do not influence complex human traits such as conduct problems in simple and direct ways (see Chapter 3 [Beauchaine, GatzkeKopp, & Gizer]). Rather, they influence human behavior through complex interactions with the environment (Rutter, 2006). By understanding the interplay between genes and environments, we are in a better position to evaluate what is known about possible genetic and environmental influences later in the chapter.

Gene–Environment Correlation Genetic and environmental influences on conduct problems and other traits may be correlated in three ways (Plomin, DeFries, & Loehlin, 1977; Rutter, 2006; Scarr & McCartney, 1983). Passive gene–environment correlation (rGE) describes situations in which genetic and environmental influences that are transmitted from parents to children are correlated. This process is likely to occur for childhood conduct problems because children with high levels of such problems often have antisocial parents who transmit genes that both predispose to antisocial behavior and affect the rearing environment (e.g., young parental age at childbirth, lower parental supervision, increased use of harsh discipline), conferring additional risk for development of childhood conduct problems (Lahey et al., 1988; Lahey, Russo, Walker, & Piacentini, 1989; Nagin, Pogarsky, & Farrington, 1997).

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Evocative or reactive rGE describes situations in which children’s genetically influenced characteristics change the environment in ways that make it more likely (positive reactive rGE) or less likely (negative reactive rGE) that he or she will manifest a particular trait or disorder. Adverse parenting practices are linked with conduct problems among children (Patterson et al., 1992) and are likely to represent examples of reactive rGE. That is, young children with ODD and early conduct problems tend to evoke exactly the kinds of coercive, harsh, rejecting, and inconsistent parenting behaviors that contribute to development of later conduct problems (Anderson, Lytton, & Romney, 1986; Ge et al., 1996; Sanson & Prior, 1999). Thus, genes that influence temperament and ODD become evocatively correlated with adverse parenting environmental risk factors. Finally, active rGE describes situations in which children’s heritable characteristics lead them to seek environments that magnify a particular trait or disorder. For example, some children selectively form friendships with delinquent peers, who foster their delinquent behavior. Because evidence exists that children’s associations with delinquent peers are themselves genetically influenced (Rowe & Osgood, 1984), active rGE is likely operative.

Gene × Environment Interaction There is also evidence that conduct problems are influenced by Gene × Environment interaction (G × E). First, heritable influences on childhood conduct problems can be mitigated by favorable social learning environments. Evidence comes from adoption studies, which show that adopted-away offspring of antisocial biological parents have fewer conduct problems when raised by well-adjusted adoptive parents as opposed to antisocial adoptive parents (Bohman, 1996; Cadoret, Yates, Troughton, Woodward, & Stewart, 1995). Second, in the Rhee and Waldman (2002) meta-analysis, the magnitudes of heritable and environmental influences on antisocial behavior differed as a function of a host of moderators, including assessment method, zygosity determination method, and age, which suggests that causal influences on antisocial behavior are malleable as a function of personal, situational, or methodological characteristics. Third, different individuals respond in different ways to the same experiences partly because of differences in their genes. Findings on this kind of G × E are summarized in the section below on molecular genetics.

Potential Environmental Mechanisms of Conduct Problems In this section, we review findings on aspects of environments as risk factors for conduct problems. In reading this section one should keep in mind that correlation does not imply causation. Thus, conduct problems may be associated with a variable that is not itself causal but linked to effects of a common (even if unidentified) causal influence.

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Birth Weight and Birth Complications. A number of pregnancy and birth factors are correlated with development of serious conduct problems (Brennan, Grekin, & Mednick, 2003), including birth complications (e.g., lack of oxygen to the fetus during labor) and low birth weight (Brennan et al., 2003), particularly in disorganized families with few resources (Arseneault, Tremblay, Boulerice, & Saucier, 2002). Although this finding may indicate that better-functioning families provide environments that lessen negative effects of birth complications, it is not usually possible to determine whether birth complications have causal effects or are related to various outcomes because of heritable and environmental influences that are correlated with them. Nonetheless, studies of genetically informative samples provide evidence that at least some perinatal factors may have causal effects on vulnerability to conduct problems (Raz, Shah, & Sander, 1996). For example, because monozygotic twins share all of their segregating genes and share all aspects of the environment that are common to twins who grow up in the same home (the equal environments assumption; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]), differences between monozygotic twins in conduct problems that are related to differential experience of a birth complication would provide strong evidence that it plays a causal role. Accordingly, van Os et al. (2001) found that monozygotic twins of lower birth weight are more likely to develop conduct problems, which suggests that prenatal complications that gives rise to low birth weight play a causal role, perhaps because they are associated with alterations in brain systems involved in vulnerability to conduct problems (Brennan et al., 2003). However, the magnitude of this relation throughout the full range of birth weight is likely to be quite small (Ficks, Lahey, & Waldman, 2013), although there may be a stronger relation at the extremes. Other studies suggest that birth complications interact with heritable vulnerabilities to conduct problems (Wichers et al., 2002). It is possible that the link between birth complications and conduct problems is stronger among males (Brennan et al., 2003). Maternal Cigarette Smoking and Substance Use During Pregnancy. Women who smoke, drink alcohol, and/or use drugs such as cocaine during pregnancy are considerably more likely to have children who develop conduct problems, even when other maternal characteristics known to be associated with conduct problems among offspring are covaried (Brennan et al., 2003; Wakschlag, Pickett, Cook, Benowitz, & Leventhal, 2002; see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]). Thus, toxic substances such as carbon monoxide in tobacco, which crosses the placental barrier to the fetus, may affect fetal brain development in ways that increase risk for conduct problems. Of course, embryos are not randomly assigned to develop in women who smoke during pregnancy versus women who don’t, and women who smoke during pregnancy differ from women who do not smoke

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in many ways. A large study that controlled extraneous background genetic and environmental risk factors has raised questions about whether apparent effects of prenatal exposure to smoking are truly causal. That is, D’Onofrio et al. (2008) found that on average women who smoked more during their pregnancies gave birth to children with more conduct problems, yet when mothers who smoked during one pregnancy smoked less (or not at all) during their other pregnancies, the level of offspring conduct problems did not vary with the level of smoking during each pregnancy. Other findings, however, indicate that even second-hand smoke exposure, including that experienced in the workplace, predicts later development of conduct problems (Gatzke-Kopp & Beauchaine, 2007). Such findings are more difficult to attribute to third variables. There is indisputable evidence that maternal alcohol use during pregnancy causes conduct problems among offspring (see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]). For example, in the same sample used to study maternal cigarette smoking, D’Onofrio et al. (2007) found a linear dose-response effect: the more alcohol consumed, including even moderate levels, the greater the risk of conduct problems among offspring. Because this relation with alcohol use was clear even when mothers drank at different levels during different pregnancies, results suggest strongly an adverse causal effect of drinking alcohol during pregnancy. Socioeconomic Status (SES). Children and adolescents from families with low income and low levels of parental education are more likely than their peers to exhibit serious conduct problems (Côté et al., 2006; Lahey, Miller et al., 1999; Lahey & Waldman, 2003). Poverty may create circumstances that foster conduct problems, or alternatively, antisocial parents who live in poverty because they did not succeed educationally and occupationally might transmit conduct problems to their offspring through common heritable and environmental mechanisms related to both poverty in parents and conduct problems in children. Longitudinal and quasi-experimental studies that minimize confounds suggest that both explanations are correct (e.g., Dohrenwend et al., 1992; Miech, Caspi, Moffitt, Wright, & Silva, 1999). In one study, family income supplements led to decreases in oppositional and conduct disorder behaviors among children (Costello, Compton, Keeler, & Angold, 2003). Low SES may be more strongly associated with childhood-onset delinquency than adolescent-onset delinquency (Lahey et al., 2006; Moffitt et al., 2001). It is not clear whether sex differences exist in the magnitude of the association between SES and conduct problems (Lahey et al., 2006; Moffitt et al., 2001). Parental Characteristics, Family Characteristics, and Parenting. Many studies indicate that a set of correlated characteristics of parents is related to conduct problems among offspring. Risk for conduct problems is highest among children of mothers and fathers with histories of antisocial behavior and substance abuse, mothers with

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low intelligence, and mothers who first give birth at young ages (Lahey et al., 2003; Lahey et al., 2006; Lahey, Miller et al., 1999; Moffitt et al., 2001). A well-controlled quasi-experimental sibling comparison study supported the hypothesis that something about being the child of a younger mother increases risk for conduct problems (D’Onofrio, Goodnight, Van Hulle, Waldman et al., 2009). In addition, women who have multiple partners and/or discordant partner relationships are more likely to have children with conduct problems (Keenan, Loeber, & Green, 1999; Lahey, Miller et al., 1999). Indeed, each change in parental relationships (e.g., from unmarried to married or married to divorced) is associated with 12% greater risk for offspring delinquency during adolescence (Goodnight et al., 2013). According to social learning theory (Patterson et al., 1992), these and other parent and family characteristics cause child conduct problems by disrupting aspects of parenting behavior per se—a view with considerable empirical support (Jaffee, Belsky, Harrington, Caspi, Moffitt, 2006; Patterson, DeGarmo, & Knutson, 2000). Furthermore, robust evidence exists that inadequate supervision and inconsistent, coercive, and punitive discipline—including physical and sexual abuse and neglect—are correlated with offspring conduct problems (Lahey, Miller et al., 1999; Patterson & Stouthamer-Loeber, 1984). Interventions that change these aspects of parenting behavior reduce child conduct problems (Beauchaine, Webster-Stratton, & Reid, 2005; Nock, 2003). Deviant Peer Influence and Gang Membership. Two robust findings suggest the importance of peers in the origins of juvenile delinquency. First, almost all crime committed by adolescents is committed in the company of other youth (Conger & Simons, 1997). Second, associating with delinquent peers is perhaps the strongest correlate of adolescent delinquency (Conger & Simons, 1997). Some evidence indicates that developing friendships with delinquent peers leads to increases in delinquency among youth who were not previously delinquent (Keenan, Loeber, & Zhang, 1995). More can be learned from studies of membership in antisocial gangs, which is a special case of delinquent peer influence. Although it is clear that engaging in conduct problems during childhood increases the likelihood that male children join gangs (Lahey, Gordon, Loeber, Stouthamer-Loeber, & Farrington, 1999), drug selling, violent behavior, and vandalism all increase sharply after youth join, compared to before gang entry and after gang exit (Gordon et al., 2004). In Moffitt’s (1993, 2003) model, peer influences are particularly important for delinquent adolescents without high levels of conduct problems as children, and subsequent research indicates consistently strong effects of peers on delinquent behavior (see Dishion, Kim, & Tein, 2016). It is important to note, however, that children’s associations with delinquent peers are influenced in part by heritable mechanisms (Rowe & Osgood, 1984). Little is currently known about sex differences in peer influences on delinquency.

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Neighborhoods and Urbanicity. Juvenile delinquency is far more common among youth who live in neighborhoods characterized by poverty, crime, and social disorganization than in other neighborhoods (Loeber et al., 1998). Sampson, Raudenbush, and Earls (1997) suggested that the most important aspects of high-crime neighborhoods are a lack of social connectedness among neighbors and the absence of working together to supervise youth and reduce crime. Meier, Slutske, Arndt, and Cadoret (2008) found that the relation of delinquency with impulsivity and callous-unemotional traits was greater in neighborhoods low in collective efficacy compared to neighborhoods high in collective efficacy. A quasi-experimental study was consistent with the hypothesis that disorganized neighborhoods yield increased risk for delinquency (Goodnight et al., 2012). In addition, Tuvblad, Grann, and Lichtenstein (2006) found that the proportion of variance in adolescent conduct problems attributable to genetic influences was lower, and the proportion attributable to environmental influences shared by siblings was greater, in such high-risk neighborhoods. This interaction, which has now been shown in several studies, provides support for the hypothesis that neighborhood factors play some causal role in conduct problems. Juvenile crime is highly concentrated in high-density cities (Laub, 1983). European studies indicate that youth who live in big cities report rates of delinquent behavior twice those of rural youth (Rutter et al., 1975; Wichström, Skogen, & Oia, 1996), but evidence from North America is inconsistent (Costello et al., 1996; Offord et al., 1987). Furthermore, a longitudinal study of a large U.S. sample indicated that although population density is correlated with greater delinquency at any one point in time, moving from a higher density region to a lower density region is not associated reductions in adolescent delinquency (Harden et al., 2009). Finally, research indicates consistently that impulsive children and adolescents are susceptible to delinquency in high-risk neighborhoods, whereas nonimpulsive children and adolescents are not (Jennings & Hahn Fox, 2016; Lynam et al., 2000; Meier et al., 2008)

NEURAL MECHANISMS It is important to relate individual differences in antisocial behavior to variations in the anatomy and physiology of neural systems because such links can illuminate our understanding of conduct problems via what we know about those neural systems. Early studies of biological correlates of conduct problems evaluated peripheral nervous system markers. These studies, and more recent examples, yield consistent findings of both low resting heart rate and low electrodermal responding as predictors of adolescent and adult conduct problems and crime (Latvala, Kuja-Halkola, Almqvist, Larsson, & Lichtenstein, 2015; Lorber, 2004; Ortiz & Raine, 2004; Raine, 2015). Low resting heart rate is interesting partly because

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it also is related to temperamental fearlessness/stimulation-seeking (Raine, 2002). These findings indicate that autonomic arousal is inversely correlated with conduct problems and positively correlated with desistance from childhood conduct problems (Lahey, Hart, Pliszka, & Applegate, 1993; Popma et al., 2006; Quay, 1993; Raine, Venables, & Williams, 1995). Similarly, less responsive hypothalamic-pituitary-adrenal (HPA) axis activity is observed among those with conduct problems in some studies (McBurnett et al., 2005; McBurnett, Lahey, Rathouz, & Loeber, 2000; Popma et al., 2006). Recent advances in brain imaging have led to studies relating brain anatomy and function to conduct problems. These studies indicate both structural and functional brain correlates of conduct problems. These correlates include abnormalities in both striatal and prefrontal/orbitofrontal cortex structure and function (e.g., Sauder, Beauchaine, Gatzke-Kopp, Shannon, & Aylward, 2012; Gatzke-Kopp et al., 2009; Ishikawa & Raine, 2003; Raine, 2002; Rubia, 2011), as well as abnormalities in white matter tracts (Decety, Yoder, & Lahey, 2015). A recent meta-analysis indicates that children and adolescents with a mixture of ODD and CD symptoms exhibit less gray matter volumes in the insula, amygdala, and frontal and temporal structures (Rogers & DeBrito, 2016). Conduct problems are also associated with atypical functional organization of neural networks (Zhou et al., 2015) and with functional connectivity between neural structures involved in impulse control (e.g., caudate). Because areas involved in behavior regulation (e.g., medial frontal cortex) appear to be altered among youth with conduct problems, deficits in top-down control over impulsive behavior are implicated (see Beauchaine et al., 2017; Shannon, Sauder, Beauchaine, & Gatzke-Kopp, 2009). There are some intriguing links among these research findings, which could lead to a more integrated theory of the neural mechanisms underlying conduct problems. Low resting heart rate may be correlated with conduct problems because the prefrontal cortex, particularly the insular cortex, plays a role in regulating autonomic arousal (Raine, 2002). It is also interesting that maternal alcohol consumption during pregnancy results in smaller frontal cortices in children (Brennan et al., 2003).

Molecular Genetics The past 15 years have witnessed initial steps in the search for genetic variants that confer vulnerability to conduct problems (see Gizer, Otto, & Ellingson, 2016). Early findings appeared to support hypotheses regarding the role of specific neurotransmitter systems in the etiology of conduct problems and tested hypotheses regarding gene–environment interplay (Rutter, 2006). A classic example is a study by Caspi et al. (2002), which yielded an interaction between childhood maltreatment and a variant in the promoter of the gene that encodes the enzyme monoamine oxidase-A (MAOA) with respect to antisocial behavior. MAOA is of interest because it regulates availability of monoamine neurotransmitters, including serotonin, dopamine,

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and norepinephrine, all of which are implicated in animal studies of aggression (Rutter, Moffitt, & Caspi, 2006). Caspi et al. (2002) found that as adults, maltreated children with the low-activity MAOA genotype exhibited significantly higher levels of conduct problems than maltreated children with the high-activity genotype. This genetic moderation of a major effect of maltreatment has since been replicated in several studies (Foley et al., 2004; Kim-Cohen et al., 2006; Nilsson et al., 2006; although see Haberstick et al., 2005 and Young et al., 2006, for null findings). Nonetheless, a meta-analysis of extant studies confirmed the interaction of MAOA with childhood maltreatment in predicting serious antisocial behavior (Kim-Cohen et al., 2006). The Caspi et al. (2002) finding could reflect gene–gene interaction instead of G × E. That is, a different gene (or set of genes) transmitted from parent to child (manifested in the parent as risk for harsh discipline and in the child as risk for aggressive conduct problems) could interact with MAOA to result in the increased risk for serious conduct problems among maltreated children, even if childhood maltreatment has no causal environmental effect. Importantly, however, evidence from two other types of studies, which are not subject to the same alternative explanation, support the G × E hypothesis. First, Newman et al. (2005) found that rhesus monkeys randomly assigned to be raised in isolation as opposed to with their mothers are more aggressive if they have the homologous low-activity MAOA genotype. Second, imaging studies among humans show that when presented with emotion-provoking stimuli, persons with the low-activity MAOA genotype show greater activation of the amygdala and less activation of the prefrontal cortex (Meyer-Lindenberg et al., 2006; Meyer-Lindenberg & Weinberger, 2006). MAOA is not the only gene that affects neural systems implicated in aggression. Catechol-O-methyl transferase (COMT), a gene that codes for an enzyme involved in the breakdown of synaptic dopamine, epinephrine, and norepinephrine, is associated with variations in frontal cortex function. Thapar et al. (2005) found evidence for a G × E interaction in which COMT is associated with increased vulnerability to childhood conduct problems among low birth weight children. This association is stronger among those with the val/val COMT genotype. There also are several findings relating conduct problems to variants in the gene that encodes the dopamine transporter (DAT1), which is involved in reuptake of dopamine from the synapse (Lee et al., 2007; Young et al., 2002), including (1) an interaction between DAT1 and positive and negative parenting (Lahey et al., 2011), and (2) an interaction between maternal insensitivity and variants of the D4 receptor gene in predicting childhood conduct problems (Bakermans-Kranenburg & van IJzendoorn, 2006). There are also reports that link a commonly studied polymorphism in the serotonin transporter gene (the 5HTTLPR) to oppositional and aggressive behavior (e.g., Haberstick, Smolen, & Hewitt, 2006). A recent meta-analysis (Ficks & Waldman, 2014) revealed a significant association between antisocial behavior and both the 5HTTLPR short allele and the promoter variant in

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MAOA, although there was substantial heterogeneity in effect sizes across studies, and evidence of a possible bias toward publishing significant findings for 5HTTLPR. A vast amount undoubtedly remains to be learned about genetic influences and gene–environment interplay, but molecular genetic studies of conduct problems are already producing intriguing findings. Even so, the proportion of variance in behavior accounted for by specific genetic markers remains quite low (on the order of a few percent), compared with research from behavioral genetic studies, which consistently suggest that genetic influences account for large proportions of the variance in various behaviors and traits, including conduct problems (see above). Furthermore, there is a disappointing lack of replication of findings on associations between genetic variants and conduct problems (McGue et al., 2013; Tielbeek et al., 2012). For extended discussion of the “missing heritability” problem in psychiatric genetics, see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer].

THEORETICAL SYNTHESIS Lahey and Waldman (2003, 2005, 2012) proposed a theoretical model that integrates current findings on the development of conduct problems (see also Lahey et al., 2003). In the Lahey and Waldman model, children are born with individual differences in dispositions to respond socially and emotionally to the environment. Variation in these dispositions among children is influenced by genes and prenatal influences and shaped by the postnatal environment from birth onward. Although definitions and labels of the dispositions vary somewhat across studies, three dispositions have been identified across many studies as being associated reliably with childhood conduct problems. Prosociality versus Callousness. Children who care about the feelings of others and want to please adults are less likely to develop serious conduct problems than children who callously disregard the wishes and feelings of others (e.g., Frick, 2006; Messer, Goodman, Rowe, Meltzer, & Maughan, 2006). In the Lahey and Waldman (2003, 2005, 2012) model, the reason is that natural consequences of common early childhood misbehaviors such as hitting and taking things from others (e.g., seeing the other child cry) serve as punishments to children who care about the feelings of others but are either neutral or reinforcing to more callous children. These individual differences lead to differential reinforcement histories that either increase or decrease the likelihood of future antisocial behavior. Callous children are particularly likely to acquire a pattern of planful, goal-directed aggression (Frick, 2006; Kempes, Matthys, Maassen, van Goozen, & van Engeland, 2006). Daring/Sensation-Seeking versus Fearful Inhibition. Children who find novelty and danger attractive and exciting are more likely to develop conduct problems than

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children who react fearfully to novel, loud, and risky situations (Biederman et al., 2001; Raine et al., 1998; Quay, 1965). Lahey and Waldman (2003, 2005, 2012) hypothesized that getting into fights and engaging in transgressions that could lead to apprehension and punishment is reinforcing to daring children but punishing to less daring children. Emotional Lability versus Emotional Stability. Children who experience high levels of negative emotionality, a heritable trait that is sometimes expressed as intense negative emotions to even minor frustrations and threats, are vulnerable to conduct problems (e.g., Hur, Hwang, & Chung, 2015). When adults attempt to control or discipline highly emotional children, their children are likely to respond with intensely oppositional, defiant, and coercive responses, often prompting adults to back down. The net result of such parent-child interactions is negative reinforcement that increases the likelihood of future oppositional-defiant and even aggressive behavior by the child (e.g., Patterson et al., 1992). Similarly, negative emotional responses to minor frustrations and provocations from other children (e.g., someone is playing with a toy that the child wants to play with) increase the likelihood of reacting in an antisocial manner (e.g., grabbing the toy), leading to positive reinforcement of the antisocial behavior (i.e., the aggressive child gets the toy). Thus, in a multitude of ways, individual differences in early socioemotional dispositions may increase or decrease the probability that a child develops childhood-onset conduct problems and persists in them. In addition, deficiencies in cognitive skills and language may interfere with socialization and thereby increase vulnerability to conduct problems (see above; Keenan & Shaw, 1997). Lahey and Waldman (2003, 2005, 2012) posit that the three socioemotional dispositions and cognitive ability play less of a role in the development of adolescent-onset conduct problems. Note that the inverses of these predispositions (i.e., prosociality, fearfulness, calm response to frustration and threat, and higher intelligence) may protect adolescents from development of delinquent behavior. At a different level of analysis, these predispositions and abilities can be understood as manifestations of individual differences in brain structure and function that are caused by genetic and environmental influences. Genes influence conduct problems partly because they affect neural systems that subserve the dispositions and abilities outlined above. Genes also influence environments that foster or reduce the likelihood of conduct problems—and are correlated with and interact with those environments. Thus, our theoretical model and others like it should incorporate variables at biological, environmental, and behavioral levels of analysis (e.g., Beauchaine & Gatzke-Kopp, 2012; Beauchaine & McNulty, 2013; Beauchaine et al., 2017). Although many tests of this model are required, an early prospective investigation confirmed the prediction that children high in both negative emotionality and sensation seeking are vulnerable

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to developing childhood conduct problems (Gilliom & Shaw, 2004). More recently, Waldman et al. (2011) showed that a substantial proportion of genetic influence on youth conduct problems is mediated by the three socioemotional dispositions under discussion, suggesting that future research on genetic bases of youth conduct problems should also focus on these socioemotional dispositions as target phenotypes.

UNRESOLVED QUESTIONS AND FUTURE DIRECTIONS Is ODD distinguishable from CD? As noted earlier, for many years there have been two different views in the literature of the relation between ODD and CD. The first view, perhaps embodied best in the ICD-10 approach to diagnostic classification, is that ODD is part of a CD diagnostic spectrum, characterizes a less severe form of CD, and is sometimes a developmental precursor to CD (WHO, 1993). Such spectrum approaches to externalizing behavior have gained increasing traction in recent years (Beauchaine & Hinshaw, 2016). The second perspective, represented in DSM-IV, is that although ODD frequently overlaps with CD, and although their symptoms are highly correlated (Angold et al., 1999; Angold & Costello, 2009; Lahey, Rathouz et al., 2008), they are relatively distinct dimensions of psychopathology with some distinct correlates and sequelae (Boden, Fergusson, & Horwood, 2010; Burke, Waldman et al., 2010; Petty et al., 2009; Rowe et al., 2010).

Mapping the Fine Structure of Youth Antisocial Behavior: ODD and CD A number of published studies are relevant to evaluating these two alternative hypotheses. The DSM-IV field trials for the disruptive behavior disorders identified two nonoverlapping sets of symptoms with greater diagnostic utility for ODD or CD, respectively (Frick et al., 1994). Many studies have subsequently supported the distinction between the DSM-IV symptoms lists for ODD and CD using factor analysis, although some ODD symptoms (intentionally bothers others and spiteful and vindictive) may poorly discriminate ODD and CD (Lahey, Applegate et al., 2004; Lahey, Rathouz et al., 2008). In addition, several recent studies suggest partitioning ODD symptoms into those that reflect affect dysregulation (e.g., “loses temper,” “is touchy or easily annoyed,” “is angry and resentful,” and “is spiteful or vindictive”) versus those that reflect more “acting-out” behavior (e.g., “argues with adults,” “actively defies,” “deliberately annoys people,” and “blames others for his or her mistakes or behaviors”) (Burke et al., 2014; Burke, Hipwell & Loeber, 2010; Stringaris & Goodman, 2009). The meta-analysis of Frick et al. (1993) revealed that two orthogonal dimensions, (1) overt versus covert and (2) destructive versus nondestructive, best described the domain under consideration. The conjunction of these two orthogonal dimensions gives rise to the four symptom dimensions of oppositionality, aggression, property violations,

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and status offenses. The authors went on to cross-validate this 2 × 2 matrix and align it with developmental framing, such that oppositionality had the earliest onset (median age = 6.0 years), followed by aggression (median age = 6.75 years), property violations (median age = 7.25 years), and status offenses (median age = 9.0 years). Thus, although they are highly correlated and therefore likely share causal influences and neurobiological mechanisms, ODD and CD are different enough to distinguish as dimensions. Yet results from factor analysis are often misinterpreted. This strategy is not suited for identifying subtypes of disorders or people (see e.g., Waller & Meehl, 1998). Rather, it identifies dimensions on which people vary. For example, consistent evidence for five personality dimensions in no way suggests five types of personality. Instead, individual differences along five dimensions yield almost unlimited expressions of personality. Similarly, factor analyses of ODD and CD symptoms do not suggest specific types of disorder. Individuals who score high on one dimension of externalizing conduct almost always score high on all others (Hinshaw, 1987), especially if their age offers opportunities to engage in criterion behaviors across syndromes (see Beauchaine & McNulty, 2013). Direct comparisons have been made between the DSM-IV model, in which ODD and CD are separate dimensions, and a model inspired by the structure of the Child Behavioral Checklist (CBCL) (Achenbach, 1978), in which aggressive CD symptoms are on the same dimension as ODD symptoms and nonaggressive CD symptoms are placed on a separate factor (Lahey, Rathouz et al., 2008). Results favored the DSM-IV perspective over both the CBCL framework and a model based on ICD-10, in which the ODD and CD symptoms loaded together on a single dimension. Thus, ODD and CD might best be considered as mathematically distinguishable yet highly correlated dimensions of psychopathology. Also, few factor analytic studies and no behavior genetic studies have investigated the validity of partitioning ODD symptoms into the “affect dysregulation” and “acting-out” dimensions, implying that further such analyses are needed.

Is the Distinction Between Aggressive and Nonaggressive CD Symptoms Useful? It is possible that the dimension of CD symptoms should also be further partitioned. In particular, although aggressive (e.g., fighting, bullying, and threat with confrontation of the victim) and nonaggressive CD behaviors (e.g., lying to “con” others, truancy, and theft without confrontation of the victim) are often highly correlated, there may be value in distinguishing between them. There is a small but informative literature in which confirmatory factor analyses (CFAs) and behavior genetic analyses have been used to test whether aggressive and nonaggressive CD symptoms are meaningfully distinguishable (see, for example, Frick et al., 1993, described above). More recently, CFAs of CD symptoms revealed greater statistical support for a model in which aggressive and nonaggressive CD symptoms load

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on two separate, but highly correlated dimensions (r = .73)1 than on a single CD symptom dimension (Tackett, Krueger, Iacono, & McGue, 2005). Researchers also have demonstrated differences between aggressive and nonaggressive conduct problems in personality dimensions: Burt and Donnelan (2008) found that several measures of aggression were correlated uniquely with higher levels on the stress reaction scale of the multidimensional personality questionnaire (Patrick, Curtin, & Tellegen, 2002), whereas nonaggressive conduct problems were uniquely correlated with lower levels on the control scale. These results suggest that the distinction between aggressive and nonaggressive CD symptoms may be useful for some purposes. A potentially important issue for future research is whether nonaggressive CD behaviors are homogenous in nature or if there are important differences between nonaggressive property violations (e.g., theft without confrontation and vandalism) and nonaggressive status offenses (e.g., truancy and staying out late without parental permission) (Frick et al., 1993). Several multivariate behavior genetics studies have examined common and unique heritable and environmental influences on aggressive and nonaggressive conduct problems. Early biometric studies of aggressive and nonaggressive conduct problems (e.g., Edelbrock et al., 1995; Eley, Lichtenstein, & Moffitt, 1999; Eley, Lichtenstein, & Stevenson, 2003) yielded three important findings. First, there were substantial heritable influences on both aggressive and nonaggressive conduct problems (as defined by the CBCL), although these were of greater magnitude for aggressive conduct problems. Second, shared environmental influences were of greater magnitude for nonaggressive conduct problems than for aggression (although the magnitude of shared environmental influences on aggression may increase during adolescence; Eley et al., 2003). Third, although there were substantial common heritable influences on aggressive and nonaggressive conduct problems, each dimension of conduct problems showed additional unique heritable influences. More recently, Tackett and colleagues found that heritable and nonshared environmental influences underlie both aggressive CD symptoms and the overlap between aggressive and nonaggressive CD symptoms, whereas substantial shared environmental influences (which were of the same magnitude as heritable influences) also underlie nonaggressive CD symptoms (Tackett et al., 2005). Aggressive and nonaggressive CD symptoms also show unique heritable and nonshared environmental influences (Gelhorn et al., 2006; Kendler, Aggen, & Patrick, 2013). A meta-analysis of biometric studies (Burt, 2009) found that heritable influences are more substantial for aggressive conduct problems than nonaggressive conduct problems (.65 vs. .48%) and that only the latter showed substantial shared environmental influences (accounting for 18% of the variance). Furthermore, the level of heritable influence on aggressive CD behaviors is stable 1. Such a high correlation approaches the reliabilities of symptom scales. Since reliability sets an upper limit on correlation, this suggests near full dependence across constructs, and calls into question whether the distinction between aggressive and nonaggressive CD symptoms is useful practically.

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from childhood through adolescence, but heritable influences on nonaggressive CD behaviors increase with age (Burt & Klump, 2009). Thus, it may be useful to distinguish between aggressive and nonaggressive CD behaviors (and perhaps between property and status offenses) in future nosologies, but further research is needed to ascertain this distinction in additional large population-based samples as well as in clinically referred samples.

Is There Sufficient Breadth of Coverage of Antisocial Behavior in Symptoms of ODD and CD? Another important but unresolved taxonomic issue is whether the extant ODD and CD criteria are broad enough to cover the full range of impairing antisocial behaviors. In particular, recent factor analytic and behavior genetic studies of reactive, proactive, and relational aggression have raised the possibility that these facets of antisocial behavior may not be sufficiently represented in the current taxonomy. Proactive and Reactive Aggression. Several factor analytic studies of reactive and proactive aggression (Dodge & Coie, 1987; Raine et al., 2006) suggest that these represent two distinct yet correlated dimensions. Although this distinction has been challenged (Bushman & Anderson, 2001), proactive aggression has been uniquely associated with delinquency, poor school motivation, poor peer relationships, single-parent status, psychosocial adversity, substance-abusing parents, and hyperactivity during childhood and with psychopathic personality, blunted affect, delinquency, and serious violent offending in adolescence (Kempes, Matthys, de Vries, & van Engeland, 2005; Raine et al., 2006). In contrast, reactive aggression has been associated with impulsivity, hostility, social anxiety, problems encoding and interpreting social cues, lower peer status, and lack of close friends in adolescence (Kempes et al., 2005; Raine et al., 2006). Biometric studies yield diverging results. A study of 172 6-year-old twin pairs (Brendgen, Vitaro, Boivin, Dionne, & Perusse, 2006) found a similar magnitude of heritable influences on proactive and reactive aggression, with a very high correlation between the heritable influences on each dimension of aggression (r = .87). A study of 1,219 9- to 10-year-old twins (Baker, Raine, Liu, & Jacobson, 2008) found significant sex differences in the magnitude of heritable and environmental influences on aggression, in which moderate heritable influences were found for boys but not for girls, whereas moderate shared environmental influences were found for girls but not boys. In contrast, no sex differences were found for mother or teacher reports of reactive and proactive aggression. Common heritable and environmental influences were each responsible for the correlation between proactive and reactive aggression, with the former being moderate-to-high and the latter being small-to-moderate in magnitude.

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There are two strong a priori arguments against including separate dimensions of proactive and/or reactive aggression in the DSM-5. First, because many of the items that define reactive aggression are similar to ODD items, any distinction between reactive and proactive aggression may overlap substantially with the distinction between ODD and CD. Second, when items that define proactive and reactive aggression were included with symptoms of psychopathology in the assessment of a large representative sample, exploratory factor analyses supported DSM-IV-like symptom dimensions of ODD and CD. Some reactive and proactive aggression items did not load on any psychopathology factor, and the ones that did loaded on either the ODD or the CD factors (Lahey, Applegate et al., 2004). Thus, there is not sufficient evidence that independent dimensions of proactive or reactive aggression should be included in future nosologies. Relational Aggression. Unfortunately, even less research is available on relational aggression, which refers to behaviors intended to hurt others by damaging their social relationships, reputation, or self-esteem but that do not involve physical harm (Archer & Coyne, 2005; Crick & Zahn-Waxler, 2003). Researchers have begun to entertain the possibility that relational aggression should be included in official nosologies, either as part of the definition of CD or as a new form of psychopathology (Keenan, Coyne, & Lahey, 2008; Keenan, Wroblewski, Hipwell, Loeber, & Stouthamer-Loeber, 2011; Moffitt et al., 2008). Recent biometric studies have examined the symptom structure of relational aggression and its associations with physical aggression. In a sample of 1,981 6- to 18-year-old twin pairs (Tackett, Waldman, & Lahey, 2009), substantial additive heritable influences and moderate shared environmental influences were found on a latent relational aggression factor that comprised both mother and child ratings, and which more strongly reflected mother than child ratings (i.e., accounting for 66% versus 9% of the variance). A study of 172 6-year-old twin pairs (Brendgen et al., 2005) examined the association between physical and relational aggression and found that heritable influences were greater in magnitude for physical than for relational aggression. It is noteworthy that there were shared environmental influences on relational but not physical aggression, and that these were equal in magnitude to the genetic influences underlying relational aggression. Phenotypic overlap between the two forms of aggression was mainly due to common heritable influences. In a sample of 7,449 7-year-old twin pairs (Ligthart, Bartels, Hoekstra, Hudziak, & Boomsma, 2005), heritable, shared environmental, and nonshared environmental influences were found on both relational and direct aggression. The phenotypic correlation between relational and direct aggression was due mainly to common heritable influences and to a lesser extent shared and nonshared environmental influences (55% to 58% genetic, 30% to 33% shared environmental, and 12% nonshared environmental influences). As with proactive and reactive aggression, the ultimate question is whether there is an incremental contribution of relational aggression in identifying children with impairing antisocial behavior. First, measuring symptoms of relational aggression

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appears to add little to identification of children and adolescents with impairing antisocial behavior over and above symptoms of ODD and CD (Keenan et al., 2008; Keenan et al., 2011). Second, when items that define relational aggression are included with DSM-IV symptoms in factor analyses, a relational aggression factor distinct from ODD and CD does not emerge (Lahey, Applegate et al., 2004). Nonetheless, some relationally aggressive behaviors load strongly on CD, suggesting that they should be considered for inclusion as symptoms that broaden the description of CD. Again, more research is clearly in order.

VALIDITY OF DIAGNOSTIC SUBTYPES OF CD There is widespread agreement that CD is a highly heterogeneous diagnostic category, both phenotypically and etiologically (Rhee & Waldman, 2002). Thus, an important issue is whether subtypes of CD should be distinguished and, if so, which subtypes are most valid and useful. Previous subtyping schemes in early editions of the DSM distinguished between socialized and undersocialized CD and between aggressive and nonaggressive CD. These models were abandoned and replaced in DSM-IV because no clear operationalization of the socialized/undersocialized distinction had been proposed and studied and because inspection of data from a longitudinal study of prepubertal children with CD (Lahey et al., 1995) found that all children with CD displayed aggression in at least one wave of the study (Lahey et al., 1998). In the DSM-IV and DSM-5, a distinction is instead made between childhood and adolescent age-of-onset subtypes based on the presence of at least one CD symptom prior to age 10. It is crucial to determine whether this or any other subtyping scheme is sufficiently valid to be incorporated into the nosology of CD.

Validity of Subtypes of CD Based on Age of Onset Considerable research has documented important differences between childhoodonset/life-course persistent and adolescence-limited forms of broadly defined antisocial behavior (see above; Moffitt, 1993, 2003). Although the prevalence of these forms of antisocial behavior, which range from mild to serious, is far higher than the diagnosis of CD, this research could be relevant to the taxonomy of CD. Childhood-onset antisocial behavior is associated with parental antisocial behavior, serious family dysfunction, perinatal complications, low IQ, and neuropsychological deficits, high levels of concurrent and earlier ADHD and ODD symptoms (and possibly with aggression), and difficulties in school performance and peer relations, whereas adolescence-limited antisocial behavior is associated with greater affiliation with deviant peers and less severe maladjustment and negative outcomes in adulthood (Hinshaw et al., 1993; Lahey et al., 2006; Moffitt, 1993, 2003; Odgers et al., 2008). Correlates of childhood-onset and adolescent-onset antisocial behavior also are quite different (Lahey et al., 2006; Lahey & Waldman, 2003; Odgers et al., 2008), which suggests different causal mechanisms.

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Thus, there is strong evidence that trajectories of broadly defined antisocial behavior differ considerably as a function of age of onset and persistence. A rather different question is whether subtypes of children and adolescents for whom antisocial behavior is severe enough to meet diagnostic criteria for CD based on age of onset should be distinguished, as they are in the DSM-5 (Moffitt et al., 2008). Most studies conducted on age-of-onset subtypes of CD used potentially biased retrospective ages of onset of symptoms (Lahey et al., 1998; McCabe, Hough, Wood, & Yeh, 2001; Stalk, Love, & Mueller, 2015), comprising weak evidence. Recently, however, in a longitudinal study of twins, youth who met criteria for CD with childhood onset exhibited higher levels of ADHD, came from more dysfunctional families, and were more likely to engage in antisocial behavior during early adulthood than children with CD with adolescent onset (Silberg, Moore, & Rutter, 2015). Furthermore, twin analyses show that heritable influences on ADHD are strongly shared with childhood-onset but not adolescent-onset CD (Silberg et al., 2015).

Validity of Subtypes of CD Based on Limited Prosocial Emotions The DSM-5 provides a new specifier to identify individuals who meet criteria for CD who also have “limited prosocial emotions.” This specifier requires the presence of at least two of four characteristics: lack of remorse or guilt, callous lack of empathy for others, unconcern about performance, and shallow or deficient affect (APA, 2013). This specifier is based on evidence that callous-unemotional (CU) traits in children are associated with severe and persistent antisocial outcomes, respond differently to treatment, and that children and adolescents with CD with limited prosocial emotions may have a different etiology than other youth with CD (see above; Frick, Ray, Thornton, & Kahn, 2014a). The introduction of this new specifier and the body of research related to it raise two important questions that should be a focus of future research. First, does a trait characterized by low prosocial emotion play a role in the origins and heterogeneity of antisocial behavior during childhood and adolescence? Second, is there enough evidence to use the limited prosocial emotions specifier in clinical practice (see the debate between Frick et al., 2014a, and Lahey, 2014)? Like others (Blair, Leibenluft, & Pine, 2014; Frick, Ray, Thornton, & Kahn, 2014b; Frick & Ray, 2015), we believe there is strong evidence that individual differences in trait-like low prosociality is a key factor increasing the likelihood that a youth’s transaction with the social environment will lead to the development of CD and poor academic performance (Ciucci, Baroncelli, Franchi, Golmaryami, & Frick, 2014; Frick & Viding, 2009; Frick & White, 2008; Lahey & Waldman, 2003, 2012; Lynam, Caspi, Moffitt, Loeber, & Stouthamer-Loeber, 2007; McMahon, Witkiewitz, & Kotler, 2010). Still, a number of different models for the underlying structure of traits related to antisocial behavior among children and adolescents have been proposed and should be compared in future studies (Dong, Wu, & Waldman , 2014; Forsman, Lichtenstein, Andershed, & Larsson, 2008; Frick & White, 2008). Each includes traits related to low prosocial concern for others as an integral component of the model

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but differs on other issues. One key issue for future research is whether shallow emotions are distinct from low prosociality (Ciucci et al., 2014; Lahey et al., 2008). Another concerns the need for further research on the etiology of CU traits. Twin studies suggest CU traits are moderately heritable (Ficks, Dong, & Waldman, 2014; Forsman et al., 2008; Viding, Blair, Moffitt, & Plomin, 2005; Viding, Jones, Frick, Moffitt, & Plomin, 2008), that they share substantial common heritable influences with CD (Viding, Frick, & Plomin, 2007), and that CD is more heritable when accompanied by high levels of CU (Forsman et al., 2008; Viding et al., 2005; Viding et al., 2008). At the same time, no studies of children who meet diagnostic criteria for CD have actually compared those with and without CU traits. Instead, most studies have used heterogeneous samples of children with ODD and CD (Lahey, 2014). Studies are needed to examine the clinical value of CU traits for subtyping children and adolescents who meet criteria for CD.

Overlap of Subtyping Schemas for CD In evaluating the validity and utility of alternative ways of subtyping CD, it is important to bear in mind that the various subtyping schemes are highly overlapping and may simply be different ways of identifying the same or highly similar youth. That is, distinctions between aggressive and nonaggressive, undersocialized and socialized, high versus low CU subtypes, and childhood- and adolescent-onset CD may largely identify the same subgroups of individuals with CD. It is possible that children who first meet criteria for CD early in childhood and continue to do so into adolescence exhibit more undersocialized, aggressive, and CU behavior than adolescents whose CD symptoms show onset in the absence of a history of childhood conduct problems (Lahey et al., 2006; Moffitt et al., 1996; Odgers et al., 2008). Thus, more research contrasting these subtyping approaches is needed to determine whether the DSM-5 limited prosocial emotions specifier provides incremental utility in subtyping CD than the age-of-onset subtypes.

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Maughan, B., & Rutter, M. (2001). Antisocial children grown up. In J. Hill & B. Maughan (Eds.), Conduct disorders in childhood and adolescence (pp. 507–552). New York, NY: Cambridge University Press. McBurnett, K., Lahey, B. B., Rathouz, P. J., & Loeber, R. (2000). Low salivary cortisol and persistent aggression in boys referred for disruptive behavior. Archives of General Psychiatry, 57, 38–43. McBurnett, K., Raine, A., Stouthamer-Loeber, M., Loeber, R., Kumar, A. M., Kumar, M., . . . Lahey, B. B. (2005). Mood and hormone responses to psychological challenge in adolescent males with conduct problems. Biological Psychiatry, 57, 1109–1116. McCabe, K. M., Hough, R., Wood, P. A., & Yeh, M. (2001). Childhood and adolescent onset conduct disorder: A test of the developmental taxonomy. Journal of Abnormal Child Psychology, 29, 305–316. McGue, M., Zhang, Y., Miller, M. B., Basu, S., Vrieze, S., Hicks, B., . . . Iacono, W. G. (2013). A genome-wide association study of behavioral disinhibition. Behavior Genetics, 43, 363–373. McMahon, R. J., Witkiewitz, K., & Kotler, J. S. (2010). Predictive validity of callous-unemotional traits measured in early adolescence with respect to multiple antisocial outcomes. Journal of Abnormal Psychology, 119, 752–763. Meier, M. H., Slutske, W. S., Arndt, S., & Cadoret, R. J. (2008). Impulsive and callous traits are more strongly associated with delinquent behavior in higher risk neighborhoods among boys and girls. Journal of Abnormal Psychology, 117, 377–385. Meier, M. H., Slutske, W. S., Heath, A. C., & Martin, N. G. (2011). Sex differences in the genetic and environmental influences on childhood conduct disorder and adult antisocial behavior. Journal of Abnormal Psychology, 120, 377–388. Messer, J., Goodman, R., Rowe, R., Meltzer, H., & Maughan, B. (2006). Preadolescent conduct problems in girls and boys. Journal of the American Academy of Child and Adolescent Psychiatry, 45, 184–191. Meyer-Lindenberg, A., Buckholtz, J. W., Kolachana, B., Hariri, A. R., Pezawas, L., Blasi, G., . . . Weinberger, D. R. (2006). Neural mechanisms of genetic risk for impulsivity and violence in humans. Proceedings of the National Academy of Sciences, 103, 6269–6274. Meyer-Lindenberg, A., & Weinberger, D. R. (2006). Intermediate phenotypes and genetic mechanisms of psychiatric disorders. Nature Reviews Neuroscience, 7, 818–827. Miech, R. A., Caspi, A., Moffitt, T. E., Wright, B. R. E., & Silva, P. A. (1999). Low socioeconomic status and mental disorders: A longitudinal study of selection and causation during young adulthood. American Journal of Sociology, 104, 1096–1131. Miller, G. E., & Chapman, J. P. (2001). Misunderstanding analysis of covariance. Journal of Abnormal Psychology, 110, 40–48. Mitchell, S., & Rosa, P. (1981). Boyhood behavior problems as precursors of criminality: A fifteen year study. Journal of Child Psychology and Psychiatry, 22, 19–33.

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C H A P T E R 15

Substance Use Disorders SANDRA A. BROWN, KRISTIN L. TOMLINSON, AND JENNIFER WINWARD

INTRODUCTION

A

lthough experimentation with alcohol and drug use during adolescence is nearly ubiquitous and considered by many to be a normal rite of passage, misuse of alcohol and drugs is a salient public health concern. It is now known that alcohol and other addictive substances affect adolescents differently than they affect adults (Brown et al., 2008; Wiley, Evans, Grainger, & Nicholson, 2008; Windle et al., 2008). Although few studies that assess biological sensitivity of children and adolescents to alcohol and drugs have been conducted, animal models consistently show marked differences in responses to and effects of alcohol on younger animals compared to adults. First, adolescent animals appear to be less sensitive to adverse effects of alcohol than adults (Casey et al., 1995; Zald & Iacono, 1998). Alcohol is less sedating, produces less motor impairment, less social and affective impairment, and fewer postintoxication (hangover) effects on adolescent animals than adult animals. Although such investigations are no longer allowed, an early study in which high doses of alcohol were administered to youth (males ages 8–15 years) indicated little behavioral change in children relative to what would be expected for the same dose (blood alcohol concentration) among adults (Behar et al., 1983). Of note, reduced sensitivity to the effects of alcohol and other substances is associated with subsequent increases in drinking per occasion and with long-term elevations in risk for development of alcohol dependence among humans (Schuckit, Smith, Anderson, & Brown, 2004). Second, compared with adult animals, adolescents exposed to low doses of alcohol exhibit greater social facilitation (Varlinskaya & Spear, 2002). Because most early alcohol and drug involvement is influenced strongly by social factors, such results suggest potential heightened reward value of alcohol and other substances during this developmental period. Third, as described 497

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later in this chapter, there are greater long-term behavioral effects and more brain impairment among adolescents than adults exposed to alcohol. Among humans, adolescent drinking and drug use are associated with physical problems (loss of consciousness), fragmentary to full blackouts (memory loss), interpersonal conflict, reduced school involvement and success, and elevated rates of risky sexual behaviors, interpersonal aggression, accidents, and injuries (see Brown, Gleghorn, Schuckit, Myers, & Mott, 1996; Brown & Tapert, 2004; Brown et al., 2008; Windle et al., 2008). Of greatest concern are suicidal ideation, attempts, and completions following alcohol/drug exposure, and increased risk associated with ideation during periods of alcohol and/or drug intoxication (e.g., Hingson, Heeren, Winter, & Wechsler, 2005; Windle, Miller-Tutzauer, & Domenico, 1992).

PREVALENCE OF ALCOHOL AND OTHER DRUG USE National surveys in the United States indicate that alcohol is consistently the drug of choice for teens, and many youth consume alcohol in a particularly hazardous fashion (Brown et al., 2008; Patrick et al., 2013). The greatest escalation in alcohol involvement occurs between 12 and 15 years of age. According to the Monitoring the Future (MTF) study (Miech, Johnston, O’Malley, Bachman, & Schulenberg, 2015), by 8th grade, 27% of students report lifetime drinking, which rises to 50% by 10th grade. The prevalence of prior 30-day use is 9% for 8th graders and 24% for 10th graders. More concerning are rates of hazardous use, including getting drunk and binge drinking (i.e., when a male consumes five or more drinks within 2 hours or a female consumes four or more drinks in the same time period). Although 3% of 8th graders and 11% of 10th graders report being drunk in the past 30 days, rates of binge drinking in the prior two weeks rise from 4% in 8th grade to 13% in 10th grade. Thus, many early adolescents who drink alcohol do so episodically and often to excess. As described later in this chapter, high doses of alcohol early in adolescence appear to have adverse effects on health across multiple developmental systems. As youth progress into middle and late adolescence, alcohol and other drug involvement continues to escalate, as does intensity of use. By 12th grade (modal age = 18 years), 66% of youth report having ingested alcohol at least once and half of all youth have been drunk at least once (Miech et al., 2015). Among high school seniors, almost 40% report using alcohol in the past 30 days, one quarter report being drunk, and 20% report five or more drinks per occasion during the prior 2 weeks. In contrast, rates of daily drinking remain low (2%), highlighting the heavy, episodic nature of youth involvement with alcohol. Recently, adolescent substance use researchers have noticed an increase in extreme binge drinking (consumption of 10 or more alcoholic drinks), a behavior that confers serious risk of alcohol poisoning and death. According to a study by Patrick and colleagues (2013), 10.5% of high school seniors in a national sample reported consuming 10 or more drinks, and 5.6% reported consuming 15 or more drinks in a single occasion at least once in the past 2 weeks.

Substance Use Disorders 499 Another commonly used drug among adolescents is nicotine. Although traditional cigarette use among adolescents has decreased over the last decade, with 14% of high school seniors reporting past month smoking in 2014 compared with 53% in 2005 (Miech et al., 2015), adolescents have increasingly turned to e-cigarettes. Indeed, e-cigarettes now have the highest 30-day prevalence of all tobacco products, which may be in part related to the incorrect perception that this method of nicotine administration is low-risk (Roditis & Halpern-Felsher, 2015). Exposure to other illicit substances is also common among adolescents. Approximately half of high school seniors report lifetime use of a drug other than alcohol or cigarettes (Miech et al., 2015). Marijuana is the most widely used illicit substance by adolescents. Forty-five percent of high school seniors report lifetime use of marijuana, and 1 in 5 seniors has smoked marijuana in the previous month. Use of drugs other than marijuana has decreased over the past decade, from 27% of high school seniors reporting lifetime use in 2005, compared with 22% in 2015. However, misuse of prescription medication including stimulants (e.g., Adderall, Ritalin) and opiates (e.g., OxyContin, Vicodin) continues to be a substantial problem among adolescents. These reported rates in national school-based samples may underestimate actual prevalence. Adolescents with problematic substance involvement have higher rates of truancy, suspensions, and expulsions than their peers (Brown, Mott, & Stewart, 1992), so those who use the most may not be represented fully in school surveys. Furthermore, among adolescents who are involved in substance abuse treatment programs, over half report not attending school immediately preceding admission to treatment (Brown, Myers, Mott, & Vik, 1994).

DSM-5 CRITERIA AND DIAGNOSTIC ISSUES Substance use disorders (SUDs) in adolescence involve self-administration of any substance that induces long term changes in mood, perception, or brain function (Bukstein & Lutka-Fedor, 2007). In general, substances are used initially by youth in social settings to produce a change in affective state or consciousness. Almost all abused substances can lead to psychological dependence, or the subjective feeling of needing the substance to function adequately. Some substances also produce physical dependence, when physiological and psychological adaptations to the substance occur. Tolerance—the need to ingest larger amounts of a substance for an effect once obtained at a lower dose—exemplifies such a physical adaptation. Another aspect of physical dependence involves the experience of withdrawal when consumption of an abused substance is ended abruptly. Indicators of SUDs among adolescents often involve physical, socioemotional, and health changes. Such changes include deterioration in appearance (e.g., rapid weight loss, unusual breath and body odors, cuts and bruises); bloodshot eyes, very large or small pupils, and watery or blank stares; increased energy or lethargy, insomnia or excessive sleep; clinically significant levels of depression or anxiety; deviant behaviors that were not evident in childhood; decreases in school grades;

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changes in social activities or peer groups; chronic coughing or sniffing; skin boils or sores; nasal bleeding; and evidence of intravenous drug use (needle tracks) or inhalation (perforated nasal septum) (Brown & Abrantes, 2005). The American Psychiatric Association recently published its fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013; see Chapter 2 [Beauchaine & Klein]). There are substantive differences between the DSM-IV and DSM-5 in diagnostic categorization and criteria for substance use disorders. Substance use disorder (SUD) in the DSM-5 combines the DSM-IV categories of substance abuse and substance dependence, with 11 criteria measured on continua from mild to severe. Endorsement of 2–3 criteria indicates a mild SUD; 4–5 criteria a moderate SUD; and 6–11 criteria a severe SUD. Drug craving was added as a criterion, and the criterion involving problems with law enforcement was eliminated. The ultimate goal of SUD diagnosis is to understand the clinical severity of the patient’s alcohol or drug use, based on effects to his or her health and his/her ability to meet major responsibilities at work, school, and home. In the DSM-IV, the distinction between abuse and dependence was based on the concept of abuse as a more mild manifestation and dependence as more severe manifestation. However, the severity of some abuse criteria, along with the high correlation between dependence and abuse symptoms in clinical populations, suggest that a unidimensional disorder assessed along a continuum better characterizes patients’ experiences (e.g., Hasin, Fenton, Beseler, Park, & Wall, 2012). Furthermore, factor analytic and item response theory analyses support the DSM-5 structure in both adult (e.g., Preuss, Watzke, & Wurst, 2014) and adolescent populations (e.g., Gelhorn et al., 2008). Adolescent SUD researchers have long been concerned about limitations of DSM-IV criteria for SUDs. For example, even when youth exhibit multiple substance use problems, DSM-IV criteria sometimes result in no diagnosis. Moreover, because there was no overlap between dependence and abuse symptoms, it was possible for an individual to exhibit multiple substance-related problems (e.g., two dependence symptoms) and not meet criteria for either abuse or dependence. In contrast, youth with only one abuse symptom could meet criteria for substance abuse. Pollock and Martin (1999) found that 31% who had alcohol dependence symptoms did not meet criteria for either abuse or dependence. Furthermore, when such adolescents were evaluated 1 year later, they exhibited outcomes more comparable to abusers than nonabusers. Similarly, among those who received treatment in publicly funded substance abuse programs, 18% did not meet DSM-IV criteria for a SUD, even though all exhibited multiple alcohol and/or drug problems that were sufficient to merit hospitalization (Aarons, Brown, Hough, Garland, & Wood, 2001). Thus, DSM-IV abuse and dependence criteria lack sufficient sensitivity to identify substance use problems among at least some adolescents. A recent study by Kelly and colleagues (2014) indicated a higher prevalence of SUD diagnoses when using DSM-5 criteria compared to DSM-IV criteria for alcohol, nicotine, and marijuana

Substance Use Disorders 501 use, although they had moderate to strong concordance rates overall. To date, no large-scale epidemiological studies have published data on prevalence rates of SUDs among adolescents using the new DSM-5 criteria. According to the most recent National Survey on Drug Abuse and Health, which used DSM-IV criteria, 5.2% of adolescents ages 12 to 17 meet criteria for substance abuse or dependence, with prevalence rates increasing from early to late adolescence (Substance Abuse and Mental Health Services Administration, 2014). Although prevalence rates of SUDs have gone down in the past decade among adults, rates for SUDs among youth ages 12–17 have remained steady. The prevalence of SUDs among youth ages 13–18 has been examined in multiple sectors of public service care including mental health, alcohol and drug, child welfare, juvenile justice, and severely emotionally disturbed groups in schools (Aarons et al., 2001). Using DSM-IV lifetime rates of SUDs, 35% of adolescents within these systems of care meet diagnostic criteria for either substance abuse or substance dependence. Alcohol and marijuana use disorders are the most prevalent, with the highest rates reported in mental health settings. Other illicit SUDs are more prevalent in juvenile justice settings. As these figures indicate, alcohol and drug involvement progresses to the level of SUD for a significant number of youth. Among adolescents, this progression occurs more rapidly than among adults. Adolescent SUDs are associated with a variety of developmentally significant impairments such as poor academic functioning (Chatlos, 1997), family problems (e.g., Dakof, 2000), health problems (Brown & Tapert, 2004), morphological and functional neuroanatomical abnormalities (Tapert et al., 2004), and psychiatric comorbidity (Abrantes, Brown, & Tomlinson, 2003). Moreover, emerging evidence from developmentally focused longitudinal studies indicates that SUDs during adolescence predict a wide range of adverse outcomes in adulthood (Anderson, Ramo, Cummins, & Brown, 2010).

HISTORICAL CONTEXT AND ETIOLOGICAL FORMULATIONS Considerable research has been devoted to understanding the onset of SUDs and progression to abuse and dependence among youth. Early etiological theories of substance use among adolescents focused on the interplay of person and environment; they include the theory of planned behavior (TPB), social learning theory (SLT), problem behavior theory (PBT), and the domain model. More recent models incorporate genetic, neurobiological, neurophysiological, and neuropsychological factors.

Environmental Models Theory of Planned Behavior (TPB). This theory, a derivative of the theory of reasoned action (Ajzen & Fishbein, 1980), has been used to explain why youth engage in various addictive behaviors. In this cognitive and behavioral theory, attitudes about using substances, perceived social norms of alcohol/drug use,

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and self-efficacy for coping in potential use situations influence youths’ intentions to use substances. Intentions, in turn, influence substance use decisions and behavior. Substance-specific attitudes result from underlying expectations about personal consequences associated with substance use and the value placed on these consequences (Ajzen & Fishbein, 1980; Goldman, Brown, Christiansen, & Smith, 1991; Petraitis, Flay, & Miller, 1995). Normative beliefs about substance use are determined by perceived use rates of others, the perception that others prefer that the individual use a substance, and personal motivation to please others. Self-efficacy about substance use refers to whether adolescents feel control over their behaviors in use situations. Two types of self-efficacy related to substance use intentions are described in this model: substance use self-efficacy, or the ability to successfully obtain and use substances, and refusal self-efficacy, or the ability to resist perceived pressures to use (Ajzen, 1988, 2001). Although support for TPB has been demonstrated for experimental substance use, causal links between substance-specific beliefs and substance uses may be more bidirectional, as proposed in other cognitively oriented models (e.g., expectancy theory). Social Learning Theory (SLT). Initially developed by Akers (1977) and subsequently refined by Bandura (1986), SLT focuses on relations between perceived contingencies and substance use. From this perspective, adolescents develop outcome expectations about the effects of substance use by observing (e.g., parents, peers, media) or by learning about the effects of substance use (e.g., discussions of use effects). SLT posits that positive social, personal, and physiological expectations, which result from attending to influential social role models, are predictive of adolescent substance use. Aspects of this model are salient in broader decision making models of deviancy among youth (Brown, Aarons, & Abrantes, 2001). Problem Behavior Theory (PBT). Problem behavior theory is a generalist model that considers substance involvement to be one of a number of deviant behaviors that typically co-occur among adolescents (Jessor & Jessor, 1977). From this perspective, adolescent deviant behavior reflects unconventionality. Thus, if an adolescent is prone to engage in one deviant behavior, he or she is likely to engage in others (Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008). Numerous studies support the high co-occurrence of multiple problems or delinquent behaviors, including marijuana and alcohol use, early and high-risk sexual behavior, illegal activity, truancy, and aggression. Compared to their peers, individuals who are high on this risk-taking characteristic are less likely to engage in health-promoting behaviors (Jessor & Jessor, 1977). Furthermore, adolescents who are at risk for deviant behaviors are more detached from their parents, more influenced by their peers, and less responsive to negative reinforcement; they also show distinct neuroanatomical response patterns reflective of poorer executive function (e.g., Zucker et al., 2006).

Substance Use Disorders 503 Domain Model. Huba and colleagues (Huba, Wingard, & Bentler, 1980) extended these models to focus on interactions among biological, intrapersonal, interpersonal, and sociocultural factors in jointly influencing adolescent substance use behavior. Biological mechanisms include genetic susceptibility, physiological reactions to substance use, and general health. Psychological state, cognitive style, personality traits, and personal values comprise the intrapersonal domain; interpersonal factors of social support, modeling, social reinforcement, personal identity, and belonging also contribute to use decisions. Finally, sociocultural and environmental factors include social sanctions of substance use, degree of availability of substances, social expectations, and environmental stressors.

Behavioral and Molecular Genetic Models Behavioral genetics studies (Beauchaine et al., 2008) have consistently linked parental alcohol and drug dependence to risk for alcohol and drug dependence among offspring (e.g., Schuckit, 1988). Although associated risks (e.g., conduct disorder) may influence outcomes, differences in behavioral, cognitive, and neurological measures are observed between offspring of alcoholics (family history positive, FHP) and offspring of nonalcoholics (family history negative, FHN). For example, when FHP adolescents are compared to FHN teens, they demonstrate greater impulsivity and rebelliousness (Knop, Teasdale, Schulsinger, & Goodwin, 1985), poorer response inhibition (Nigg et al., 2006), and poorer neuropsychological performance (Tapert & Brown, 1999; Tarter & Edwards, 1988). Additionally, nonalcoholic FHP youth demonstrate a lower physiological and subjective response to alcohol compared to FHN youth with similar levels of prior alcohol exposure (Newlin, 1994). These preexisting vulnerabilities, which predispose FHP adolescents to problematic substance use, are consistent with behavioral genetics studies indicating that a substantial portion of risk for SUDs is heritable and over half is nondrug specific (e.g., Kendler, Prescott, Myers, & Neale, 2003; Tsuang et al., 1998). Molecular genetics research has identified a number of candidate liability genes that affect liver enzyme activity (e.g., Wall, Shea, Chan, & Carr, 2001), serotonin neurotransmission (e.g., Schuckit et al., 1999), and dopamine neurotransmission in the mesolimbic reward system (e.g., Limosin, Loze, Rouillon, Adès, & Gorwood, 2003). These findings are consistent with prominent animal models of abuse and dependence that implicate primary reward pathways (e.g., Robinson & Berridge, 2003).

Gene × Environment Interaction Models Multiple genetic pathways, which interact with environmental risk factors and life experiences, have been proposed. Cadoret and colleagues (1995) found support for

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a direct pathway from parental alcoholism to drug abuse and dependency among male offspring, and for an indirect pathway from parental antisocial personality disorder to externalizing behaviors and eventually drug abuse and dependence. More recent research has uncovered genetic markers associated with level of physiological responding (LR) to the effects of alcohol, a heritable trait that predicts alcohol use disorders (AUDs) (e.g., Joslyn et al., 2008). Schuckit et al. (2004) reported that perceptions of lower response to alcohol among children who are offspring of alcoholics predate personal exposure, and identified a pathway of genetic risk in which the effect of family history is mediated through LR, which in turn predicts higher use per drinking episode, developing eventually into patterns of alcohol dependence 20 years later (Schuckit et al., 2005). More recent data from the Collaborative Study on the Genetics of Alcoholism (COGA) provides direct evidence for a Gene × Environment (G×E) interaction in predicting both age to first drink, and age to first DSM-5 alcohol use disorder symptom. The protective effect associated with a variant in the rs1229984 gene in alcohol dehydrogenase 1B (ADH1B) was attenuated among youth whose best friends drank alcohol (Olfson et al., 2014). Maturation Theory. Maturation theory (Tarter et al., 1999) is a transactional/ ontogenic process model of the development of early-onset SUDs. According to this theory, deviations in somatic and neurological maturation, along with stressful and adverse environments, predispose children to difficulties in regulating affect and behavior. Children with difficult temperaments in infancy are predisposed to oppositional behaviors. Family conflict then leads to conduct problems, and in turn to SUDs (Beauchaine & McNulty, 2013; Dawes et al., 2000; Tarter et al., 1999). Maturation theory incorporates a transactional perspective in which from the moment of conception, genetic and environmental interactions result in developmental sequences of events leading to increased risk for substance use disorders. Thus, no single genetic or nongenetic factor dominates risk for addictive disorders. Instead, clusters of vulnerabilities interact with environmental experiences to culminate in one of many phenotypic patterns of addiction. Expectancy Theory. Expectancy theory (Goldman et al., 1991; Goldman & Rather, 1993) has emerged as an alcohol/drug-specific integrative model of youth substance involvement because it considers multiple system levels of potential influence on youth substance use, as well as processes through which these systems interact over time in the context of development. Expectancies of the effects of alcohol and drugs are understood to reflect both content of cognitions (e.g., immediate and distal consequences of use), memories of prior use (which influence access to perceived consequences), and motivation (e.g., neural activation patterns). Expectancies, which are influenced by both genetic and environmental factors including learning experiences, are proximal to youth substance use decisions and

Substance Use Disorders 505 are continually modified via acquisition of updated cognitive content and adapted physiological and neuroanatomical responses (Anderson, Schweinsburg, Paulus, Brown, & Tapert, 2005). Such modifications occur with each use experience or substance-related exposure, increasing the likelihood of use in future high-risk situations. Thus, vulnerabilities present in childhood (e.g., genetic predispositions, temperament) affect learning processes by (a) influencing self-selection of environments (active gene-environment correlation), (b) directing attention to specific rewards, and (c) magnifying the subjective experience of the reward itself. In concert, these unfolding processes build a network of alcohol and drug expectancies, which dominate adolescent use decisions. For example, genetically influenced individual differences in alcohol metabolism influence motivation-related expectancies and drinking behavior (McCarthy, Brown, Carr, & Wall, 2001).

ENVIRONMENTAL RISK FACTORS AND GENETIC VULNERABILITIES Given widespread use of alcohol over the course of adolescence, as well as exposure to diverse substances during this period, there is great interest in discovering factors that increase vulnerability and risk for early-onset use and predict escalation to frequent use or involvement with other substances, high dose drinking, and/or emergence of associated problems. A broad range of vulnerabilities and risk factors has been identified, yet few are specific to alcohol/drugs. Clusters of co-occurring risk factors appear to facilitate progression of certain use trajectories (Tarter, et al., 1999; Windle et al., 2008). These developmental trajectories may be viewed as a succession of intermediary phenotypes that, depending on the severity of alcohol/drug consequences, may reach threshold for a SUD diagnosis. A diagnosis of a SUD (the endpoint phenotype) is multidimensional and developmentally variable. Because the phenotype varies across development, exemplifying processes of heterotypic continuity (see Chapter 1 [Hinshaw]), the significance of individual risk and protective factors changes as youth traverse changing demands during adolescence. The following summarizes alcohol/drug-specific vulnerabilities, risk factors, and protective factors for children and adolescents. Vulnerabilities and risk factors range from biogenetic (e.g., liver enzyme activity) to intraindividual (e.g., personality), interpersonal (family, peers), and environmental (community, cultural). Multiple risk factors often co-occur or are nested in certain contexts (families with alcohol/drug dependent parents). Certain biological vulnerabilities are dynamic in that they provoke and interact with environmental risk factors to shape developmental experiences. For example, youth with genetically influenced sensation-seeking tendencies seek out risky environments, which provide exposure to substance use and reinforcement for use, as well as involvement in other problematic behaviors.

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This process exemplifies both active and evocative rGE (Beauchaine et al., 2008; see also Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Temperament, which is present early in life, may directly and indirectly influence substance involvement among adolescents (Windle, 1990). Several heritable temperamental traits are associated with vulnerability to substance use and substance use problems among adolescents. In particular, difficult temperament (Windle, 1990), high sensation-seeking (Zuckerman, 1994), behavioral disinhibition (McGue, Iacono, Legrand, Malone, & Elkins, 2001), impulsivity (Baker & Yardley, 2002), aggression (Kuo, Yang, Soong, & Chen, 2002), and behavioral undercontrol (Colder & Chassin, 1993) are linked to early-onset of use and problems throughout adolescence. These vulnerabilities reflect lower inhibitory control over behavior; they have neurochemical and neurophysiological substrates that influence youth decision-making in both positive social situations, where teens have initial alcohol/drug exposure, and in more distressing contexts such as high-risk relapse situations (Cyders et al., 2007; Smith et al., 2007). Other temperamental features such as trait anxiety and anxiety sensitivity influence youth motivation for alcohol, cigarette, and marijuana use (Comeau, Stewart, & Loba, 2001). Psychiatric Comorbidity. Substance use disorders and concomitant mental health problems may develop independently, one may cause or exacerbate the other, or common mechanisms may underlie both. High rates of comorbid mental health disorders among adolescents with SUDs are observed in both community and clinical samples. In a large community sample of 14- to 18-year-olds, two thirds of adolescents who met diagnostic criteria for a SUD also met lifetime criteria for at least one other Axis I disorder (Lewinsohn, Rohde, & Seeley, 1995). The Methods for the Epidemiology of Child and Adolescent Mental Disorders (MECA) study obtained similar rates of comorbidity in a stratified community sample of youth, ages 9 to 18 (Kandel et al., 1997). Among weekly drinkers, two thirds met criteria for a DSM-IV psychiatric disorder. Furthermore, among those who used illicit drugs three or more times in the past year, 85% of girls and 58% of boys met criteria for a nonalcohol-, nondrug-related disorder. In their review of community based samples, Armstrong and Costello (2002) found that 60% of adolescent substance users evidenced a comorbid disorder. Rates of psychiatric disorders are even higher among substance abusing adolescents who are in treatment (Abrantes et al., 2003; Greenbaum, Foster-Johnson, & Petrila, 1996). Adolescents in inpatient substance abuse treatment report rates of other mental health disorders ranging from 68% to 82% (Novins, Beals, Shore, & Manson, 1996; Stowell & Estroff, 1992). Conversely, one third to one half of adolescents who are admitted to acute care psychiatric settings meet criteria for one or more SUDs (Grilo et al., 1995). More recent reviews addressing comorbid SUDs and psychiatric disorders (Brown & Abrantes, 2005; Cornelius, Salloum, Bukstein, & Clark, 2003) are

Substance Use Disorders 507 consistent in identifying both externalizing disorders (attention deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder) and internalizing disorders (anxiety, depression) among youth with SUDs. Age of Onset. The age at which involvement with psychoactive substances is initiated has important epidemiological and developmental implications. Clearly, not all youth exposed to psychoactive substances develop substance use disorders. However, age of first use is a reliable risk factor for later substance use problems and disorders. According to the National Longitudinal Survey of Youth (NLSY), the odds of developing alcohol dependence decrease by 9% for each year that onset of drinking is delayed, although the authors discuss the possibility that early onset drinking may be part of a cluster of deviance prone behavior among some youth (Grant, Stinson, & Harford, 2001), so causality cannot be inferred. Early onset of alcohol and marijuana use is predictive of binge drinking in adolescence (D’Amico et al., 2001). Among youth who receive treatment for substance use disorders, age of alcohol initiation occurs at 11 years, with progression to weekly alcohol use by age 13. Other drug use is initiated by age 13.7 years and progresses to regular use within a year (Brown et al., 1996). Among substance abusing adolescents with comorbid psychopathology, age of onset of drug initiation is earlier, with first use at 12.4 years and weekly use at 13.3 years (Abrantes, et al., 2003). Family Influences. Disruptions in family relations and functioning, along with parental psychopathology, are precursors, correlates, and consequences of adolescent SUDs. A family history (FH) of alcohol and/or drug dependence is associated with a four- to ninefold risk of SUDs in male offspring, and a two- to threefold risk in female offspring. This transmission appears to be nonspecific to SUDs, extending to delayed or deficient behavioral, emotional, and cognitive regulation (Tarter et al., 1999). A positive FH is also associated with elevated rates of comorbid mental health disorders and SUDs during adolescence and with altered neurocognitive and neurophysiological functioning during childhood and adolescence. For example, compared to youth without family histories, children with FH show (a) altered neural responses—as measured using electroencephalography—in response to novel stimuli, (b) different neural activation responses to memory tests, (c) blunted inferior parietal responses to inhibition tasks, and (d) lower executive functioning on neurocognitive tests (Brown & Tapert, 2004; Schweinsburg et al., 2004; Tapert & Brown, 2000; Tapert, et al., 2004). Parental deviance and psychopathology also confer risk for SUDs through lack of parental involvement and/or low levels of parent-child affection (Baer & Bray, 1999; Loukas, Zucker, Fitzgerald, & Krull, 2003; Sadava, 1987; Zucker et al., 2006). Inconsistent discipline, lower monitoring of behavior, excessive punishment, and permissiveness are all risk factors for SUDs among adolescents (Brody & Forehand, 1993; Chilcoat & Anthony, 1996; Gilvarry, 2000; Williams & Hine, 2002). In addition,

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family conflict predicts disruptive behavior in children, which elevates risk for SUDs during adolescence (Loukas et al., 2003; Zucker et al., 2000). The extent to which parents monitor youth activities also influences selection of peers (Brown et al., 1992; Chassin, Pillow, Curran, Molina, & Barrera, 1993) and, consequently, future risk. Peers. Peer influences are one of the most significant and consistent risk factors for adolescent substance involvement (Bates & Labouvie, 1995; Fergusson, Horwood, & Lynskey, 1995). Higher perceptions of peer use and more friends who engage in substance use and deviant behaviors (Barnes, Farrell, & Banerjee, 1994; Epstein & Botvin, 2002; Vik, Grizzle, & Brown, 1992) create greater access to substances and lead to the adoption of beliefs and values consistent with a drug use lifestyle (Tapert, Stewart, & Brown, 1999). Furthermore, associations with substance abusing and deviant peers mediate relations between parental alcoholism, low socioeconomic status, and family conflict on the one hand, and substance abuse during adolescence on the other (Fergusson & Horwood, 1999). Stress. Stressful life experiences increase substantially during early and middle adolescence, as does heightened reactivity to stress, especially for girls (Arnett, 1999). Stressful life events are correlated with substance use, and when occurring in the context of economic adversity, predict progression of substance involvement over the course of adolescence (e.g., Pandina & Schuele, 1983; Wills, Vaccaro, & McNamara, 1992). Youth from alcohol-abusing families experience more life stress and rate stressful life events as more negative than youth from families with no parental alcohol or substance abuse (Brown, 1989). Consistent with a transactional framework, the stress-substance involvement association is bidirectional, as adolescent alcohol and drug use provokes substantial stress in the form of subsequent physical, academic, legal, family, peer, and emotional problems (Tate, Patterson, Nagel, Anderson, & Brown, 2007).

Protective Factors Protective factors are not simply the absence of risk characteristics. Rather, they are distinct characteristics or circumstances that are associated with decreased likelihood of engaging in health-damaging behaviors, despite the presence of one or more significant risk factors (Jessor, Van Den Bos, Vanderryn, Costa, & Turbin, 1995). Protective factors for substance use and associated problems among adolescents include certain temperamental traits, high intelligence, social support, involvement with conventional peers, religiosity, and low-risk taking (Brown et al., 2001; Gilvarry, 2000). Competence skills (e.g., decision making, self-efficacy) and psychological wellness also protect against alcohol involvement across adolescence (Epstein & Botvin, 2002).

Substance Use Disorders 509 As a biologically based example, a genetic deficiency in the Km aldehyde dehydrogenase (ALDH2) isoenzyme is associated with adverse reactions to alcohol. This genetic polymorphism is more prevalent among northern Asians (Chinese, Japanese, and Koreans) than among Caucasians, African Americans, and Native Americans, and results in adverse physiological responses to alcohol, including flushing, tachycardia (increased heart rate), hypotension (low blood pressure), nausea, and vomiting (Luczak, Glatt, & Wall, 2006). These physiological responses protect against development of heavy drinking patterns by lowering the positive reinforcement value of alcohol. By disrupting both regular use and binge onset, ALDH2 polymorphisms and related expectancies may also act to deter progression to other drugs, although this remains to be determined.

DEVELOPMENTAL PATHWAYS TO ABUSE AND DEPENDENCE Adolescents typically seek autonomy and independence from their parents. Across species, increases in exploration, risk-taking, and independence appear during this developmental transition. It is therefore not surprising that middle school and high school students explore new activities and experiment with substance use (Schinke, Botvin, & Orlandi, 1991). In the United States, initiation of substance use typically occurs in early to middle adolescence, with use of gateway substances including cigarettes, alcohol, and marijuana (Kandel, Yamaguchi, & Chen, 1992), followed by other illegal drugs in late adolescence for a subset of gateway substance users. This sequence varies across different ethnic groups and among multiethnic adolescents (Chen et al., 2002). Binge drinking and more diversified substance involvement peaks during late adolescence and early adulthood (Chen & Kandel, 1995). Transitions out of the family of origin to independent and less restrictive living situations predict greater access to and acceptance of use of alcohol and other substances (Chassin & Ritter, 2001; Kypri, McCarthy, Coe, & Brown, 2004). Transition to adult roles including work, marriage, and parenthood are associated with a decline in substance involvement and abuse/dependence symptoms (Chilcoat & Breslau, 1996; Gotham, Sher, & Wood, 1997; Zucker, et al., 2006). Thus, a portion of substance abusing youth mature out of problematic use when anticipating or transitioning into adult responsibilities (Chassin & Ritter, 2001) or changing environments (Schulenberg, Maggs, & Hurrelmann, 1997). Biological vulnerabilities and environmental risk factors operate in concert to predict SUDs in adolescence. Although certain factors predict substance involvement during adolescence directly, mediators and moderators at multiple system levels influence initiation and progression of adolescent substance involvement. Thus, a multitude of trajectories may lead to abuse and dependence among youth. Several broad developmental pathways to SUDs in adolescence have been proposed.

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Deviance Prone Pathway. Zucker and colleagues (2000) have described a parental alcoholism/deviance proneness pathway that operates as a risk factor for behavioral difficulties among offspring. Behavior problems including conduct disorder elevate risk for early substance involvement and persistent deviant behaviors in adolescence. Because parental SUDs and psychopathology are associated with ineffective parenting, risk for behavioral and cognitive problems are elevated among offspring. These, in turn, result in emotional distress and affiliation with substance using, deviant peers, and eventually in offspring’s own substance involvement and continued problematic behaviors (Sher, 1994). A key feature of this model involves the child’s reduced ability to self-regulate emotional distress and inhibit behaviors, which elevates risk for development of substance use problems in adolescence. Difficulties with self-regulation are reflected in executive functioning deficits, which have been demonstrated in neuropsychological and neuroimaging studies of adolescent substance abusers (e.g., Anderson et al., 2005; De Bellis et al., 2000; Giancola & Parker, 2001). Although the primary emphasis among substance abuse researchers has been on environmental mechanisms operating within high-risk families, genetic factors also contribute to heterotypic continuity among adolescents on the deviance-prone trajectory (e.g., Beauchaine & McNulty, 2013; Beauchaine et al., 2008). Furthermore, heritable vulnerabilities and environmental risk factors likely interact to reinforce one another in affecting SUD outcomes, through both passive and evocative rGE (Beauchaine et al., 2008; Rutter, 2007). Negative Affectivity Pathway. A second developmental pathway to SUDs is through deficient regulation of negative affect. This pathway is associated with both exposure to environmental stressors and temperamental negative emotionality (Colder & Chassin, 1993, 1997; Cooper, Frone, Russell, & Mudar, 1995). Substance use among adolescents on this trajectory is also mediated by peer use and/or adolescent-onset deviant behavior, and it is associated with elevated incidence of comorbid internalizing disorders (Abrantes et al., 2003). Although the negative affectivity pathway has received support in both cross-sectional and cross-cultural research (e.g., Rose et al., 1997), associations between negative affectivity and substance involvement have been modest in prospective high-risk studies (Chassin & Ritter, 2001). Only about one quarter of those who evidence negative affectivity as children and who possess poor self-regulation and coping skills exemplify this trajectory (Colder & Chassin, 1993; Cooper et al., 1995). Enhanced Reinforcement Pathway. Some youth are less sensitive to the effects of substances and consequently use substances more frequently and/or in greater quantities (Chassin & Ritter, 2001; Schuckit, et al., 2004). Low response to alcohol is associated with higher positive reinforcement expectancies. This pathway is genetically mediated and is associated with physiological response differences to

Substance Use Disorders 511 pharmacological effects of substances (e.g., Conrod, Peterson, & Pihl, 1997; Schuckit et al., 2004). Genetically influenced physiological and subjective responses appear to affect use decisions via expectancies, which develop in part as a result of individual reactions to alcohol (McCarthy et al., 2001). Thus, in addition to the physiological effects of substances, positive cognitions and outcome expectancies develop based on personal use of a substance and continued drinking. In turn, escalation to abuse occurs via effects of both expectancies and continued use on decision making (Schuckit et al., 2004).

EFFECTS OF ADOLESCENT ALCOHOL USE ON BRAIN DEVELOPMENT Until recently, the extent of human brain development during and after puberty was not fully appreciated. With the advent of structural and functional neuroimaging and science-based behavioral tests, a more complete understanding of adolescent brain development has emerged. Early adolescence is characterized by secondary and tertiary expanses of the cerebral cortex in prefrontal, parietal, and temporal regions (Giedd et al., 1999). Subcortical structures in the medial temporal lobe that are dense in sex-steroid receptors—particularly the amygdala and hippocampus—also exhibit substantial development during this period (De Bellis et al., 2001; Sowell et al., 2004; Toga & Thompson, 2003). Throughout adolescence, there is a decrease in gray matter volume associated with dendritic pruning of synaptic connections, particularly in the prefrontal and orbitofrontal cortices (Giedd, et al., 1999; Gogtay et al., 2004). Important functional changes also unfold as myelination proceeds from more posterior to anterior regions (Huttenlocher, 1990; Paus et al., 1999), with prefrontal regions maturing last (Huttenlocher & Dabholkar, 1997; Sowell, Thompson, Holmes, Jernigan, & Toga, 1999). The higher-order association cortices undergo similar changes subsequent to primary sensory motor cortices. As a result of both synaptic refinement and myelination, white matter volume increases, as does the density, organization, and integrity of white matter pathways (Barnea-Goraly et al., 2005; Giedd, et al., 1999; Jernigan & Gamst, 2005). These structural and functional changes that unfold during adolescence contribute to maturing neurocognitive processes and increased regional specificity of processing during cognitive and behavioral tasks. Some of these neuroanatomical changes appear to be both hormone and experience dependent. Exposure to neurotoxins including alcohol and other substances during this phase of brain maturation may interrupt neurodevelopment and key processes associated with cognitive and behavioral functioning. Neuroimaging studies of white matter microstructure (Bava et al., 2009; Jacobus et al., 2009; McQueeny et al., 2009); brain morphometry (Medina et al., 2008; Medina, Schweinsburg, Cohen-Zion, Nagel, & Tapert, 2007); brain functioning (Schweinsburg, Schweinsburg, Nagel,

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Eyler, & Tapert, 2011; Squeglia, Schweinsburg, Pulido, & Tapert, 2011; Tapert et al., 2007); and neuropsychological functioning among adolescent substance users (Medina et al., 2007; Squeglia, Spadoni, Infante, Myers, & Tapert, 2009; Tapert, Granholm, Leedy, & Brown, 2002) provide evidence of the potentially adverse effects of substance use on the developing adolescent brain.

Neuroanatomical Consequences Compared to adult brains, adolescent brains show differential sensitivity to alcohol-induced brain changes, most notably in the frontal cortex, corpus callosum, and hippocampus. Adolescent substance users consistently exhibit altered white matter organization. McQueeny and colleagues (2009) compared white matter microstructure between adolescent binge drinkers and nonbinge-drinking controls and found that binge drinkers exhibit altered anisotropy in frontal, cerebellar, temporal, and parietal regions. Another study compared the brains of adolescents who engaged in heavy marijuana and alcohol use to the brains of demographically matched controls. Substance-using youth showed poorer white matter integrity in several areas, including fronto-parietal circuitry implicated in attention, and pathways connecting the frontal and temporal lobes (Bava et al., 2009). Furthermore, youth diagnosed with a substance use disorder show poorer prefrontal and parietal white matter organization (Clark, Chung, Thatcher, Pajtek, & Long, 2012), whereas adolescents with comorbid alcohol use and other disorders have smaller prefrontal cortex (PFC) white matter volumes compared to controls (De Bellis et al., 2005; Medina et al., 2008). The hippocampus also appears to be affected by drinking in adolescence. Both the left and right hippocampi are smaller among adolescent AUD participants than controls (De Bellis et al., 2000). These findings were replicated in studies of adolescents with AUDs without significant histories of other substance use or psychiatric conditions. Left hippocampal volumes were smaller among teens with AUDs than demographically matched controls, and youth with greater severity of AUDs had the smallest left hippocampi (Medina et al., 2007; Nagel, Schweinsburg, Phan, & Tapert, 2005). Finally, white matter microstructure abnormalities in the corpus callosum are observed in adolescents with histories of alcohol abuse and dependence (Tapert & Schweinsburg, 2005; Tapert, Theilmann, Schweinsburg, Yafai, & Frank, 2003). Researchers have used fMRI to examine effects of adolescent substance use on neural activity. In a study comparing spatial working memory (SWM) of binge-drinking adolescents and matched controls, findings revealed sex differences in brain activity, with female binge drinkers showing less SWM-related activity than female controls, and male binge drinkers exhibiting greater SWM-response than

Substance Use Disorders 513 male controls (Squeglia et al., 2011). Studies of neural activity in marijuana-using adolescents reveal altered brain responses during verbal learning (Schweinsburg et al., 2011), and increased activation during attentional control and inhibitory processing tasks compared with nonusing controls (Abdullaev, Posner, Nunnally, & Dishion, 2010; Tapert et al., 2007). Thus, marijuana users appear to exert more effort when trying to self-regulate (Dishion, Felver-Grant, Abdullaev, & Posner, 2011).

Neurocognitive Consequences In addition to impairing growth and integrity of certain brain structures, adolescent substance use is also associated with decrements in neuropsychological function. Adolescent substance users exhibit deficits in visuospatial and attentional performance (Squeglia et al., 2009; Tapert et al., 2002), memory (Brown, Tapert, Granholm, & Delis, 2000), and executive function (Giancola & Moss, 1998; Thoma et al., 2011). Furthermore, heavy drinking during adolescence is associated with poorer neurocognitive functioning into adulthood, particularly in visuospatial abilities and attention (Tapert & Schweinsburg, 2005). Executive Functioning. Studies of adolescents with AUDs consistently reveal deficits in tests of planning and executive function (Giancola & Mezzich, 2000; Giancola & Moss, 1998; Giancola, Shoal, & Mezzich, 2001; Moss, Kirisci, Gordon, & Tarter, 1994). Adolescents, ages 15–16, with comorbid alcohol and substance use disorders commit commission errors twice as often as controls when responding to nontarget stimuli (Tarter, Mezzich, Hsieh, & Parks, 1995). Binge drinkers, ages 18–20, also show less advantageous decision making on the Iowa Gambling Task (Goudriaan, Grekin, & Sher, 2007), and female, binge-drinking young adults, ages 18–30, are less able to inhibit responses to an alerting stimulus during vigilance tasks. Thus, binge drinking is associated with deficits in inhibitory control (Townshend & Duka, 2005). Deficits in executive function and inhibition are particularly concerning given the slow rate of neurocognitive recovery in the frontal lobe from ethanol exposure (Fein et al., 1994). Executive function scores also predict age at first drink, with students who use alcohol prior to sixth grade showing less advanced decision making skills (Brown et al., 2009). Thus, it may well be the case that deficits in executive function both precede and result from substance use, exemplifying the kinds of transactional paths that characterize developmental psychopathology models (Chapter 1 [Hinshaw]). Visuospatial Abilities. Reduced visuospatial performance seems to be associated particularly with alcohol exposure in adolescence (Garland, Parsons, & Nixon, 1993; Nichols & Martin, 1996; Sher, Martin, Wood, & Rutledge, 1997).

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Adolescent and young-adult heavy drinkers, ages 13–24, perform poorly on spatial operation assessments (Tapert & Brown, 1999; Tapert et al., 2002) and block design tasks (Sher et al., 1997; Tapert et al., 2004). A study comparing detoxified, alcohol-dependent adolescents, ages 15–16, to control teens found that aspects of visuospatial cognition were poor among AUD adolescents (Brown, Tapert, Granholm, & Delis, 2000). Binge-drinking teens and young adults, ages 18–35, perform worse than nonbinge drinkers on spatial working memory and pattern recognition tasks (Weissenborn & Duka, 2003). Among female adolescents, ages 12–18, increased drinking predicts greater reductions in visuospatial performance on complex figure delay tasks (Squeglia et al., 2009). Worse visuospatial ability continues in the decade following treatment for AUD youth (Hanson, Medina, Padula, Tapert, & Brown, 2011; Tapert & Brown, 1999; Tapert et al., 2002), with frequent drinkers performing more poorly on delayed recall complex figure tasks (Hanson et al., 2011). Learning and Memory. Verbal and spatial working memory abilities improve throughout adolescence, with older teens responding more accurately and more quickly (Brown et al., 2009). Alcohol use during this time appears to interfere with these improvements. Neuropsychological studies of adolescents with AUDs demonstrate deficits in verbal and nonverbal memory (Brown et al., 2000; Tapert et al., 2001). Poorer verbal learning and recognition discriminability were identified among detoxified 15- to 16-year-old teens with protracted alcohol exposure (Brown et al., 2000). Female young adults, ages 18–25, with greater withdrawal history perform worse on verbal working memory tasks (Tapert et al., 2001). Another study among 13- to 18-year-old adolescents indicated that heavy use of alcohol is related to impaired learning of verbal material and poorer free recall after a short delay (Hanson et al., 2011). Studies also indicate that alcohol-dependent youth underutilize semantic learning strategies, which may lead to poorer retention rates after a short delay (Brown et al., 2000; Hanson et al., 2011). AUD youth, ages 15–17, made more perseveration errors when recalling recently learned words on the California Verbal Learning Test (Tapert et al., 2004). Nonverbal memory deficits have also been identified among AUD youth (Brown, et al., 2000). A study of detoxified teens, ages 15–16, revealed a 10% deficit in AUD teens’ ability to recall nonverbal information that was previously presented to them; visual reproduction rates were significantly lower in the alcohol-dependent teens than among controls (Brown et al., 2000). Female young adults, ages 18–25, with greater alcohol withdrawal histories performed worse on nonverbal, working memory tasks than controls (Tapert et al., 2001). Interestingly, more self-reported alcohol withdrawal symptoms predicted poorer performance on learning and memory in a sample of teens with histories of heavy drinking (Mahmood, Jacobus, Bava, Scarlett, & Tapert, 2010).

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SUMMARY AND CONCLUSIONS Child and adolescent development involves substantial changes across levels of analysis ranging from biological, cognitive, social-emotional, and behavioral, varying with genetics, community, and cultural factors. Changes in any of these systems may influence a variety of aspects of early alcohol and other drug involvement (e.g., onset, escalation, problems). As should be clear from this review, exposure to alcohol and other drugs can directly and indirectly alter normal development of both biological and social systems. Bidirectional and in some cases synergistic effects may produce short-term consequences that quickly resolve (hangovers). In other cases, such effects may alter developmental trajectories in ways that affect long term adult functioning (e.g., SUDs, frontal lobe development). Furthermore, vulnerabilities and risk factors noted above may predispose a subset of youth to develop problematic use patterns, once again exemplifying transactional processes. Thus, understanding youth development and the emergence of alcohol and drug problems among adolescents requires both an appreciation for processes and tasks of normative adolescent development and knowledge of effects of and mechanisms of progression along continua of alcohol and drug dependences. Regardless of which etiological model of substance use disorders applies, core developmental processes and system-specific stages must be considered to account for the complex symptom matrix presented by youth with SUDs. Joint consideration of both mechanisms of development and processes of adolescent addiction progression should facilitate better prediction of vulnerability and risk, improve research paradigms, and create substance interventions for this prevalent adolescent disorder.

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P A R T IV

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C H A P T E R 16

Anxiety Disorders CARL F. WEEMS AND WENDY K. SILVERMAN

HISTORICAL CONTEXT

T

he OXFORD ENGLISH DICTIONARY reports evidence for the noun “anxiety” as early as the 1500s, and interest in childhood anxiety problems can be traced as far back as the writings of Hippocrates in ancient Greece (Silverman & Treffers, 2011). Descriptions of child anxiety and phobias also can be found in writings from early America (e.g., Benjamin Rush), and in many famous case reports (e.g., Freud’s Little Hans; Watson’s Little Albert; for a review, see Silverman & Treffers, 2001). In the second edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-II; American Psychiatric Association [APA], 1968) there was only one specific childhood anxiety category: overanxious reaction. The DSM-III (APA, 1980) then introduced—and the DSM-III-R (APA, 1987) retained—a new broad category termed anxiety disorders of childhood and adolescence. Within this broad category, three specific anxiety disorders were introduced: (1) separation anxiety disorder, (2) overanxious disorder, and (3) avoidant disorder. Children could also receive diagnoses of other anxiety/phobic disorders classified among adults, with identical criteria to the adult depictions. The fourth version of the DSM (APA, 1994) witnessed major changes. The broad category of anxiety disorders of childhood and adolescence (and its subcategories: overanxious disorder, avoidant disorder) were abandoned. Only separation anxiety disorder was retained as a distinct child anxiety disorder under “other disorders of infancy, childhood, or adolescence.”

DIAGNOSTIC ISSUES AND DSM-5 CRITERIA The extant empirical data and classification of anxiety disorders is based largely on DSM-IV (1994, 2000) criteria. However, the fifth edition of the DSM was released in May 2013 (APA, 2013). Several changes to the classification scheme for anxiety 531

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disorders were made. Most anxiety disorders included in the DSM-IV continue in the DSM-5. These include (a) specific phobias, (b) social anxiety disorder, (c) generalized anxiety disorder, (d) separation anxiety disorder, and (e) panic disorder. Obsessive compulsive disorder (OCD) and posttraumatic stress disorder (PTSD) are no longer classified as anxiety disorders. Instead, they are classified as impulse control and stress-related disorders, respectively. However, we retain discussion and discuss studies on these disorders in this chapter because research on them helps make up the corpus of scientific knowledge on anxious emotion and its disorders. Moreover, the Research Domain Criteria strategy (see below) is aimed at classifying mental disorders based on dimensions of observable behavior and neurobiological measures, and this perspective continues to closely link anxiety, impulse control, and stress-related disorders. Finally, selective mutism is now classified as an anxiety disorder in DSM-5. Specific phobias are characterized by extreme and unreasonable fears of a specific object or situation such as dogs, loud noises, or the dark. Symptom criteria remain unchanged from the DSM-IV, except adults no longer must recognize that their anxiety or fear is excessive or unreasonable. Symptoms must also now be present for at least 6 months for all ages in order for a diagnosis of specific phobia. Social anxiety disorder is characterized by an extreme and (previously in the DSM-IV) unreasonable fear of being embarrassed or humiliated in front of other youth or adults. In the DSM-5, individuals no longer have to recognize that their anxiety is excessive or unreasonable to receive one of these diagnoses. Next, generalized anxiety disorder is characterized by persistent and excessive worry about a number of events or activities. Separation anxiety disorder is characterized by excessive worry concerning separation from home or loved ones. Specific symptoms of separation anxiety disorder remain unchanged, although the wording of criteria has been modified slightly and updated. For example, attachment figures may include children of adults with separation anxiety disorder, and avoidance behaviors may occur in the workplace as well as at school. In contrast to the DSM-IV, diagnostic criteria no longer specify that age of onset must be before 18 years, and duration criterion—“typically lasting for 6 months or more”—have been added for adults. Separation anxiety disorder was moved from the DSM-IV section “Disorders Usually First Diagnosed in Infancy, Childhood, or Adolescence” into the section on anxiety disorders. Panic disorder is characterized by sudden and severe attacks of anxiety. The biggest change in the DSM-5 is that panic disorder and agoraphobia are no longer linked but are instead recognized as two separate disorders. There are no significant changes to criteria for panic attacks. However, the DSM-5 removes the description of different kinds of panic attacks and lumps them into one of two categories—expected and unexpected. Although considered in a separate section of the DSM-5, OCD and PTSD are also important to review here. Obsessive compulsive disorder (OCD) is characterized by recurrent thoughts or behavior patterns severe enough to be time consuming, distressful, and highly interfering, including repeated thoughts about contamination,

Anxiety Disorders 533 repeated doubts, having things in a particular order, and aggressive or horrible impulses. In addition, a child or adolescent who experiences a catastrophic or otherwise traumatic event may develop posttraumatic stress disorder (PTSD; APA, 1994, 2000). The traumatic event must involve a situation in which someone’s life has been threatened or severe injury has occurred. Following exposure to the trauma, the youth may exhibit agitated or confused behavior as well as intense fear, helplessness, anger, sadness, horror, and/or denial. Youth with PTSD usually avoid situations or places that remind them of the trauma. They also may become depressed, withdrawn, emotionally unresponsive, and detached from their feelings. The DSM anxiety disorders exhibit high rates of comorbidity with one another (Costello, Egger, & Angold, 2004; Curry, March, & Hervey, 2004). In fact, only secondary features outlined above, such as social concerns in social anxiety disorder and worries about separation in separation anxiety disorder, distinguish the anxiety disorders from one another. Moreover, genetic risk for anxiety disorders appears to be nonspecific (Gregory & Eley, 2011). With the exceptions of OCD and possibly PTSD, there is little evidence that anxiety disorders are related differentially to treatment outcomes, although it is recognized that some null findings may be attributed in part to insufficient sample sizes (see Berman, Weems, Silverman, & Kurtines, 2000; see also Dadds, James, Barrett, & Verhulst, 2004; Saavedra & Silverman, 2001).

PREVALENCE A meta-analysis by Costello, Egger, Copeland, Erkanli, and Angold (2011) reported prevalence estimates for any anxiety disorder of 12.3% for children between ages 6 and 12, with the following disorders being most prevalent: specific phobia (6.7%), separation anxiety disorder (3.9%), social phobia (2.2%), and generalized anxiety disorder (1.7%). For adolescents, the prevalence estimate for any anxiety disorder was 11.0%. The following specific disorders were most prevalent: specific phobia (6.6%), social phobia (5.0%), separation anxiety disorder (2.3%), generalized anxiety disorder (1.9%), and panic disorder (1.1%).

ETIOLOGY The following sections examine heritable vulnerabilities, environmental risk, their interaction, and other potential factors in the etiology of anxiety disorders.

Heritable Vulnerabilities Twin studies suggest that about a third of the variance in childhood anxiety symptoms is accounted for by heritable influences (see Eley, 2001; Gregory & Eley, 2011). Heritability may help to account for children’s early anxious styles, including physiological reactivity and avoidance behaviors. However, genes do not act directly on behavior. Genes code for proteins, which in turn affect brain structures

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and regulative processes, such as neurotransmitter receptors. The heritability of anxiety also depends on many other factors including the type of anxiety assessed, the age and sex of the population, specific assessment methods, and whether anxiety is viewed as a personality trait or a clinical disorder (Gregory & Eley, 2011). It is also important to note that the heritability of anxiety increases considerably in adolescence and young adulthood, a phenomenon seen for other psychiatric disorders (Bergen, Gardner, & Kendler, 2007). This pattern suggests that genetic vulnerabilities unfold across development as they correlate and interact with mounting environmental risks (Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Molecular genetics studies of anxiety have also grown rapidly. These investigations have focused on genes that encode components of the 5-HT and GABA systems. Results have been mixed, but the strongest findings have linked genes to anxiety-related traits such as behavioral inhibition, not specific disorders. Temperamentally inhibited children display many of the same behavioral, affective, and physiological characteristics as children with anxiety disorders. These characteristics include avoidance and withdrawal from novelty, clinging or dependence on parents, fearfulness, and autonomic hyperarousal (Kagan, Reznick, & Snidman, 1987). Such children are more likely than their peers to respond to potentially fearful situations (e.g., interactions with a stranger, separations from mother) with heightened physiological reactivity, which may result from a lower threshold of amygdalar and hypothalamic activity. Such findings point to the importance of differentiating between primary and secondary features of anxiety (see above). For example, the gene encoding the GABA-synthetic enzyme GAD65 has been associated with behavioral inhibition, a risk factor for anxiety disorders such as panic disorder, yet no genes encoding GABA receptors have been linked directly to panic disorder (see Gordon & Hen, 2004). One of the most promising lines of research suggests that polymorphisms in the promoter region of the gene for the 5-HT transporter (5-HTT) may also be associated with behavioral inhibition, particularly among individuals who are exposed to environmental risk (Fox et al., 2005). Temperament theorists have drawn from genetic models, suggesting that anxiety stems from biological predispositions to react negatively to novelty (Biederman et al., 1990; Biederman et al., 1993; Kagan, Reznick, & Gibbons, 1989; Kagan et al., 1987; Kagan, Reznick, & Snidman, 1988; see Lonigan, Phillips, Wilson, & Allan, 2011 for review).

Environmental Risk Factors Behavioral conceptualizations of anxiety disorders propose respondent (classical or Pavlovian conditioning), vicarious (social modeling), and operant (Skinnerian conditioning) mechanisms of acquiring fear. Limitations of early classical conditioning accounts involving direct pairing of stimuli with aversive events (e.g., a large dog bites a child, resulting in fear of dogs) have prompted theorists to posit multiple learning pathways to anxiety and phobic disorders (Bouton, Mineka, &

Anxiety Disorders 535 Barlow, 2001). Although the following focuses on specific conditioning events, it is important to realize that many individuals with phobias and anxiety disorders do not develop these problems following a single exposure to a feared stimulus (Bouton et al., 2001), bespeaking the role of preexisting vulnerabilities and additional risk factors in generating clinical-level problems. Although recent conceptualizations highlight the complexities involved in learning processes with respect to fear and anxiety development and maintenance, we focus here on three pathways posited by Rachman (1977). One pathway is through classical aversive conditioning (Wolpe & Rachman, 1960). A large body of research suggests that exposure to traumatic events is associated with increased risk for anxiety disorders, particularly PTSD (indeed, the diagnostic criteria for PTSD mandate exposure to life-threatening trauma). Events that have been researched extensively as traumatic during childhood include experiences of child abuse, maltreatment, and exposure to community violence. Between 25% and 55% of youth with histories of physical and sexual abuse meet criteria for PTSD (Ackerman, Newton, McPherson, Jones, & Dykman, 1998; Kiser, Heston, Millsap, & Pruitt, 1991). Exposure to natural disasters, such as earthquakes and hurricanes, is also associated with PTSD symptoms in youth (e.g., La Greca, Silverman, Vernberg, & Prinstein, 1996). Furthermore, the level of PTSD symptoms a child or adolescent experiences is related to the number of disaster exposure events (La Greca, Silverman, & Wasserstein, 1998). Importantly, preexisting vulnerabilities such as trait anxiety confer susceptibility to postdisaster PTSD and predict symptoms beyond exposure to the stressful event (La Greca et al., 1998). The second of Rachman’s pathways is vicarious acquisition through observational learning or modeling. Via this pathway, children may acquire fears by observing actions of salient others such as parents, caregivers, siblings, or friends (Bandura, 1982). For example, a child who sees his or her mother react fearfully to a dog may begin to model this reaction. The third pathway is through verbal transmission of information. Through this mechanism, children may acquire fears by talking about fearful things with parents, caregivers, siblings, or friends. For example, the type of information (positive vs. negative) youth receive about a potential fear stimulus (e.g., an animal) changes the valence of fear beliefs (Field 2006; Field, Argyris, & Knowles, 2001; Field & Lawson, 2003). Ollendick, Vasey, and King (2001) suggested a fourth pathway to anxiety problems through negative reinforcement, also called escape conditioning. This account suggests that if a child learns to cope with normal anxiety and fear responses through avoidance of the anxiety- or fear-provoking stimulus, then “normal” anxiety responses may be maintained at high levels and may eventually turn into problematic anxiety. Withdrawal from the stimulus may be negatively reinforced by reduction in anxiety after withdrawing. In addition, avoidance may be positively reinforced by caregivers through approval of avoidance behaviors, and/or tangible rewards. In this way, the child does not experience feared stimuli, which maintains

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anxious and avoidant responses. Considerable evidence exists to support these learning pathways in anxiety disorders generally (Craske et al., 2009) and in childhood anxiety in particular (see Ollendick et al., 2001 for a review). Interpersonal theories focus on children’s relationships with others, emphasizing peer (Bell-Dolan, Foster, & Christopher, 1995) and parent (Berg, 1976; Bögels & Phares, 2008; Creveling, Varela, Weems, & Corey, 2010; Dadds, Barrett, Rapee, & Ryan, 1996) influences on childhood anxiety. Social contextual approaches further suggest that factors such as poverty, parental psychopathology, exposure to trauma, and exposure to violence can exacerbate vulnerability to anxiety disorders. According to attachment theory, for example, a child’s interactions with the environment are influenced by the underlying quality of the parent-child relationship, and a number of factors influence the quality of that relationship (e.g., poverty, parental psychopathology). Attachment theory suggests that human infants form enduring emotional bonds with their caretakers (Bowlby, 1977; Cassidy, 1999). When caretakers are responsive to a child, the resultant emotional bonds can provide a lasting sense of security that continues even when the caretaker is not present. However, an inconsistently responsive caretaker, a neglectful caretaker, or some other disruption in the parent-child bond may be associated with insecure attachment. Children with insecure attachments have particular difficulty during separations from their parents (Ainsworth, Blehar, Waters, & Wall, 1978). Reactions of children with anxiety disorders such as separation anxiety disorder (SAD) can be similar to those reported of insecurely attached children in the Strange Situation (Ainsworth et al., 1978). For example, children with SAD protest desperately when separation is imminent, cry, and become agitated during separation, and may act angrily or aggressively toward the parent upon return. Warren, Huston, Egeland, and Sroufe (1997) found that children classified as anxious/resistant in their attachment (assessed at 12 months of age) are more likely than children with other types of attachment to have anxiety disorders at age 17, even when controlling for measures of temperament and maternal anxiety. Overcontrolling parenting may also influence childhood anxiety, although it is indeterminate whether such patterns are maintaining factors or truly causal. For example, anxiety in either member of the mother-child dyad tends to elicit maternal overcontrol during interactions (Whaley, Pinto, & Sigman, 1999; Woodruff-Borden, Morrow, Bourland, & Cambron, 2002), and higher levels of maternal control are observed in anxious mother-child dyads than in control dyads (e.g., Siqueland, Kendall, & Steinberg, 1996). Costa and Weems (2005) tested a model of the association between maternal and child anxiety that included mother and child attachment beliefs and children’s perceptions of maternal control as mediators. Maternal anxiety was associated with child anxiety and maternal anxious attachment beliefs, whereas child anxiety was associated with maternal anxious attachment beliefs, child insecure attachment beliefs, and children’s perceptions of maternal control. Maternal anxious attachment beliefs mediated the association between maternal and child anxiety. Taken together, research suggests that parents who exhibit

Anxiety Disorders 537 overcontrolling, overinvolved, dependent, or intrusive behavior may (a) prevent youth from facing fear-provoking events, a developmentally important task that allows children to face fear; and/or (b) send the message that particular stimuli are threatening, which may reinforce a child’s or adolescent’s anxiety (Rapee, 1997; 2009; Rapee, Wignall, Hudson, & Schniering, 2000; Vasey & Ollendick, 2000).

G × E Interactions Environmental models emphasize experiences in which behaviors of parents, for example, are learned by children. However, some of these processes may be influenced by genes. For example, in the Fox et al. (2005) study, neither genotype (i.e., the short allele for serotonin transporter) nor low social support predicted behavioral inhibition in isolation. However, their interaction was predictive. Thus, among children with relatively low social support, the short allele associated with behavioral inhibition. Such findings point to the importance of considering Gene × Environment interactions in developmental psychopathology research on childhood anxiety (Chapter 3 [Beauchaine et al.]). Moreover, the literature implicates parenting behaviors as unique predictors, moderators of other predictors (e.g., Costa, Weems, & Pina, 2009), and/or mediators of other influences (Costa & Weems, 2005), with possible bi-directional influences over time (Silverman, Kurtines, Jaccard, & Pina, 2009). Moreover, the particular polymorphisms in genes identified (e.g., the 5-HTT) may not simply lead to increased risk under negative environments but rather may be associated with malleability to both positive and negative environmental influences (Belsky & Beaver, 2011; Belsky, & Pluess, 2009). To understand the development of anxiety symptoms, a number of biological, cognitive, behavioral, and social risk factors are salient. Some of these may be part of “normal” developmental processes (e.g., biological maturation) or atypical experiences (e.g., traumatic events). Symptoms of anxiety and phobic disorders can be understood as stemming from transactions among biological vulnerabilities (e.g., genetic, neural) and environmental risk factors (e.g., parenting, exposure to trauma) that produce emotion dysregulation, distress, and impairment. These factors give rise to undifferentiated anxious emotion. Specific anxiety disorders are then shaped by additional biological, cognitive, behavioral, and social processes (Vasey & Dadds, 2001; Weems & Stickle, 2005). For example, genetic predispositions and early experiences may render a child vulnerable to elevated levels of anxiety and distress, sometimes termed trait anxiety (or behavioral inhibition). More specific experiences, such as being excluded or teased by peers upon school entry, may foster the development of social anxiety; a frightening experience with a dog may result in an animal phobia; and biological vulnerability such as the experience of uncued physiological arousal may lead to panic disorder. In understanding the etiology of childhood anxiety we use a developmental perspective that emphasizes complex transactions among various vulnerabilities and factors (Weems, 2008; Weems & Silverman, 2006). From this perspective, behavior

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patterns among individuals have varying trajectories over time (Baltes, Reese, & Lipsitt, 1980; Baltes, Reese, & Nesselroade, 1988), resulting in both equifinality and multifinality. Specific etiological influences on the development of problematic anxiety span genetics, temperament, and physiology (biology); operant, observational, and respondent learning (associative learning); information processing and stimulus/ event interpretation (cognition); attachment relations and sociability (socialization), and interactions among these processes in diverse contexts (e.g., parent-child relationships, family, home, school, community).

Other Important Processes Central Nervous System. The concept of a behavioral inhibition system (BIS) is a good starting point for understanding central nervous system structures involved in anxiety responses (Gray & McNaughton, 2000). The BIS comprises the septohippocampal system, including the amygdala, noradrenergic projections of the locus coeruleus, and serotonergic projections of the median raphe (Gray 1982; Gray & McNaughton, 2000). Important for this discussion, the BIS is activated under conditions of perceived threat, helping to avoid exposure to punishment and danger. Overactivation of the BIS is associated with excessive fear, hyperarousal, and negative emotionality. Among septohippocampal structures, the amygdala has received particular attention in theories of the pathogenesis of anxiety (e.g., Davis, 1998; LeDoux, 2000). The amygdala is a collection of nuclei found in the anterior portion of the temporal lobes. It functions to evaluate the emotional significance of incoming stimuli after receiving input from the cortex, hippocampus, and thalamus. The amygdala projects to other brain structures in the frontal cortex (related to choice), the hippocampus (memory consolidation), the striatum (approach/avoidance), and the hypothalamus and brain stem (autonomic responses, startle, corticosteroid response; see Gordon & Hen, 2004; LeDoux, 2000). Importantly, no single structure, neurotransmitter, or gene controls the experience of anxiety or any other complex behavioral trait (see Pine, 2011). As noted, negative affect is an important component of anxiety. Functional brain studies suggest normal threat assessment and emotional learning may involve differential hemispheric activation. Electroencephalography (EEG) research has demonstrated increased right prefrontal and anterior temporal region activation during the experience of negative emotion and increased left prefrontal activation during the experience of positive emotion (Davidson, 1998). Furthermore, increased left prefrontal activation is associated with the ability to suppress startle responses to negative stimuli (Davidson, 1998; Davidson, Marshall,

Anxiety Disorders 539 Tomarken, & Henriques, 2000). Davidson et al. (2000) have also demonstrated greater relative right versus left prefrontal activation among adults with social anxiety and depression. These findings have been replicated in infants (Davidson & Fox, 1989) and in school-age children (8 to 11 years) with diagnosable anxiety disorders (Baving, Laucht, & Schmidt, 2002). Similar findings have emerged in anxious youth using functional magnetic resonance imaging (fMRI) techniques, with research implicating frontal regions in anxiety disorders in youth (Carrion et al., 2008). The greater spatial resolution of fMRI has recently shown sensitized amygdala and hippocampal activation to facial expressions in youth with PTSD symptoms. For example, youth with PTSD symptoms (ages 11–17) show faster right amygdala activation in response to angry faces than age- and sex-matched controls (Garrett et al., in press). In addition, a number of neurotransmitters and neurotransmitter systems have been implicated in anxiety and anxiety-related behavior. Both gamma-aminobutyric acid (GABA) and serotonin (5-HT) have been the foci of research. This interest follows from findings that anxiolytic (antianxiety) medications such as benzodiazepines and selective serotonin reuptake inhibitors (SSRIs) modulate GABA and 5-HT neurotransmission. The noradrenergic system has also been implicated in the expression of anxiety disorders (Gordon & Hen, 2004). Another theoretically important system is the hypothalamic-pituitary-adrenal (HPA) axis. Activation of the HPA axis often follows from fight-flight reactions in response to stress and fear, although its time course is considerably longer, spanning minutes to hours. Fear reactions are associated with elevations in secretion of cortisol, a corticosteroid hormone produced by the adrenal cortex (see Gunnar, 2001). The release of cortisol is controlled by the hypothalamus, where corticotropin-releasing hormone (CRH) is secreted. CRH then stimulates the pituitary gland, resulting in the release of adrenocorticotrophic hormone (ACTH), which in turn causes the adrenal cortex to release cortisol. Cortisol helps to regulate behavioral and emotional responding through a feedback loop to the pituitary and hypothalamus. Cortisol secretion may underlie protective mechanisms upon exposure to danger. However, prolonged exposure to glucocorticoids such as cortisol may also be neurotoxic and related to anxiety problems. For example, animal studies demonstrate hippocampal atrophy in rats and primates exposed to either chronic psychological stress or elevated levels of glucocorticoids (see Sapolsky, 2000). Maltreated children with PTSD symptoms, as well as those diagnosed with PTSD, demonstrate dysregulation in diurnal cortisol rhythms (Carrion et al., 2002; De Bellis, Baum, et al., 1999). Symptoms of social anxiety disorder are associated with heightened cortisol reactivity among clinic referred youth (Granger, Weisz, & Kauneckis, 1994; see Gunnar, 2001 for a review). Youth who experience severe

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stress are more likely to display reductions in cerebral volume and frontal lobe asymmetry, possibly due to effects of prolonged cortisol secretion (Carrion et al., 2001; De Bellis, Keshavan, et al., 1999). Carrion, Weems, and Reiss (2007) found that cortisol levels were associated with changes in the volume of the hippocampus in a sample of youth ages 8–14 who were exposed to traumatic stressors. Here, higher cortisol levels were related to decreases in hippocampal volume over a 1-year period. Finally, research on behavioral inhibition, a risk factor for later childhood anxiety disorders (see Chapter 7, this volume [Kagan]), has linked the construct with increased cortisol levels in very young children (e.g., Kagan, Reznick, & Snidman, 1987; 1988). Carrion, Weems, Richert, Hoffman, and Reiss (2010) also found decreased prefrontal cortical volume associated with increased bedtime cortisol in traumatized youth. Peripheral Nervous System. A growing body of research suggests that youth with anxiety disorders are marked by excessive sympathetic nervous system (SNS) activity, expressed by increased heart rate, blood pressure, and electrodermal responding (e.g., Beidel, 1991; Carrion et al., 2002). Behavioral inhibition is also associated longitudinally with elevated heart rates in community samples of youth (Kagan et al., 1987, 1988). Data available from youth with elevated anxiety scores also suggest different physiological responses to anxiety provoking stimuli. For example, in a community sample of children with and without test-taking anxiety, Beidel (1991) found significant group differences in pulse rate and systolic blood pressure during social evaluative tasks. Scheeringa, Zeanah, Myers, and Putnam (2004) found that young children with symptoms of PTSD exhibited higher heart rates during recall of their trauma memories than matched controls. In a community-recruited sample, Weems, Zakem, Costa, Cannon, and Watts (2005) examined skin conductance and heart rate responses among youth exposed to a fear-eliciting stimulus (video of a large dog), and their relation to youth- and parent-rated anxiety symptoms and cognitive bias. Heart rate and skin conductance were associated with youth ratings of anxiety disorder symptoms. These responses were associated independently with youth-rated symptoms of anxiety but not depression. There are also individual differences in absolute levels of arousal that individuals comfortably tolerate. Indeed, research on anxiety sensitivity shows that absolute levels of arousal are not crucial for anxiety problems to develop (Schmidt, Lerew & Jackson, 1997). Anxiety sensitivity involves the belief that anxiety sensations (e.g., heart beat awareness, increased heart rate, trembling, shortness of breath) have negative social, psychological, and/or physical consequences (Reiss, Silverman, & Weems 2001). One’s interpretation of arousal symptoms appears to be especially important in influencing his or her experience of anxiety (Reiss, 1991; Weems, Hammond-Laurence, Silverman, & Ginsburg, 1998). In other words, some individuals experience considerable negative affect and distress with relatively little physiological arousal. Thus, an important component in the development of anxiety disorders is cognitive appraisal, interpretation, and recall.

Anxiety Disorders 541

Cognitive Processes Proponents of cognitive and information processing models propose disruptions in various stages of cognition, such as encoding, interpretation, and recall, as contributing to the etiology and maintenance of anxiety disorders (see Field, Hadwin, & Lester, 2011; Vasey, Dalgleish, & Silverman, 2003). According to these models, anxious children have biased interpretations, judgments, and memories, as well as attentional selectivity (Vasey & MacLeod, 2001). These biases are hypothesized to work together to foster and maintain heightened anxiety. Weems and Watts (2005) developed a model of cognitive influences on childhood anxiety, positing that attentional biases may foster selective encoding of threat information into memory, and such selective attention could increase negatively biased threat memories. Memory biases, in turn, may become internalized in cognitive working models or cognitive schemas, fostering interpretive and judgment biases. For example, existing threat memories may bias attention toward only the threatening part of the situation and away from mitigating aspects of the situation (such as safety signals), thereby fostering anxiety-provoking interpretations. Existing threat memories may then bias new interpretation of the event and help to consolidate existing interpretation and judgment biases. In conjunction with biological and learning accounts, cognitive factors may foster or hamper learning acquisition, exacerbate biological predispositions, and maintain anxiety disorder symptoms. Selective attention, memory biases, interpretation biases (e.g., negative cognitive errors) and judgment biases are four broad forms of cognitive processes that have begun to garner attention in relation to youth anxiety symptoms. First, selective attention involves focusing attention toward a category of stimuli (e.g., threatening stimuli) when such stimuli are placed in a context with other categories of stimuli (e.g., neutral or other nonthreatening stimuli). An exciting line of research using modification of attentional biases has suggested causal linkages. When attentional biases toward threat are trained in nonanxious individuals, anxiety symptoms increase (e.g., Mathews & Mackintosh, 2000; Mathews & MacLeod, 2002; Salemink, van den Hout, & Kindt, 2010). Moreover, training anxious individuals away from threat (i.e., attention bias modification training) improves anxiety disorder symptoms (e.g., Amir, Beard, Burns, & Bomyea, 2009), with evidence for a similar effect in youth samples (Bar-Haim, Morag, & Glickman, 2011). Recent work further suggests the possibility of attention bias modification training as an augment to cognitive behavior therapy, particularly for anxious children who fail to respond to cognitive behavior therapy (Bechor et al., 2014). Second, memory biases refer to a predisposition to recall threatening information (see Vasey & MacLeod, 2001). Similar to research on selective attention, evidence supports a link between memory biases and anxiety problems in children (Daleiden, 1998; Moradi, Taghavi, Neshat-Doost, Yule, & Dalgleish, 2000). Third, interpretive bias involves a predisposition toward negative or erroneous interpretations of neutral, ambiguous, or potentially threatening stimuli. Negatively

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biased cognitions have long been implicated in both anxiety and depression (e.g., Beck, 1976). Clinically anxious youth who are presented with ambiguous vignettes and asked to explain what is happening are more likely to provide interpretations indicating threat than nonanxious controls (Barrett, Rapee, Dadds, & Ryan, 1996). A well-validated measure for assessing negative interpretive biases is the Children’s Negative Cognitive Error Questionnaire (CNCEQ; Leitenberg, Yost, & Carroll-Wilson, 1986). Research using the CNCEQ suggests that cognitive biases are associated with symptoms of anxiety and can be assessed validly in both children and adolescents (Epkins, 1996; Leitenberg et al., 1986; Leung & Wong, 1998; Weems, Berman, Silverman, & Saavedra, 2001). Research has also implicated anxiety sensitivity as a risk factor for panic attacks, panic disorder and other anxiety problems (e.g., Maller & Reiss, 1992; Schmidt et al., 1997; Schmidt, Lerew, & Jackson, 1999; Weems et al., 1998; Weems et al., 2001; Weems, Hammond-Laurence, Silverman, & Ferguson, 1997). Anxiety sensitivity can be thought of as an interpretation bias that anxiety sensations have negative social, psychological, and/or physical consequences (Reiss, 1991) and so involves a negative interpretation of anxiety related sensations. Anxiety sensitivity differentiates youth with panic disorder from youth with other anxiety disorders (Kearney, Albano, Eisen, Allan, & Barlow, 1997) and predicts the onset of panic attacks in adolescents (Hayward, Killen, Kraemer, and Taylor, 2000; Weems, Hayward, Killen, & Taylor, 2002). Finally, judgment bias involves negative and/or lowered estimates of the individual’s coping ability or style. Several investigators have emphasized a key role for the construct of control in anxiety and anxiety disorders in youth (e.g., Capps, Sigman, Sena, Henker, & Whalen, 1996; Cortez & Bugental, 1995; Granger et al., 1994; Muris, Schouten, Meesters & Gijsbers, 2003; see Chorpita & Barlow, 1998; Weems & Silverman, 2006 for reviews). Chorpita and Barlow (1998) described how early childhood experiences with diminished control may result in a cognitive style that increases the probability of interpreting events as out of one’s control. Based on these findings, Chorpita and Barlow proposed a model in which perceived control (or lack thereof) may represent a psychological vulnerability for anxiety problems. Barlow’s (2002) model of anxiety suggests that a perceived lack of control over “external” threats (events, objects, situations that are fear producing) and/or negative “internal” emotional and bodily reactions are central to the experience of anxiety problems. Nonpathological anxiety is differentiated from pathological anxiety both by subjective anxiety responses to the experience and by the belief that the event is uncontrollable. Empirical support exists for the importance of control cognitions in youth (Ginsburg, Lambert, & Drake, 2004; Muris et al., 2003; Weems, Silverman, Rapee, & Pina, 2003). In terms of integrating such processes, Watts and Weems (2006) examined links among selective attention, memory bias, cognitive errors, and anxiety problems in a community sample of youth ages 9 to 17. Selective attention, memory bias, and cognitive errors were each independently associated with childhood anxiety symptoms. Cannon and Weems (2010) found that both interpretive biases (CNCEQ)

Anxiety Disorders 543 and judgment biases (ACQ-C) each provided incremental discrimination of youth meeting DSM-IV criteria for anxiety disorders from matched comparison youth. Furthermore, Weems, Costa, Watts, Taylor, & Cannon (2007) found that each was independently associated with anxiety symptoms in a community sample.

DEVELOPMENTAL PROGRESSION Childhood anxiety disorders are associated with adult anxiety and depressive disorders (Pine, Cohen, Gurley, Brook, & Ma, 1998). As noted earlier, results from prospective longitudinal studies of childhood anxiety disorders indicate widely varying stabilities, ranging from 4% to 80% (e.g., Keller et al. 1992; Last, Perrin, Hersen, & Kazdin, 1996; March, Leonard, & Swedo, 1995; Newman et al., 1996). Such wide-ranging stability estimates may exist for several reasons, including the type of disorder, the informant, the sample, and the amount of time between evaluations. Interestingly, studies show similarly wide estimates even for the same anxiety disorder across similar time frames. For example, Last et al. (1996) found that 13.6% of youth with social phobia retained the diagnosis after 3 to 4 years, whereas Newman et al. (1996) found that 79.3% of youth with social phobia retained the diagnosis after 0 to 3 years. The main difference between these studies was the age of participants (5 to 18 in Last et al.; 11 to 21 in Newman et al.). As already noted, heritable influences on anxiety increase with age, which could account for some of the discrepancy. Other possible sources of inconsistency include types of assessment instruments used, sample variations, different definitions of impairment, and limited understanding and use of empirically derived developmental information about classification of anxiety disorders (see Curry et al. 2004; Scheeringa, Peebles, Cook, & Zeanah, 2001). Some authors have posited specific age differences in the onset and expression of phobic and anxiety disorders in youth (Westenberg, Siebelink, & Treffers, 2001; Warren & Sroufe, 2004, Weems, 2008). Drawing on stage theories (e.g., Loevinger, 1976), Westenberg et al. (2001) suggested that the predominant expression of fear and anxiety symptoms may be tied in part to sequential developmental challenges. For example, children ages 6 to 9 years have begun the process of individuation and are expressing autonomy from their parents. The developmental challenge is self-reliance, but this challenge is likely to give rise to concerns about separation from or loss of parents. In contrast, youth ages 10 to 13 years are gaining insight into mortality and broader world concerns. Finally, emerging social understanding and comprehension in adolescence may lead to a predominance of social and evaluative concerns (see Warren & Sroufe, 2004; Westenberg et al., 2001). According to both Westenberg et al. (2001) and Warren and Sroufe (2004), separation anxiety symptoms and animal fears are the predominant expression of anxiety in children ages 6 to 9, compared with generalized anxiety symptoms and fears concerning danger and death in children ages 10 to 13, and social anxiety symptoms and social/performance related fears in adolescents around

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ages 14 to 17. Epidemiological data from community samples are fairly consistent with these assertions (see Costello et al., 2004), providing empirical evidence that the predominant expression of phobic and anxiety symptoms is tied to normative developmental milestones. Research in clinical samples also suggests that separation anxiety disorder is more common in children, whereas social phobia is more common in adolescents (Weems et al., 1998; Weems, Silverman, Saavedra, Pina, & Lumpkin, 1999). Research examining specific fear and anxiety symptoms dimensionally across age ranges also supports the notion of sequential developmental differences in the expression of symptoms (Chorpita, Yim, Moffitt, Umemoto, & Francis, 2000; Ollendick, Matson, & Helsel, 1985; Ollendick, King, & Frary, 1989). In terms of a priori tests of the developmental hypothesis, Westenberg, Siebelink, Warmenhoven, and Treffers (1999) reported that separation anxiety disorder precedes overanxious disorder (using DSM-III-R criteria). Moreover, Westenberg, Drewes, Siebelink, and Treffers (2004) found that children’s self-rated fears of physical danger and punishment decrease with age and self-rated fears of social and achievement evaluation increase with age, controlling for overall fears. Weems and Costa (2005) tested this developmental view and found that specific symptoms dominated at certain ages (e.g., separation anxiety in children 6 to 9 years, death and danger fears in youth 10 to 13 years, and social anxiety and fears of criticism in youth 14 to 17 years). These findings suggest that models of the etiology of childhood anxiety should consider differences across childhood and adolescence in developmental expression. This concept has been termed “heterotypic continuity” (e.g., Moffitt, 1993).

COMORBIDITIES As noted above, comorbidity among anxiety disorders (i.e., homotypic comorbidity) in youth is substantial, with estimates as high as 50% in population studies (see Costello et al., 2004) and as high as 70% in clinical samples (Weems et al., 1998). Furthermore, across studies, comorbidity of anxiety disorders with ADHD (an example of heterotypic comorbidity) ranges from 0 to 21%, with conduct disorder and oppositional defiant disorder from 3% to 13%, and with depression from 1% to 20% (Costello et al., 2004). In general, there is a high degree of association between depression and anxiety. Similar findings in community samples suggest that comorbidity is not just a function of referral biases. Rates of comorbidity exceed those predicted by intersecting base rates (see Costello et al., 2011; Curry et al., 2004).

CULTURAL CONSIDERATIONS A large body of literature indicates cultural and ethnic differences in the expression of anxious symptoms. Latino children experience higher levels of internalizing symptoms than White non-Latino children, both in terms of anxious and somatic

Anxiety Disorders 545 complaints (Ginsburg & Silverman, 1996; Pina & Silverman, 2004; Roberts, 1992; Varela, Vernberg, Sanchez-Sosa, Riveros, Mitchell, et al., 2004; Varela, Vernberg, Sanchez-Sosa, Riveros, Mashunkashey, et al., 2004). Little is known about mechanisms underlying this cultural variability. Investigators have focused on effects that culture-specific socialization practices and family variables may have on emotion expression. For example, some have speculated that because Latino culture is characterized by a collectivistic ideal, emotions and willingness to express emotions will tend to be consistent with cultural norms (Triandis, Leung, Villareal, & Clack, 1985). In a collectivistic cultures, interdependence and subordination to the group are cultivated through strict social norms and expectations of conformity, self-restraint, and social inhibition. Thus, symptom elevations in anxiety reflect the societal emphasis on those particular mood states and behaviors (Weisz, Suwanlert, Chaiyasit, & Walter, 1987). From this perspective, individualistic cultures such as the United States, which emphasize autonomous, outgoing, self-promoting behaviors, should have more children with disruptive behavior problems because this type of expression is supported (Weisz et al., 1987). An alternative explanation for an association between collectivistic cultures and internalizing symptoms is that an emphasis on the control of emotions may stifle children’s understanding and managing of their internal states (Varela, Vernberg, Sanchez-Sosa, Riveros, Mitchell et al., 2004; Varela, Vernberg, Sanchez-Sosa, Riveros, Mashunkashey, et al. 2004). From this perspective, social constraints on expressing emotions may lead to a failure to develop emotion regulation skills, which predicts greater emotional difficulties.

SEX DIFFERENCES Girls and women experience higher levels of anxiety and related symptoms than boys and men (Silverman & Carter, 2006), at a roughly 2:1 girl:boy ratio (Costello et al., 2004). These findings are consistent with research on youth self-reports of fear, which show that girls report more fears than boys (Ginsburg & Silverman, 2000; Ollendick, Langley, Jones, & Kephart, 2001; Ollendick et al., 1985; Ollendick et al., 1989). Although these findings are consistent, mechanisms responsible for the observed sex differences remain obscure. Twin studies suggest there may be a genetic basis for the sex difference, as the heritable contribution to individual differences in anxiety appears to be greater for females (Eley, 2001). Genetic influences do not rule out socialization processes in expression of symptoms, or in girls’ increased willingness to report certain types of symptoms (Ginsburg & Silverman, 2000; Rutter, Caspi, & Moffitt, 2003). Recent research also has broadened the theoretical frame for understanding observed sex differences. For example, in a sample of clinic referred anxious youth, Carter, Silverman, and Jaccard (2011) found that early pubertal development and self-reported gender-role orientation (high levels of masculinity had low levels of anxiety symptoms) were significant contributors to levels of youth anxiety.

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RESEARCH DOMAIN CRITERIA The National Institute of Mental Health is developing, “for research purposes, new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.” (For the full text, see http://www.nimh .nih.gov/about/strategic-planning-reports/index.shtml#strategic-objective1). This strategy has been named the Research Domain Criteria Project (RDoC). The RDoC research framework can be considered as a matrix, the rows of which correspond to specified dimensions of function and the columns of which are levels of analysis (from molecules and genes to observable behavior). The functions comprise several specific constructs (many have already been discussed above), with each couched within large classes of constructs. A few specific examples are worth noting. For example, within the broader class of Negative Valence Systems, fear is a core RDoC construct, central to anxiety disorders and linked to brain regions in the amygdala, hippocampus, and HPA axis. Within the broader Cognitive Systems Cognitive (Effortful) control is a central construct also linked to anxiety disorders. Within Arousal/Regulatory Processes, stress regulation is another central construct. In our research in this area, anxiety is viewed as higher-order feeling state produced by specific brain mechanisms responsible for basic emotion (Damasio, 2003). We define anxiety in this chapter as the product of a multicomplex response system, involving affective, behavioral, physiological, and cognitive components (e.g., Barlow, 2002; Lang, 1977). Worry, for example, is one component of anxiety that can be viewed as a cognitive process preparing individuals to anticipate future danger. Fear, in contrast, is part of the response system that fosters preparation for either freezing to avoid impending punishment or escaping as part of the fight/flight response (Barlow, 2002; Gray & McNaughton, 2000; Mathews, 1990). A core defining feature of anxiety is emotion dysregulation of the anxiety response system (Weems, 2008). Such dysregulation may involve intense and disabling worry that does not help anticipate true future danger, or intense fear reactions in the absence of true threat. Distress and impairment may also result from dysregulation in corresponding negative emotional states (e.g., being upset or overconcerned). For convenience, we refer to these primary features of anxiety problems as anxious emotion. These core features of anxiety may be expressed behaviorally (e.g., avoidance), cognitively (e.g., concentration difficulties), physiologically (e.g., dizziness, racing heart), or interpersonally (e.g., difficulty making friends). These features cut across all of the anxiety disorders in the DSM-IV. In contrast, secondary features of anxiety are aspects that differentiate specific categories of anxiety disorder (Weems, 2008). For example, worry about separation from parents is specific to separation anxiety disorder, being embarrassed in public is specific to social anxiety disorder, and uncued panic attacks are specific to panic disorder (APA, 1994).

SUMMARY AND CONCLUSIONS In this chapter, we provided a developmental psychopathology approach to describing continuity and change in childhood anxiety disorders. Such an approach

Anxiety Disorders 547 suggests that a comprehensive theory of anxiety disorders requires differentiation between primary and secondary features. Primary features of problematic anxiety are (a) dysregulation of the anxiety response system, and (b) negative affect and distress/impairment, which result from physiological arousal. Secondary features are aspects that distinguish the DSM-IV (APA, 1994) anxiety disorders from one another (e.g., interpersonal concerns in social anxiety disorder, uncued panic attacks in panic disorder). Significant advances have been made in understanding the developmental psychopathology of childhood anxiety disorders: Research has identified biological, behavioral, cognitive, interpersonal, and contextual processes important to understanding the origins of childhood anxiety. The developmental psychopathology view can be summarized via a hypothetical child’s emotional development from childhood through adulthood. This child may be behaviorally inhibited early in his or her life. This behavioral inhibition is likely to be the product of genetic risk factors (e.g., short 5HTT allele), which correlate and potentially interact with environmental risks (e.g., low social support, parental reinforcement of avoidance). A child exposed to this combination of genetic vulnerability and environmental risk is likely to experience elevated anxiety (i.e., dysregulation of anxious emotion and corresponding distress), which is in turn shaped by normative developmental processes and individual experiences. For example, a child with a propensity for elevated arousal and avoidance may live with parents who are not skilled in reducing the child’s anxious responding or who model withdrawn or anxious behaviors in social contexts. Such parents are at risk for being anxious themselves. The child may also be exposed repeatedly to socially challenging events that he or she is allowed to avoid. This avoidance may result in a failure to develop cognitive, social, and behavioral skills for facing social situations. Vulnerability to developing an anxiety disorder is high for this child, and the specific set of risk factors may potentiate social anxiety. Early in the child’s life, resultant emotion dysregulation may manifest as separation anxiety disorder. Later, especially in adolescence, social anxiety disorder may result. We encourage research that tests hypothesized factors of influence on shaping continuity and change in both primary and secondary features of anxiety. Our view emphasizes trajectories (versus purely categorical approaches) throughout the period of childhood and adolescence, to determine common and unique pathways in anxious emotion. We also encourage research aimed at clarifying the role of the factors that are hypothesized to shape these pathways. We suggest that taking an approach that emphasizes the distinction between core and secondary features of anxious emotion will facilitate understanding of the basic developmental psychopathology of anxiety, and also individual variation in expression of anxious emotion. In closing it is important to note that the research literature indicates that interventions, such as cognitive behavioral therapy, are efficacious in reducing anxiety disorders in youth (see Silverman, Pina, & Viswesvaran, 2008; Silverman & Motoca, 2011). Results suggest that cognitive behavior therapy is efficacious across

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various types of childhood anxiety disorders and age groups (Kendall, 1994; Scheeringa, Weems, Cohen, Amaya-Jackson, & Guthrie, 2011), can serve preventive functions (Dadds, Holland, Barrett, Laurens, & Spence, (1999), and is associated with long-term positive outcomes (Saavedra, Silverman, Morgan-Lopez, & Kurtines, 2010). We encourage the use of intervention research to further the understanding the mechanisms involved in anxiety disorder development as well as the use of the empirical literature on the developmental psychopathy of anxiety to inform the next generation of intervention research.

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Schmidt, N. B., Lerew, D. R., & Jackson, R. J. (1997). The role of anxiety sensitivity in the pathogenesis of panic: Prospective evaluation of spontaneous panic attacks during acute stress. Journal of Abnormal Psychology, 106, 355–364. Schmidt, N. B., Lerew, D. R., & Jackson, R. J. (1999). Prospective evaluation of anxiety sensitivity in the pathogenesis of panic: Replication and extension. Journal of Abnormal Psychology, 108, 532–537. Scheeringa, M. S., Peebles, C. D., Cook, C. A., & Zeanah, C. H. (2001), Toward establishing procedural, criterion, and discriminant validity for PTSD in early childhood. Journal of the American Academy of Child and Adolescent Psychiatry, 40, 52–60. Scheeringa, M. S., Weems, C. F., Cohen, J., Amaya-Jackson, L., & Guthrie, D. (2011). Trauma-focused cognitive-behavioral therapy for posttraumatic stress disorder in three through six year-old children: A randomized clinical trial. Journal of Child Psychology and Psychiatry, 52, 853–860. Scheeringa, M. S., Zeanah, C. H., Myers, L. & Putnam, F. (2004). Heart period and variability findings in preschool children with post-traumatic stress symptoms. Biological Psychiatry, 55, 685–691. Silverman, W. K. & Carter, R. (2006). Anxiety disturbance in girls and women. In J. Worell & C. Goodheart (Eds.), Handbook of girls’ and women’s psychological health (pp. 60–68). New York, NY: Oxford University Press. Silverman, W. K., Kurtines, W. M., Jaccard, J., & Pina, A. A. (2009). Directionality of change in youth anxiety treatment involving parents: An initial examination. Journal of Consulting and Clinical Psychology, 77, 474–485. Silverman, W. K., & Motoca, L. (2011). Psychosocial interventions for anxiety disorders in children: An update and future directions. In W. K. Silverman & A. Fields (Eds.), Anxiety disorders in children and adolescents: Research, assessment and intervention (2nd ed.). Cambridge, England: Cambridge University Press. Silverman, W. K., Pina, A. A., & Viswesvaran, C. (2008). Evidence-based psychosocial treatments for phobic and anxiety disorders in children and adolescents. Journal of Clinical Child & Adolescent Psychology, 37, 105–130. Silverman, W. K., & Treffers, P. D. A. (Eds.). (2001). Anxiety disorders in children and adolescents: Research, assessment and intervention. Cambridge, England: Cambridge University Press. Siqueland, L., Kendall, P. C., & Steinberg, L. (1996). Anxiety in children: Perceived family environments and observed family interaction. Journal of Clinical Child Psychology, 25, 225–237. Triandis, H. C., Leung, K., Villareal, M. J., & Clark, F. L. (1985). Allocentric versus idiocentric tendencies: Convergent discriminant validation. Journal of Research in Personality, 19, 395–415. Varela, R. E., Vernberg, E. M., Sanchez-Sosa, J. J., Riveros, A., Mashunkashey, J., & Mitchell, M. (2004). Parenting practices of Mexican, Mexican American, and European American families: Social context and cultural influences. Journal of Family Psychology, 18, 651–657.

Anxiety Disorders 557 Varela, R. E., Vernberg, E. M., Sanchez-Sosa, J. J., Riveros, A., Mitchell, M., & Mashunkashey, J. (2004). Anxiety reporting and culturally associated interpretation biases and cognitive schemas: A comparison of Mexican, Mexican American, and European American families. Journal of Clinical Child and Adolescent Psychology, 33, 237–247. Vasey, M. W., & Dadds, M. R. (2001) (Eds.) The developmental psychopathology of anxiety. London, England: Oxford University Press. Vasey, M. W., Dalgleish, T., & Silverman, W. K. (2003). Research on information processing factors in child and adolescent psychopathology: A critical commentary. Journal of Clinical Child and Adolescent Psychology, 32, 81–93. Vasey, M. W., & MacLeod, C. (2001). Information-processing factors in childhood anxiety: A review and developmental perspective. In M. W. Vasey & M. R. Dadds (Eds.), The developmental psychopathology of anxiety (pp. 253–277). London, England: Oxford University Press. Vasey, M. W., & Ollendick, T. H. (2000). Anxiety. In M. Lewis & A. Sameroff (Eds.), Cognitive interference: Theory, methods, and findings (pp. 117–138). Hillsdale, NJ: Erlbaum. Warren, S. L., Huston, L., Egeland, B., & Sroufe, L. A. (1997). Child and adolescent anxiety disorders and early attachment. Journal of the American Academy of Child and Adolescent Psychiatry, 36, 637–644. Warren, S. L., & Sroufe, L. A. (2004). Developmental issues. In T. H. Ollendick & J. S. March (Eds.), Phobic and anxiety disorders in children and adolescents: A clinician’s guide to effective psychosocial and pharmacological interventions (pp. 92–115). New York, NY: Oxford University Press. Watts, S. E., & Weems, C. F. (2006). Associations among selective attention, memory bias, cognitive errors and symptoms of anxiety in youth. Journal of Abnormal Child Psychology, 34, 838–849. Weems, C. F. (2008). Developmental trajectories of childhood anxiety: Identifying continuity and change in anxious emotion. Developmental Review, 28, 488–502. Weems, C. F., Berman, S. L., Silverman, W. K., & Saavedra, L. S. (2001). Cognitive errors in youth with anxiety disorders: The linkages between negative cognitive errors and anxious symptoms. Cognitive Therapy and Research, 25, 559–575. Weems, C. F., & Costa, N. M. (2005). Developmental differences in the expression of childhood anxiety symptoms and fears. Journal of the American Academy of Child and Adolescent Psychiatry, 44, 656–663. Weems, C. F., Costa, N. M., Watts, S. E., Taylor, L. K., & Cannon M. F. (2007). Cognitive errors, anxiety sensitivity and anxiety control beliefs: Their unique and specific associations with childhood anxiety symptoms. Behavior Modification, 31, 174–201. Weems, C. F., Hammond-Laurence, K., Silverman, W. K., & Ferguson, C. (1997). The relation between anxiety sensitivity and depression in children referred for anxiety. Behaviour Research and Therapy, 35, 961–966.

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Weems, C. F., Hammond-Laurence, K., Silverman, W. K., & Ginsburg, G. S. (1998). Testing the utility of the anxiety sensitivity construct in children and adolescents referred for anxiety disorders. Journal of Clinical Child Psychology, 27, 69–77. Weems, C. F., Hayward, C., Killen, J. D., & Taylor, C. B. (2002). A longitudinal investigation of anxiety sensitivity in adolescence. Journal of Abnormal Psychology, 111, 471–477. Weems, C. F., & Silverman W. K. (2006). An integrative model of control: Implications for understanding emotion regulation and dysregulation in childhood anxiety. Journal of Affective Disorders, 91, 113–124. Weems, C. F., Silverman W. K., Rapee, R., & Pina, A. A. (2003). The role of control in childhood anxiety disorders. Cognitive Therapy and Research, 27, 557–568. Weems, C. F., Silverman, W. K., Saavedra, L. S., Pina, A. A., & Lumpkin, P. W. (1999). The discrimination of children’s phobias using the Revised Fear Survey Schedule for Children. Journal of Child Psychology and Psychiatry and Allied Disciplines, 35, 941–952. Weems, C. F., & Stickle, T. R. (2005). Anxiety disorders in childhood: Casting a nomological net. Clinical Child and Family Psychology Review, 8, 107–134. Weems, C. F., & Watts, S. E. (2005). Cognitive models of childhood anxiety. In C. M. Velotis (Ed.) Anxiety Disorder Research (pp. 205–232). Nova Science Publishers, Inc.: Hauppauge, NY. Weems, C. F., Zakem, A., Costa, N. M., Cannon, M. F., & Watts, S. E. (2005). Physiological response and childhood anxiety: Association with symptoms of anxiety disorders and cognitive bias. Journal of Clinical Child and Adolescent Psychology, 34, 712–723. Weisz, J., Suwanlert, S., Chaiyasit, W., & Walter, B. (1987). Over- and undercontrolled referral problems among children and adolescents from Thailand and the United States: The Wat and Wai of cultural differences. Journal of Consulting and Clinical Psychology, 55, 719–726. Westenberg, P. M., Drewes, M. J., Siebelink, B. M., & Treffers, P. D. A. (2004). A developmental analysis of self-reported fears in late childhood through mid-adolescence: Social-evaluative fears on the rise? Journal of Child Psychology and Psychiatry, 45, 481–496. Westenberg, P. M., Siebelink, B. M., & Treffers, P. D. A. (2001), Psychosocial developmental theory in relation to anxiety and its disorders. In W. K. Silverman & P. D. A. Treffers (Eds.), Anxiety disorders in children and adolescents: Research, assessment and intervention (pp. 72–89). Cambridge, England: Cambridge University Press. Westenberg, P. M., Siebelink, B. M., Warmenhoven, N. J., & Treffers, P. D. A. (1999). Separation anxiety and overanxious disorders: Relations to age and level of psychosocial maturity. Journal of the American Academy of Child and Adolescent Psychiatry, 38, 1000–1007.

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C H A P T E R 17

Obsessive-Compulsive and Related Disorders EMILY RICKETTS, DEEPIKA BOSE, AND JOHN PIACENTINI

INTRODUCTION

O

bsessive-compulsive and related disorders (OCRDs), as newly classified in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM; American Psychiatric Association [APA], 2013), include obsessive-compulsive disorder (OCD), body dysmorphic disorder (BDD), trichotillomania (hair-pulling disorder; HPD), hoarding disorder (HD), and excoriation (skin-picking disorder; SPD). Obsessive compulsive disorders are classified together in a chapter of the DSM-5 due to shared pathophysiology and phenomenological features, including preoccupation, urges, and repetitive behaviors, with some disorders (BDD, HPD, and SPD) focused more on body features (Phillips, Stein, et al., 2010; Van Ameringen, Patterson, & Simpson, 2014; Stein, Craske, Friedman, & Phillips, 2014). This new classification scheme, which removes OCRDs from the anxiety disorders section of the DSM, provides exciting opportunities for enhancing our conceptualization and understanding of these disorders and their relations with one another. The present chapter provides a historical context for these disorders and a summary of the most recent findings for OCRDs with respect to diagnosis, prevalence, sex differences, clinical course, etiology, and cultural considerations, with a focus on pediatric populations. This chapter also provides an overview of relevant mechanisms underlying and unifying OCRDs.

HISTORICAL CONTEXT The recognition of behaviors consistent with obsessive-compulsive and related disorders goes back several thousand years. Descriptions of obsessive-compulsive 560

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behavior first appeared at least 4,000 years ago (Kolada, Bland, Newman, 1994). Similarly, hair pulling was performed during times of grief as a mourning ritual in ancient Chinese, Roman, Greek, and Egyptian societies with this practice continuing through the late 1800s (Lewis, 2013).

Obsessive-Compulsive Disorder, Hoarding Disorder, and Body Dysmorphic Disorder Obsessive-compulsive symptoms have been documented on a consistent basis since the 1600s, with several reports of religious scrupulosity resulting in exorcisms (Alvarenga, Hounie, Mercadante, Miguel, & do Rosario, 2007). By the late 1700s and early 1800s, medical conceptualizations of obsessive-compulsive symptoms began to develop, particularly in France, with a number of medical scholars describing different emotional, volitional, and intellectual aspects of the disorder. However, a German scholar, Carl Westphal, first described OCD most similarly to its current conceptualization. He referred to symptoms using the term Zwangsvorstellung, which was translated as obsession in England and as compulsion in the United States. These terms were later combined to produce our current terminology (Berrios, 1989). By the late 1800s, psychological conceptualizations of OCD emerged (Alvarenga et al., 2007). During this time, George Miller Beard introduced the concept of neurasthenia, featuring obsessive-compulsive behaviors in addition to several other somatic and internalizing symptoms. In the early 1900s, Pierre Janet and Sigmund Freud centered their theories of neurasthenia solely on obsessive-compulsive features (Taylor, 2001). Janet purported that patients with these symptoms, termed psychasthenic illness, had personality deficits, featuring anxiety, lack of energy, and doubt. Freud developed a psychoanalytic account of obsessive-compulsive symptoms, which he termed obsessive neurosis. He proposed that obsessions resulted from repressed sexual, aggressive, or blasphemous impulses (Kolada, Bland, & Newman, 1994; Moritz, Kempke, Luyten, Randjbar, & Jelinek, 2011). Initially, these conceptualizations referred exclusively to adults, as it was thought that obsessions could only present among those with a high degree of self-knowledge and awareness. The first report of OCD in a child was by Janet, who in 1906 described symptoms in a 5-year-old (Alvarenga et al., 2007). OCD was first recognized in the modern psychiatric nomenclature in 1980, with its classification as an anxiety disorder in the DSM-III (APA, 1980). Its classification as an anxiety disorder remained until publication of the DSM-5, in which it was moved to the OCRD category based on research showing etiologic disparities between OCD and anxiety disorders (Van Ameringen, Patterson, & Simpson, 2014). Hoarding was first acknowledged during the era of psychoanalysis as a feature of the anal-retentive personality type, later retermed obsessive compulsive personality disorder. However, hoarding symptoms were officially acknowledged for the first time as a diagnostic symptom of OCPD in the DSM-III-R (APA, 1987). Hoarding later

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became linked to OCD in the DSM-IV (APA, 1994), possibly due to the addition of two hoarding items on the Yale-Brown Obsessive Compulsive Scale, which over the years became the standard for clinician-administered rating scales for OCD, with influence regarding subsequent rating scales (Mataix-Cols et al., 2010). However, research in the past decade (Pertusa, Frost, & Mataix-Cols, 2010) has identified differences in both symptom presentation and biological correlates of hoarding versus OCD, which led to the recent reclassification of HD as distinct from OCD. BDD symptoms have been recognized for over a century, referred to by several terms, including dermatologic hypochondriasis, beauty hypochondria, dermatophobia, and dermatological nondisease (Castle, Phillips, & Dufresne, 2004; Phillips, 1991). In 1891, Enrico Morselli developed the term dysmorphophobia to characterize anxiety and concerns regarding an imagined physical deformity (Mufaddel et al., 2013). However, dysmorphophobia was not recognized in the DSM until publication of the third edition, in which it was categorized as an atypical somatoform disorder. In the DSM-III-R, the term dysmorphophobia was replaced by BDD and recategorized as a distinct somatoform disorder (Phillips, Wilhelm, et al., 2010; Munro & Stewart, 1991). Because of research showing similarities between the phenomenology of BDD (i.e., mental preoccupation, repetitive body checking, and grooming behaviors) and OCD (intrusive thoughts, worries, and compulsions), BDD has been recategorized as an OCRD in DSM-5 (Phillips, Wilhelm, et al., 2010).

Hair Pulling Disorder and Skin Picking Disorder Hair pulling during times of distress has been referenced within written works, including the Bible, the Iliad by Homer, the Shakespearean play Troilus and Cressida, and works of art, including de Oude’s 17th-century sculpture, The Women from the Mad House, as well as Pugin’s 1809 illustration of St. Luke’s Hospital (Christenson & Mansueto, 1999). Acknowledgement of hair pulling as a medical problem was first noted by Hippocrates. However, it was trichophagia, or eating of the hair, that was first recognized as a disorder in modern medicine in the late 1700s by Baudamant, a French physicist, who documented a case of trichobezoar (mass of hair in the stomach) due to hair eating. Hair pulling was not recognized as a medical problem until 1889, when a French dermatologist, Francois Henri Hallopeau, developed the term trichotillomania in reference to a man who pulled out all of his body hair (Chamberlain, Odlaug, Boulougouris, Fineberg, & Grant, 2009; Christenson & Mansueto, 1999). Trichotillomania was first included in the DSM-III-R as an impulse control disorder not elsewhere classified, where it remained with minor revisions through publication of DSM-IV-TR (APA, 2000; Christenson & Mansueto, 1999; Stein, Grant et al., 2010). Objections to the term mania within trichotillomania have been prevalent, with many alternatives suggested but none adopted until publication of DSM-5, which refers to the disorder as HPD. As noted above, HPD is categorized in

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DSM-5 as an OCRD based on empirical findings (Stein, Grant, et al., 2010) showing overlap in the urge-repetitive behavior cycle and in neurobiology. Repetitive skin picking has long been acknowledged as a problem within the medical field. In 1875, Erasmus Wilson, an English surgeon and dermatologist, used the term neurotic excoriation to describe repetitive skin picking exhibited by some patients (Odlaug, Chamberlain, Harvanko, & Grant, 2012). In 1898, Brocq, a French dermatologist, coined the term acne excoriee to refer to skin picking observed among adolescent females with acne (Odlaug & Grant, 2010). It has also been referred to as pathologic skin picking, compulsive skin picking, dermatillomania, and psychogenic excoriation (Odlaug & Grant, 2010). Because of evidence showing impairments related to recurrent skin picking, and the similarities with HPD, recurrent skin picking is now recognized as an OCRD in DSM-5 (Stein, Grant, et al., 2010).

DSM-5 CRITERIA AND DIAGNOSTIC ISSUES The DSM-5 diagnostic criteria for the obsessive-compulsive and related disorders reflect both their shared features, most notably, repetitive behaviors, as well as their heterogeneity, including the variable prominence of anxiety as a central focus and a differential emphasis on cognitive, sensory and bodily triggers (Van Ameringen, Patterson, & Simpson, 2014).

Obsessive-Compulsive Disorder, Hoarding Disorder, and Body Dysmorphic Disorder OCD is characterized by obsessions (i.e., intrusive repetitive thoughts, images, urges, and/or impulses that are ignored, suppressed, or neutralized through thoughts or action) and/or compulsions (i.e., nonsensical repetitive behaviors or mental rituals triggered by obsessive thoughts, or performed in a ritualistic manner to reduce anxiety or prevent the occurrence of a feared event) (APA, 2013; Leckman et al., 2010). OCD phenomenology is rather consistent across development, and the majority of adults and youth with OCD present with both obsessions and compulsions. However, children with a very early age of onset (Rettew, Swedo, Leonard, Lenane, & Rapoport, 1992), or with comorbid tic disorders (Mansueto & Keuler, 2005) are more likely to present with compulsions exclusively. There also may be difficulty distinguishing compulsions from tics because of the higher rates of comorbid tic disorders in youth relative to adults (Mansueto & Keuler, 2005). With respect to specific content of worry, children commonly report obsessive thoughts centered on contamination or aggressive/harm-related content (Geller et al., 2001; Garcia et al., 2009; Hanna, 1995)—along with compulsions, including washing, cleaning, and checking behaviors (Garcia et al., 2009; Hanna, 1995). Additionally, research shows higher rates of hoarding compulsions among youth with OCD relative to adults (Geller et al., 2001). Although most individuals with

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OCD recognize the irrationality of their obsessions and compulsions, some lack insight into the severity and nonsensical nature of their symptoms, with poorer insight noted in children relative to adults (Geller et al., 2001). Children with OCD show significant impairment in school, social, and home domains, which is not surprising given that functional impairment is a criterion for the disorder in the DSM-V. Youth with OCD may have difficulty completing or focusing on schoolwork related to obsessions and/or compulsions. Symptoms may also result in chronic tardiness. Socially, children may display significant difficulties being in groups of strangers, being touched by others, and/or allowing others to touch personal possessions. At home, children display problems with household chores, personal hygiene-related activities, and preparing for bed (Piacentini, Bergman, Keller, & McCracken, 2003). Significant relational problems with siblings and parents, and family accommodation often result (Lebowitz & Bloch, 2012). Hoarding disorder refers to continuous difficulty disposing or letting go of belongings regardless of their objective value, caused by perceptions that belongings are needed and linked to emotional distress related to their disposal. Resulting is accumulation of clutter within the home, which impedes daily living activities, or which would impede them without interception by family members or outsiders (APA, 2013). Among children, old clothing, books, school papers, childhood toys, and useless novelty items are commonly hoarded. In severe cases youth may report difficulty discarding valueless items (i.e., spoiled foods, empty food containers, clothing lint, used napkins; Plimpton, Frost, Abbey, & Dorer, 2009; Storch et al., 2011). Both adults and children hoard due to perceived intrinsic or emotional value or perceived need for items in the future (Pertusa et al., 2008; Storch et al., 2011). However, children with HD may also display personification of objects—the application of human qualities to items. Children with HD may also exhibit excessive essentialism, fusing specific emotions or self-identity with personal objects. Hoarding disorder may also be associated with excessive acquisition of objects, most commonly through purchasing items, receiving free items, or stealing or borrowing items without returning them (Plimpton et al., 2009; Timpano et al., 2011). However, research generally shows lower rates of excessive acquisition among children relative to adults, perhaps related to parental monitoring and intervention during childhood (Ivanov et al., 2013; Storch et al., 2011). Additionally, both youth and adults with HD show tendencies toward procrastination, perfectionism, and indecision (Plimpton et al., 2009; Timpano et al., 2011). Hoarding disorder may be associated with serious impairment in multiple domains, depending on symptom severity. Among both children and adults, hoarding often leads to significant emotional distress and family tensions, as family members attempt to intervene by clearing clutter (Tolin, Frost, Steketee, Gray, & Fitch, 2008). Accommodation of symptoms is common among families of adults with HD (Wilbram, Kellett, & Beail, 2008) and children (Plimpton et al., 2009; Storch et al., 2011). Hoarding also results in occupational impairment, health hazards

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(e.g., fire, poor sanitation, mold, risk of falling), and increased risk for weight gain, obesity, and chronic health conditions due to collected food and hazardous living conditions (Storch et al., 2011; Tolin et al., 2008). Even so, many with HD lack insight into their symptoms and how hoarding negatively affects themselves and the lives of family members (Tolin, Fitch, Frost, & Steketee, 2010). Insight may be even lower among youth with clinically significant hoarding (Plimpton et al., 2009). Body dysmorphic disorder refers to a preoccupation with one or multiple physical flaws that are minor or nonexistent to outside observers. Such preoccupation triggers (a) repetitive behaviors, such as mirror checking, camouflaging, body touching, skin picking, overgrooming, clothes changing, and reassurance seeking; and/or (b) cognitions such as comparing oneself to others (APA, 2013; Phillips et al., 2006). High rates of skin picking may be observed among adolescents with BDD (Phillips et al., 2006). The most common body sites of preoccupation include the skin, hair, and nose. However, among adolescents primary sites differ slightly, with concerns about the skin, hair, stomach, and teeth among the most common (Phillips et al., 2006). Individuals with BDD often lack insight into their symptoms (Eisen, Phillips, Coles, & Rasmussen, 2004), particularly for adolescents more than adults (Phillips et al., 2006). BDD results in significant impairment in social, academic, occupational, and family functioning for children and adults (Phillips et al., 2006). School dropout, unemployment, and remaining housebound due to BDD are common in severe cases (Phillips et al., 2006). Individuals with BDD experience significant emotional distress, even including suicidality. Indeed, adolescents with BDD endorse higher rates of past suicide attempts relative to adults with the disorder (Phillips et al., 2006). Research on clinical features of BDD among children is limited but characteristics of the disorder appear generally consistent across development (Albertini & Phillips, 1999; Phillips et al., 2006).

Hair Pulling Disorder and Skin Picking Disorder Trichotillomania (HPD) is marked by repetitive pulling out of one’s hair, which is difficult to inhibit, leading to hair loss or thinning (APA, 2013). Adolescents may report (a) rising tension or urge prior to pulling and (b) relief following a pulling episode. In fact, these were diagnostic criteria in the DSM-IV-TR. However, they are often not endorsed by adults with HPD (Christenson, Mackenzie, & Mitchell, 1991; Lochner et al., 2011) or younger children (King et al., 1995; Panza, Pittenberger, & Bloch, 2013; Reeve et al., 1992; Walther et al., 2014). These criteria were therefore excluded from the DSM-5. Individuals with HPD may pull hair from anywhere on their bodies, but the most common (from most to least) pulling sites are the scalp, eyebrows, and eyelashes. However, younger children are more likely to pull solely from their scalp relative to older children and to adults (Cohen et al., 1995; Walther et al., 2014; Wright & Holmes, 2003), with the number of pulling sites rising with age (Walther et al., 2014; Woods et al, 2006). Most individuals use their fingers to pull their hair, but some also use other objects such as tweezers, combs/brushes

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or pencils; some may even break hair at the shaft or rub it until it breaks (Christenson, Mackenzie, et al., 1991). Individuals often engage in prepulling (i.e., hair stroking, hair twirling, searching for particular hairs) and postpulling (e.g., examining the root, touching the root to the lips/biting the root; letting the hair float to the floor, sticking the root to a surface, collecting hair in a pile, or swallowing the hair), which may result in serious medical complications (e.g., trichobezoar; Bouwer & Stein, 1998) that may require surgical intervention (Labouliere & Storch, 2012). Hair pulling may occur automatically (without awareness) or in a focused manner, both of which may be present in the same individual. However, younger children have less awareness of pulling compared to older children and adults (Walther et al., 2014). Common pulling triggers include tactile cues, stress, and boredom (Odlaug & Grant, 2008b). Hair pulling results in significant shame and embarrassment. Individuals may attempt to cover hair loss with make up or hair pieces or may avoid certain social or recreational activities. Social impairment is common. Individuals experience teasing from peers and attempt to keep pulling a secret, both of which increase hair pulling severity (Panza et al., 2013) and emotional distress and impairment (Walther et al., 2014). DSM-5 diagnostic criteria for skin picking disorder (SPD) align closely with those for HPD. SPD is characterized by repetitive picking or scratching of one’s skin, which is difficult to stop or decrease and results in tissue damage (e.g., sores, scabs, scars, wounds). As in HPD, some endorse an urge or tension before picking and pleasure or relief following picking (Keuthen, Koran, Aboujaoude, Large, & Serpe, 2010). Individuals may pick at any body site, but most commonly pick their face (Flessner & Woods, 2006; Odlaug & Grant, 2008a). Some also endorse picking at other people’s skin (Odlaug & Grant, 2008a). Most individuals use their fingernails to pick, but some use tweezers or other objects (e.g., knives, pins; (Odlaug & Grant, 2008a). Common triggers to picking are sensory cues, sight, boredom, and stress (Odlaug & Grant, 2008a). Skin picking onset often coincides with development of acne, and later generalizes to healthy skin (Wilhelm et al., 1999). Prepicking behaviors include viewing or touching the skin, and postpicking behaviors may include rolling the skin or residue between the fingers, wiping it onto a towel, placing it in the trash, or eating it (Wilhelm et al., 1999). Like HPD, skin picking may occur out of awareness or in more focused fashion, with individuals typically showing both characteristics over the course of illness (Walther, Flessner, Conelea, & Woods, 2009). Similarly to HPD, individuals with SPD endorse deficits in social functioning. Specific problems endorsed by adults with SPD include physical damage to the skin, efforts to conceal it, avoidance of social or romantic situations, school or work impairment, and emotional distress (e.g., guilt, shame). Additionally, picking may result in financial burden related to treatments and concealment efforts (typically involving makeup, hair styles, or clothing), with severity of symptoms associated with impairment (Flessner & Woods, 2006; Tucker,

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Woods, Flessner, Franklin, & Franklin, 2010). Little is known about the clinical phenomenology of SPD in youth, or the extent to which the disorder differs across development.

Differential Diagnosis Among Obsessive Compulsive Related Disorders With respect to accurate diagnosis of obsessive compulsive disorders, differentiating symptoms from those in other psychiatric disorders often poses challenges. Although OCD symptoms may appear similar to those for generalized anxiety disorder (GAD) (see Chapter 16 [Weems & Silverman]), ruminations of GAD tend to be based on real-life concerns (e.g., fear of an earthquake, getting robbed), whereas OCD-related obsessions tend to be more intrusive, irrational, and often coupled with compulsions (APA, 2013; Stein, Fineberg et al., 2010). Furthermore, OCD is characterized by distinct neurocognitive deficits and neural correlates (e.g., fronto-striatal hyperactivity, attenuated amygdala responses to threat), which have not been reported in anxiety disorders (Stein, Fineberg, et al., 2010). Such differences were a major impetus for creating a separate chapter for OCRDs in the DSM-5. Distinguishing between specific OCD symptoms and tics may also pose a challenge. Simple tics (e.g., eye blinking and throat clearing) are typically distinguishable from OCD compulsions based on their involuntary nature, brevity, and lack of purpose (Mansueto & Keuler, 2005). In addition, tics tend to be preceded by a premonitory sensory urge, whereas compulsions tend to be preceded by obsessions (APA, 2013). Distinguishing complex tics (e.g., repeating actions a certain number of times, or until it feels “just right”) from OCD compulsions, however, is less clear (Mansueto & Keuler, 2005). Care should be taken to distinguish hoarding symptoms within OCD from HD. Thoughts associated with HD do not have the defining elements of obsessions, which are typically undesired, and distress-inducing (Frost, Steketee, & Tolin, 2012). Instead, individuals with HD typically fail to voice concerns regarding their symptoms until others become distressed (Rachman, Elliott, Shafran, & Radomsky, 2009). Hoarding within the context of OCD is typically associated with thoughts of contamination, harm, and feeling incomplete or obsession-related avoidance, and accumulation of more unusual items (APA, 2013; Pertusa et al., 2008). Furthermore, excessive accumulation of items is less likely to be present in OCD-related hoarding (APA, 2013). In some instances, differentiating between BDD and OCD may also be difficult. The major distinguishing feature is that the former focuses solely on appearance (APA, 2013); it is also associated with poorer insight and higher rates of lifetime suicidal ideation, major depressive disorder, and substance use disorder (Phillips et al., 2007).

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Complicating differentiation of BDD from other body focused OCRDs is that SPD is common in individuals with BDD (i.e., rates of skin picking in BDD have been reported to be as high as 45%). However, BDD is diagnosed only when skin picking is solely related to improving perceived deficits (APA, 2013; Grant, Menard, & Phillips, 2006). Additionally, BDD, HPD, and SPD involve symptoms above and beyond normal grooming and appearance concerns. Body dysmorphic disorder, for example, is characterized by excessive preoccupation and repetitive behaviors that are difficult to control, time consuming, and distressing (APA, 2013). Additionally, HPD is not diagnosed when hair is removed for cosmetic reasons or for individuals who twist or bite their hair (APA, 2013). As for distinguishing between body-focused obsessive-compulsive related disorders and OCD, although both HPD and OCD are characterized by ritualistic behaviors and compulsions, hair pulling in HPD and skin picking in SPD are rarely performed in response to a fear or obsession (APA, 2013; Lochner, Seedat, et al., 2005). Additionally, ages of onset for HPD and OCD are different, with HPD presenting later than OCD (Lochner, Kinnear, et al., 2005). Furthermore, Lochner et al. reported that OCD patients have higher rates of comorbidity, maladaptive beliefs, harm avoidance, and sexual abuse than those with HPD. Moreover, HPD is associated with poorer inhibition of motor responses, whereas OCD is associated with deficits in cognitive flexibility (Chamberlain, Fineberg, Blackwell, Robbins, & Sahakian, 2006). With respect to SPD and OCD, although skin lesions resulting from excessive washing may occur in individuals with OCD, SPD would not be diagnosed in such a scenario (APA, 2013). Finally, neurodevelopmental disorders (e.g., autism, stereotypic movement disorders) and other medical conditions (e.g., brain injury, autoimmune conditions, or other diseases) should be ruled out prior to diagnosis.

PREVALENCE Although the prevalence of OCD in youth has been reasonably well-characterized, representative population-based data are lacking for the remaining OCRDs in this age range. Extrapolation from adult and small-scale pediatric studies suggest these other disorders may each affect up to 2% of children and adolescents with an even higher prevalence if strict impairment criteria are relaxed.

Obsessive-Compulsive Disorder, Hoarding, and Body Dysmorphic Disorder Pediatric OCD is a common form of psychopathology in children and adolescents, with a point-prevalence rate of approximately 2.7% (Honjo et al., 1989; Rapoport et al., 2000; Zohar et al., 1992), and lifetime prevalence rate between 1.8% and 5.5% (Peterson, Pine, Cohen, & Brook, 2001). Subclinical OCD is also experienced by many children and adolescents, with rates ranging from 10.2–18.3% (Alvarenga et al., 2015; Canals, Hernández-Martínez, Cosi, & Voltas, 2012; Vivan et al., 2014).

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Although nationally representative studies of the prevalence of hoarding disorder (HD) are not available, one population-based twin study found a 2% point-prevalence of hoarding disorder in adolescents. However, this rate increased to 3.7% when the requirement for significant room clutter was excluded (Ivanov et al., 2013). To date, large-scale epidemiological studies examining the prevalence of BDD among children and adolescents are lacking. However, many adults with BDD report that their symptoms began during childhood and adolescence (Albertini & Phillips, 1999; Phillips, 1991; Sobanski & Schmidt, 2000). One small-scale study yielded a point prevalence of 2.2% in a community sample of 566 adolescents ages 14–19 years (Mayville, Katz, Gipson, & Cabral, 1999), similar to the prevalence found (2.4%) in a U.S. survey of adults (Koran, Abujaoude, Large, & Serpe, 2008).

Hair Pulling Disorder and Skin Picking Disorder The prevalence of HPD is estimated to range from 0.6–3.5% (Bruce, Barwick, & Wright, 2005; Christenson, Pyle, & Mitchell, 1991; King et al., 1995; Tolin et al., 2008). The prevalence of subclinical hair pulling (i.e., hair pulling for purposes other than grooming) was reported at 6.5% in a community sample (Duke, Bodzin, Tavares, Geffken, & Storch, 2009) and 9.7% in a college sample (Duke, Keeley, Ricketts, Geffken, & Storch, 2010). It is notable, however, that prevalence studies of pediatric HPD are limited (Duke et al., 2010). Multiple methodological limitations increase the challenge of discerning a true prevalence rate for HPD, including small sample sizes, differing definitions of hair pulling across studies, and the secretive nature of individuals who have HPD (Duke et al., 2010). Research on the prevalence of SPD is highly lacking among youth. However, parent- and teacher-report of picking at fingers, lips, and sores in 100 preschool children ages 3 to 6 revealed frequency estimates of 6% and 7%, respectively (Foster, 1998). Despite a lack of prevalence estimates in youth, results from a national telephone survey of adults in the United States show a point-prevalence estimate of 0.2% for SPD (Keuthen et al., 2010). Findings from the same study showed that 16.6% of the sample reported pathological skin picking with noticeable damage at some point in their lives and 1.4% reported noticeable skin picking with significant functional impairment (Keuthen et al., 2010). Nonclinical community and college samples endorse SPD at rates of 5.4% and 3.8%, respectively (Hayes, Storch, & Berlanga, 2009; Keuthen et al., 2000).

DEVELOPMENTAL PROGRESSION OCD and the other OCRDs share a typically common onset in puberty or early adolescence and a gradually worsening course over time. Although these disorders are considered chronic, symptom fluctuation is not uncommon with greater waxing and waning in childhood and adolescence as opposed to adulthood.

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Obsessive-Compulsive Disorder, Hoarding, and Body Dysmorphic Disorder For pediatric OCD, age of onset ranges from 9 to 11 (Hanna, Himle, Curtis, & Gillepsie, 2005; Rettew et al., 1992; Swedo, Rapoport, Leonard, Lenane, & Cheslow, 1989; Taylor, 2011). Symptoms evolve in topography and fluctuate in severity over time. Pediatric OCD is more likely to remit relative to adult onset OCD, with remission rates of 10 to 50% by late adolescence (Zohar, 1999). The age of onset for hoarding symptoms ranges from 10 to 20 years old (Ayers, Saxena, Golshan, & Wetherell, 2010; Tolin, Meunier, Frost, & Steketee, 2010), with one study finding a mean of 13.4 for significant hoarding symptoms (Grisham, Frost, Steketee, Kim, & Hood, 2006). On average symptoms steadily worsen through adulthood, following symptom onset, with mild symptoms coinciding with pubertal onset, moderate symptoms in the mid-20s, and severe symptoms in middle age and beyond (Ayers et al., 2010; Grisham et al., 2006). In a large Internet sample of adults with self-reported hoarding symptoms, the majority (73%) endorsed a chronic course, 21.2% reported an increasing course, 5.2% a remitting course, and 0.7% a decreasing course (Tolin, Meunier, et al., 2010). Among youth, BDD symptom onset ranges from 11.8 to 13.5 years of age (Albertini & Phillips, 1999), with subclinical BDD symptom onset beginning at a mean age of 11.3 (Phillips et al., 2006). For most youth, the clinical course is chronic, with 97% endorsing a continuous course in a study of 33 youth with BDD. The majority (87%) endorsed symptom worsening over time, with a small percentage (13%) reporting a steady symptom trajectory (Albertini & Phillips, 1999). One study indicated that a higher percentage of adolescents endorsed a chronic course relative to adults (Phillips et al., 2006).

Hair-Pulling Disorder and Skin-Picking Disorder Most hair pullers report symptom onset during adolescence although pulling during childhood is not uncommon (Christenson, Pyle, et al., 1991; Cohen et al., 1995; du Toit, van Kradenburg, Niehaus, & Stein, 2001; Panza et al., 2013). The clinical course for HPD is typically chronic, with symptoms fluctuating in intensity over time and relapse commonly occurring (Kratochvil & Bloch, 2009). In many cases, hair pulling may begin in infancy or early childhood. The mean age of onset for skin picking is typically pubertal, ranging from 12 to 16 years of age (Cullen et al., 2001; Lochner, Simeon, Niehaus, & Stein, 2002; Odlaug & Grant, 2008a; Wilhelm et al., 1999). However, in dermatologic patient samples, skin picking exhibits an onset later in adulthood (i.e., age 30 to 40; Arnold et al., 1998; Fruensgaard, 1984; Snorrason, Belleau, & Woods, 2012). Little is known about the clinical course in SPD among children; however, among adults the course is often chronic, with symptoms waxing and waning in severity, and periods of complete symptom absences being rare (Wilhelm et al., 1999).

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SEX DIFFERENCES There is no clear pattern of male-female prevalence rates across the OCRDS prepubertally; however, females are overrepresented in each of these disorders from adolescence on suggesting that developmental factors may play a role in their etiology or chronicity. Although the literature is mixed, there is also some evidence of sex-related phenotypic differences in these conditions.

Obsessive Compulsive Disorder, Hoarding Disorder, and Body Dysmorphic Disorder Pediatric OCD is more prevalent among males, with a sex ratio of 3:2 (Eichstedt & Arnold, 2001; Fireman, Koran, Leventhal, & Jacobson, 2001; Geller, 2006; Hanna, 1995; Kessler et al., 2005). However, during puberty the sex ratio changes, with a bias toward females (Kalra & Swedo, 2009). Research shows an earlier age of onset among males, with symptoms presenting between 9 and 11 years, versus 11 and 13 years among females (Kessler et al., 2005). Females tend to report more contamination and aggression obsessions and cleaning rituals, and males tend to report more sexual and symmetry obsessions and odd rituals. These findings derive mostly from adult samples (Lensi et al., 1996). However, sex differences in symptom types have also been found in pediatric samples (Zohar, 1999). Hoarding symptoms in pediatric populations are more prevalent among females than males (Mataix-Cols, Nakatani, Micali, & Heyman, 2008). Similar sex differences have also been reported in a more recent population-based twin study of 15-year olds in Sweden (Ivanov et al., 2013). Sex differences in heritability of HD have also been reported, with a higher heritability for adolescent males (Ivanov et al., 2013). Among adults, males and females have similarities in various clinical features of BDD, including symptom severity, topography of repetitive behaviors, suicidality, and receipt of cosmetic procedures. However, females are more likely to have a comorbid eating disorder, and males are more likely to display genital preoccupation and muscle dysmorphia (Phillips, Wilhelm, et al., 2010). These sex differences were not found in an adolescent sample (Mayville et al., 1999). However, body dysmorphic symptoms are experienced more intensely among adolescent girls relative to boys.

Hair-Pulling Disorder and Skin-Picking Disorder Conflicting findings have been reported for sex differences in HPD (Grant, Stein, Woods, & Keuthen, 2012). However, studies tend to report an equal sex distribution of HPD in early childhood (Swedo & Leonard, 1992; Winchel, 1992; Grant et al., 2012) and a significant female preponderance beginning in adolescence (Walther, Ricketts, Conelea, & Woods, 2012). However, contrary to prior findings, a recent study showed a strong female sex bias among young children, ages 0 to 5 years

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(Walther et al., 2014). Reasons for this discrepancy are unclear; however, it is possible that hormonal changes during puberty may play a role (Keuthen et al., 1997). In addition, higher rates of treatment seeking among females may contribute (Hanna, 1997). This may be related to the relative ease with which males can shave or cut their hair which may reduce stigma and ease pulling (Duke et al., 2010). Beyond sex differences in prevalence and treatment seeking, one study showed female youth displayed greater pulling-related distress and higher rates of anxiety and depressive disorders relative to males (Panza et al., 2013). Among adults, males have shown greater relative rates of facial, arm, and stomach hair pulling, and higher rates of comorbid substance use disorder, with females being more likely to be younger and unmarried (Grant et al., 2016). Skin picking also appears to be more common among females, with a reported sex ratio of 8:1 (Arnold, Auchenbach, & McElroy, 2001; Teng, Woods, & Twohig, 2006). Findings from both a sample of adolescent psychiatric inpatients (Grant, Williams, & Potenza, 2007) and a nonclinical sample (Hayes et al., 2009) support this sex difference. As in HPD, reasons for the sex difference may be due in part to increased treatment seeking among females (Snorrason, Belleau, & Woods, 2012). In addition, hormonal changes may play a role (Wilhelm et al., 1999).

COMORBIDITIES Obsessive Compulsive Disorder, Hoarding Disorder, and Body Dysmorphic Disorder Pediatric OCD is a commonly comorbid disorder with significantly higher rates of comorbid anxiety (31%), tic (9%–25%), disruptive behavior (36%–51%) and mood (39%–62%) disorders observed in clinical samples of children and adolescents than expected (Geller, 2006). The distribution of comorbid disorders in non–treatment-seeking pediatric OCD samples has not been well characterized. Higher rates of disruptive behavior disorders (i.e., oppositional defiant disorder and conduct disorder) and chronic tic disorders have been found for adolescent relative to child samples (Garcia et al., 2009; Geller, Biederman, Faraone, Agranat et al., 2001), while comorbid depressive disorder shows the opposite pattern (Fireman et al., 2001; Peris et al. 2010). The former finding may be attributable to the fact that males, for whom tic and disruptive disorder are more common, make up a higher proportion of prepubertal onset OCD cases (Swedo et al., 1989; Kalra & Swedo, 2009). There is a paucity of research on comorbidity in childhood HD. However, a review by Storch and colleagues (2011) suggests hoarding among youth is commonly comorbid with ADHD, anorexia nervosa, autism spectrum disorders, and Prader-Willi syndrome. In a study of adolescents with hoarding symptoms, ADHD was present in 10%, with OCD and ASD each occurring in 2.9%. No other diagnoses were assessed (Ivanov et al., 2013).

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Research on BDD comorbidity among youth is also limited. However, in a very small study of 33 children and adolescents with BDD, investigators found several comorbid disorders, including major depression (70%), psychotic disorders (36%), OCD (36%), and anxiety disorders (61%)—particularly social phobia (30%). In addition, histories of suicidal ideation and attempts were present in 67% and 21% of the sample, respectively (Albertini & Phillips, 1999). In a separate study, eating disorders were present in 4% of psychiatric inpatients with BDD (Dyl, Kittler, Phillips, & Hunt, 2006). For lifetime prevalence, studies show that BDD is associated with major depression (73%–81%), psychotic disorders (2%–36%), OCD (28%–40%), and anxiety disorders (64%–67%), especially social phobia (30%–39%). In addition, substance use disorders (6%–44%), eating disorders (6%–17%), somatoform disorders (3%), and hypochondriasis (3%) have been observed. (Albertini & Phillips, 1999; Phillips et al., 2006). Finally, adolescents with BDD exhibit lower rates of lifetime panic disorder than adults with the disorder but higher rates of past suicide attempts (Phillips et al., 2006).

Hair-Pulling Disorder and Skin-Picking Disorder Hair-pulling disorder among youth is commonly comorbid with depressive disorders (9%–31%), and anxiety disorders (14%–30%), with GAD being relatively common, along with both ADHD (9%–17%) and ODD (6.5%; Franklin et al., 2008; Panza et al., 2013; Rozenman, Peris, Gonzalez, & Piacentini, 2016; Tolin, Franklin, Diefenbach, Anderson, & Meunier, 2007; Walther et al., 2014). Comorbid OCD (3%–5%), tic disorders (3%–6%), and eating disorders (4%) do not appear to occur above expected rates in youth with HPD (Franklin et al., 2008; Panza et al., 2013; Tolin et al., 2007; Walther et al., 2014). Body-focused repetitive behaviors are prevalent in youth with HPD, with skin picking (2%–20%) most common, followed by nail biting (13%), and lip/cheek biting (5%; Panza et al., 2013; Walther et al., 2014). In a recent parent-report Internet study, young children, ages 0–5 years, showed significantly lower rates of psychiatric comorbidity and body-focused repetitive behaviors relative to older children, ages 6–10 (Walther et al., 2014). However, oral-digital habits of thumb and finger sucking are commonly observed in young children with HPD (Reeve, Bernstein, & Christenson, 1992; Santhanam, Fairley, & Rogers, 2008). Although data on SPD comorbidity are limited, skin picking is rather common among those with Prader-Willi syndrome, occurring at rates of 70% to 95% in youth (Dykens & Kasari, 1997; Whitman & Accardo, 1987; Wigren & Hansen, 2003) and 82% to 86% in mixed youth and adult samples (Didden, Korzilius, & Curfs, 2007; Symons, Butler, Sanders, Feurer, Thompson, 1999). In these mixed-age samples, skin picking is associated with self-injurious behaviors and compulsive behaviors (Didden et al., 2007; Symons et al., 1999). In addition, skin picking is observed among youth with Smith-Magenis syndrome (Edelman et al., 2007) and other

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intellectual disabilities (Lang et al., 2010). Despite a lack of research on comorbidity in children, research with adults shows skin picking to be commonly comorbid with major depressive disorder, OCD, anxiety disorders (especially GAD and Social Phobia), and other body-focused repetitive behaviors (e.g., HPD, pathological nail biting, BDD (Lochner et al., 2002; Neziroglu, Rabinowitz, Breytman, & Jacofsky, 2008; Odlaug & Grant, 2008a; Odlaug & Grant, 2010; Wilhelm et al., 1999).

CULTURAL CONSIDERATIONS Obsessive-Compulsive Disorder, Hoarding Disorder, and Body Dysmorphic Disorder Clinical reports of OCD appear in most Western countries and in other countries such as Egypt, Saudi Arabia, Turkey, Pakistan, India, Sri Lanka, Singapore, and Hong Kong (de Silva, 2006). Many cross-cultural similarities have been reported in terms of the age of onset, comorbidity, and sex distribution (Fontenelle & Hasler, 2008). Specifically, onset during adolescence, comorbidity with anxiety disorders, and higher rates in male youth and female adults with OCD appear to be common across cultures (Lewis-Fernández et al., 2010). Cross-cultural differences in OCD have also been reported. For example, African Americans and Asian Americans with OCD endorse greater contamination symptoms compared to European Americans (Wheaton, Berman, Fabricant, & Abramowitz, 2013). Additionally, Asian Americans report elevated levels of obsessive beliefs related to perfectionism (Wheaton et al., 2013). Ethnic differences have also been found in youth samples. For example, Austin and Chorpita (2004) found that Native Americans and Filipino Americans score significantly higher on OCD symptoms than Japanese Americans and Caucasians. Relations between OCD symptoms and religious affiliation have also been reported (de Silva, 2006). For example, Sica, Novara, and Sanavio (2002) found that two religious groups in Italy scored higher than a nonreligious group on measures of obsessionality, perfectionism, inflated responsibility, overimportance of thoughts, and control of thoughts. To date, no research has addressed cultural differences in hoarding among children. However, there are a few studies of adult populations. African Americans with clinically significant OCD exhibit hoarding at similar rates as Caucasians (Williams, Elstein, Buckner, Abelson, & Himle, 2012). In contrast, more hoarding symptoms were endorsed in a Chinese sample relative to a U.S. sample, with hoarding among Chinese participants associated with beliefs about wastefulness and usefulness of items, as opposed to broader beliefs (i.e., emotional attachment, memory, control, responsibility, wastefulness, usefulness, and aesthetic qualities) among U.S. participants (Timpano et al., 2015). In a sample of Japanese patients with OCD, 32% displayed hoarding symptoms—a rate similar to that found in Western countries (Matsunaga, Hayashida, Kiriike, Nagata, & Stein, 2010).

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Clinical features of BDD appear to be similar across countries (Phillips, 2005). Reported similarities include sex ratios, particular body areas that are disliked, types of compulsive BDD behaviors, and levels of associated distress and functional impairment (Phillips, Wilhelm, et al., 2010). Despite these similarities, some cultural differences have been noted. For example, eyelid concerns and worry about displeasing other people by being unattractive are more common in Japan compared to Western countries (Phillips, 2005). The Japanese diagnostic system also discusses shubo kyofu (phobia of a deformed body), a subtype of tajin kyofusho (fear of people), which relates to concerns about offending others as a result of a deformed body. One study reported that 10% of participants with tajin kyofusho also had BDD (Matsunaga et al., 2001). Koro, a fear that one’s genitals are shrinking or are retracting into one’s body, is another condition that occurs primarily in Southeast Asian countries (Phillips, Wilhelm, et al., 2010); it may be related to BDD. Although koro and BDD both include a focus on distress over one’s body, they differ in that individuals with koro believe that the feared event will cause death (Bernstein & Gaw, 1990; Cheng, 1997; Johan & Wolfgang, 2007). In addition, findings from a cross-cultural comparison between German and American students suggest a higher prevalence of subclinical BDD (i.e., body image concerns and resulting preoccupation) among Americans compared to Germans. However, the prevalence of probable BDD at a clinical level appeared to be equal in both groups (Bohne, Keuthen, Wilhelm, Deckersbach, & Jenike, 2002). African Americans reported lower rates of body dissatisfaction than other ethnic groups (i.e., Caucasian, Hispanic, and Asian; Mayville et al., 1999).

Hair-Pulling Disorder and Skin-Picking Disorder Cross-cultural studies of HPD have primarily explored group differences between African Americans and Caucasians. McCarley, Spirrison, and Ceminsky (2002) reported similar prevalence rates of HPD between both groups but found that African Americans were more likely to (a) exhibit a higher rate of pulling in response to itchy/inflamed skin, (b) report pleasure and relief as a result of hair pulling, and (c) experience higher rates of noticeable hair loss. These differences, however, were not replicated in a later study (Dubose & Spirrison, 2006). Research also indicates a negative association between private regard (the extent to which one feels positively or negatively toward being a member of their community) and feelings of happiness and relief experienced after pulling among African Americans (Neal-Barnett & Stadulis, 2006). A negative correlation between humanist ideology (what being African American means in terms of belief and behaviors) and relief during a pulling episode among African Americans has also been reported (Neal-Barnett & Stadulis, 2006). More recently, Neal-Barnett and colleagues (2010) performed a large-scale Internet study to assess differences between Caucasians (n = 1290) and minorities

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(i.e., African Americans and Latinos; n = 103) with HPD. The minority versus Caucasian groups demonstrated a similar sex distribution, age of onset, median income, and degree of education, yet minority group members were less likely to (a) report pulling from the eyebrows and eyelashes, (b) report increased tension before a pulling episode, and (c) utilize treatment. No differences were found for treatment improvement, however. Furthermore, minority group members reported greater interference with home management, whereas Caucasians reported higher interference with academics (Neal-Barnett et al., 2010). The prevalence of pathological skin picking appears to be similar across the United States (3.8%), Germany (2.2%), and Turkey (2.04%; Bohne et al., 2002; Calikusu, Kucukgoncu, Tecer, & Bestepe, 2012; Keuthen et al., 2000). Age of onset and sex differences also appear to be similar across cultures (Calikusu et al., 2012; Grant, Odlaug, Chamberlain, Keuthen, et al., 2012). Among American, German, and Turkish student samples, pimples appear to be the most common trigger for picking (Keuthen et al., 2000; Bohne et al., 2002; Calikusu et al., 2012). However, cultural differences were found in the prevalence of picking healthy skin. Approximately 18% of German students reported picking healthy skin, whereas rates in the American and Turkish samples were 7.7% and 9.8%, respectively. Finally, pleasure and tension relief are more frequent among German and Turkish students than American students (Calikusu et al., 2012).

ETIOLOGY Obsessive Compulsive Disorder The heterogeneity of OCD, both pediatric and adult, indicates a complex etiology characterized in part by deficits in cortico-striato-thalamo-cortical (CSTC) circuitry. There is also clear support for a genetic basis. However, the role of environmental risk is less well understood, complicating identification of gene by environment interactions. Additional work is also needed to disentangle the etiological characteristics underlying the overall disorder from those primarily relevant for specific symptom dimensions or subtypes. Neurobiology. Structural imaging studies among youth with OCD show increased volumes in orbitofrontal and striatal regions relative to controls, whereas decreased volumes in basal ganglia and thalamic volumes are often observed in adult populations with the disorder (Abramovitch, Mittelman, Henin, & Geller, 2012). Studies using resting-state fMRI have also found reduced connectivity between specific regions in CSTC circuitry in adolescents with OCD as compared to healthy controls (e.g., Bernstein et al., 2016; Fitzgerald et al., 2011). Given the involvement of components of this circuitry in the regulation of habitual behavior, disruptions in connectivity may underlie the inflexible and ritualistic behavior observed in OCD (Bernstein et al., 2016). Functional imaging studies

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have revealed reduced frontostriatal activation in children with OCD, as in adults (Abramovitch et al., 2012). As an example, Fitzgerald et al. (2013) found hypoactivation of dorsolateral prefrontal cortex during error, but not conflict, processing for youth with either OCD or non-OCD anxiety disorders as compared to healthy controls suggesting that this activation pattern may underlie the broader anxiety spectrum rather than just OCD. However, structural and/or functional abnormalities have also been observed in the cingulate gyrus, thalamus, and corpus callosum (MacMaster, O’Neill, & Rosenberg, 2008). Additionally, neurotransmitter dysfunction within cortico-striatothalamo-cortical circuity has been implicated in the pathophysiology of pediatric OCD. Similarly to adults, support exists for dysfunction within the glutamatergic, serotoninergic, and dopaminergic systems (Boileau, 2011; O’Neill, Piacentini, Chang, Levitt, Rozenman, Bergman, Salamon, Alger, & McCracken, 2012). Genetic Vulnerabilities. Evidence from a number of family studies suggests higher familiality for childhood-onset OCD relative to adult symptom onset (Walitza et al., 2010). With respect to heritability of OCD symptoms, a similar pattern holds, with a review of twin studies reporting heritability estimates ranging from 45% to 65% for childhood obsessive-compulsive symptoms and 27% to 47% for adult symptoms (van Grootheest, Cath, Beekman, & Boomsma, 2005). A longitudinal analysis of heritable and environmental influences on OCD symptoms in youth between the ages of 4 and 16 showed moderate stability in OCD symptoms, with heritable factors explaining 59% to 80% of stability, with nonshared environmental effects influencing symptom stability to a lesser degree (Krebs, Waszczuk, Zavos, Bolton, & Eley, 2015). Recently, multivariate methods, including structural equation modeling, have been used to overcome methodological issues such as small sample size and ascertainment bias, and yield much more precise heritability estimates for OCD (Browne, Gair, Scharf, & Grice, 2014). Perhaps the most conclusive OCD twin study to date using structural equation modeling reported concordance rates of 0.52 for monozygotic and 0.21 for dizygotic twins with an overall OCD heritability rate of 48% (Monzani, Rijsdijk, Harris, & Mataix-Cols, 2014). Heritability rates drop consistently as the genetic distance to the proband relative increases (e.g., from first-degree to second-degree to third-degree, etc.) (Mataix-Cols et al., 2013). In their combined genome-wide association study of OCD and Tourette’s syndrome, Yu et al. (2015) identified distinct components to the genetic architecture of these two disorders. However, consistent with prior work showing some shared genetic variance, findings also indicated that individuals with comorbid OCD and Tourette’s/chronic tics may have a different underlying genetic susceptibility than those with OCD alone. Studies of recurrence risk, or the likelihood that a child or other biologically related family member will be affected by OCD already present in the family,

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are often used to provide additional, albeit non-DNA-based, evidence of the genetic bases of OCD (Browne et al., 2014). Collectively, the literature suggests a recurrence risk for lifetime OCD among first-degree relatives of 10%–20% (range 6%–55%) or much higher than that for individuals from nonaffected families (Browne et al., 2014). Although linkage studies have implicated multiple moderate to large effect candidate genes (Hu et al., 2006; Nicolini et al., 2009), most research support exists for SLC1A1, a glutamate transporter gene (Stewart et al., 2013), followed by genes implicated in serotonergic (Taylor, 2013) and dopaminergic neurotransmission (Walitza et al., 2008). Preliminary evidence also supports a role for SLITRK1 (Ozomaro et al., 2013). Environmental Risk Factors. Although extant knowledge clearly supports a genetic basis for OCD, current understanding of the role that environmental risk plays in the etiology of the disorder is considerably less clear. In their attempt to reconcile findings in this area, Brander et al. (2016) conducted a systematic review of 128 studies (from 9,960 initial records identified) Although several potential risk factors were identified, these authors concluded that, “at present, no environmental risk factors have been convincingly associated with OCD.” The potential risks identified by Brander et al. fall into three broad categories: (1) perinatal complications, (2) reproductive cycle, and (3) stressful life events. Perinatal insults were the most consistently described risk with five of six studies reviewed reporting significant associations with OCD. Birth complications have been reported to be associated with an increased risk of shameful thoughts and symmetry/ordering, whereas poor motor skills in childhood appear to be associated with an increased risk for harm/checking (Grisham et al., 2011). Prenatal events (e.g., edema during pregnancy and prolonged labor; Vasconcelos et al., 2007) and streptococcal infections (Murphy et al., 2010) have also been reported as increasing OCD risk. Unfortunately the quality of this literature is relatively poor with the majority of studies based on small clinical samples and retrospective in nature. Among reproductive cycle events, the strongest evidence relates to the postpartum period and following miscarriage. Frydman et al. (2014) reported that recent pregnancy in self or significant other led to a 13.2-fold increase for late- compared to early- and normal-onset OCD in a cross-sectional sample of 1,001 OCD cases. Geller, Klier, & Neugebauer, (2001) reported an eightfold increase in OCD for women within 6 months after miscarriage compared to community controls. Similarly to the perinatal risk literature, the above findings needed to be interpreted cautiously due to methodological limitations. Anecdotal reports of OCD triggered by psychosocial change or trauma are common in clinical settings, and stressful and traumatic life events have been associated with illness onset and symptom patterns/severity in both youth (Gothelf,

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Aharonovsky, Horesh, Carty, & Apter, 2004) and adults (Cath, van Grootheest, Willemsen, van Oppen, & Boomsma, 2008; Lochner et al., 2015). Adverse childhood events, including sexual and physical abuse and neglect, have also been studied with mixed results (Brander et al. 2016). As Brander et al. note, the vast majority of this literature is complicated by reliance on retrospective data and failure to control for family-wide vulnerability factors. Finally, although considerable effort and attention has been brought to bear on the role of infectious agents, more specifically group-A hemolytic streptococcal (GABHS) infections, in the etiology of OCD, leading to the development of PANDAS (Pediatric Autoimmune Neuropsychiatric Disorders Associated with Strep) (Murphy & Pichichero, 2002), the literature remains highly conflicted. Unfortunately, in the absence of large-scale, community-based, prospective studies, empirical support for infectious agents as a meaningful risk factor for OCD is slight (Brander et al., 2016; de Oliveira & Pelajo, 2010). Gene × Environment Interactions. The literature on gene-environment interactions for OCD is relatively limited and primarily confined to adult samples. In a meta-analysis of twin studies, Taylor (2011) showed that nonshared environmental factors accounted for greater variance in OCD symptoms as age increased. Real et al. (2013) identified a Gene × Environment interaction for resistance to treatment with serotonin reuptake inhibitors (clomipramine, fluoxetine, sertraline, paroxetine, fluvoxamine, or citalopram) in OCD. Across the entire sample, pharmacologic resistance in OCD was associated with three single nucleotide polymorphisms (SNPs) in the glutamate transporter gene (SCL1A1). However, with regard to the most strongly associated SNP (rs3087879), risk of resistance was significant only among OCD patients without a history of life stress at disorder onset. Of additional importance, the Real et al. study provides support for glutamatergic involvement in the pathophysiology of OCD.

Hoarding Disorder Given its relatively recent designation as a DSM mental disorder (American Psychiatric Association, 2013), the relative lack of research regarding the neurobiological, genetic, and environmental etiologies of Hoarding Disorder is not surprising. However, available evidence suggests a more distinct than shared etiology when compared with OCD (Mataix-Cols et al., 2010). Neurobiology. Although studies have assessed neural correlates of hoarding symptoms among adults with OCD, they have clear limitations with respect to drawing inferences regarding neural deficits particular to HD. However, in recent years some imaging studies of HD have emerged in the adult literature. In a small study of neural correlates of decision making in HD, refusal to discard

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belongings was associated with greater neural activity within the superior temporal gyrus, middle temporal gyrus, medial frontal gyrus, anterior cingulate cortex, precentral gyrus, and cerebellum relative to decisions to discard personal items, possibly indicative of emotional over-attachment and worries about making incorrect decisions (Tolin, Kiehl, Worhunsky, Book, & Maltby, 2009). An extension of this study compared neural correlates of decision making regarding whether or not to discard items among those with HD, OCD, and healthy controls. Relative to those with OCD and healthy controls, individuals with HD exhibited decreased activation in the anterior cingulate cortex and insula when making decisions regarding items not belonging to them, and increased activation when making decisions about items belonging to them. Activation during decision making regarding personal belongings was associated with severity of hoarding, indecision, and “not just right” feelings. Subsequent research supports findings of abnormalities in brain regions associated with decision making in HD (Tolin, Stevens, Nave, Villavicencio, & Morrison, 2012). A recent comparison of neural activity in HD and OCD supports hoarding as clinically distinct from OCD, given that HD is associated with reduced activity in the middle frontal gyrus, implicated in error monitoring and emotional processing during a response inhibition task, whereas OCD was associated with increased orbitofrontal activation—which is implicated in reward processing—during the same task (Tolin, Witt, & Stevens, 2014). Genetic Vulnerabilities. Hoarding is familial, with one sibling study showing significant similarity within adult sibling pairs with respect to hoarding symptoms (Hasler et al., 2007). Although studies of heritability of HD are limited, heritabilities of hoarding symptoms in 15-year-old twins are different for males versus females. For males, a heritability estimate of 32% was found, with nonshared environment contributing to 64% of risk, and shared environment contributing 4%. Among females a heritability estimate of 2% was found, with shared environmental effects accounting for a moderate percentage (32%) of risk, and nonshared environment accounting for the majority (65%) of risk (Ivanov et al., 2013). In adult twin studies, heritabilities are 42% for hoarding symptoms; for clinically significant hoarding symptoms, they are 36% for within a multisex sample (Taylor, Jang, & Asmundson, 2010), and 50% in a female-only sample (Iervolino et al., 2009). There is a paucity of research on candidate genes for HD. However, findings from a study of pathological hoarding within families with OCD showed an association between OCD and a marker on chromosome 14 in families with two or more relatives with hoarding symptoms (Samuels et al., 2007). Obsessive compulsive disorder with hoarding symptoms was associated with higher rates of the L/L genotype of the COMT Val158Met polymorphism relative to OCD without

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hoarding in an Afrikaner subgroup (Lochner, Kinnear, et al., 2005). Additionally, researchers have noted the sharing of hoarding phenotype alleles: 4q34–34, 5q35.2–35.3, and 17q25 among sibling pairs in which both siblings had Tourette’s disorder (Zhang et al., 2002). Environmental Risk Factors. There are limited data on the environmental risk factors and Gene × Environment interactions in HD (Mataix-Cols et al., 2010). However, stressful and traumatic life events are implicated in onset and exacerbation of hoarding symptoms (Cromer, Schmidt, & Murphy, 2007; Landau et al., 2011).

Body Dysmorphic Disorder Neuroimaging studies have identified characteristic deficits in visual and emotional processing and connectivity in adults with BDD. However, genetic, environmental, and gene by environment risk factors are less well understood. Neurobiology. Imaging studies indicate that compared with controls, individuals with BDD show (a) more detailed encoding relative to holistic analysis (i.e., left prefrontal and temporal cortices) when processing others’ faces (Feusner, Townsend, Bystritsky, & Bookheimer, 2007); (b) deficits in inhibitory control, flexible responding (orbitofrontal cortex), and visual processing (i.e., visual cortex) during own-face processing (Feusner, Bystritsky, Hellemann, & Bookheimer, 2010); and (c) deficits in encoding of places and scenes (parahippocampal cortex), and directed and sustained attention (posterior cingulate and precuneus) during processing of images of non-face images (i.e., houses; Feusner, Hembacher, Moller, & Moody, 2011). Additionally, increased BDD symptom severity is associated with deficits in cognitive flexibility, response inhibition, visual processing (Feusner et al., 2010), motor perception, analysis of shapes and three-dimensional figures within objects, and perception and discrimination of faces, forms, colors, and spatial cues (Feusner et al., 2011). Additionally, fiber disorganization in white matter tracts connecting visual with emotion/memory processing systems have been observed in individuals with BDD (Buchanan et al., 2013; Feusner et al., 2013). More specifically in their BDD sample, Feusner et al. (2013) found poor insight negatively correlated with fractional and linear anisotropy and positively correlated with mean diffusivity in the inferior longitudinal fasciculus and the forceps major. Findings from the first brain network analysis of BDD suggest deficits in whole brain network structure, the extent of which is associated with symptom severity. Abnormalities in connectivity between brain regions implicated in visual and emotional systems are also observed (Arienzo et al., 2013).

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Findings from morphometric studies are inconsistent, with some showing abnormalities in the size and volume of the orbital frontal cortex and anterior cingulate, as compared to controls (Atmaca et al., 2010; Buchanan et al., 2014; Rauch et al., 2003), and frontostriatal systems (Wei et al., 2013), and other studies showing no differences (Feusner et al., 2009; Madsen et al., 2015). With respect to neurochemistry, there is some evidence of serotonergic dysfunction in BDD—as controlled studies show that selective serotonin reuptake inhibitors are associated with symptom reduction (Phillips & Hollander, 2008; Li, Arienzo, & Feusner, 2013). There is also preliminary evidence of DA system involvement in BDD (Vulink, Planting, Figee, Booij, & Denys, 2016). Genetic Vulnerabilities. Research on the heritability of BDD is highly limited and largely inconclusive. For example, a self-report study of family history found only 6%–7% of BDD patients to report at least one first-degree relative with the disorder (Phillips, Gunderson, Mallya, McElroy, & Carter, 1998; Phillips, Menard, Fay, & Weisberg, 2005). According to one twin study, the heritability of body dysmorphic symptoms is .44 (Monzani, Risjdijk, Anson, et al., 2012). There is a lack of research assessing genetic correlates of BDD, with pilot research providing preliminary evidence for genes implicated in serotonergic and GABA-ergic system function (Phillips et al., 2015). Environmental Risk Factors. Childhood abuse and neglect are associated with BDD (Didie et al., 2006; Phillips et al., 2010). For example, participants with BDD report higher rates of emotional and sexual abuse than those with OCD (Neziroglu, Khemlani-Patel, & Yaryura-Tobias, 2006). Additionally, appearancerelated teasing may increase risk of BDD (Buhlmann, Cook, Fama, & Wilhelm, 2007). Gene × Environment Interactions. Research on gene-environment interactions in BDD is lacking. However, preliminary studies suggest possible gene-environment effects on body dissatisfaction. In a study of the effects of parental divorce on disordered eating behavior (binge eating, weight preoccupation, and body dissatisfaction) among twins, divorce moderated the heritability of body dissatisfaction, such that higher heritabilities were observed among twins with divorced parents relative to those from intact families (Suisman, Alexandra Burt, McGue, Iacono, & Klump, 2011). However, in an extension of this work with an expanded measure of body dissatisfaction, no differences in heritabilities of body dissatisfaction between twins from divorced and intact families were observed. (O’Connor, Klump, VanHuysse, McGue, & Iacono, 2015). Rather, nonshared environmental effects on body dissatisfaction were higher in twins from divorced families relative to those from intact families.

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Hair-Pulling Disorder and Skin-Picking Disorder Research on the neurobiology of HPD is limited, with even fewer studies devoted to understanding the neural correlates of SPD. Although the OCRDs share a collective genetic vulnerability, evidence suggests a second shared liability factor for HPD and SPD (Monzani et al., 2014). Significant gene by environment risk interactions have yet to be identified for these disorders. Neurobiology. Imaging research with pediatric samples is limited to a pilot fMRI study assessing brain activation among 9- to 17-year-old children with HPD during completion of two versions (visual, visual plus tactile) of a laboratory task designed to evoke hair pulling urges. Relative to controls, increased activation was found in brain regions associated with visual processing of emotional stimuli (temporal cortex), processing emotional stimuli and reward (dorsal posterior cingulate gyrus), reward/habit learning (putamen), and self-processing, episodic memory retrieval, and visuo-spatial imagery (precuneus) (Lee et al., 2010). Among adults findings have been mixed. Some studies show abnormalities in brain regions implicated in reward learning (striatum [Chamberlain et al., 2008]; putamen [O’Sullivan et al., 1997]); response inhibition, motor control, and error monitoring (anterior cingulate cortex [Chamberlain et al., 2010; Grant, Odlaug, Hampshire, Schreiber, & Chamberlain, 2013]; cerebellum [Keuthen et al., 2007]); memory and emotion processing (amygdalo-hippocampal formation [Chamberlain et al., 2008]); emotion regulation (temporal cortex; bilateral cortical regions [Chamberlain et al., 2008; Chamberlain et al., 2010]); and self-monitoring and awareness (precuneus, temporal lobe [Odlaug, Chamberlain, Derbyshire, Leppink, & Grant, 2014]). Other studies show no differences in patterns of regional brain activation between HPD participants and healthy controls (Rauch et al., 2007; Roos, Fouche, Stein, & Lochner, 2013, even though correlations between mean diffusivity within white matter tracts of the fronto-thalamic pathway and longer HPD duration/increased hair pulling severity are noted (Stein, Coetzer, Lee, Davids, & Bouwer, 1997). In a recent volumetric comparison among those with HPD, SPD, and healthy controls, HPD was associated with reduced cortical thickness of the right parahippocampal gyrus relative to SPD and healthy controls, possibly related to dissociative symptoms among those with HPD. Skin picking disorder was associated with greater bilateral ventral striatum volumes, decreased right frontal hemisphere thickness, and increased bilateral cuneus thickness relative to HPD and healthy controls, suggesting involvement of the mesolimbic reward system in SPD (Roos, Grant, Fouche, Stein, & Lochner, 2015). Also, SPD was associated with abnormalities in anterior cingulate cortices, implicated in response inhibition (Grant et al., 2013). In the first functional imaging study of SPD, underactivation in the bilateral dorsal

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striatum, bilateral anterior cingulate, and right medial frontal regions was observed during an executive functioning task (Odlaug, Hampshire, Chamberlain, & Grant, 2016). This pattern suggests deficits in habit formation, self-monitoring, and response inhibition regions. Deficits in neurotransmission have also been implicated in HPD, with support for the role of the serotonergic and dopaminergic systems based on animal research and medication trials among humans (Woods & Houghton, 2014). Positive symptom response to N-acetylcysteine, a glutamate modulator, is also observed among adults (Grant, Odlaug, & Kim, 2009), suggesting deficits in the glutamatergic system (Woods & Houghton, 2014). However, no benefits were found in children (Bloch, Panza, Grant, Pittenger, & Leckman, 2013; Grant, Odlaug, Chamberlain, Keuthen, et al., 2012). Genetic Vulnerabilities. Results of a family study of individuals with HPD and clinically significant hair pulling and their relatives showed that hair pulling/HPD is familial, although a familial link between HPD/clinically significant hair pulling and skin picking among relatives was not observed (Keuthen, Altenburger, & Pauls, 2014). Findings from a recent twin study indicate that HPD is 76% heritable (Novak, Keuthen, Stewart, & Pauls, 2009). Heritability estimates are lower—about .40—for clinically significant skin picking (Monzani, Rijsdijk, Cherkas, et al., 2012). Research on molecular genetic correlates of HPD is limited. However, there is preliminary evidence for involvement of the 5-HT receptor 2A (Hemmings et al., 2006). In addition, in mouse models of grooming disorders, mice with genetic deletion of the Sapap3 gene, which is involved in glutamatergic synaptic efficiency, engage in repetitive grooming (Welch et al., 2007). Among humans, at least one study supports the role of Sapap3 in HPD (Züchner et al., 2009), with another showing a nonsignificant association after controlling for multiple testing (Boardman et al., 2011). Sapap3 may also be implicated in SPD. In a study of families who were phenotyped for OCD, 4 of 6 Sapap3 variants were associated with skin picking but not OCD symptoms (Bienvenu et al., 2009). SLITRK1 and HOXB8 are also candidate genes based on research conducted among humans with HPD, and animal models, respectively (Greer & Capecchi, 2002; Zuchner et al., 2006). Environmental Risk Factors. An understimulating environment, severe activity restriction, and boredom are associated with HPD and SPD (Gupta, Gupta, & Haberman, 1986; Teng, Woods, Marcks, & Twohig, 2004; Snorrason et al., 2012). Additionally, histories of trauma and stress are associated with both disorders (Snorrason et al., 2012). Research is needed to elucidate the role of gene-environment interactions in HPD and SPD. A recent investigation of heritabilities of all OCRDs yielded estimates of .48 (OCD), .32 (HPD), .47 (SPD), .51 (HD), and .43 (BDD). Heritable vulnerability to

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OCRDs was best explained by a two-factor liability model. All disorders loaded on the first factor with nonshared genetic vulnerability being strongest for OCD, HD, and BDD, and weaker for HPD and SPD. Hair pulling disorder and SPD loaded independently onto a second factor, suggesting that heritable mechanisms could be part specific to these disorders (Monzani et al., 2014).

NEUROPSYCHOLOGICAL FUNCTIONING While several neurocognitive correlates of pediatric OCD have been identified, questions remain regarding their clinical significance and specificity (Abramovitch et al., 2015). Pediatric research for the remaining OCRDs is very limited, with deficits in response inhibition most commonly identified in adult samples.

Obsessive Compulsive Disorder Findings regarding neuropsychological function among youth with OCD are mixed, with deficits sometimes but not always found in executive functioning, planning, cognitive flexibility, response inhibition, and verbal and nonverbal memory. The types of deficits exhibited by youth with OCD are similar to those found among adults. However, the pediatric literature shows greater between-study inconsistencies relative to research with adult populations—possibly due to maturational neural changes that occur across development, along with the heterogeneous age ranges of youth enrolled in studies (Abramovitch et al., 2012; Boileau, 2011). A recent meta-analysis of neuropsychological findings in pediatric OCD identified nine subdomains of deficits, including planning, response inhibition/interference control, set shifting/cognitive flexibility, verbal memory, nonverbal memory, processing speed, working memory, visuospatial functions, and attention. Results showed small but nonsignificant effect sizes (ranging from -0.04 to 0.28) for all subdomains, suggesting neuropsychological deficits shown by children with OCD may not actually be clinically meaningful (Abramovitch et al., 2015)—or that domain-specific deficits are not as important as cross-domain problems.

Hoarding Disorder Research on neuropsychological deficits among children with HD is highly limited. Findings from a retrospective chart review showed patients with learning disabilities and hoarding symptoms to have slower verbal learning relative to those without hoarding (Testa, Pantelis, & Fontenelle, 2011). Furthermore, in a study of youth with OCD, those who displayed both hoarding and symmetry/ordering symptoms showed more cognitive impairment relative to other symptom dimensions (McGuire et al., 2014). Among adults, findings are also mixed, with previous

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research limited by inclusion of mixed hoarding and OCD samples (Mataix-Cols, Pertusa, & Snowdon, 2011). However, research using compulsive hoarding or HD samples reveals deficits in sustained attention (Grisham, Brown, Savage, Steketee, & Barlow, 2007; Tolin, Villavicencio, Umbach, & Kurtz, 2011), memory (Hartl et al., 2004; Tolin et al., 2011), planning/problem-solving (Grisham, Norberg, Williams, Certoma, & Kadib, 2010), organizational strategies (Hartl et al., 2004), decision making (Grisham & Norberg, 2010), categorization of personal (Wincze, Steketee, Frost, 2007) and nonpersonal items (Luchian, McNally, & Hooley, 2007) relative to controls. Additionally, there is some evidence to support deficits in response initiation and spatial ability (Grisham et al., 2007).

Body Dysmorphic Disorder There is a lack of research on neuropsychological deficits among youth with BDD and only a handful of studies with adults. Hanes (1998) found individuals with OCD and BDD to perform equally poorly on the New Tower of London task suggesting impairments in executive function. Both BDD and OCD groups performed similarly to healthy controls but better than a schizophrenia group on measures of memory, language, and visuospatial construction. Conversely in their study of BDD adults, Deckersbach et al. (2000) identified significant deficits in verbal and visuospatial memory, using the California Verbal Learning Test and Rey-Osterreith Complex Figure task, respectively. More recently, Toh, Castle, & Rossell (2015) found matched adult BDD and OCD groups to demonstrate poor overall neuropsychological performance on the Repeatable Battery for the Assessment of Neuropsychological Status as compared to matched controls. Performance was negatively correlated with illness severity for both groups. In addition, the BDD group performed about a half standard deviation worse than the OCD group on the Immediate Memory and Attention tasks.

Hair-Pulling Disorder Neuropsychological findings in pediatric HPD are limited, with one recent study showing that after controlling for age and attention deficits, youth with HPD perform well in terms of inhibitory control, with no associations between performance and symptoms (Brennan, Francazio, Gunstad, & Flessner, 2016). Among adults, some studies show deficits in response inhibition relative to controls (Chamberlain, Fineberg, Blackwell, Robbins, & Sahakian, 2006; Odlaug, Chamberlain, Harvanko, & Grant, 2012; Odlaug et al., 2014), whereas others find no such deficits (Bohne, Savage, Deckersbach, Keuthen, & Wilhelm, 2008; Grant, Odlaug, & Chamberlain, 2011). Several studies have also investigated cognitive flexibility in HPD, with most showing null findings (Chamberlain et al., 2006; Grant et al., 2011). However, there is preliminary evidence for deficits in cognitive flexibility among adults with childhood-onset picking (Odlaug et al., 2012). Additional cognitive

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deficits have been observed in visuospatial learning, spatial working memory, and divided attention (Chamberlain et al., 2009; Snorrason, Belleau, & Woods, 2012). Also, an examination of attentional bias in HPD revealed attentional avoidance of hair-related images relative to neutral ones when images were shown at longer stimulus durations (Lee, Franklin, Turkel, Goetz, & Woods, 2012).

Skin-Picking Disorder At present, studies of youth with SPD are lacking. However, a recent comparison of cognitive impairment in early- and late-onset SPD revealed poorer inhibition relative to controls in both groups, but poorer set shifting/cognitive flexibility in the later-onset group only (Grant, Odlaug, & Chamberlain, 2012). Research on neurocognitive impairment in SPD is also limited, with mixed findings (Snorrason et al., 2012). Some studies show deficits in inhibitory control relative to controls (Grant et al., 2011; Odlaug, Chamberlain, & Grant, 2010). These deficits are not found in motor or decision-making impulsivity but rather in emotional impulsivity (Snorrason, Smári, & Ólafsson, 2011). As is the case for HPD, most studies assessing cognitive flexibility have found it to be intact (Grant et al., 2011; Odlaug et al., 2010). There is preliminary evidence for slower reaction times and greater avoidance to skin picking-related images in SPD relative to controls, possibly indicative of implicit affective distraction, with higher initial distraction predicting greater improvements in symptoms following treatment but worse symptoms for those not treated (Schuck, Keijsers, & Rinck, 2012).

RESEARCH DOMAIN CRITERIA The Research Domain Criteria (RDoC) refers to an NIMH research classification system developed to better characterize dimensional constructs or symptoms that cut across psychiatric disorders, at multiple levels of analysis spanning genes to behavior (Chapter 2 [Beauchaine & Klein]). Ultimately, this may provide an important alternative to our current mental health diagnostic systems such the DSM, which group behavioral and cognitive symptoms into psychiatric categories or diagnoses that do not map on well to biological or behavioral measures (Cuthbert & Insel, 2013). One assumption of RDoC is that advanced understanding of pathophysiologies of psychiatric disorders, including OCRDs, will spur development of more effective treatments that target underlying disease mechanisms directly. Dimensional constructs are listed under one of five domains, including negative valence systems, positive valence systems, cognitive systems, systems for social processes, and arousal/regulatory systems. Negative valence systems refer to reactions (e.g., anxiety, fear, and loss) to unpleasant environmental contexts. Within this dimension, sustained threat, which refers to attentional bias to threat, is implicated in OCD. Studies assessing attentional bias in pediatric OCD are limited, but there is preliminary support for attentional bias modification interventions for pediatric

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OCD (Najmi & Amir, 2010). The acute threat subdimension is likely also relevant to pediatric OCD, as research shows reductions in amygdalar volumes in pediatric OCD following treatment with serotonin reuptake inhibitors (Szeszko et al., 2004). The amygdala and its interconnections are important in fear conditioning. Additionally, children with OCD show deficits in extinction of a fear-conditioned skin conductance response (McGuire et al., 2016), providing support for the role of acute threat in childhood OCD. Positive valence systems are also implicated in OCRDs. With regard to initial responsiveness to award attainment, deficits in reward processing circuity, measured using both the gain and loss anticipation phases of a monetary incentive delay task, are found in both OCD (Jung et al., 2013) and HPD (White et al., 2013). In the former, OCD individuals evidenced significantly increased connectivity of the nucleus accumbens with the posterior insula and occipital regions compared to controls. During loss anticipation, the OCD group demonstrated a connectivity pattern involving the striatum, thalamus, and cerebellum that was largely nonoverlapping with that of the controls (Jung et al., 2013). Voon et al. (2015) examined the influence of motivation on reward and loss outcomes in matched OCD and healthy adults using a novel two-step sequential decision task. The OCD group was less goal oriented (model-based) and more habitual (model-free) to reward outcomes and displayed the opposite pattern to loss outcomes. In addition, compulsion severity was correlated with habitual learning in the reward condition, while obsession severity was correlated with greater switching after loss outcomes consistent with avoidant responding to aversive stimuli. This subdimension has also been linked conceptually to SPD but experimental studies are lacking. Findings on reward learning are mixed in HD (Slyne & Tolin, 2014). However, Fullana et al. (2004) found high scores on the hoarding dimension of the Yale-Brown Obsessive Compulsive Scale to positively correlate with sensitivity to punishment and negatively correlate with novelty seeking. Research examining the role of the positive valence system in BDD is lacking. However, the subdimension of habit learning has clear conceptual relations to the OCRDs. The greatest evidence exists for the relation of habit learning to OCD through findings of basal ganglia dysfunction in both youth and adults with OCD (Graybiel, 2008) and behavioral deficits in habit learning tasks among adults with OCD (Gillan et al., 2011). At present, more research is needed to investigate the role of habit learning, related neural circuity, and genetic correlates of OCD and other OCRDs among youth. Currently, the greatest support exists for an association between OCRDs and the cognitive systems domain of RDoC. In fact, pediatric OCD is associated with deficits in most subdimensions within this domain (Abramovitch et al. 2015). However, research on pediatric HD is lacking, with only preliminary evidence for deficits in language (i.e., verbal learning). Research with adults suggests greater involvement of the cognitive system, as in OCD. In BDD, there is most evidence for deficits in visual perception, with preliminary evidence suggesting deficits in declarative memory,

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and cognitive control, including response inhibition. However, this evidence is based on studies of adult samples (Fang & Wilhelm, 2015). In HPD and SPD, findings are mixed with respect to cognitive control (i.e., response inhibition, and updating/cognitive flexibility), with the only study among youth with HPD showing enhanced response inhibition after covarying age and attention (Brennan et al., 2016). Arousal/regulatory systems are also implicated in OCRDs. Specifically, a recent meta-analysis supported the role of arousal, circadian rhythms, and sleep and wakefulness in OCD. However, this meta-analysis excluded studies with children because of the influence of developmental changes on sleep (Nota, Sharkey, & Coles, 2015). Additionally, a limited number of studies provide preliminary support for the role of sleep and wakefulness in HPD, SPD, and HD among adults.

SUMMARY AND CONCLUSIONS Although the five disorders making up the obsessive-compulsive and related disorders (OCRDs) classification have only been formally grouped since the advent of DSM-5 in 2013, they have been linked by their similar phenomenology and comorbidity for considerably longer. Spurred in part by the DSM-5 development process, a concerted research effort has led to significant gains in our knowledge of the shared pathophysiology underlying the OCRDs and provided additional support for the concept of an OCD spectrum. However, although datasets are nearing the size necessary to greatly enhance our understanding of the genetic architecture for OCD, large-scale, prospective, population-based research is needed to clarify the shared and unique environmental risks underlying this and the other OCRDs as well as inform future GxE studies. Prospective longitudinal neuroimaging and neurocognitive studies are also needed to identify the early diatheses and developmental pathways of these disorders. Finally, the observable nature of the behaviors characterizing OCD, HD, BDD, HPD, and SPD phenomenology make them ideally suited for RDoC-based research and hopefully lead to a better understanding of the full range of human behavior.

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C H A P T E R 18

Depressive Disorders DANIEL N. KLEIN, BRANDON L. GOLDSTEIN, AND MEGAN FINSAAS

INTRODUCTION

D

epressive disorders in youth are a significant social and public health problem. Depressed children and adolescents often exhibit significant impairment in family, school, and peer functioning, and this impairment may persist after recovery from the depressive episode. Depressed adolescents are also at risk for school dropout and unplanned pregnancy. Moreover, depression is the leading risk factor for youth suicide; it is also a risk factor for other psychiatric and general medical conditions (Gibb, 2014; Rudolph & Flynn, 2014). Depression is a complex phenomenon, for a number of reasons. First, the term refers to (a) a mood state; (b) a clinical syndrome that can be caused by a variety of nonpsychiatric factors such as neuroendocrine disorders and psychoactive substances; and (c) a psychiatric disorder. In addition, it may be difficult to distinguish from normative reactions to major stressors, such as bereavement (Wakefield, 2013). We will emphasize depressive disorders, particularly major depressive disorder (MDD), but we also consider dimensional measures of depressive symptoms. Second, many of the processes responsible for the pathogenesis of MDD remain unknown, although we discuss a number of biological and psychological vulnerabilities and environmental risk factors that may contribute to etiology, at least for some individuals. It is likely that the depressive disorders are multifactorial conditions—caused by combinations of many etiological factors. Moreover, depressive disorders are probably etiologically heterogeneous, meaning there are different forms of depression caused by different sets of etiological processes. As a result, depressive disorders are characterized by both equifinality and multifinality. Consistent with the idea of etiological heterogeneity, depression exhibits equifinality in that a variety of different developmental pathways may lead to the same clinical syndrome. Depression is also characterized by multifinality in that it is unlikely that

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Depressive Disorders 611 any set of etiological factors is specific to depression. Rather, the same factor may contribute to a variety of outcomes depending on other moderating or mediating variables (e.g., other vulnerabilities as well as risk and protective factors).

HISTORICAL CONTEXT Recognition of child and adolescent depressive disorders did not emerge until the late 1970s. Before then, childhood depression was thought to be rare because children had not yet developed the cognitive capacity to experience symptoms such as guilt and hopelessness. In reaction, many clinicians came to believe that to the extent that children did experience depression, it was expressed in behavioral disturbances such as behavior problems, enuresis, and somatic concerns (i.e., “masked depression”). However, in the late 1970s, Puig-Antich, Blau, Marx, Greenhill, and Chambers (1978), Carlson and Cantwell (1980), and several other groups demonstrated that children and adolescents can and do meet full adult criteria for MDD. More recently, Luby and colleagues have shown that with some modifications in the criteria, even preschoolers can be diagnosed with MDD (Luby et al., 2002).

Diagnostic Issues and DSM-5 Criteria The 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) defines MDD in children and adolescents as a period of persisting depressed or irritable mood or loss of interest or pleasure that lasts at least 2 weeks and is accompanied by a variety of other symptoms, including low energy and fatigue; inappropriate feelings of guilt or worthlessness; difficulty thinking, concentrating, or making decisions; sleep disturbance (insomnia or hypersomia); appetite disturbance (eating too little or too much or significant weight loss or gain); psychomotor disturbance (either retardation [extreme slowing in movement and speech], or agitation [extreme restlessness]); and thoughts of death or suicidal thoughts or behavior. Persistent Depressive Disorder (PDD) is a similar, but more chronic condition, characterized by a period of depressed or irritable mood that is present for at least half the time for at least 1 year and is accompanied by several other depressive symptoms. PDD subsumes the category of Dysthymic Disorder (DD) in previous DSMs, but includes other forms of persistent depression, such as chronic MDD and MDD superimposed on dysthymia. There are a number of questions related to the diagnosis and classification of depression in children and adolescents. These include whether depression manifests differently at different ages; the degree of continuity between child, adolescent, and adult depression; the location and nature of the boundary between depression and normal variations in mood; and identification of more homogeneous subtypes. We discuss each of these in turn below.

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Age-Specific Manifestations. Symptoms of MDD are fairly similar in school-aged children, adolescents, and adults, although hypersomnia, melancholic1 and psychotic symptoms, and suicide attempts are somewhat more frequent in adolescents than in children (Rao & Chen, 2009). In addition, manifestations of particular symptoms vary as a function of children’s levels of cognitive and social development. For example, very young children have shorter episodes and prepubertal children may lose interest in play but, for obvious reasons, do not experience decreased libido (Luby et al., 2002; Weiss & Garber, 2003). There have also been several studies exploring whether there are developmental differences in the structure of the depressive syndrome, but findings are inconsistent (Weiss & Garber, 2003). Homotypic Continuity. Two forms of continuity can be distinguished. Homotypic continuity refers to a disorder that has similar clinical manifestations across development; heterotypic continuity refers to situations in which a disorder can be expressed differently at different points in development, yet with stability of the underlying construct. Most studies have reported significant homotypic continuity between adolescent and adult depression, but there is less evidence for homotypic continuity of prepubertal with adolescent and adult depression. Three relevant lines of research include studies of clinical presentation, longitudinal course, and risk factors such as familial aggregation. As summarized above, depressed children, adolescents, and adults tend to exhibit similar symptoms despite some developmental variations. Most follow-up studies indicate that adolescents with MDD are at elevated risk for major depressive episodes as adults (e.g., Lewinsohn, Rohde, Klein, & Seeley, 1999; Weissman, Wolk, Goldstein et al., 1999). In a notable exception, Copeland, Shanahan, Costello, and Angold (2009) reported that the association between depression in adolescence and young adulthood disappeared after adjusting for comorbid anxiety and externalizing disorders in adolescence. However, they combined all depressive disorders, including depressive disorder not otherwise specified (D-NOS), which is likely to be a particularly unstable diagnosis. It is also important to note, as Miller and Chapman (2001) articulated, that statistically partialing the effects of anxiety from depression makes little sense if both disorders are highly associated and share a common etiology (Cummings, Caporino, & Kendall, 2014). In follow-up studies of prepubertal children results have been inconsistent. Some studies have found that depressed children are at increased risk for depression in adolescence and/or adulthood (Geller, Zimmerman, Williams, Bolhofner, & Craney, 2001; Luby, Gaffrey, Tillman, April, & Belden, 2014), but many have not found evidence of greater risk (Copeland et al., 2009; Harrington, Fudge, 1. Melancholia is a subtype of MDD that features severe anhedonia, lack of mood reactivity to events in the environment, psychomotor disturbance (i.e., retardation or agitation), severe loss of appetite or weight, excessive guilt, and diurnal variation in which mood is worst in the morning.

Depressive Disorders 613 Rutter, Pickles, & Hill, 1990; Weissman, Wolk, Wickramaratne et al., 1999). Instead, childhood depression appears to be associated with a range of subsequent disorders. Finally, evidence exists that many of the risk factors playing a role in the onset of adult depression also predict the onset of depression in children and adolescents (Bufferd et al., 2014; Shanahan, Copeland, Costello, & Angold, 2011). Two particularly well-studied risk factors are family history of depression and gender. Children and adolescents of depressed parents have a significantly greater risk of depression than offspring of nondepressed parents (Gotlib & Colich, 2014; Weissman et al., 2006). In addition, there are significantly higher rates of MDD in the first-degree relatives of depressed adolescents than healthy adolescents or those with other psychiatric disorders (e.g., Klein, Lewinsohn, Seeley, & Rohde, 2001). Family studies have also found higher rates of MDD in the relatives of depressed compared to healthy children, although less evidence exists for differences from relatives of children with other psychiatric disorders (e.g., Kovacs, Devlin, Pollock, Richards, & Mukerji, 1997). As discussed below, there are significant differences in rates of depression between males and females, and these differences vary as a function of age. Thus, females and males exhibit similar rates of depression in childhood, but the risk of depression becomes approximately twice as high in females compared to males beginning in adolescence (Hyde, Mezulis, & Abramson, 2008; Hilt & Nolen-Hoeksema, 2014). Thus, there appears to be strong evidence for continuity between adolescent and adult depression. However, some forms of childhood-onset depression may be continuous with adolescent and adult depression, but other forms of childhood depression could represent a different kind of psychopathology. Boundaries. Some investigators believe that boundaries established by the DSM-5 definition of depression are too broad and thereby include many individuals with demoralization or transient and normative responses to stress (e.g., Wakefield, 2013). Given the major developmental transitions—and emotional intensity—that characterize adolescence, it may be particularly difficult to distinguish MDD from normal variations in mood during this period. Conversely, current boundaries for depressive disorders could be too strict, as subthreshold depressive symptoms are common in youth yet are still a strong predictor of later MDD (Kovacs & Lopez-Duran, 2010). For example, Klein, Shankman, Lewinsohn, and Seeley (2009) found that approximately two thirds of adolescents with subthreshold depressive symptoms developed MDD by age 30, with the highest risk among those with chronic or recurrent subthreshold symptoms. A related question is whether the boundary between MDD and normal variations in mood is discrete or continuous. Research on subthreshold depression is consistent with the existence of a continuum, although these investigations cannot rule out the possibility of a qualitative distinction, with the boundary set at a lower

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level of severity than is present in DSM-5. More formal tests of discreteness have been conducted using taxometric procedures. Indeed, some studies have reported evidence for a qualitative distinction (e.g., Solomon, Ruscio, Seeley, & Lewinsohn, 2006), whereas others have supported a continuum (e.g., Hankin, Fraley, Lahey, & Waldman, 2005). Subtypes. Because depression is so heterogeneous, considerable effort has been devoted to delineate more homogeneous subtypes of mood disorders in adults. Subtypes have been proposed on the basis of differential symptom presentation (e.g., psychotic, melancholic, atypical) and developmental course (e.g., age of onset, recurrence, persistence, and seasonal pattern). However, subtyping has largely been ignored in child and adolescent depression. Despite some intriguing findings for melancholia (e.g., Luby, Mrakotosky, & Heffelfinger, 2004), the validity of distinct subtypes of depression in children and adolescents is largely unexplored.

PREVALENCE Studies of representative community samples indicate that depression is rare in early childhood, increases somewhat in middle/late childhood, and rises sharply in adolescence. The 3-month prevalence of depression in preschoolers is approximately 1% to 2% (Bufferd, Dougherty, Carlson, & Klein, 2011; Egger & Angold, 2006). In a meta-analysis of 26 studies, Costello, Erkani, and Angold (2006) found that the point prevalence of MDD was 2.8% in school-age children and 5.7% in adolescents. Consistent with these estimates, in a nationally representative sample, Merikangas et al. (2010) reported that the 12-month prevalence of MDD and DD disorder was 2.5% in children ages 8–11 and 4.8% in adolescents ages 12–15. By mid- to late adolescence, the lifetime prevalence of depression is very similar to rates in adults. For example, in a large representative sample of 13–18 year-olds, the 12-month prevalence rates for MDD and DD were 7.5% and 1.3%, respectively (Avenevoli, Swendsen, He, Burstein, & Merikangas, 2015).

DEVELOPMENTAL PROGRESSION AND COMORBIDITY The average age of onset of MDD in community samples of children and adolescents is about 14 years (Ormel et al., 2015). Almost all youth with an episode of MDD recover from the episode, although many continue to experience residual symptoms and length of episodes varies widely. In clinical samples, the mean duration of MDD episodes is approximately 7–8 months, and DD persists for an average of 48 months (Birmaher, Arbelaez, & Brent, 2002; Kovacs, 1996). However, depressive episodes are much shorter in community samples (Rohde, Lewinsohn, Klein, Seeley, & Gau, 2013). Most youth with DD experience superimposed episodes

Depressive Disorders 615 of MDD, a phenomenon referred to as “double depression,” now diagnosed as PDD in DSM-5. A number of naturalistic follow-up studies have reported high rates of relapse and recurrence, with a substantial proportion of depressed juveniles experiencing another episode within several years (Birmaher et al., 2002; Kovacs, 1996; Rohde et al., 2013). As noted earlier, adolescents, but not necessarily children, with MDD have an increased risk of experiencing a recurrence in adulthood. A number of predictors of the duration and recurrence of MDD episodes in children and adolescents have been identified. Variables associated with a longer time to recovery include earlier age of onset, greater severity, suicidality, double depression, comorbid anxiety disorders or disruptive behavior disorders, depressotypic cognitions, and adverse family environments (Birmaher et al., 2002). Predictors of recurrence include early onset, severity, psychotic symptoms, suicidality, previous MDD episodes, double depression, residual symptoms after recovery, a family history of MDD (particularly if recurrent), an adverse family environment or maltreatment, temperamental low positive emotionality (or low extraversion), trait anxiety, depressotypic cognitions, early pubertal development, poor academic functioning, and recent stressors (Birmaher et al., 2002; Lewinsohn, Rohde, Seeley, Klein, & Gotlib, 2000; Wilson, Hicks, Foster, McGue, & Iacono, 2014). Children and adolescents with MDD and DD are also at risk for developing manic and hypomanic episodes (Geller et al., 2001; Kovacs, 1996). The probability of “switching” to bipolar disorder is higher in patients with psychotic symptoms, psychomotor retardation, a family history of bipolar disorder, and/or a high familial loading for mood disorders (Geller, Fox, & Clark, 1994). Genetic risk for bipolar and nonbipolar depressive disorders is only partially overlapping (see Chapter 21 [Blader, Roybal, Sauder, & Carlson]). Thus, switching presumably occurs among youth with bipolar disorder whose first presentation of mood disturbance is depression. Factor analytic studies of the structure of depression in youth indicate that there is considerable covariance among depressive and anxiety symptoms, as well as between internalizing and externalizing symptoms (e.g. Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011; Olino, Dougherty, Bufferd, Carlson, & Klein, 2014). Indeed, the majority of children and adolescents with MDD or DD also meet criteria for other psychiatric disorders. In a meta-analysis of studies using community samples, Angold, Costello, and Erkanli (1999) reported that depressed children and adolescents were 8.2 times more likely than nondepressed youths to have an anxiety disorder, 6.6 times more likely to have conduct disorder (CD), and 5.5 times more likely to have attention-deficit/hyperactivity disorder. Juvenile depression also frequently co-occurs with oppositional-defiant, substance use, eating, and developmental disorders (Angold et al., 1999). As is universally the case, comorbidity is even greater in clinical samples (Kovacs, 1996).

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There are a number of potential explanations for the high rates of comorbidity (Klein & Riso, 1993). For example, each disorder may predispose to development of the other. Cummings et al. (2014) hypothesize that the interpersonal impairment associated with early social and separation anxiety can lead to depression, and depression can interfere with the development of social skills, prompting social anxiety. Consistent with this perspective, anxiety disorders in children and adolescents predict subsequent depressive disorders, and depression in youth also predicts subsequent anxiety (e.g., Copeland et al., 2013). Alternatively, comorbidity may be due to shared etiological factors. Clark and Watson’s (1991) tripartite model proposes that the temperament trait of negative emotionality (NE) predisposes to both depression and anxiety, increasing the chances that the two disorders will co-occur. Cross-sectional and longitudinal studies show that NE is associated with both depressive and anxious symptoms and disorders in youth (Klein, Dyson, Kujawa, & Kotov, 2012). Other shared etiological influences may also play a role. For example, depressive and anxiety disorders have overlapping genetic influences (Lahey et al., 2011), and these shared genes may be responsible for least part of the association of NE with both disorders (Rhee, Lahey, & Waldman, 2015). Shared etiology may be a particularly good explanation of the comorbidity between depression and generalized anxiety disorder (GAD), as GAD has the greatest genetic overlap with depression and the strongest association with NE of the anxiety disorders (Cummings et al., 2014). The concept of heterotypic continuity assumes that different phenotypes evident at two or more points in time are caused by the same etiological processes, which are expressed differently at different points in development. In practice, however, as the etiologies of the major mental disorders are unknown, the term is used more broadly to refer to having two differing manifestations of an underlying trait or disorder at two different points in time (i.e., sequential comorbidity). Explanations for comorbidity may not be mutually exclusive, as multiple processes are likely to be involved (Cummings et al., 2014). For example, in a longitudinal twin study, Eaves, Silberg, and Erkanli (2003) reported that childhood anxiety influences the development of depression in adolescence through three distinct pathways: one in which the same genes influence early anxiety and later depression (i.e., heterotypic continuity); a second in which the genes that affect early anxiety increase sensitivity to adverse life events, indirectly increasing risk for depression (an example of gene-environment interaction, as discussed later); and a third in which genes that increase risk for early anxiety increase exposure to depressogenic environmental influences (an example of gene-environment correlation, also discussed later). Comorbidity between depression and disruptive behavior disorders is also complex. There is growing evidence of overlapping genetic influences between CD and depression in children (Lahey et al., 2011) and the presence of genetic pathways from parental depression to offspring CD (e.g., Singh et al., 2011). Moreover, at least part of the association between depression and CD is due to shared relations with

Depressive Disorders 617 oppositional defiant disorder (ODD), which is often a precursor of both conditions (e.g., Burke & Loeber, 2010). Importantly, negative affective symptoms in ODD (e.g., anger and resentment; touchiness and irritability) are a much stronger predictor of later depression than the oppositional behavior features per se (e.g., defiant and noncompliant behaviors; argumentativeness with adults) (Stringaris & Goodman, 2009). This view is consistent with the possibility that NE is a final common pathway to depression (Rhee et al., 2015). Thus, research on comorbidity indicates that, in accordance with the concepts of equifinality and multifinality, depression is both predicted by, and predictive of, anxiety and disruptive behavior disorders (Ormel et al., 2015).

SEX DIFFERENCES One of the best-established findings in the depression literature is that rates of depressive symptoms and disorders in males and females are similar in childhood or even somewhat greater in males, but begin to increase markedly in females starting at age 12–15 (Hyde et al., 2008; Hilt & Nolen-Hoeksema, 2014). A variety of explanations for the increased rate of depression in adolescent girls have been considered, with most focusing on sex differences in the many biological, psychological, and social changes during this period. There has been some support for the role of hormones and pubertal timing, sometimes in relation to contextual factors. For example, in a longitudinal study of adolescent females, Angold, Costello, Erkanli, and Worthman (1999) found that increases in estrogen and testosterone levels were associated with the onset of depressive disorders. Biological changes at the time of puberty may interact with broader social environmental factors. For example, physical changes associated with puberty may lead to greater dissatisfaction with one’s body among girls than boys, which may predict onset of depression (Stice, Hayward, Cameron, Killen, & Taylor, 2000). More generally, early-maturing girls are at particularly high risk for depression compared to their peers (e.g., Copeland et al., 2010), perhaps because they are faced with expectations, pressures, and reactions from peers that they are not ready to handle. Indeed, recent work suggests that the relationship between early puberty and depression is mediated by peer stress (Conley, Rudolph, & Bryant, 2012). Another set of explanations suggests that increased rates of depression in adolescent females may relate to girls’ experiencing more stress, particularly interpersonal stressors, than boys during the transition to adolescence (e.g., Hankin, Mermelstein, & Roesch, 2007). Moreover, females are more sensitive to effects of stress, experiencing more depression than males at similar levels of stress (Hankin et al., 2007). This last finding suggests that stress may activate preexisting differences in susceptibility. Thus, a third set of explanations posits that females have greater vulnerabilities than males and that these vulnerabilities interact with the stressors and challenges of adolescence to produce higher rates of depression (Hyde et al., 2008). A number of vulnerabilities have been hypothesized. For example, because

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of a combination of biological and socialization processes, girls may have greater affiliative needs than boys, rendering them more vulnerable to interpersonal stressors (Rudolph, 2009). Females may also be more prone to cope with adversity and dysphoric moods by ruminating—a style associated with depression (Hilt & Nolen-Hoeksema, 2014). Indeed, Hankin (2009) found that the interaction between rumination and stress explained much of the sex difference in increasing levels of depressive symptoms in adolescents, such that girls who experienced more stress and were more prone to ruminate exhibited the largest increases in depressive symptoms. There is growing evidence that sex differences in vulnerability to depression may be present at an early age. For example, even in early childhood girls exhibit higher levels of temperamental fearfulness (Else-Quest, Hyde, Goldsmith, & Van Hulle, 2006) and attentional biases toward negative emotional stimuli (Kujawa et al., 2011).

ETIOLOGY Both genetic and environmental influences appear to play important roles in depression in children and adolescents, and research is beginning to elucidate their interplay. Genetic Vulnerabilities. Family studies indicate that there are elevated rates of MDD in the first-degree relatives (parents and siblings) of children and adolescents with MDD (e.g., Klein et al., 2001; Kovacs et al., 1997). Similarly, high-risk studies have documented increased rates of MDD in the offspring of parents with MDD (e.g., Klein, Lewinsohn, Seeley, Rohde, & Olino, 2005; Weissman et al., 2006). However, genetically informative designs (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer) are required to determine whether familial aggregation is due to genetic or environmental factors. Most studies examining genetic and environmental contributions to youth depression have used twins. Twin studies use structural equation modeling to partition variance in twin resemblance into additive genetic factors, shared environmental factors (those aspects of the environment that make twins similar to one another), and unique environmental factors (aspects of the environment that make twins different from one another). Most of these studies have focused on depressive (or depressive and anxiety) symptoms, rather than diagnoses, and results have varied somewhat as a function of the child’s age and sex and the informant (parent or child). Nonetheless, most twin studies indicate that additive genetic factors contribute to youth depression (Frani´c, Middeldorp, Dolan, Ligthart, & Boomsma, 2010). A number of studies have assessed twins repeatedly over development. Findings regarding the relative magnitude of contributions of genes and environment in childhood, adolescence, and adulthood have been inconsistent (Frani´c et al.,

Depressive Disorders 619 2010; Nivard et al., 2015). However, it appears that genetic influences are largely responsible for the stability of depressive symptoms over development (Frani´c et al., 2010; Nivard et al., 2015). In addition, evidence exists for genetic innovation and attenuation. That is, new genetic influences on depression emerge as children grow older, whereas some genetic influences evident in younger children diminish with age (Frani´c et al., 2010; Nivard et al., 2015). These findings also suggest that genotype-phenotype associations may differ as a function of development: similar phenotypes may reflect different genetic and environmental influences at different ages, and the same genetic and environmental factors may be expressed as different phenotypes in different developmental periods (i.e., heterotypic continuity). The genetic structure of internalizing symptoms (i.e., depression and anxiety) also changes over development, with evidence of independent genetic influences on depression and anxiety in childhood, shared genetic influences on depression and some anxiety disorders in adolescence, and shared genetic influences on depression and all anxiety disorders in young adulthood (Waszczuk, Zavos, Gregory, & Eley, 2014). Recent studies using three other types of designs have further highlighted the role of environmental factors in the etiology of youth depression. In an adoption study, Tully, Iacono, and McGue (2008) found that adolescents who were raised by adoptive mothers with MDD had a significantly higher rate of MDD than adolescents who were raised by nondepressed adoptive mothers. In several studies examining the children of identical twins, offspring of depressed twins had higher rates/levels of depression than offspring of nondepressed co-twins, despite similar genetic relationships (e.g., Singh et al., 2011). Finally, Lewis, Rice, Harold, Collishaw, and Thapar (2011) examined the transmission of depressive symptoms in parents who used assisted conception (i.e., in vitro fertilization). They found significant associations between parent and child depressive symptoms, but the magnitude of the correlations was similar for the genetically related and genetically unrelated parent–child pairs. Thus, there is support for the role of both genetic and environmental influence in the development of youth depression, although further work is need to determine why the evidence for genetic factors in recent children of twins and assisted conception designs is weaker than in traditional twin studies. In addition, genetic and environmental influences and their associations with depression and related clinical phenotypes appear to change over the course of development, consistent with both equifinality and multifinality. These studies provide important information on the role of genetic and environmental factors in depression, but they cannot identify the particular genes and/or environmental processes involved. The two major approaches to identifying specific genes are linkage and association studies (see Chapter 3 [Beauchaine et al.]). Linkage studies examine the relationships between genetic markers with known chromosomal locations and the occurrence of disorder within families. “Linkage” between a genetic marker and a disorder suggests that the marker, or a gene that is in close

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proximity to it, contributes to the etiology of the disorder. This approach has been successful in disorders that are caused by a single gene with large effects (e.g., Huntington’s disease), but may be less useful for disorders that are caused by many genes, none of which has major effects. Indeed, genome-wide linkage studies on adult depression have yielded few consistent findings (Flint & Kendler, 2014). Association studies compare the frequencies of single nucleotide polymorphisms (SNPs) between groups of depressed and nondepressed individuals. This approach is more powerful than linkage studies in detecting genes with small effects. Specific SNPs (candidate genes) may be selected due to a hypothesized role in the pathophysiology of the disorder (e.g., genes involved in the regulation of neurotransmitters, such as serotonin [5-HT], brain plasticity and response to stress, such as brain-derived neurotrophic factor [BDNF] and FK506 binding protein 5 [FKBP5], or inflammation, such as Interleukin-1 beta [IL-1β]). However, results of candidate gene studies have proven difficult to replicate, and existing theories of the neurobiology of depression may not consider genes that could prove important in MDD. Hence, the candidate gene approach has been largely replaced by genome-wide association studies (GWAS), in which large numbers of SNPs across the genome are examined without regard to theoretical preconceptions (Flint & Kendler, 2014). To date, GWAS studies of depression have focused on adults, and produced only a small number of findings that collectively account for a very small portion of variance (Flint & Kendler, 2014). Thus, there is a discrepancy between the results of these studies and the heritability estimates derived from twin studies—a problem referred to as “missing heritability.” The reason for this discrepancy is not clear. One possibility is that the genes influencing MDD have very small effects and existing studies have not had adequate power to detect them. This stance is supported by studies using genome-wide complex trait analysis (GCTA), a method that correlates the genetic resemblance among a large sample of individuals estimated through GWAS with the individuals’ resemblance on a phenotype. Using GCTA, Lubke et al. (2012) estimated that gene variants with very small effects could account for most of the heritable variance in MDD. Another possibility is that rather than being caused by many common genetic variants, each with small effects, common disorders may be due to rare genetic mutations, such as copy number variants (CNVs), each of which may have a large effect on a small number of cases (McClellan & King, 2010). This phenomenon would represent an extreme example of equifinality, in that what we regard as a single disorder would actually consist of hundreds, if not thousands, of subgroups, each associated with a different genetic abnormality. If many of these abnormalities influence the same neural system, however, it could help elucidate the pathophysiology of the disorder and identify novel targets (the common pathway) for intervention. Preliminary evidence suggests that recurrent MDD in adults may be associated with a greater burden of deletion CNVs (Rucker et al., 2013). Finally, other possible explanations include gene-environment interactions and epigenetic effects (discussed later).

Depressive Disorders 621 Environmental Risk Factors. Cross-sectional studies indicate that stressful life events are associated with depressive symptoms/disorders in children and adolescents, and longitudinal studies reveal that life stressors predict the onset of depressive episodes and increases in depressive symptoms in youth (Grant et al., 2014). Consistent with diathesis-stress models of psychopathology, youth with preexisting vulnerabilities/risk factors, such as parental MDD (Morris, Ciesla, & Garber, 2010), and cognitive vulnerabilities like negative inferential style and rumination (Cole et al., 2008; Michl, McLaughlin, Shepherd, & Nolen-Hoeksema, 2013), are more likely to experience depressive episodes or increased symptoms following life stressors than less vulnerable youth. In addition, depressed youth may become more sensitized to life stress with a greater number of episodes. Thus, associations between stress and depression appear to be weaker for an initial depressive episode—with higher levels of stress required to elicit it—whereas stress-depression associations are stronger and lower levels of stress are necessary for subsequent episodes (Morris et al., 2010; Stroud, Davila, Hammen, & Vrshek-Schallhorn, 2011). In some cases, depressed individuals contribute to the occurrence of stressors that they experience. The stress generation model (Hammen, 1991) suggests that depression increases the likelihood of self-generated stressful events; in turn, these “dependent” events exacerbate depressive symptoms. Indeed, longitudinal studies indicate that depressive symptoms/disorders in youth are associated with subsequent dependent life events (e.g., Uliaszek et al., 2012). Moreover, Rudolph, Flynn, Abaied, Groot, and Thompson (2009) found that among adolescent girls, depressive symptoms predicted subsequent dependent life events, which then predicted increased depression. These findings could be due to gene-environment correlations (discussed later), given evidence that a genetic predisposition for depression predicts a greater number of dependent negative life events (Silberg et al., 1999). A recent study also indicates that stress generation effects are stronger for adolescents than adults, indicating that the link between stress and depression may change over development (Morris, Kouros, Hellman, Rao, & Garber, 2014). Beyond stressful life events, a number of other environmental factors have been implicated in depression in youth, although the nature of these relations is complex and may include gene-environment correlations. A number of studies indicate modest but consistent associations of maladaptive parenting and maltreatment with child and adolescent depression. Thus, in both clinical and community samples, depressed youth and their parents report lower levels of parental warmth and communication and higher levels of parental criticism, intrusiveness, and maltreatment than nondepressed youth (Gibb, 2014; McLeod, Weisz, & Wood, 2007; Yap & Jorm, 2015). Moreover, similar findings have been reported using observations of parent-child interactions (McLeod et al., 2007). Although most data are cross-sectional or retrospective, longitudinal studies have also shown that maladaptive parenting predicts later increases in depressive symptoms

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(e.g., St. Clair et al., 2014). It is likely that effects of maladaptive parenting and maltreatment on depression are indirect, influencing other processes that in turn increase risk. For example, adverse parenting and maltreatment are associated with youth’s dysregulated neurobiological and behavioral responses to stress (Dougherty, Klein, Rose, & Laptook, 2011), cognitive vulnerabilities (Gaté et al., 2013), interpersonal deficits (Rudolph, 2009), and later life stressors (Hazel, Hammen, Brennan, & Najman, 2008). In addition, early adversity may sensitize children to the effects of subsequent life stressors, so depressive symptoms are provoked at lower levels of stress in children with versus without a history of adversity (e.g., Oldehinkel, Ormel, Verhulst, & Nederhof, 2014). A number of studies have documented that depressed children and adolescents have difficulties with peer (and for adolescents, romantic) relationships (Davila, 2008; Rudolph, 2009). Depressed children report poorer friendships and experience greater peer rejection and victimization than nondepressed children (Gibb, 2014; Rudolph & Flynn, 2014). In part, these phenomena reflect negative self and relationship appraisals, which share genetic influences with depression (Lau, Belli, Gregory, & Eley, 2014). However, peer and teacher reports also indicate that depressed children have deficits in social skills and difficulties with interpersonal relationships, and observational studies indicate that depressed children are more withdrawn and isolated, and more hostile and aggressive, than their peers (Rudolph, 2009). Associations between peer relationships and depression appear to be reciprocal and transactional (Prinstein, Borelli, Cheah, Simon, & Aikins, 2005). Difficulties with peer relationships prospectively predict increases in depression (e.g., Witvliet, Brendgen, Van Lier, Koot, & Vitaro, 2010). On the other hand, depression is also associated with subsequent reductions in peer support (e.g., Oppenheimer & Hankin, 2011). Interpersonal difficulties may play a particularly important role in depression during the adolescent transition because of the increasing salience of peer and romantic relationships at that time. (Rudolph & Flynn, 2014). Gene-Environment Interplay. The relationship between genetic and environmental risk factors is complex, with the possibility of both gene-environment correlations and gene-environment interactions (see Chapter 3 [Beauchaine et al.]). Gene-environment correlation refers to situations in which certain genotypes increase risk of exposure to high risk environments. There are three types of gene-environment correlations. Passive gene-environment correlations refer to the fact that children usually inherit their genes from the same people who raise them, so their genotypes and childrearing environments are correlated. Evocative gene-environment correlation refers to the possibility that the child’s genes are expressed in ways that evoke certain reactions from others. For example, Hayden et al. (2013) reported that children with a particular variant of the dopamine active

Depressive Disorders 623 transporter gene (9-repeat DAT1) exhibited greater negative affectivity toward their parents, who showed greater hostile and less scaffolding behavior toward the child. Finally, active gene-environment correlation refers to the fact that as children grow older, they have more opportunity to choose their environments, such as activities and peers (i.e., “niche-picking”). For example, some of the same genes that predispose to depression appear to increase the likelihood that adolescents will experience higher rates of dependent life events (stressors that they help create), which in turn increase levels of depression (Silberg et al., 1999). As discussed earlier, identifying specific genes implicated in depression has proven difficult. One reason for this could be that, consistent with diathesis-stress models, individuals with a genetic predisposition may not develop the disorder unless they experience significant environmental stress (a Gene × Environment interaction). A number of studies have reported gene-environment interactions for depression at both the aggregate and molecular levels (Frani´c et al., 2010). As an example of the latter, Caspi and colleagues (2003) reported that young adults with a short allele in the promoter region of the serotonin transporter gene (5-HTTLPR) had an increased rate of depressive disorders, but only when exposed to stressful life events. Similar findings have been reported for depressive symptoms in children from preschool age to adolescence (Bogdan, Agrawal, Gaffrey, Tillman, & Luby, 2014; Frani´c et al., 2010), although there have also been a number of failures to replicate this work. In a meta-analysis, Karg, Burmeister, Sjeddem, and Sen (2011) found that when only studies using methodologically rigorous assessments of life stress are considered, data support a moderating role of stress on 5-HTTLPR in predicting depression, with the strongest findings emerging for early and chronic stressors, such as childhood maltreatment. Interestingly, some studies have suggested that not only does the 5-HTTLPR short allele predispose to depression in the presence of stress, but it is also associated with lower than expected levels of depression in positive environments. This is consistent with Belsky and Pluess’s (2009) differential susceptibility theory, which argues that “plasticity genes” confer greater sensitivity to both positive and negative environments. In one study, among adolescent boys with the short 5-HTTLPR allele, lower family support was related to a higher depressive symptoms and high family support to a lower number of depressive symptoms (Li, Berk, & Lee, 2013). This theory may help explain resilience, or positive adaptation in the face of adversity. Evidence that genes influence sensitivity to the environment is paralleled by recent findings indicating that the environment also influences the expression and regulation of genes (epigenetics). Studies of rodents and primates show that maternal behavior and separation can have lifelong effects on neuroendocrine stress responses and neurotransmitter systems that are dysregulated in depression (see later)—and that these effects are mediated by changes in gene expression.

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For example, maternal behavior in rats influences methylation of binding sites on the pup’s glucocorticoid receptor gene that regulates expression of that receptor, thereby influencing long-term neuroendocrine stress reactivity (Zhang & Meaney, 2010). Such models are now being extended to humans. For example a recent study of identical twins in which one was depressed and the other was not found differential methylation at several sites using genome-wide methylation analysis (Dempster et al., 2014). Epigenetic processes may explain how different genes influence depression at different stages of development (Frani´c et al., 2010; Nivard et al., 2015), but it remains to be determined how well methylation in peripheral tissue generalizes to the brain, and findings are often difficult to replicate (Dempster et al., 2014).

CULTURAL CONSIDERATIONS Epidemiological studies examining ethnic/racial differences in the prevalence of depression in youth have yielded inconsistent findings (e.g., Merikangas et al., 2010). However, adolescent sexual minorities consistently experience high rates of depressive symptoms (Marshal et al., 2011). There are relatively few comparisons of the prevalence of child and adolescent depression in different countries and cultures. However, some studies have compared symptoms on rating scales across cultures. For example, Rescorla et al. (2012) examined data on the Child Behavior Checklist from almost 70,000 youth in 44 societies. They found small to medium effects of society on levels of depressive symptoms as assessed using parent, child, and teacher reports. Whereas epidemiological studies of adults typically report higher rates of depression in the United States compared to most Asian countries, levels of depressive and other internalizing symptoms appear to be higher among adolescents in some Asian and African societies than in the United States, and sex differences evident in adolescents in the United States are not present in Chinese adolescents (Ryder, Sun, Zhu, Yao, & Chentsova-Dutton, 2012). The literature on adult depression suggests that in some Asian societies, depression is expressed more through somatic than psychological symptoms (Ryder et al., 2012). In addition, it has been proposed that depressive symptoms are expressed in a fashion that is discrepant with normative emotional responses. In the United States, where expression of emotions (particularly positive emotions) is valued, depression is associated with dampened responding to emotion-evoking stimuli. In contrast, in China, which has traditionally emphasized emotional moderation and control, depression may take the form of unchanged or enhanced responding to emotional stimuli (Ryder et al., 2012). However, it is unclear whether these cross-cultural differences in the expression of depression in adults can be generalized to children and adolescents.

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RESEARCH DOMAIN CRITERIA The National Institute of Mental Health’s Research Domain Criteria (RDoC) initiative (Insel et al., 2010) seeks to elucidate a set of broad biobehavioral systems (or domains) that are evident across species and are presumed to underlie much of both typical and atypical behavior. RDoC conceptualizes each domain at multiple units of analysis, including genes, cells, molecules, circuits, physiology, behavior, and self-report. These domains cut across traditional diagnostic categories, and it is hoped that they will ultimately lead to a more valid approach to classifying mental disorders. The RDoC domains most germane to depression include the positive valence system (PVS; e.g., reward and approach motivation), negative valence system (NVS; e.g., threat and loss), and cognitive system (e.g., cognitive or effortful control). We now discuss recent research relevant to each domain. Temperament. One of the goals of the RDoC initiative is to map pathways from genes to disorders. These pathways include a number of intermediate biological and behavioral processes, often termed intermediate phenotypes. One set of pathways from genes to depression may be mediated by child temperament. Two temperament traits that have been linked to depression and map closely onto RDoC’s NVS and PVS are high NE and low positive emotionality (PE), respectively. NE, which is closely related to neuroticism, refers to a propensity to experience negative affects, such as fear, sadness, and irritability, often in response to stress. PE, which overlaps with extraversion, refers to a propensity to positive affect (e.g., joy, exuberance), appetitive drive/approach behavior, and sociability. In addition to cross-sectional associations of these traits with depression, there is growing evidence linking them to risk for later depression (Klein et al., 2012). For example, young children of depressed parents have higher levels of NE and/or lower levels of PE than the offspring of nondepressed parents (Olino, Klein, Dyson, Rose, & Durbin, 2010). As noted above, this pattern may in part reflect shared genetic influences between NE and MDD. In addition, longitudinal studies indicate that high NE and low PE in children and adolescents predict subsequent depressive symptoms (e.g., Bould et al., 2014; Dougherty, Klein, Durbin, Hayden, & Olino, 2010). These effects may be exacerbated by low effortful control (Kotelnikova, Mackrell, Jordan, & Hayden, 2015). Finally, there is growing evidence that high NE and low PE are associated with neuroendocrine dysregulation, information processing biases, and interpersonal problems that may mediate associations between early temperament and later depression (for a review, see Klein et al., 2012). Cognitive Processes. Cognitive processes in depression are closely related to the RDoC NVS and PVS, and also involve systems involved in cognitive control (Joormann & Arditte, 2014). Abnormalities have been reported in a number of

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cognitive processes in depressed youth, including dysfunctional attitudes, negative attributional (or inferential) styles, rumination, and attention and memory biases, although they may not be specific to depression (Alloy et al., 2012; Gibb, 2014). Many of these abnormalities precede the onset of youth depression and may increase vulnerability to depressive episodes, whereas others appear to co-occur with or result from depressive symptoms (LaGrange et al., 2011). There are also reciprocal relationships between depressive symptoms and many cognitive factors such as negative self-perceptions, attributional style, and rumination (Jacobs, Reinecke, Gollan, & Kane, 2008). In addition, links between depressive cognitions and symptoms change over the course of development, as cognitive styles become more stable and the strength of their associations with symptoms increase with age (LaGrange et al., 2011). Dysfunctional attitudes, such as believing that one must be perfect in order to be loved, and negative attributional styles, in which negative events are viewed as having internal, global, and stable causes, are tied to the NVS and may be vulnerability factors for youth depression (Jacobs et al., 2008). Cole et al. (2008) reported that attributional style interacted with negative life events to predict depressive symptoms in adolescents. Interestingly, this effect did not emerge until eighth grade, suggesting that the relation between attributional style and depression changes from childhood to adolescence. Rumination, the tendency to respond to distress by focusing on the causes and consequences of one’s problems rather than engaging in more active coping, is also associated with the NVS and cognitive control system. Rumination increases risk of depression, particularly in conjunction with negative life events, and may partially mediate sex differences in rates of depressive symptoms in adolescents (Hilt & Nolen-Hoeksema, 2014; Michl et al., 2013). The association between depressive symptoms and rumination appears to be reciprocal, as depressive symptoms also predict increases in rumination (Nolen-Hoeksema, Stice, Wade, & Bohon, 2007). In addition, co-rumination—continually discussing problems with peers—prospectively predicts depression onset, as well as greater severity and longer duration of episodes (Stone, Hankin, Gibb, & Abela, 2011). Finally, attention and memory biases favoring the processing of negative and hindering positive information also play a role in depression, and reflect interactions between dysregulated NVS and PVS and inefficient cognitive control (Joormann & Arditte, 2014). Depressed children and adolescents exhibit attentional biases toward sad faces, and similar patterns have been observed among girls of depressed mothers (Hankin, Gibb, Abela, & Flory, 2010; Kujawa et al., 2011), although some studies have found a bias away from sad faces (Gibb, Benas, Grassia, & McGeary, 2009). There also appear to be biases in memory retrieval, particularly for self-relevant material, in depression. Thus, children and adolescents with depression symptoms and offspring of depressed mothers recall fewer positive and more negative self-descriptive words than nondepressed youth (Goldstein, Hayden, & Klein,

Depressive Disorders 627 2014). Finally, overgeneral autobiographical memory, characterized by difficulties recalling specific past events, has also been linked to depressive symptoms in children and adolescents (Raes, Verstraeten, Bijttebier, Vasey, & Dalgleish, 2010), predicting later depressive symptoms in adolescents who are at familial risk for depression (Rawal & Rice, 2012). Neuroendocrinology. As described above, stressful life events are related to depression and one biological pathway that might mediate this association is dysregulated hypothalamic pituitary adrenal (HPA) axis functioning. Indeed, HPA axis abnormalities are present in depressed children and adolescents and often linked to constructs within the NVS (i.e., sustained threat and loss). Depressed youth often exhibit higher basal cortisol levels than their healthy peers and are more likely to fail to suppress cortisol production after ingesting the synthetic corticosteroid dexamethasone (Lopez-Duran, Kovacs, & George, 2009). In addition, several studies have reported that depressed youth have greater and more prolonged cortisol responses to laboratory stressors than nondepressed youth (Lopez-Duran et al., 2009). Abnormalities in cortisol reactivity to stress are also evident in children at risk for depression, although this link may be moderated by other factors, including hostile parenting (Dougherty, Klein, Rose, & Laptook, 2011), as well as the child’s temperament and cognitive vulnerability to depression (Hayden et al., 2014; Mackrell et al., 2014). Developmental stage may also moderate the relation between cortisol reactivity to stress and depression (Colich, Kircanski, Foland-Ross, & Gotlib, 2015). Elevated levels of morning cortisol may also be associated with risk for developing MDD. Several studies have reported that children of depressed parents exhibit elevated morning cortisol (e.g., Dougherty et al., 2009). Moreover, an elevated cortisol awakening response predicts later depressive symptoms and MDD onset in youth, although these effects may diminish over time (Vrshek-Schallhorn et al., 2013). Finally, HPA axis dysregulation may influence the course of depression, as elevated basal cortisol in depressed adolescents is associated with a longer duration of episodes (Rao, Hammen, & Poland, 2010). Brain Structure and Function. The PVS and NVS have complex, overlapping neural circuitry that include areas involved in cognitive control. Key areas in both systems include the prefrontal cortex (i.e., the dorsal lateral prefrontal cortex [DLPFC], orbitofrontal cortex [OFC], and medial prefrontal cortex [mPFC], which are involved in executive function, decision-making, and emotion processing and regulation; the anterior cingulate cortex (ACC; implicated in attention and error-monitoring); the amygdala (involved in processing motivationally salient stimuli); the hippocampus (implicated in memory consolidation), and the basal ganglia (the striatum, substantia nigra, and subthalamic nucleus, which are involved in habit and reinforcement learning). In addition, the mesolimbic pathway

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(projections from the ventral tegmental area to the nucleus accumbens) is strongly implicated in reward processing. These circuits undergo significant changes during puberty, as reward sensitivity and orientation toward peers are heightened (Crone & Dahl, 2012). Cross-sectional and longitudinal studies using structural magnetic resonance imaging (MRI), event-related potentials (ERP), task-based and resting-state functional MRI (fMRI), and diffusion tensor imaging (DTI) have explored the role of atypical brain development in depression. MRI studies reveal structural differences in the amygdala and hippocampus, both of which play critical roles in processing emotional and particularly negative stimuli. A review concluded that, similar to depressed adults, amygdala and hippocampal volumes are smaller among depressed and high-risk youth compared to healthy controls (Hulvershorn, Cullen, & Anand, 2011). Reduced hippocampal volume has also been found in youth with family histories of depression, suggesting that it is not simply a concomitant or consequence of depression (Chen, Hamilton, & Gotlib, 2010). Other work indicates that structural differences may interact with environmental factors, such as childhood adversity and maternal aggression, to predict depression, although the pattern of interactions is inconsistent (e.g., Mannie et al., 2014; Yap et al., 2008). A number of studies have used task-based and resting-state fMRI and ERPs to examine brain function in depressed youth and youth at familial risk for depression. This work suggests that there may be functional abnormalities in PVS and NVS circuits involved in reward and emotion processing. Both fMRI and ERP studies find that depressed youth (Forbes & Dahl, 2012; Kerestes, Davey, Stephanou, Whittle, & Harrison, 2014) and offspring of depressed parents (Kujawa, Proudfit, & Klein, 2014; Sharp et al., 2014) exhibit diminished neural responses to rewards. Moreover, decreased neural responding to reward predicts subsequent MDD onset and increased depressive symptoms (Forbes & Dahl, 2012; Proudfit, Bress, Foti, Kujawa, & Klein, 2015). Additional fMRI research reveals increased activation of the amygdala in response to negative stimuli (e.g., fearful, angry, or sad faces) in depressed youth and offspring of depressed parents (Kerestes et al., 2014). In contrast, ERP studies have found blunted neural responding to negative stimuli, suggesting emotional disengagement, in depressed and at-risk youth (Proudfit et al., 2015). Finally, results of fMRI studies of depressed youth examining prefrontal cortex activation in tasks requiring cognitive control have been inconsistent (Kerestes et al., 2014). In addition to identifying structural and functional differences in individual brain regions, investigators have begun to examine how aberrations in functional connectivity (or co-activation) among brain regions play a role in youth depression. For example, during a reward task, adolescent boys with a history of depression exhibited greater positive connectivity between the nucleus accumbens and the mPFC, areas of the brain associated with reward processing and cognitive control, in response to monetary reward compared to controls (Morgan et al., 2015).

Depressive Disorders 629 This pattern may indicate that the mPFC overregulates, or dampens, reward-related activation in the nucleus accumbens among individuals with a history of depression. In the absence of a task, resting-state fMRI allows researchers to examine background connectivity within neural circuits. One neural circuit that may be disrupted in youth with depression is the default mode network (DMN, which includes the mPFC, medial temporal lobe, precuneous, posterior cingulate cortex). The DMN is typically activated during self-referential processing (e.g., daydreaming, recall of autobiographical memories), and deactivated during engagement with external stimuli (e.g., laboratory tasks). However, depressed youth exhibit abnormal patterns of DMN activation that may be related to clinical phenomena such as rumination (Gaffrey, Luby, Botteron, Repovs, & Barch, 2012; Kerestes et al., 2014). Depressed youth may also exhibit abnormal connectivity in the salience network (involved in evaluating and integrating the significance of stimuli) and the limbic network (involved in threat detection, response, and learning) (Pannekoek et al., 2014). The structural basis of aberrant connectivity within neural circuits can be examined by exploring disruptions of white matter (WM) integrity (i.e., demyelination and axonal loss) using diffusion tensor imaging (DTI), which examines the way water molecules spread in neural tissue. Aghajani et al. (2013) found that compared to healthy controls, clinically depressed youth had decreased integrity of WM tracts associated with cognitive and emotional functioning (i.e., the corpus callosum and uncilate fasciculus). Other studies have explored how WM integrity may be a vulnerability marker for depression. Huang, Fan, Williamson, and Rao (2011) found altered WM integrity in adolescents of depressed parents compared to controls in areas associated with emotional and motivational processes and cognitive control (left cingulum, splenium of the corpus callosum, superior longitudinal fasciculi, uncinate, and inferior fronto-occipital fasciculi).

SYNTHESIS AND FUTURE DIRECTIONS Child and adolescent depression is multifactorial and etiologically heterogeneous— an outcome of multiple developmental pathways (equifinality) operating at multiple levels of analysis (see Hankin, 2012). Few risk factors are specific to depression (multifinality), and etiological overlap with other psychiatric disorders contributes to the comorbidity that is ubiquitous in youth depression. Genetic and environmental factors both play a role in youth depression, but their relative influence may vary over the course of development. The nature of the genetic effects is poorly understood. There may be multiple common gene variants each with small effects, rare mutations with large effects on only a small number of cases, or some combination of both. In addition, due to developmentally mediated changes in gene expression, the relation between genotype and phenotype varies over time, with the same set of genetic influences producing different phenotypes

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at various points in development and different genetic factors producing similar phenotypes at different ages. Genetic influences operate through intermediate phenotypes such as temperament and susceptibility to stress. They are further mediated and/or moderated by a number of other risk factors including early adversity, dysregulation in key hormonal and neural systems, cognitive biases, interpersonal problems, and life stress. These risk factors may also have independent effects on depression, and they may be exacerbated by depressive symptoms. We are still a long way from being able to formulate a comprehensive model of depression in children and adolescents. For heuristic purposes, however, we briefly outline one plausible but undoubtedly oversimplified model of the etiopathogenesis of youth depression. The two major sets of distal causes include genetic susceptibilities and early (including prenatal) adversities. These two sets of distal causes often co-occur (i.e., passive gene-environment correlation), and may have additive or interactive effects. Genetic susceptibilities may be expressed in the form of temperamental vulnerabilities that, at the behavioral level, are manifested as high NE and low PE, and are associated with dysregulation in the neurocircuity of the NVS and PVS. Early adversity may also directly influence these temperamental and neurobiological vulnerabilities and have enduring effects on stress response systems. As the child enters the early school-age years, temperamental vulnerabilities are elaborated cognitively, leading to the emergence of depressotypic cognitive styles/ biases. Over time, these temperamental and cognitive vulnerabilities can lead to interpersonal deficits that in turn reinforce negative cognitive styles/biases and generate dependent stressors that may sensitize neurobiological stress response systems. When these temperamental/neurobiological/cognitive vulnerabilities and environmental stressors combine, either additively or interactively, to exceed a critical threshold, the emotional and cognitive precursors of depression escalate to the point of a diagnosable disorder. This can occur at virtually any point during the life span. However, because of developmental effects on gene expression, the development of critical neurobiological circuitry (e.g., heightened sensitivity to reward and peers together with slower maturation of cognitive control systems), and the developmental challenges that emerge at this time, this escalation is particularly likely to occur in adolescence. This process is much more likely to occur in females, due to sex differences in vulnerability factors and a greater increase in depression-relevant (e.g., interpersonal) stressors in this period. To further our understanding of child and adolescent depression, research is needed to clarify the relations between depressive phenotypes at different developmental stages, as well the relations between depressive and nondepressive phenotypes across development. Genetically informative designs, along with prospective longitudinal studies of high risk and community samples of infants and young children prior to the onset of depressive disorders, are necessary to elucidate the processes and mechanisms involved in the etiopathogenesis of youth

Depressive Disorders 631 depression. It is particularly important to continue the search for intermediate phenotypes and attempts to trace the complex pathways among genes, neurobiology, and behavior. This search includes elucidating the role of early adversity on neurobiological and psychosocial sources of risk, and understanding the reasons for the major increase in depression among females in early adolescence. Knowledge of genetics and neurobiology continues to grow at a rapid pace and is increasingly being applied within a developmental perspective. This growth provides grounds for guarded optimism for progress in understanding the etiopathogenesis of depression in youth.

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C H A P T E R 19

The Development of Borderline Personality and Self-Inflicted Injury ERIN A. KAUFMAN, SHEILA E. CROWELL, AND MARK F. LENZENWEGER

INTRODUCTION

B

orderline personality disorder (BPD) and self-inflicted injury (SII) are distinct psychiatric problems that share many defining features and often co-occur (i.e., 40%–90% of those with BPD engage in SII; APA, 2004). Both are severely impairing and costly, leading to intense suffering for affected individuals and those around them. BPD and SII are each associated with significant psychiatric comorbidity, frequent treatment utilization, and elevated risk for suicide (De La Fuente & Bobes, 2009; Gunderson, 2010). Although SII is a criterion for BPD, the symptom is neither required nor sufficient for a BPD diagnosis (American Psychiatric Association [APA], 2013). Furthermore, SII is observed in many conditions (e.g., depression), and suicide rates are elevated for those with most psychiatric diagnoses relative to unaffected individuals (APA, 2013). Given this set of observations, BPD and SII are often studied independently, which obscures shared etiological links. Indeed, similar mechanisms appear to underlie both, including biological vulnerabilities, contextual risk factors, personality traits, and acquired behavior and emotion regulatory strategies. There is also accumulating evidence that SII is a developmental precursor to BPD for many but not all individuals (Crowell, Beauchaine, & Linehan, 2009; Groschwitz et al., 2015; Lamph, 2011). Many adolescents engage in SII. Each year, approximately 4,600 youth aged 10 to 24 die by suicide and approximately 157,000 receive medical attention for self-injury in U.S. emergency departments (Centers for Disease Control and Prevention

We would like to acknowledge Theodore P. Beauchaine for his contribution to a prior version of this chapter.

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[CDC], 2015). Furthermore, estimated lifetime prevalence of suicide ideation, planning, and attempts among American 13- to 18-year-olds are 12.1%, 4.0%, and 4.1%, respectively (Nock et al., 2013). Nonsuicidal self-injury (NSSI) often precedes suicidal behavior and affects between 15% to 20% of community adolescents (Brent, 2011; Heath, Baxter, Toste, & McLouth, 2010; Klonsky, Victor & Saffer, 2014), although some find higher rates (Hilt, Cha, & Nolen-Hoeksema, 2008; Somer, et al., 2015). Discrepant definitions of SII, assessment methods (e.g., interview vs. questionnaire), and sampling differences may explain inconsistent findings. Epidemiological surveys suggest that BPD affects about 2% of adults in the community (Lenzenweger, 2008; Paris, 2010). However, in clinical samples, up to 10% of outpatients and 20% of inpatients are diagnosed with BPD (Comtois & Carmel, 2014; Widiger & Trull, 1993). Rates are higher still among those with high treatment utilization. One study found that 42% of randomly sampled outpatients with a history of frequent inpatient psychiatric hospitalization met BPD criteria (Comtois & Carmel, 2014). Although the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5; APA, 2013) discourages diagnosing personality disorders before age 18, BPD occurs among adolescents and reliable precursors appear even earlier (Bradley, Zittel Conklin, & Westen, 2005; Hallquist, Hipwell, & Stepp, 2015; Stepp, Olino, Klein, Seeley & Lewinsohn, 2013). Epidemiological surveys in the United States and China suggest that approximately 1%–3% of adolescents meet criteria for BPD (Johnson, Cohen, Kasen, Skodol, & Oldham, 2008; Leung & Leung, 2009; Zanarini, Horwood, et al., 2011). These rates are similar to those observed among adults. The presence of borderline pathology (BP) in adolescence portends negative outcomes in adulthood, such as low academic/occupational attainment, high service utilization, and low partner involvement (Stepp, 2012; Winograd, Cohen, & Chen, 2008). Even though BPD features often decline with age (Lenzenweger, Johnson, & Willett, 2004; Zanarini, Frankenburg, Reich, & Fitzmaurice, 2012), there is high rank-order stability of impairment over time (Bornovalova, Hicks, Iacono, & McGue, 2009). Given the frequency and clinical severity of these problems, early identification and prevention of SII and BPD is of utmost importance. Although emerging evidence suggests that self-injury and BPD have identifiable developmental precursors, empirical research is limited. In this chapter, we describe how biological vulnerabilities interact with environmental risks to eventuate in SII and BPD. Consistent with our theoretical model (Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009; Crowell, Derbidge, & Beauchaine, 2014; Crowell et al., 2009), we highlight areas of etiological overlap between BPD and SII and note how BPD and SII sometimes reflect multifinal outcomes of common neurobiological vulnerabilities.

HISTORICAL CONTEXT Literatures describing SII and BPD have emerged from largely independent research traditions. Suicide researchers conducted most available studies of self-harm and

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until recently, many scholars did not distinguish between suicidal and nonsuicidal SII (Linehan, 1997). Meanwhile, BPD researchers conceptualized SII as a manifestation of underlying personality pathology (i.e., a behavioral expression of negative emotionality and/or impulsivity), rather than an etiological precursor to BPD or a disordered behavior in its own right (Muehlenkamp, Ertelt, Miller, & Claes, 2011). Because SII and BPD were studied separately for so many years and the literatures addressing each remain largely independent, we review their histories separately.

Self-Inflicted Injury Self-inflicted injury is an umbrella term that covers all volitional acts of self-harm, ranging from nonsuicidal self-injury (NSSI) to completed suicide. One of the most useful distinctions in the literature is between SII with and without suicidal intent (e.g., Zlotnick, Mattia, & Zimmerman, 1999) and most recent research falls into one of these two domains. Nonetheless, for decades, suicidal self-injury and NSSI were presumed to result from common unconscious sources such as a displaced desire to kill (see Simpson, 1950; Zilboorg, 1936). We know now that suicidal and nonsuicidal self-injury serve distinct functions (see Crowell et al., 2014). Offer and Barglow (1960) were among the first to observe this distinction when they identified a subgroup of hospitalized youth who self-harmed in the absence of suicidal intent. These authors demonstrated that NSSI is often learned and may serve both instrumental and emotional functions. Following this study, research on suicidal SII and NSSI began to diverge. Although important distinctions between these forms of self-harm were made, a number of methodological challenges also emerged. First, researchers developed incorrect assumptions about differences between NSSI and suicidal behavior. For example, suicidal intent was often inferred from lethality of self-injurious behavior. Behaviors resulting in low-levels of bodily harm such as cutting, burning, and bruising were frequently categorized as nonsuicidal (e.g., Simpson, 1975), whereas behaviors with a high probability of causing bodily harm such as hanging, gun injuries, and asphyxiation were assumed to be suicidal (e.g., Seiden, 1978). We now know that deriving intent from lethality of means can be misleading. Second, because different researchers studied NSSI and suicidal SII, similarities and differences between these behaviors and those who engage in them were missed. Third, research on SII is conducted largely within rather than across diagnostic groups and age ranges. This limits generalizability of study findings and obfuscates potential transdiagnostic mechanisms of risk. For example, risk for suicide and self-injury is often researched within narrowly defined samples (e.g., those with major depression or schizophrenia), or within specific age groups (adolescents, young adults, and ageing adults). As a result, what would be convergent findings across these literatures frequently go undetected and transactional life-span theories of SII have only begun to emerge (see Crowell & Kaufman, in press; Crowell et al., 2014; Shneidman, 1991 for exceptions).

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Current research on adolescent SII is focused on (a) achieving a better understanding of etiological mechanisms; (b) integrating adolescent SII within a broader research context, including mechanism-based studies and theoretical model development; (c) better representing SII within the DSM; (d) detecting who is at risk for SII; and (e) developing best practice guidelines for treating adolescent SII (e.g., Claassen, Harvilchuck-Laurenson & Fawcett, 2014; Claassen, Pearson, et al., 2014; Gratz, 2003; Van Orden, Witte, Holm-Denoma, Gordon, & Joiner, 2011).

Borderline Personality Disorder The term borderline originally emerged when clinicians encountered difficulties diagnosing patients as either psychotic or neurotic. Such patients were considered to be on “the borderline of psychosis and neurosis” (Stern, 1938, p. 467), as early practitioners were unsure whether the patient would later develop psychotic disorders such as schizophrenia, neurotic disorders such as anxiety and depression, or vacillate between such conditions (Knight, 1953). Kernberg (1967) was among the first to delineate a borderline personality organization as a specific and recognizable personality pattern, distinct from both psychotic and neurotic conditions. Subsequently, two important reviews (Gunderson & Singer, 1975; Spitzer, Endicott, & Gibbon, 1979) outlined criteria that would be used for the formal DSM-III BPD diagnosis (APA, 1980). Soon thereafter, researchers began focusing on assessment and validity of BPD criteria. New diagnostic measures were developed, such as the Millon Clinical Multiaxial Inventory (MCMI; Millon, 1983) and the International Personality Disorders Examination (IPDE; Loranger, 1999; Loranger, Susman, Oldham, & Russakoff, 1988). These instruments spurred research on patterns of comorbidity, correlates of BPD, and behavioral or pharmacological treatments (e.g., Linehan, 1993; Loranger, Oldham, & Tulis, 1982; Soloff, 2000; Zanarini et al., 1998b). Within the past 25 years, theory-driven experimental studies have emerged that focus on biological vulnerabilities and psychosocial risk factors (e.g., Koenigsberg et al., 2009; Lenzenweger & Depue, in press; Stanley & Siever, 2010). There is also a push to reduce heterogeneity within the BPD diagnosis by identifying more homogeneous subgroups (Lenzenweger, Clarkin, Yeomans, Kernberg, & Levy, 2008) and to reduce artificial comorbidity between BPD and other personality disorders.

Borderline Pathology Among Youth For decades, misunderstandings about the chronicity and stability of personality pathology led many to question the existence of BPD among youth. Despite general reluctance to diagnose BPD prior to the age of 18, research on childhood BP evolved in parallel with the adult literature. Each began with clinical descriptions of individuals who could not be classified as either psychotic or neurotic (Geleerd, 1958; Weil, 1953). Preliminary attempts to identify diagnostic criteria for BP in childhood

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emerged (e.g., Bemporad, Smith, Hanson, & Cicchetti, 1982; Kernberg, Weiner, & Bardenstein, 2000). However, consensus on childhood criteria for the disorder was never reached (Vela, Gottlieb, & Gottlieb, 1983). Although sparse, prospective studies indicated that children with BP go on to develop a wide range of disorders as adults (Lofgren, Bemporad, King, Lindem, & O’Driscoll, 1991). Researchers who continued this line of work discovered that many early-life risk factors are neither sensitive nor specific to BPD (Paris, 2014). Thus, there is little evidence that BPD can be diagnosed reliably and validly among children. Still, great interest exists in identifying childhood precursors and delineating possible developmental trajectories to BPD (Stepp, 2012). In contrast to child studies, considerable research indicates that BPD can be diagnosed reliably among adolescents. There has been a fivefold increase in empirical studies examining adolescent BPD over the past 10 years (Sharp & Tackett, 2014). Longitudinal research indicates that several behavioral syndromes precede the emergence of BPD (Stepp, Burke, Hipwell & Loeber, 2012). For example, prospective studies indicate that oppositional defiant disorder (ODD), attention-deficit/ hyperactivity disorder (ADHD), and marijuana use among male children predict BPD symptoms during early adulthood (Burke & Stepp, 2012). Similarly, girls with the combined-subtype of ADHD in childhood show higher rates of SII in early adulthood than controls and than girls with the inattentive-subtype (Hinshaw et al., 2012). Internalizing disorders and traits also confer vulnerability to SII and BPD (Hudson, Zanarini, Mitchell, Choi-Kain, & Gunderson, 2014; Sharp & Fonagy, 2015). Given complex longitudinal pathways to these conditions and phenotypically diverse expressions of common vulnerabilities, researchers should identify developmental antecedents associated probabilistically with later SII and BPD. We hypothesize that SII and BPD often represent two points along a heterotypically continuous trajectory and that SII can increase risk for BPD (Crowell et al., 2009). Recent longitudinal research following adolescents who were treated for NSSI indicates that by young adulthood, approximately half self-injured within the previous year, half met criteria for BPD, and over half attempted suicide. Furthermore, earlier age of self-harm onset and a longer duration of NSSI during adolescence predict adult BPD (Groschwitz et al., 2015). Thus, some evidence indicates that SII is an early marker of borderline liability. However, additional longitudinal research is needed (Hallquist et al., 2015; Stepp et al., 2013).

DIAGNOSTIC, TERMINOLOGICAL, AND CONCEPTUAL ISSUES The DSM-5 includes SII in the criterion sets of major depressive disorder (MDD; e.g., suicide attempts), and BPD (e.g., recurrent suicidal behavior, gestures, or threats, or self-mutilating behavior). Because diagnosing BPD in adolescence remains controversial (e.g., Sharp & Fonagy, 2015; Stepp, 2012), many practitioners diagnose self-injuring youth with MDD (Miller, Rathus, & Linehan, 2007). As noted above, however, suicide risk is elevated for almost all forms of psychopathology,

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even though self-injury does not appear in most criterion sets (APA, 2006). Furthermore, although suicidality may resolve during the course of a disorder, it can also endure beyond remission of the primary psychiatric condition (e.g., Malone, Haas, Sweeney, & Mann, 1995; Mehlum, Friis, Vaglum, & Karterud, 1994). There have been ongoing efforts spanning many decades to list SII within the DSM as a standalone diagnosis (e.g., APA, 2010; Kahan & Pattison, 1984). Indeed, suicidal self-injury and NSSI are currently included in the DSM-5 as provisional diagnoses requiring further study (APA, 2013). However, evidence indicates that creating another disorder may not be useful. One recent study found that only 74% of participants being treated for NSSI met diagnostic criteria for the new NSSI disorder (Washburn, Potthoff, Juzwin, & Styer, 2015). The authors concluded that criterion-based diagnosis captured severe presentations of NSSI, yet overlooked other individuals in need of clinical attention. Thus, a more dimensional approach would be preferable. From a developmental psychopathology perspective, focusing on stable underlying traits that give rise to SII should be more fruitful than adding new clinical disorders. Adding diagnoses based on narrower phenotypes can obscure etiological connections across disorders if boundaries are drawn in the wrong places. Many persons who engage in suicidal SII have also engaged in NSSI, and common vulnerabilities and risk factors shape both behavioral syndromes (Crowell et al., 2012). Thus, studying the conditions together (with recognition of potential separability) is likely to yield better understanding of their emergence and maintenance than would be gleaned from parsing them into more constricted phenotypes. Unfortunately, the current classification system presents a series of “independent” disorders. Although many differ topographically, the DSM obscures common etiologies across various symptom presentations (Krueger, 2013). Another controversial topic concerns the latent structure of BPD. As with most DSM disorders, BPD is defined using a polythetic criterion set (APA, 2013). Although a more streamlined and dimensional approach to diagnosis has been proposed, at present, any five out of nine symptoms warrant a diagnosis, resulting in 151 different potential combinations (Skodol, Gunderson, et al., 2002). In spite of this heterogeneity, certain BPD features occur across most cases. For example, more than 90% of those with BPD endorse the affect dysregulation criterion (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2004a). Others claim identity disturbance is a defining characteristic of the disorder, differentiating BPD from other forms of psychopathology (Meares, Gerull, Stevenson, & Korner, 2011; Wilkinson-Ryan & Westen, 2000). Researchers have begun to examine whether BPD criteria are best conceptualized as a single underlying dimension or as multiple components. Factor analytic studies usually support a one-factor solution (Clifton & Pilkonis, 2007; Johansen, Karterud, Pedersen, Gude, & Falkum, 2004; Sanislow et al., 2002). Although Sanislow and colleagues (2002) argued for a three-factor solution, the latent variables were too highly correlated to be considered separate factors.

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Current evidence supports a continuous latent structure to BPD (Bornovalova et al., 2010; Trull, Distel, & Carpenter, 2011; Haslam, Holland, & Kuppens, 2012). Studies of adults indicate two latent classes that differentiate individuals based on symptom severity rather than by distinct profile types, suggesting a dimensional approach to diagnosis may be most appropriate (Bornovalova et al., 2010; Clifton & Pilkonis, 2007; Shevlin, Dorahy, Adamson, & Murphy, 2007; Thatcher, Cornelius, & Clark, 2005). Preliminary research with adolescents also found a two-class solution. However, rather than differentiating groups based on symptom severity, researchers identified subgroups characterized by primarily internalizing or externalizing symptoms (Ramos, Canta, de Castro & Leal, 2014). By adulthood, both internalizing and externalizing symptoms characterize BPD (Eaton et al., 2011; Shin et al., 2009), but these data suggest two developmental pathways to the diagnosis (see Stepp, Whalen & Pedersen, 2014). Researchers have also have applied model-based taxonomies for studying heterogeneity within the BPD diagnosis based on antisocial, aggressive, and paranoid features (Hallquist & Pilkonis, 2012; Lenzenweger et al., 2008; see also Yun, Stern, Lenzenweger & Tiersky, 2013). Lenzenweger and colleagues (2008) found three phenotypically distinct groups. Group 1 had low levels of antisocial, aggressive, and paranoid features; Group 2 had elevated paranoid features; and Group 3 had high antisocial and aggressive features. Hallquist and Pilkonis (2012) found similar subtypes in their sample where four groups emerged: (1) high anger and aggression, (2) high anger and mistrustfulness, (3) identity problems and low anger, and (4) low aggression and low mistrustfulness. Individuals in each group may have developed BPD via distinct pathways. Understanding etiologies is essential for prevention and treatment efforts (Paris, 2007). For example, those who arrive at BPD via an externalizing pathway may respond to interventions targeting poor behavioral inhibition (Kaufman, Crowell, & Stepp, 2015). Further research is needed to replicate findings and to assess whether intervention strategies should differ by BPD subtype.

ETIOLOGICAL FORMULATIONS The remainder of this chapter is informed by our biosocial model of borderline development (e.g., Crowell et al., 2009) in which we hypothesize the following (see Figure 19.1): 1. Trait impulsivity and emotional sensitivity are early-emerging, biologically based vulnerabilities that confer risk for SII, BPD, and other disorders characterized by poor behavioral control, such as those on the externalizing spectrum. 2. Emotional lability is shaped and maintained within high-risk contexts characterized by emotional invalidation and intermittent reinforcement of intense emotional expressions.

The Development of Borderline Personality and Self-Inflicted Injury

Biologically Based Heritable Vulnerabilities Individual differences in temperament, including emotional sensitivity, negative affect/irritability, and trait impulsivity • Genetically and epigenetically based differences in monoamine neurotransmitter functioning (e.g., DA, 5HT) • Neuroanatomical and psychophysiological differences •

Life Span Development

Infancy

Child characteristics contribute to the parent-child relationship

High-Risk Family Context Coercive interaction patterns including: (1) harsh and inconsistent discipline, (2) repeated escalating aversive exchanges, and (3) excessive verbosity and nagging • Invalidation of emotional expressions leading to a pattern of vacillation between emotional inhibition and increasingly extreme emotional displays • Pattern continues and intensifies over development leading to stable patterns of behavior, interpersonal interactions, and emotional expressions •

Preschool years Parent-child relationships shape developing biological systems (including neurotransmitter and HPA systems) via epigenetic processes and G x E interactions

Elementary school

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Adolescence

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Internalizing High anxiety Emotionally inhibited Identity distress Socially dependent Dissociative symptoms

Externalizing Oppositional/defiant Emotionally reactive Risky behaviors Deviant peer group Legal involvement

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Adulthood

• • • •

Emergence of Increasingly Stable BPD Traits and Behaviors Behavioral: avoidance/withdrawal, frequent impulsive behaviors (including self-injury) Interpersonal: angry and emotionally labile exchanges, problematic peer relationships Emotional: generalized emotional sensitivity, mood-dependent behavior, sadness, shame Cognitive: hopelessness, helplessness, disorganization, dissociation, low self-efficacy

Maladaptive behaviors serve an emotion regulation/avoidance function and become reinforcing

Borderline Personality Disorder Diagnosis Repetitive Self-Inflicted Injury Increased Suicide Risk

Figure 19.1

Biosocial developmental model of BPD and SII.

3. Biological vulnerabilities interact with environmental risks over time to potentiate internalizing and externalizing psychopathology characterized by increasingly severe behavior and emotion dysregulation.

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4. By adolescence, these Biology × Environment interactions spur a set of recognizable problems and maladaptive coping strategies characteristic of BPD, such as SII. 5. Early features of borderline pathology such as SII may further increase risk for BPD by impairing one’s abilities to navigate stage-salient developmental tasks, form supportive social bonds, and develop healthy coping strategies. We ascribe to a developmental psychopathology perspective in which BPD and SII can be conceptualized as outcomes of numerous transactional processes, interacting risk factors, and causal events. This framework is especially useful for understanding the origins and varying manifestations of psychopathology across development (Beauchaine et al., 2009; Cicchetti & Rogosch, 2002; see Chapter 1 [Hinshaw]). The concepts of multifinality and equifinality are particularly salient with respect to BPD and SII (Beauchaine et al., 2009). Some youth with borderline features or SII may develop BPD whereas others will not (multifinality). Those who develop BPD as adults arrive at this outcome via diverse developmental trajectories (equifinality), some of which include borderline features or SII in youth (for other models see Fonagy, Target, & Gergely, 2000; Judd & McGlashan, 2003; Kernberg, 1967, 1975).

FAMILIALITY AND HERITABILITY Research on BPD and SII suggests that strong biological underpinnings influence development of both conditions. Family, twin, and adoption research consistently implicate heritable factors for BPD, BPD features, SII, and key vulnerabilities that underlie both conditions (Amad, Ramoz, Thomas, Jardri & Gorwood, 2014; Jang, Livesley, Vernon, & Jackson, 1996; Livesley, Jang, & Vernon, 1998; Tidemalm et al., 2011). For example, available research indicates approximately 31% to 49% of observed heritability in BPD features can be attributed to additive genetic effects (Distel et al., 2010; Jang et al., 1996; Livesley et al., 1998). Twin studies yield heritability coefficients between .35 and .69 for a BPD diagnosis, with a mean estimate of approximately .40 and no evidence of shared environmental effects (see Amad et al., 2014 for a review). Unsurprisingly, borderline pathology aggregates in families. First-degree relatives of those with BPD show a three- to fourfold increase in risk for the disorder compared with relatives of probands without BPD (Gunderson et al., 2011). Some evidence indicates this familiality of BP is transmitted via a single latent construct or heritability factor (Gunderson et al., 2011; Reichborn-Kjennerud et al., 2013). However, other biological and familial evidence suggests that core BPD features emerge due to distinct, albeit highly correlated, vulnerability factors that also predispose to other common conditions. One study of 2,794 Norwegian young-adult twins yielded a single, highly heritable factor on which all 9 BPD criteria load, whereas environmental factors influenced only affective and interpersonal dimensions (Reichborn-Kjennerud et al., 2013). This supports our transactional

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developmental theory in which heritable vulnerabilities are shaped into the hallmark presentation of emotion dysregulation and interpersonal dysfunction via environmental influences. A second population-based twin study yielded a heritability coefficient for borderline traits of .35 and found the vulnerability to these traits were closely related to those for MDD through both genetic and environmental pathways (Reichborn-Kjennerud et al., 2010; see also Zanarini, Barison, Frankenburg, Reich, & Hudson, 2009). Overall, family studies of those with BPD reveal significant familial aggregation of mood and impulse control disorders (see White, Gunderson, Zanarini, & Hudson 2003, for a review). Twin research has also been used to examine dimensional approaches to BP and psychiatric comorbidity between BPD and other disorders. Dimensional models conceptualize personality pathology as stemming from extremes of continuously distributed personality traits (Skodol et al., 2005; Lenzenweger & Depue, in press). The combination of high neuroticism and low agreeableness best predicts BP; high neuroticism, low agreeableness, and low conscientiousness are genetically influenced vulnerabilities to BP (Distel, Trull, et al., 2009). SII also aggregates within families, including suicide and suicide attempts (e.g., Brent & Mann, 2005). Increased concordance of SII is observed among monozygotic (MZ) compared with dizygotic (DZ) twins (Baldessarini & Hennen, 2004), and one population-based study found that risk for suicide was higher among full siblings compared with maternal half-siblings, despite exposure to similar family contexts. Biologically related cousins also exhibited higher rates of suicide than controls. Despite clear heritable effects, there are also shared environmental influences within families. Adoption studies are consistent with these results. Adoptees of biological relatives who complete suicide consistently display higher rates of suicide completion themselves compared with adoptees of relatives who die by other causes (Tidemalm et al., 2011; Ernst, Mechawar & Turecki, 2009). Furthermore, parental suicide confers similar estimates of suicide risk for children who were adopted away versus those reared with their biological families (Ernst et al., 2009). Wilcox and colleagues (2012) found children who are exposed to their adoptive mother’s psychiatric hospitalization are at higher risk for being hospitalized for a suicide attempt themselves only if they also had a biological parent with SII. Across nearly every study, familial risk for SII persists even with other psychiatric disorders included as covariates. Following such adjustments, researchers find a 2- to 12-fold increase in rates of suicide among first-degree relatives of suicide victims (Brent et al., 2015; Mann et al., 2009). Although a proliferation of research has sought to pinpoint the heritable contributions to SII, the literature is replete with null results, idiosyncratic findings, and numerous failures to replicate (Turecki, 2014)—which is common in psychiatric genetics research (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). The inability to identify specific genetic alleles that confer vulnerability to SII speaks to the complexity of psychiatric problems (Brezo, Klempan & Turecki, 2008). Although the source of heritable risk for suicide is unknown, mood symptoms and Cluster B

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personality traits, such as impulsive aggression, are associated with suicide attempts and co-aggregate in suicide pedigrees (Brent & Mann, 2005; Bridge et al., 2015; McGirr et al., 2008; McGirr et al., 2009). Consistent with these results, family and twin studies support the hypothesis that trait impulsivity and emotional lability are vulnerabilities for SII and also for BPD. Impulsivity predisposes to several psychiatric disorders, with heritability coefficients of about .80 (Beauchaine et al., 2009; Krueger et al., 2002) whereas affective instability has heritability coefficients of about .50 (Livesley et al., 1998; Widiger & Simonsen, 2005). These traits co-aggregate in family members of those with BPD and SII more so than in relatives of individuals with other diagnoses (Silverman et al., 1991; Zanarini , Frankenberg, Yong, et al., 2004) and appear to arise from individual differences in neurotransmitter function (Beauchaine, Hinshaw, & Pang, 2010; Gratz, 2003; White et al., 2003).

GENETICS AND NEUROTRANSMITTER DYSFUNCTION The literature on biological vulnerabilities to SII and BPD has grown considerably in recent years. We focus our discussion on two monamine neurotransmitter systems that have received the most attention: serotonin (5HT) and dopamine (DA; see Beauchaine et al., 2009; Crowell et al., 2009; Depue, 2009; Gurvits, Koenigsberg, & Siever, 2000; Lenzenweger & Depue, in press; Turecki, 2014 for integrative reviews of neurotransmitter dysfunction, BPD, and SII). Although other biological systems are also relevant, space constraints prohibit a full review. Functional magnetic resonance imaging (fMRI) studies have identified reduced functional connectivity and dysfunctional activation patterns within dopaminergic fronto-limbic brain systems for individuals with BP (see Hughes, Crowell, Uyeji, & Coan, 2012). Studies measuring peripheral psychophysiology have also identified differences between healthy controls and those with BPD and SII across biological systems that govern behavioral inhibition and emotion regulation capacity (Crowell et al., 2005; Crowell et al., 2012; Crowell et al., 2013; Kuo & Linehan, 2009; Thorell, 2009). Finally, atypical acetylcholine, norepinephrine, endogenous opioids, and the limbic-hypothalamic-pituitary-adrenal axis functioning have also been implicated in the etiology of these conditions (LHPA; Bandelow, Schmahl, Falkai, & Wedekind, 2010; Coryell & Schlesser, 2001). Relations between dysregulated neurotransmitter activity and psychopathology are extremely complex (see Bandelow et al., 2010; Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011). However, some reliable findings have emerged. DA activity is consistently linked to novelty-seeking, reward dependence, impulsivity, and aggression (Beauchaine et al., 2010; Buckholtz et al., 2010; Castellanos & Tannock, 2002; Depue & Collins, 1999; Sagvolden, Aase, Johansen, & Russell, 2005). Both animal and human studies indicate that 5HT activity is associated with impulsive aggression, trait and state anxiety, and mood regulation (see e.g., Christianson et al., 2010; Silva et al., 2010). These neurochemical systems are functionally

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interdependent. For example, mesolimbic DA activity is also implicated in mood and emotion regulation (e.g., Dremencov, el Mansari, & Blier, 2009; Forbes & Dahl, 2012) and 5HT gates mesolimbic dopamine activity (Adell & Artigas, 2004). Thus, it is important not to oversimplify the functional roles that these neurotransmitters play in shaping complex forms of behavior.

Dopamine The mesolimbic DA system matures early in life and is a central neural substrate underlying disinhibition (see Beauchaine & McNulty, 2013; Kalivas & Nakamura, 1999). The mesolimbic DA network is highly sensitive to environmental input. For example, exposure to drugs and stress during pregnancy can alter DA signaling through epigenetic mechanisms, potentiating behavioral impulsivity (e.g., Hunter, Minnis, & Wilson, 2011). Mesolimbic DA dysfunction also arises in high-stress postnatal environments, such as those characterized by coercion, invalidation, and/or trauma, as experience-dependent effects influence the developing midbrain and cortical DA systems (Arnsten, 2009). Compromises to the early-maturing mesolimbic system can also affect neurodevelopment in the later-maturing mesocortical DA system (see Beauchaine et al., 2008; Sagvolden et al., 2005). The mesocortical DA system innervates brain regions involved in executive functions, such as decision making and planning (see, e.g., Floresco & Magyar, 2006) and dysfunction in this network is also associated with behavioral impulsivity (see, e.g., Kim & Lee, 2011). Furthermore, mesocortical and mesolimbic DA networks are interconnected through a feedback system (Louilot, LeMoal, & Simon, 1989) and compromised functional connectivity between these networks appears to be another neural substrate of impulsivity (see Beauchaine & McNulty, 2013). Hypodopaminergic functioning has been linked to suicide and behavioral traits characteristic of BPD, including SII, anger, emotional lability low positive affectivity, trait irritability, and trait impulsivity (Beauchaine et al., 2009; Osuch & Payne, 2009; Sagvolden et al., 2005; Sher & Stanley, 2009). Roy, Karoum, and Pollack (1992) measured urinary output of DA and its major metabolites (homovanillic acid and dihydroxyphenylacetic acid) and found that depressed suicide attempters had reduced peripheral markers of DA compared with depressed participants without a history of suicidal behavior. Moreover, patients who reattempted during a 5-year follow-up had significantly smaller urinary outputs of peripheral and total DA compared with those who did not reattempt suicide, those who had never attempted, and typical controls. This investigation corroborated earlier studies (Roy, Dejong, & Linnoila, 1989), suggesting that attenuated DA may contribute to suicidal behavior. Moreover, depleted levels of norepinephrine and dopamine metabolites have been found in the cerebrospinal fluid of individuals who completed suicide, providing further evidence that these neurotransmitters are depleted among this population (Roy et al., 1986; Mann & Currier, 2010).

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Research on the molecular genetics of DA neurotransmission and suicide has been mixed, but points to autoreceptors in the D2 family. These receptors control rates of DA synthesis and release (Harsing, 2008). For example, studies find reduced function in D2 receptors among individuals with a history of suicidal behavior (Pitchot et al., 2001). A well-characterized functional DRD2 polymorphism may be associated with suicide, but multiple alleles have been implicated across different samples (Johann, Putzhammer, Eichhammer, & Wodarz, 2005; Suda et al., 2009). The A2 allele of the TaqIA polymorphism is also associated with suicide risk, and impulsivity more broadly (Jasiewicz et al., 2014; Suda et al., 2009). Despite some support for a role of the DRD2 gene in suicidal behavior, there is a host of null findings and few studies examine the same polymorphisms (Giegling et al., 2008). Thus, further research is needed. Studies of DA among those with BPD are even more limited. A small study of adults with BPD with or without NSSI yielded no group differences in cerebrospinal fluid levels of DA or 5HT (Stanley et al., 2010). However, the authors did not include a non-BPD clinical group or healthy control group for comparison. BPD symptoms are associated with abnormalities on the 9-repeat allele of the dopamine transporter gene (DAT1, SLC6A3), even when childhood exposure to abuse and neglect are accounted for (Joyce et al., 2006). Another study conducted with two young adult samples found that the DRD4–616 C/G promoter variant was associated with BPD and two DRD2 polymorphisms (Taq1A and Taq1B) were related to impulsive, self-damaging behaviors (Nemoda et al., 2010). These findings support a hypodopaminergic model of BPD, yet this diagnosis is characterized by considerable heterogeneity. Further research with well-defined samples is needed and small-scale genetic associations should be interpreted with caution until replicated with Gene × Gene and Gene × Environment interaction studies (see Chapter 3 [Beauchaine, Gatzke-Kopp, and Gizer]).

Serotonin Serotonin is linked to a range of neurobehavioral processes. Impulsive aggression and mood dysregulation are associated with specific genetic polymorphisms and dysfunctions within the 5HT system. Moreover, deficits in central 5HT are linked with mood disorders, aggression, and suicidal behaviors among adolescents and adults (Amad et al., 2014; Mann, 2003; Osuch & Payne, 2009; Zalsman et al., 2011). Numerous studies find that individuals with BPD have a blunted prolactin response to fenfluramine challenge, suggesting reduced central 5HT activity (e.g., Soloff, 2000). Individuals who attempt and complete suicide also often have abnormal serotonergic neurotransmission (Mann & Malone, 1997; Roy et al., 1986) and several abnormalities related to serotonin expression appear to be linked to the pathogenesis of suicidal behavior (Ryding, Lindström & Träskman-Bendz, 2008). Genetic studies have focused on several 5HT candidate genes among those with BPD and SII, including those that code for tryptophan hydroxylase (TPH;

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a rate-limiting enzyme in the biosynthesis of 5HT), the promoter region of the serotonin transporter (5HTTlpr), the 5HT1b, 5HT1a, and 5HT2a receptors, and others (Ni, Chan, Chan, McMain, & Kennedy, 2009; Skodol, Siever et al., 2002). Surprisingly, recent meta-analytic findings did not support an association between BPD and TPH1 or 5-HTT (Amad et al., 2014). However, one meta-analysis revealed associations between TPH2 variants and BP symptoms such as emotional lability and SII as well as between certain serotonin receptor genes (HTR2A, HTR2C) and suicide, impulsive behavior, emotional lability, and other BP traits (see Amad et al., 2014). Similarly, a postmortem study of adolescent suicide victims found higher levels of 5HT2A proteins, 5HT2A receptors, and mRNA expression (Pandey et al., 2002). Longitudinal research following over 1,200 individuals for 22 years found three variants of the 5HTR2A gene interacted with physical and/or sexual abuse histories to predict suicidal behavior. Moreover, these genes were distinct from those that interacted with stress to predict depression, indicating the etiological pathways to that depression and suicidality are at least partially independent (Brezo et al., 2010). 5HT1A receptors have also received substantial attention, yet a recent meta-analysis of the most commonly studied functional polymorphism (rs6295) produced no association with suicidal behavior (González-Castro et al., 2013). The gene coding for the serotonin transporter (5HTT) is the most widely studied link between 5HT and SII (Zalsman, 2010). The gene that encodes 5-HTT in humans (SLC6A4) has a functional promoter polymorphism (5-HTTLPR) that is typically defined by two common variants—a short allele (s), and a long allele (l; Heils et al., 1996). The s variant is associated with lower transcription of SLC6A4 and reduced availability and function of 5-HTT in the brain (Canli & Lesch, 2007). Meta-analytic findings suggest that the s allele is associated with suicidal behavior independent of psychiatric diagnosis (Li & He, 2007), yet several studies indicate that the l allele may confer risk for suicidality (e.g., Shinozaki et al., 2013; Wang et al., 2009). Some have failed to replicate associations between 5-HTTLPR and suicidal behavior (e.g., Lee et al., 2015; Maurex, Zaboli, Öhman, Åsberg, & Leopardi , 2010) and recent research also claims there are three, rather than two, functional 5-HTTLPR variants. Thus, previous research using biallelic analyses may be undermined by the conflation of two functionally distinct l alleles (Risch et al., 2009). Some discrepancies in the literature may be explained by failure to account for environmental influences. Indeed, evidence suggests that 5-HTTPLPR low-activity variants interact with stressful life events to predict suicide risk (e.g., Caspi et al., 2003; Roy et al., 2009). One prospective study investigated variation in the 5HTTLPR gene and its relation to suicide ideation among maltreated and control children from low-SES environments (Cicchetti, Rogosch, Sturge-Apple, & Toth, 2010). Maltreated children were at highest risk for suicidal ideation, regardless of the number of s alleles. Furthermore, s allele status did not predict suicidal ideation among children who experienced many different forms of abuse. However, when risk exposure was lower (one to two types of abuse as compared to three to four) s-allele carriers (s/s, s/l) demonstrated higher levels of suicidal ideation than l/l-allele carriers.

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This finding converges with other reports in the literature, and demonstrates the complexity of G × E interactions (see McGuffin et al., 2010; Uher & McGuffin, 2009). As another example, interactions between short allele status (s/s or s/l) and stressful life events predict higher impulsivity scores among adults with BPD (Wagner, Baskaya, Lieb, Dahmen, & Tadifa, 2009). Finally, Lyons-Ruth (e.g., 2008) followed a high-risk sample from infancy to young adulthood and found that each short allele of the 5HTTLPR conferred a twofold increased risk for borderline or antisocial features. Thus, those with the s/s polymorphism were at fourfold higher risk. There are several explanations for why candidate gene association studies often produce conflicting or null results. BPD and SII are characterized by heterogeneity and comorbidities that emerge due to multiple genes and epigenetic processes. Environmental influences are rarely taken into account in candidate gene studies despite evidence of G × E interactions in BPD and SII development (Caspi et al., 2003; Distel, Rebollo-Mesa, et al., 2009; Distel et al., 2010; Distel, Carlier, et al., 2011; Distel, Middeldorp, et al., 2011). Unfortunately, researchers often search for genetic effects on psychopathology rather than for genetic effects that confer vulnerability to environmental risk (Caspi, Hariri, Holmes, Uher & Moffitt, 2010). Furthermore, psychologists and geneticists typically examine G × E interactions through different methods, which can lead to discrepant outcomes (Duncan, Pollastri & Smoller, 2014). Many studies have relatively small samples, which greatly limits statistical power. Finally, there is poor continuity of which genes and polymorphisms are investigated.

Other Biological Vulnerabilities Chronic stress exposure can lead to elevated LHPA axis responses. A growing body of research links altered negative feedback of LHPA axis, assessed via the dexamethasone suppression test (DST), to both SII and suicidal behavior. Although not a useful indicator of depression given inadequate sensitivity (many depressed individuals exhibit normal LHPA axis function; see Beauchaine & Marsh, 2006), nonsuppression of cortisol in response to the DST predicts future suicide among depressed individuals. Coryell and Schlesser (2001) found that depressed psychiatric inpatients who exhibited nonsuppression were at 14-fold greater risk of completed suicide across a 10-year period than depressed psychiatric inpatients with normal DST results. Meta-analytic results also support cortisol nonsuppression as a predictor of later death by suicide (Lester, 1992). Recently, Beauchaine, Crowell, and Hsiao (2015) examined post-DST cortisol levels as a correlate of suicidal ideation and SII among adolescent girls with histories of depression with and without self-harm. In contrast to Coryell and Schlesser’s findings, these authors observed that lower post-DST cortisol (i.e, greater suppression) was associated with suicidal ideation and SII, beyond both parental and combined parent-/self-reports of internalizing and externalizing behavior. These results are consistent with Joiner’s (2005) acquired capability hypothesis for SII whereby repeated exposure to painful or fearful experiences leads to blunted stress responding. Over time,

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repetitive SII may result in lowered self-preservation instinct, down regulation of stress responding, and higher likelihood of suicide. Several research groups have also observed LHPA axis dysregulation in BPD (Carrasco et al., 2007; Lieb et al., 2004; Wingenfeld et al., 2007; Wingenfeld, Spitzer, Rullkötter, & Löwe, 2010). Some have suggested cortisol nonsupression is driven by SII, PTSD, and/or a history of abuse, rather than BPD specifically (Krishnan, Davidson, Rayasam, & Shope, 1984; Wingenfeld et al., 2010). Yet, researchers recently investigated the contribution of genetic variants in HPA axis functioning among a large sample of individuals with BPD and a history of trauma, BPD without a history of trauma, and typical controls (Martín-Blanco et al., 2015). Their findings suggest a contribution of HPA axis genetic variants to the pathogenesis in both BPD groups, although unique polymorphisms were associated with childhood trauma. There is also emerging interest in neurobiological correlates of social affiliation and sensitivity to social threat among those with BPD (Bertsch et al., 2013; Lenzenweger & Depue, in press). Norepinephrine, oxytocin, vasopressin, and endogenous opiate activity have each been implicated in these processes (Heinrichs & Domes, 2008; Lenzenweger, 2010). Stanley and Siever (2010) propose that those with BPD may have low basal opioid levels in limbic circuitry coupled with a supersensitivity of 𝜇-opioid receptors. The opioid system plays a role in self-soothing, separation distress, and distress from social exclusion, whereas 𝜇-opioid receptors influence social behavior and affect regulation. Transient increases in opioid activity appear to be associated with heightened responses to painful cues, negative experiences, and emotions (Stanley & Siever, 2010). Therefore, oxytocin dysregulation in BPD may to contribute to difficulties establishing trust, reading social cues, and forming relationships. Stanley and Siever’s theory is supported by studies demonstrating that social threat hypersensitivity is reduced following administration of oxytocin (Bertsch et al., 2013). Neuroanatomical studies of SII risk and BPD have typically focused on the prefrontal cortex (PFC), particularly the ventromedial PFC and its connections with limbic system structures such as the amygdala (see Hughes et al., 2012, for a review). Some have theorized that PFC deficits contribute to suicidal and other impulsive behaviors by diminishing individuals’ capacity to inhibit strong impulses (see Mann, 2003). The ventromedial PFC is rich in both DA- and 5-HT-modulated neurons and is strongly connected with the basolateral amygdala—a region implicated in processing social and emotional stimuli (LeDoux, 1992; Shaw et al., 2005). Theories suggest that frontolimbic dysfunction among those with BPD leads to emotion regulation deficits and impulsive aggression (e.g., Davidson, Putnam, & Larson, 2000). Structural abnormalities in the amygdala and related brain regions have also been linked to BPD and SII risk directly. Gray matter volume in the left centromedial amygdala (responsible for behavioral responses in the face of emotional stimuli) is correlated negatively with BPD symptom severity (Niedtfeld, Schulze, Krause-Utz, Demirakca & Bohus, 2013). Although different studies have found conflicting volumetric results depending on which area of the

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amygdala is examined (see Schinele, Leutgeb & Wabnegger, 2015 for a review), meta-analytic findings show an average amygdala reduction of 13% for individuals with BPD compared to healthy controls (Ruocco, Amirthavasagam & Zakzanis, 2012). A recent voxel-based morphometry study found that compared to healthy controls, women with BPD had enhanced volume in the laerobasal region of the amygdala (Schinele et al., 2015). Further, SII was related to smaller somatosensory cortex volumes—a region that is typically activated in response to pain. Taken together, these results show relatively small structural abnormalities can map onto distinct behavioral markers of risk.

CONTEXTUAL AND FAMILY RISK FACTORS Poor impulse control and emotion dysregulation are core features of BPD that have empirically supported biological correlates. However, these traits are shaped considerably by environmental risk exposure. For example, impulsive youth who are biologically vulnerable and also raised in high-risk family and neighborhood environments are at heightened risk for substance use, teen pregnancy, and juvenile justice system involvement, compared with vulnerable youth who are raised in protective environments (Hallquist et al., 2015; Lynam et al., 2000). Context plays such a vital role in shaping biological vulnerabilities that although there are a wide range of externalizing disorders, they can largely be traced to a single, highly heritable latent trait (see Beauchaine & McNulty, 2013; Krueger et al., 2002). In fact, common vulnerabilities underlie internalizing and externalizing psychopathology broadly (Caspi et al., 2014; Lahey, et al., 2015). Risk factors often accumulate across development such that early contexts (e.g., family) influence later risk exposure in peer, school, romantic, and work contexts. There are a number of accounts of family-level risk factors for BPD (Barnow, Spitzer, Grabe, Kessler, & Freyberger, 2006). We draw from the extensive literature on the emergence of mood lability among impulsive youth as family processes that shape emotion dysregulation have been well-delineated in such samples and may translate to youth at risk for SII and BPD (see Beauchaine et al., 2009; Snyder, Edwards, McGraw, & Kilgore, 1994; Snyder, Schrepferman, & St. Peter, 1997). In fact, we have suggested that extreme emotional lability observed among those with BPD is shaped and maintained in high-risk developmental contexts characterized by intermittent reinforcement of emotionally labile behaviors and chronic invalidation of intense emotional expressions (Beauchaine & Zalewski, in press; Crowell et al., 2009). Because our model is transactional and ecological, we acknowledge that (a) children play an active role in shaping their environments and (b) the family system is affected by variables other than parenting (e.g., financial strain, isolation). We briefly review the literature on parenting and parent-child interactions, which provide rich targets for intervention. Researchers who study the development of externalizing behavior problems have found that repeated escalating exchanges between children and their parents

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function as a training ground for emotionally labile behavior patterns, which contribute to the emergence oppositional defiant disorder, conduct disorder, and ASPD (Beauchaine, Gatzke-Kopp, & Mead, 2007; Patterson, Chamberlain, & Reid, 1982; Patterson, DeBaryshe, & Ramsey, 1989; Patterson, Dishion, & Bank, 1984). These coercive interactions involve negative reinforcement of emotional outbursts (e.g., a tantrum is negatively reinforced when the parent withdraws the request to tidy up), excessive verbosity and nagging (e.g., prolonged debates following requests), and threats of punishment that are often not followed through. This pattern of escape conditioning gradually increases negative interactional patterns in both parent and child, leading to increased lability and anger. Importantly, the function of these increasingly aversive exchanges is to escape conflict (e.g., when yelling leads to child compliance the conflict ends). Thus, both parent and child are motivated to continue coercive patterns even though such exchanges are unpleasant. Although developed independently, Linehan’s (1993) developmental model of BPD proposes that the disorder emerges, in part, due to an invalidating caregiving environment. Within this context, a child’s expressions of emotion are often rejected, invalidated, and disregarded. As a consequence, the child learns to inhibit his/her emotions, especially those that are negative and unpleasant. She/he also learns to escalate emotional expressions to in order to generate a response from caregivers. Thus, invalidating environments haphazardly punish emotional expressions, while intermittently reinforcing extreme emotional displays. Both of these models claim emotional lability is shaped within families via operant conditioning and labile reactive parenting. The theories differ, however, in their hypotheses regarding when and why emotional outbursts occur. In Patterson’s coercion model, aversive behaviors emerge as a means of avoiding demands, whereas Linehan suggests that negative affect increases when the child has unmet emotional needs. Both processes may occur in youth at risk for BPD (see Beauchaine et al., 2009), but it is also possible that distinct operant conditioning patterns contribute to multifinality in the development of ASPD and BPD. Children at risk for BPD may also evoke different parenting strategies than children on an antisocial trajectory. Importantly, boys with ASPD and girls with BPD often come from the same families (Beauchaine et al., 2009), suggesting common vulnerabilities and risks. Given the interplay between biological vulnerabilities and psychosocial risk factors in the ontogenesis of BP, future studies should evaluate their interactions and transactions. Although cross-sectional, in a study of self-injuring adolescents and their mothers, dyads in the SII group expressed higher levels of negative affect and lower levels of positive affect and cohesiveness during a conflict task (Crowell et al., 2008). Adolescent serotonin levels interacted with dyadic negativity and conflict to account for 64% of the variance in self-injury, yet very little variance in SII was accounted for by main effects. Unfortunately, most studies of SII and BPD have not assessed Biology × Environment interactions. Research examining neglect and abuse—potent forms of invalidation—has produced conflicting results, possibly because biological vulnerabilities were not

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assessed. Although several studies have reported increased risk for SII among victims of abuse (See Lang & Sharma-Patel, 2011, for a recent review), meta-analytic findings indicate that the relation between childhood sexual abuse (CSA) and SII is relatively small (Klonsky & Moyer, 2008). The authors concluded that CSA is not causal in the development of SII and instead believe that the two are modestly related because of their correlation with the common risk factors. However, the studies included in this meta-analysis did not assess biological factors, such as the short allele of the 5HTTLPR, which is a known vulnerability for SII (see above). Similarly, the relation between childhood abuse and BPD has also been debated extensively. Many individuals with BPD describe a history of neglect (92%), physical abuse (25% to 73%), and/or sexual abuse (40% to 76%; Zanarini, 2000). Thus, researchers have examined trauma as one etiological factor in BPD development (Gratz, Latzman, Tull, Reynolds, & Lejuez, 2011; Soloff, Lynch, & Kelly, 2002). Prospective longitudinal research with female adolescent detainees indicates that posttraumatic stress, depressive symptoms, and dissociation during detention predicted BPD in adulthood (Krabbendam et al., 2015). Zanarini, Laudate, et al. (2011) examined retrospective reports of childhood sexual abuse among BPD participants enrolled in their 10-year longitudinal study. They reported six predictors of self-injury: (1) female sex, (2) dysphoric cognitions, (3) dissociative symptoms, (4) depression, (5) history of childhood sexual abuse, and (6) sexual assaults as an adult. Thus, traumatic experiences may increase risk for SII among those with BPD—yet the authors did not assess vulnerability factors. A more recent longitudinal discordant twin study used biometric modeling to examine heritable and environmental influences on links between child abuse, internalizing disorders, and externalizing disorders in childhood on BPD traits in adulthood (Bornovalova et al., 2013). Ultimately, there was little evidence for childhood abuse having a causal effect on BP. Rather, the association between childhood abuse and BP appear to stem from common genetic influences that, in some cases, also overlap with internalizing/externalizing disorders. Thus, BPD traits in adulthood may be best accounted for by heritable vulnerabilities to internalizing and externalizing disorders. Consistent with these findings, Gratz and colleagues (2011) found core vulnerabilities and risk factors to psychopathology, including impulsivity, affective instability, and emotional abuse each accounted for a significant amount of the variance in childhood BP features. They also examined the interaction between affective dysfunction (defined as emotional lability, sensitivity, intensity, and reactivity) and emotional abuse and found that emotional abuse predicated BP features for youth who were high on affect dysregulation but not for those who scored low on this trait. Thus, although there appears to be a relation between BPD and childhood maltreatment, researchers highlight the importance of viewing no single event as causal in the pathogenesis of BPD (Zanarini et al., 1998a). An important theme across theories of parenting and SII/BPD is one of emotional lability and/or instability. Coercive family processes involve intermittent reinforcement patterns and inconsistent parenting strategies. Similarly, the invalidating

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environment theory suggests that emotional displays are only occasionally effective for the child to get his or her needs met, resulting in increasingly distressed communication patterns. Taken together, these theories indicate that static measures of parenting and the parent-child relationship may miss important family dynamics. For example, clinicians report that parents of self-injuring adolescents are neither lax nor controlling but vacillate between both extremes (Miller et al., 2007). Future research should examine mechanisms through which parental lability fosters dysregulation in children using repeated measures approaches (e.g., daily diaries) across extended time periods.

SUMMARY AND CONCLUSIONS In this review, we have highlighted how BPD and SII develop through Biology × Environment interactions. The relative weights and composition of specific vulnerabilities and risk factors vary widely across individuals and developmental stages, although some appear more commonly than others. It is understandable that studies have produced diverse findings regarding the etiology of these conditions. We have also presented our biosocial developmental model of BPD and evidence in favor of our developmental hypotheses. We additionally theorize that early-emerging BP features exacerbate risk for adult BPD by impairing youths’ ability to navigate stage salient developmental tasks, form supportive interpersonal relationships, and develop effective regulatory strategies. Although longitudinal research following children and adolescents with BP features is only beginning to emerge (e.g., Bernstein, Cohen, Skodol, Bezirganian, & Brook, 1996; Burke & Stepp, 2012; Crick, Murray-Close, & Woods, 2005; Conway, Hammen & Brennan, 2015; Stepp et al., 2012), there can be little doubt that BP traits cause significant impairment and risk for suicide. Unfortunately, much of the available literature measures risk factors and biological vulnerabilities in isolation, limiting our knowledge of how they act together to bring about SII and/or BPD. Ideally, future studies will map heritable vulnerabilities beginning in infancy and assess neurological functioning, parental psychopathology, and health behaviors across development to better elucidate the specific etiological pathways to disorder. Contextual risk and behavioral outcomes should be examined at multiple levels of analysis. By early adolescence, researchers can assess BPD criteria with developmentally normed measures and follow vulnerable youth forward into adulthood to better understand the factors influencing the multifinal outcomes often observed among this population. Understanding the pathogenesis of BPD and the specific mechanisms by which this disorder arises will allow us to better prevent its occurrence. Because the BPD diagnosis has historically been stigmatized and understudied among youth, adolescents with BP often go unidentified and untreated. Providing early intervention for those with any BPD traits could fundamentally alter the developmental trajectory away from BPD and toward more adaptive outcomes.

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Zalsman, G., Patya, M., Frisch, A., Ofek, H., Schapir, L., Blum, I., . . . Tyano, S. (2011). Association of polymorphisms of the serotonergic pathways with clinical traits of impulsive-aggression and suicidality in adolescents: A multi-center study. World Journal of Biological Psychiatry, 12, 33–41. Zanarini, M. C. (2000). Childhood experiences associated with the development of borderline personality disorder. Psychiatric Clinics of North America, 23, 89–101. Zanarini, M. C., Barison, L. K., Frankenburg, F. R., Reich, D. B., & Hudson, J. I. (2009). Family history study of the familial coaggregation of borderline personality disorder with axis I and nonborderline dramatic cluster axis II disorders. Journal of Personality Disorders, 23, 357–369. Zanarini, M. C., Frankenburg, F. R., Dubo, E. D., Sickel, A. E., Trikha, A., Levin, A., Reynolds, V. (1998a). Axis I comorbidity of borderline personality disorder. American Journal of Psychiatry, 155, 1733–1739. Zanarini, M. C., Frankenburg, F. R., Dubo, E. D., Sickel, A. E., Trikha, A., Levin, A., Reynolds, V. (1998b). Axis II comorbidity of borderline personality disorder. Comprehensive Psychiatry, 39, 296–302. Zanarini, M. C., Frankenburg, F. R., Hennen, J., Reich, D. B., & Silk, K. R. (2004). Axis I comorbidity in patients with borderline personality disorder: 6-year follow-up and prediction of time to remission. American Journal of Psychiatry, 161, 2108–2114. Zanarini, M. C., Frankenburg, F. R., Reich, D. B., & Fitzmaurice, G. (2012). Attainment and stability of sustained symptomatic remission and recovery among patients with borderline personality disorder and axis II comparison subjects: A 16-year prospective follow-up study. American Journal of Psychiatry, 169, 476–483. Zanarini, M. C., Frankenburg, F. R., Yong, L., Raviola, G., Reich, D. B., Hennen, J., . . . Gunderson, J. G. (2004). Borderline psychopathology in the first-degree relatives of borderline and axis II comparison probands. Journal of Personality Disorders, 18, 449–447. Zanarini, M. C., Horwood, J., Wolke, D., Waylen, A., Fitzmaurice, G., & Grant, B. F. (2011). Prevalence of DSM-IV borderline personality disorder in two community samples: 6,330 English 11-year-olds and 34,653 American adults. Journal of Personality Disorders, 25, 607–619. Zanarini, M. C., Laudate, C. S., Frankenburg, F. R., Reich, D. B., & Fitzmaurice, G. (2011). Predictors of self-mutilation in patients with borderline personality disorder: A 10-year follow-up study. Journal of Psychiatric Research, 45, 823–828. Zilboorg, G. (1936). Differential diagnostic types of suicide. Archives of General Psychiatry, 35, 270–291. Zlotnick, C., Mattia, J. I., & Zimmerman, M. (1999). Clinical correlates of selfmutilation in a sample of general psychiatric patients. Journal of Nervous and Mental Disease, 187, 296–301.

PART V

OTHER DISORDERS

C H A P T E R 20

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents BRUCE D. PERRY

Essentially, all models are wrong, but some are useful. —George E. P. Box

F

or more than 150 years, trauma- and stressor-related problems have been studied extensively from multiple perspectives, including those represented by neuroscience, developmental psychology, genetics, epidemiology, the social sciences, medicine, and psychiatry, to name a few. These interdisciplinary perspectives, which are all reflected in the developmental psychopathology approach to conceptualizing mental illness (see Chapter 1 [Hinshaw]) bring different and often complementing insights. Yet, as these various perspectives have converged, defining and delineating “trauma- and stressor-related” disorders has become significantly more challenging and at times controversial. In some important ways, clinical work and research related to trauma among children and adolescents is at an important crossroads; multiple useful directions can, and will, emerge from this junction, but for students, clinicians, and researchers who are interested in trauma “disorders” and trauma-informed practice, program, and policy, this a messy but exciting time. A brief review of evolving formulations regarding trauma- and stressor-related mental health issues can provide perspective to current efforts to understand complex interrelationships among developmental experiences and physical, emotional, behavioral, social, and cognitive functions. 683

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HISTORICAL CONTEXT Humankind has always experienced chaos, threat, violence, rape, war, traumatic death, and a host of other known traumatic and stressful events. And humankind has always known the emotional toll that these experiences exact on individuals. Indeed trauma-related symptoms have been documented across all of recorded history. According to Abdul-Hamid and Hacker Hughes (2014), archeological records from Assyria in 1300 B.C. mention combat exposure causing King of Elam’s “mind change.” Homer’s Iliad (725 B.C.) describes the emotionally distraught Ajax after losing a competition with Odysseus for the fallen Achilles’ armor. Ajax comes under a “spell” from Athena, slaughters a herd of sheep thinking they are the enemy, and then when he comes to his senses, is shamed and commits suicide (later the basis for the famous play Ajax, by Sophocles: 450 B.C.). Herodotus (approx. 440 B.C.) describes trauma-like symptoms among warriors following the battle of Marathon. Hysterical blindness was described in one warrior after the man standing next to him was killed, although the blinded warrior “was wounded in no part of his body” (Waterfield & Dewald, 1998). Herodotus also wrote of the Spartan commander Leonidas, who, at the battle of Thermopylae in 480 B.C., dismissed men from combat knowing they were mentally exhausted from previous battles. Trauma-related syndromes similar to the current DSM-5 diagnosis of post-traumatic stress disorder (PTSD) were described as “irritable heart” of the U.S. Civil War (DaCosta, 1871) and “shell shock” following combat in World War I (Myers, 1915). Early neglect-related conditions similar to the DSM-5 diagnoses of reactive attachment disorder (RAD) have also been recorded throughout history. Frederick II, the Emperor of Germany, while seeking to determine the “language of God,” raised dozens of children in a silent, emotionally neglectful manner. These children spoke no language; and all of them died in childhood (see van Cleve, 1972). Study of the effects of stress and trauma on mental health played major roles in the emergence of modern neurology and psychiatry. Jean-Martin Charcot (1825–1893), who is often considered the founder of modern neurology, hypothesized that fits of “hysteria” and “hystero-epilepsy,” seen in both female and male patients, were associated with earlier traumatic experiences, including industrial accidents and combat exposure (see Ellenberger, 1970; Goetz, 1987). Pierre Janet (1859–1947), a student of Charcot, continued to study hysteria and hypnosis-induced trance states, and ultimately coined the term dissociation to describe detachments from reality that occurred when individuals with unspecified mental weaknesses were stressed. Present-day clinicians recognize these as common trauma-related symptoms. The historical record of the study of trauma in childhood is not as extensive. Sigmund Freud (1909), who was aware of the work of Charcot and Janet, described treatment of a specific phobia in a 5-year-old child, Hans. This was one of the earliest descriptions of potential trauma-related symptoms among children. Although Freud’s interpretation was somewhat complex, he made the observation of a previous distressing (if not traumatic) experience that might

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 685 account for Han’s specific fears related to horses. Hans’ family lived across from a coaching inn—a hotel where travelers in coaches could stop for food and lodging. Horses pulling heavily laden carts were most upsetting to Hans. As a younger child when he was outside with his nurse, he observed a horse collapse and die in the street. This horse was pulling a bus of passengers. Hans was frightened by the fallen horse and the clattering of its hooves against the cobbles. Again, the present day, trauma-informed clinician would describe cue-specific reactivity and avoidant symptoms in Hans’ behaviors. Creation of a specific conditioned fear response, and generalization of it to similar stimuli, was a foundational experiment of modern psychology. A pioneer of American psychology, John Watson, intentionally created a phobia in a toddler (Watson & Rayner, 1920). In the classic case study “Little Albert,” Watson created cue-specific reactivity by conditioning Albert to be fearful of a neutral cue. Albert demonstrated symptoms of intrusion, altered arousal, and avoidance—key symptoms of PTSD in the DSM-5. It is probable that many of the phobias in psychopathology are true conditioned emotional reactions either of the direct or the transferred type . . . . Emotional disturbances . . . must be retraced along at least three collateral lines—to conditioned and transferred responses set up in infancy and early youth in all three of the fundamental human emotions Watson and Rayner, 1920, p. 317 Another pioneer of psychology, Mary Cover Jones, reported the first progressive desensitization treatment of a young child when she successfully treated his phobia of rabbits and other soft and, white materials (Cover Jones, 1924). Core principles of this approach form the basis for some current evidence-based or evidence-informed treatments for trauma among both children and adults, including systematic desensitization and trauma-focused cognitive behavioral treatment (TF-CBT). Interestingly, Cover Jones did not believe successful treatment of Peter would persist. She suggested that poverty, maternal depression, permeating distress, chaos, and emotional abuse in the family would undermine his progress: His “home” consists of one furnished room which is occupied by his mother and father, a brother of nine years and himself. Since the death of an older sister, he is the recipient of most of the unwise affection of his parents. His brother appears to bear him a grudge because of this favoritism, as might be expected. Peter hears continually, “Ben is so bad and so dumb, but Peter is so good and so smart!” His mother is a highly emotional individual who can not get through an interview, however brief, without a display of tears. She is totally incapable of providing a home on the $25 a week which her husband steadily earns. In an attempt

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This clinical insight foreshadowed complexities that contribute to our current understanding and study of developmental trauma. It was in this same era when conceptualizations of homeostasis, stress, and distress were initially articulated. Walter Cannon (1914) coined the term homeostasis, and described physiological mobilization of multiple systems in the body under threat as the “fight or flight” response. This term continues to be used to encapsulate a complex array of emotional, behavioral, and physiological changes seen in arousal responses to threat. A less well-known area of Cannon’s work examined an extreme manifestation of dissociative responding—“voodoo” death (Cannon, 1942). The dissociative continuum involves a graded set of adaptive responses, including vasovagal activation, to immobilizing, inescapable, or painful stimuli/threat (Perry, Pollard, Blakely, Baker, & Vigilante, 1995; Porges, 2011). Under extreme threat (perceived or real) both “fight or flight” and dissociative responses can co-occur, leading to a complex mixture of physiological, emotional, behavioral, and cognitive responses (see Perry et al., 1995). Hans Selye (1936) first used the term stress in physiology to describe “nonspecific response of the body to any demand.” Selye’s organizing framework for understanding effects of stressors on the body—general adaptation syndrome—continues to be useful. This three-phase process begins with the organism at homeostasis. Once a stressor is perceived, the alarm phase begins, which is a sympathetic nervous system dominated “fight or flight” reaction. A second phase, resistance, involves efforts of the body to restore physiological functioning to homeostasis (i.e., back to normal). This involves activation of the parasympathetic nervous system. The third state, exhaustion, occurs if the stressor persists beyond the body’s capacity to restore homeostasis. This leads to dysfunction within organ systems in the body and potentially, death (see Chapter 4 [Compas, Gruhn, & Bettis]). As Selye wrote “Every stress leaves an indelible scar, and the organism pays for its survival after a stressful situation by becoming a little older” (Selye, 1936, p. 32). But this was just a start of understanding the complex role of stress and trauma in neuropsychiatric disorders.

ETIOLOGY Over the past 75 years, a torrent of research in neuroscience and related fields has explored various aspects of stress, distress, trauma, and resilience among both animals and humans. A major area of convergence is the central role that a set of neural

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 687 networks plays in maintaining homeostasis, mediating “alarm” and “resistance” responses to stress. These neural networks become altered via allostatic mechanisms when stressors are of sufficient duration, intensity or pattern (for reviews, see Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011; Mead, Beauchaine, & Shannon, 2010; Chapter 4 [Compas, Gruhn, & Bettis]). A set of neural systems (including adrenergic, noradrenergic, dopaminergic, and serotonergic) originate in the brainstem and diencephalon and have wide distribution throughout the brain. These networks comprise the core of a “bottom-up” component of the stress response. Along with a top-down set of neural networks (see Beauchaine, 2015; Rauch & Drevets, 2009), they provide integrated responses to novelty, challenge, and threat. Collectively, they modulate and regulate almost all brain functions, including those subserved by neuroendocrine systems including the limbic hypothalamic pituitary adrenal (LHPA) axis, neuroimmune systems, and the autonomic nervous system, thereby playing crucial roles in all of our variegated, heterogeneous stress response capabilities (see also Perry, 2008). Abnormal development or regulation of any component or subcomponent of one or more of these neural networks can result in functional problems and cause symptoms of psychopathology (see Beauchaine et al., 2011). There are many mechanisms through which the functional capacity of these systems can be affected. A brief overview follows.

Genetics Certain genetic vulnerabilities influence the nature and flexibility of individual’s stress responses. To date, most major candidate genes are associated with regulation of adrenergic, noradrenergic, and dopaminergic and serotonergioc neural networks. In animal models, for example, genetic differences in expression of phenylethanolamine N-methyltransferase (PNMT), an enzyme that converts noradrenaline to adrenaline (see Vantini et al., 1983) in two strains of rats lead to a cascade of group differences in stress-response neurobiology that have significant functional consequences, with one strain being more sensitive to stressors (Perry, Stolk, Vantini, Guchhait, & U’Prichard, 1983). Similar genetically mediated individual differences in sensitivity to stressors among humans are observed. Caspi and colleagues (2001, 2003), for example, reported that the short allele of the serotonin transporter gene (5-HTTLPR) confers vulnerability to depression following stressful life events, a finding that, despite being controversial early on (see e.g., Fergusson, Horwood, et al., 2011; Risch et al., 2009), has since gained acceptance following several replications (see Karg, Burmeister, Shedden, & Sen, 2011). Other gene-environment interactions involving differential sensitivity to stress have been reported for genes involved in production and regulation of monoamine oxidase (MAO-A; e.g., Fergusson,

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Boden, et al., 2011; Kim-Cohen, Caspi, Taylor, et al., 2006), corticotropin-releasing hormone (CRHR; Tyrka et al., 2009); and tyrosine hydroxylase (TH; see Cicchetti, Rogosch & Thibodeau, 2012). Nevertheless, much work remains. Smoller (2015) summarized the current status of this area in a recent review: Available data suggest that stress-related disorders are highly complex and polygenic and, despite substantial progress in other areas of psychiatric genetics, few risk loci have been identified for these disorders. Progress in this area will likely require analysis of much larger sample sizes than have been reported to date. The phenotypic complexity and genetic overlap among these disorders present further challenges Smoller, 2015, p. 297

Epigenetics Stressors of various kinds affect gene expression and regulation via DNA methylation and modification of histones (proteins that regulate DNA structure; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). These epigenetic alterations in DNA structure (as opposed to sequence) are best characterized in animal models, where true experiments—including random assignment to stressful and nonstressful conditions—can be conducted (see e.g., Meaney & Szyf, 2005). Maternal stress exposure at key prenatal periods can alter long-term function of several behavior regulation and stress response systems among offspring (e.g., Daskalakis et al., 2013), including both (a) monoamine neural networks implicated in mood and emotion regulation, motivation, social affiliation, and attachment (see Beauchaine et al., 2011); and (b) the LHPA axis, which, as outlined above, coordinates neural and neuroendocrine responses to stress (e.g., Lupien, McEwen, Gunnar, & Heim, 2009). Although epigenetics is a relatively new field, the potential impact of epigenetic mechanisms of intergenerational transmission of vulnerability (or resilience) to psychopathology may be profound. To date, however, studies among humans remain suggestive, not conclusive (Klengel & Binder, 2015). Although environmentally induced, epigenetic alterations in gene expression clearly accumulate across the lifespan (e.g., Fraga et al., 2005), and are dissociated with adverse rearing conditions (e.g., Tyrka, Price, Marsit, Walters, & Carpenter, 2012), drawing clear links to psychopathology is difficult without the capacity to conduct true experiments. Nevertheless, research demonstrates epigenetic changes in gene expression across an ever broadening range of psychiatric conditions (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). In a recent study that received considerable media attention, Yehuda and colleagues (2015) reported prenatally acquired FKBP5 methylation, which was presumed to be trauma-induced among Holocaust survivors and their offspring. It now seems clear that epigenetics will be

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 689 a major focus of future research into determinants of stress-responding and other behavior regulation systems.

Early Development A related area of research on etiologies of trauma- and stress-related disorders is the study of effects of early experience. Seymour Levine, a pioneer in this area, demonstrated that brief stressors in the infant period of rat pup development can result in dramatic alterations in neuroendocrine stress responding (e.g., Levine, 1957, 1994, 2005). Maternal nurturing behaviors (e.g., physical touch, grooming) are key to healthy development of the stress response systems among both rats and nonhuman primates (e.g., Schanberg, Evoniuk, & Kuhn, 1984). In these studies, timing and pattern of stress activation (or deprivation) is critical (Meaney, 2001; Claessens et al., 2011). Some stressors alter neurodevelopment when delivered at certain ages but not others, and some patterns of stress delivery (generally predictable, controllable, and moderate) result in healthier development, whereas other patterns (generally unpredictable, uncontrollable, or extreme) result in apparent sensitization (increased reactivity) of the stress response system to future challenges. Animal studies of maternal deprivation are homologous to observations of clinicians who work with institutionalized and severely neglected children (see Perry, 2002 for review). Early life stressors, without extreme deprivation, can also lead to abnormalities in stress-responding, and in functioning of other neural networks involved in reward processing, behavior regulation, and social affiliation (see Beauchaine et al., 2011; Broekman, 2011; Meaney, 2001; Perry, 2002; Tronick & Perry, 2015). Among humans, primary caregivers (often mothers) serve as external stress regulators for developing children (Beeghly, Perry & Tronick, 2016). Attentive, attuned, and responsive caregiving provides a pattern of stress response activation/deactivation (i.e., when the infant is hungry, cold, or thirsty, and therefore stressed, she cries—the alarm phase—and the caregiver responds, thereby returning the infant to homeostasis) that encourages a moderate, predictable, and controllable pattern of behavioral responding that leads to resilience. In contrast, overwhelmed, depressed, dysregulated caregivers struggle with consistency in responding, providing their infants with unpredictable, episodic care (and stress response activation/deactivation) that leads to sensitized stress reactivity and a cascade of secondary developmental sequelae (e.g. Perry, Hambrick & Perry, 2015). Other early developmental stressors such as poverty, with related food and housing insecurity, can create a sensitized pattern that leads to risk for health and mental health issues. Nurturing and supportive maternal care can buffer some of the adverse effects of poverty (Miller et al., 2011). Similarly, high quality early childhood programs for at-risk children can buffer some stress-related negative health outcomes associated with early childhood adversity (see Campbell et al., 2014).

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Childhood Adversity and Resilience Developmental adversity following infancy can also precipitate trauma- and stressor-related disorders, especially when coupled with neurobiological vulnerability. An expansive number of studies demonstrate (a) development of trauma-related neuropsychiatric disorders, including PTSD, following various forms childhood trauma (e.g., exposure to domestic violence, sexual abuse, catastrophic public events, maltreatment [for review see Saunders & Adams, 2014]); and (b) a role of childhood trauma as a causal or additive factor in expression of psychiatric disorders that are usually not conceptualized as being trauma- or stressor related, including major depression (see Teicher & Samson, 2013) and schizophrenia/psychotic disorders (see, e.g., Read, Perry, Moskowitz, & Connolly, 2001; Read, Fosse, Moskowitz, & Perry, 2014). Retrospective studies of effects of adversity (i.e., trauma- and stress-related problems) on all aspects of health and welfare has resulted in major shifts in policy and practice. The epidemiological Adverse Childhood Experience (ACE) studies (e.g., Fellitti et al., 1998) demonstrate that adversity in childhood results in “dose-dependent” increases in risk for the top nine major causes of death in adulthood. Risk for suicide, mental health problems, substance abuse, and a host of other untoward outcomes is also increased by childhood adversity (Anda et al., 2006). In fact, childhood adversity may play a role—at least for many individuals—in expression of most DSM disorders. Green et al. (2010), in a representative sample of 9,282 adults, found that childhood adversities (CAs), especially those in a maladaptive family functioning cluster (parental mental illness, substance abuse disorder, criminality, family violence, physical abuse, sexual abuse, and neglect) correlated strongly with onset of many DSM-IV disorders. Furthermore, simulations suggested that CAs are associated with 44.6% of all childhood-onset disorders, and 25.9% to 32.0% of later-onset disorders. These findings complement studies that examine the role of maltreatment in prevalent disorders of childhood (i.e., major depression, ADHD, conduct disorder, anxiety disorders). In a review and analysis of maltreatment as a major co-existing factor in DSM disorders, Teicher and Samson (2014) make the case that for any given disorder, maltreated versus nonmaltreated individuals should be conceptualized as distinct subtypes, and that an ecophenotype modifier be added to the DSM to facilitate research and clinical intervention. Clearly, developmental adversity, including trauma and exposure to extreme stress, can result in adverse outcomes. However, not all children who are exposed to trauma develop symptoms. In fact, there are identified vulnerabilities and risk factors that increase the likelihood of adjustment problems following trauma (e.g., previous history of exposure to trauma), and factors that predict resilience (e.g., nurturing families, community and cultural connections; see Ungar & Perry, 2012). Understanding resilience is crucial for understanding the etiology of trauma- and stress-related disorders, and for developing more effective treatments.

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 691 Cicchetti (2013) in reviewing research on resilience among maltreated children summarized the state of this area: “The majority of the research on the contributors to resilient functioning has focused on a single level of analysis and on psychosocial processes. Multilevel investigations have begun to appear, resulting in several studies on the processes to resilient functioning that integrate biological/genetic and psychological domains. Much additional research on the determinants of resilient functioning must be completed before we possess adequate knowledge based on a multiple levels of analysis approach that is commensurate with the complexity inherent in this dynamic developmental process.” Cicchetti, 2013, pp. 402 Complex interactions across levels of analysis (i.e., genes, the epigenome, neural systems, physiological networks, organ systems, individuals, families, communities, and cultures), and the developmental timing, patterns, intensity, and nature of stress-activating experiences (i.e., sensitizing vs. resilience-building) for any given individual imply a staggering number of potential phenotypic outcomes following developmental adversity and trauma (i.e., multifinality; see Chapter 1 [Hinshaw]). This is a major challenge to past, present, and proposed efforts to categorize, study, and diagnose trauma- and stressor-related mental disorders among humans.

DIAGNOSTIC ISSUES AND DSM-5 CRITERIA The DSM model of conceptualizing, categorizing, and naming psychiatric disorders based solely on symptom clusters has its origins in the 1800s and early 1900s. The first official categorization was the label of “idiocy/insanity,” which was part of the 1840 census. The National Commission on Mental Hygiene and the American Psychiatric Association (APA) developed a Statistical Manual for the Use of Institutions of the Insane in 1917. This precursor to the DSM included 22 diagnoses. The first DSM (DSM-I), published in 1952, specified 108 mental disorders (Grob, 1991). In the DSM-I, which was influenced heavily by Adolph Meyer’s psychobiology, all psychiatric disorders were characterized as reactions to stress (see Chapter 2 [Beauchaine & Klein]). Accordingly, all disorders had the word “reaction” in their titles (e.g., depressive reaction), including the stress-related diagnosis, “gross stress reaction.” Interestingly, this diagnosis disappeared in the second version of the DSM (DSM-II, 1968). Post-traumatic stress disorder (PTSD), the major trauma-related disorder in the DSM-5, did not appear in the DSM until 1980 (DSM-III). In 2013, the APA published the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The trauma and stressor-related disorders category reflects the most recent efforts of the APA and its appointed academic

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contributors and workgroup members to create diagnostic criteria to categorize neuropsychiatric signs and symptoms—and in some cases life histories—into meaningful clusters to promote further study and development of effective clinical interventions. In the DSM-5, trauma- and stressor-related disorders comprise a new category. Five distinct disorders are included: acute stress disorder (ASD), adjustment disorders (AD), disinhibited social engagement disorder (DSED), PTSD, and reactive attachment disorder (RAD). Two indistinct disorders are also included: other specified trauma- and stressor-related disorders and unspecified trauma- and stressor-related disorders. Diagnostic criteria for these disorders are summarized briefly below (each major diagnostic criterion is listed in capital letters).

Acute Stress Disorder A. Exposure to a trauma (see below for definition) B. Presence of nine (or more) symptoms from any of the five major symptom categories—intrusion, negative mood, dissociation, avoidance, and arousal—which appear to be associated with the traumatic event C. Duration of three days to one month after the trauma D. Impairment in functioning E. Symptoms not attributable to another cause (e.g., substance of abuse, medical condition, brief psychotic disorder)

Adjustment Disorders A. Development of emotional or behavioral symptoms in response to an identifiable stressor occurring within 3 months of the onset of stressor B. Clinically significant level of symptoms C. Symptoms do not meet criteria for another mental disorder D. Symptoms are not attributable to normal bereavement E. Once the stressor is gone, symptoms do not persist past 6 months

Disinhibited Social Engagement Disorder A. The child actively approaches and interacts with unfamiliar adults, in an overly familiar fashion B. Such approach behavior is not due to impulsivity (e.g., ADHD) C. The child has history of insufficient care such as described for RAD D. Such care is presumed to be causal to A E. The child has a developmental age of at least nine months

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Posttraumatic Stress Disorder Six years and up: A. Exposure to a trauma B. Intrusive symptoms (e.g., intrusive ideations, repetitive play with traumarelated themes, distressing dreams) C. Avoidant symptoms (e.g. avoidance of evocative cues or trauma-associated people, places, or experiences) D. Altered mood and cognitions (e.g., guilt, dysphoria, anhedonia) E. Altered arousal and reactivity (e.g., increase startle response, irritability, hypervigilance) F. Duration of more than one month G. Significant functional impairment H. Symptoms are not due to other causes (e.g., substance use, medical condition) Criteria for PTSD for children who are younger than age six years are essentially the same, aside from developmentally appropriate emotional, cognitive, and behavioral manifestations of intrusive, avoidant, affective, and arousal symptoms.

Reactive Attachment Disorder A. A consistent pattern of inhibited, emotionally withdrawn behavior toward adult caregivers, manifested by both B. Social and emotional disturbance C. Extremes of insufficient care (e.g., social neglect, institutionalization, repeated changes in primary caregiver); as well as D. Care in C that is presumed to be causal of A E. The behavior is not attributable to autism spectrum disorder F. Symptoms are evident before age five years G. The child has a developmental age of at least nine months Specific criteria for, and the very existence of, trauma- and stressor-related disorders in the DSM have changed multiple times since 1980, as newer versions were published. In the DSM-IV and DSM-IV-TR, for example, PTSD and ASD were categorized as anxiety disorders, whereas RAD was categorized as a disorder usually first diagnosed in infancy, childhood, or adolescence, AD was a stand-alone disorder, and DSED—a new disorder in the DSM-5—was previously a subtype of RAD (disinhibited attachment disorder). For ASD, AD, and PTSD, exposure to a traumatic or stressful event is a required diagnostic criterion. However, the definition of trauma is different in the DSM-5 than in the DSM-IV and DSM-IV-TR (see below). For RAD and DSED, social neglect (absence of necessary caregiving during

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childhood) is a required diagnostic criterion. As this summary implies, individuals who meet diagnostic criteria for trauma- and stressor-related disorders can display a remarkably heterogeneous range of emotional, behavioral, social, and cognitive symptoms, many of which overlap with other DSM-5 disorders (see discussion of comorbidity below).

PREVALENCE Prevalence rates of DSM-5 trauma- and stress-related disorders among children and adolescents are not well established. However, inferences can be drawn from the few studies that addressed prevalences of similar disorders in previous instantiations of the DSM, both among youth and adults. Rates of AD among adult clinical populations, for example, are high; in some cases up to 20% of outpatient samples. Prevalence rates of PTSD among children and adolescents are not well determined, but lifetime prevalence estimates of PTSD range from 8 to 12%. In contrast, RAD is likely rare, given that only about 10% of neglected children are affected (Gleason et al., 2011). Perhaps the more important question for both research and clinical purposes is the prevalence of traumatic experiences. Exposure to childhood adversity and trauma are common (Saunders & Adams, 2014). Public health surveillance using ACEs in multiple settings demonstrates very high rates of exposure to adversity among children and adolescents. In typical public school classrooms in the state of Washington, for example, only 6 in 30 children have an ACE score of 0, whereas 10 have an ACE score of 4 or more (Family Policy Council: WA). In juvenile justice populations, rates of exposure to multiple trauma are astoundingly high—in excess of 85% (e.g., Baglivio et al., 2014). In the National Survey of Children’s Exposure to Violence (NSA), 20% of all youth and 41% of victims of any of four types of victimization that were measured experienced more than one type. In fact, exposure to multiple types of victimization/trauma is very common among children and adolescents, characterizing 20% to 48% of all youth depending on the number of victimization types measured (Finkelhor, Turner, Shattuck, & Hamby, 2015). The prevalence of trauma and its complex heterogeneous outcomes poses a major challenge to the DSM model of delineating mental disorders (see also Chapter 2 [Beauchaine & Klein]).

Clinical and Research Challenges of DSM Model Since 1980, when PTSD was introduced in the DSM-III, clinicians and researchers have had to deal with complexities posed by developmental manifestations of trauma-related problems. A simple example is in conceptualization of Criterion A, experiencing a trauma. The DSM-5 defines trauma as “exposure to actual or threatened death, serious injury, or sexual violence in one or more of four ways: (a) directly experiencing the event; (b) witnessing, in person, the event occurring to others; (c) learning that such an event happened to a close family member

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 695 or friend; and (d) experiencing repeated or extreme exposure to aversive details of such events, such as with first responders. Actual or threatened death must have occurred in a violent or accidental manner, and experiencing cannot include exposure through electronic media, television, movies or pictures, unless it is work-related” (DSM-5, pp. 271). In order to meet diagnostic criteria for PTSD for adults and children older than age 6 years, the individual must endorse Criterion A. What is considered traumatic is different in the DSM-5 than in previous versions of the DSM; the subjective experience of the individual during the traumatic event is no longer part of Criterion A. Almost as soon as PTSD among children was described (Terr, 1983), clinicians began to see at least two subtypes. Single traumatic events often result in different presentations compared with multiple traumatic experiences. This led Terr (1991) to refer to Type I and Type II variants of childhood PTSD. Moreover, developmental trauma was associated such complex mixtures of symptoms that it could mimic many other DSM diagnoses. As described above, heterogeneous symptom clusters that are observed following trauma result in very high rates of comorbidity. Attention-deficit/hyperactivity disorder, conduct disorder, major depression, substance abuse disorder, dissociative disorders, and psychotic disorders are commonly co-diagnosed with PTSD. Strict application of DSM criteria yields combinations of comorbid diagnoses that are often of little use to clinicians, and produce major confounds for researchers. Complex developmental sequelae of trauma and neglect challenge the validity and clinical and research utility of DSM formulations of trauma-related disorders (see van der Kolk, 2005). During development of the DSM-5, academics and clinicians who work with traumatized and maltreated children, and others, urged, unsuccessfully, for adoption of a developmental trauma disorder to address some of these complexities (van der Kolk et al., 2009). Yet such an additional disorder cannot address the multidimensional and complex physiological, emotional, social, behavioral, and cognitive effects of developmental adversity, trauma, and neglect. As the statistician George Box said, “Essentially, all models are wrong, but some are useful.” For trauma-related disorders, the DSM model may have reached the limits of its utility. The major limitation of the DSM model involves defining disorders based upon symptoms—not pathophysiology (see Chapter 2 [Beauchaine & Klein]). In stark contrast, diagnosis in medicine focuses on identifying underlying disease processes/pathophysiology (see e.g., Beauchaine & Cicchetti, 2016). This model has evolved over the last 150 years, and was only possible following development of methods that allow more direct and detailed examination of organs and cells, (e.g., microscopes, x-ray, ultrasound, fMRI), and identification of dynamic physiological processes and biomarkers (e.g., chemicals, enzymes, DNA-related factors in blood and other tissue). These advances have allowed clinicians and researchers to move from a symptom and sign dominated model of diagnosing to a specific disease process model (Berger, 1999).

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The human brain, however, is both much more complex than any other organ in the body, and much less accessible for direct examination of functioning of its various neural networks. The brain has roughly 86 billion neurons, 420 trillion synapses, and 2.5 quadrillion depolarizations/min, which mediate hundreds of complex functions including speech, abstract cognition, and fine motor control. In comparison, the heart has roughly 2 billion cells and mediates only a handful of much simpler functions such as pumping blood. In 1952, when the DSM was first introduced, no technologies or lab tests existed to provide any basis for clustering neuropsychiatric conditions. At the time, symptom-based diagnosing was therefore logical and necessary. However, reliance on this system is vestigial. By the time newer technologies evolved to directly examine complex neural networks across various brain regions, the field of psychiatry, including its clinical practice, training programs, research frameworks and, medical-economic model, were all dependent on the DSM symptom clustering model. Although good arguments can be made that the so-called medical model of diagnosing disorders is inadequate for the complexities of neuropsychiatric problems, there is value in examining diagnostic practices for other diseases. This simple examination illustrates the nature of clinical and research problems that arises when using symptom-based clustering to define neuropsychiatric disorders. Problem 1: Similar Signs and Symptoms May Be Caused by Multiple Pathophysiological Processes (Equifinality). If a person presents at the emergency room with severe chest pain (a symptom) and high heart rate (a physical sign), the clinical team will need to determine the underlying cause in order to provide effective treatment. Chest pain and elevated heart rate can be caused by dozens of different pathophysiological processes including coronary artery blockage, pancreatitis, gall bladder problems, lung infections, indigestion, or a gastric ulcer. Although history and additional symptoms help narrow the search for actual causes of these problems, a set of tests that assess biomarkers helps evaluate the physiological status of the various organs and physiological processes that may be involved (e.g., elevated heart muscle enzymes in the blood indicate a heart attack, elevated white blood cells indicate infection, elevated liver enzymes indicate blockage of the gall bladder, ultrasound of the abdomen identify masses or blockages, x-rays of the chest identify lung infection). Biomarkers tell the clinical team about functioning of organ systems that may be responsible for symptoms. Once the actual pathophysiology is determined, a suitable intervention can be started (see also Beauchaine & Marsh, 2006). Perhaps no other category of DSM-5 disorders illustrates this potential problem in clinical settings as much as the trauma-related disorders. Consider a teacher who deals with an inattentive, restless, and generally dysregulated 10-year-old boy. His homework is rarely turned in on time, and is always messy and usually incorrect. He doesn’t finish tests on time, and his social skills are lagging. The teacher suspects he has ADHD and that he needs medication, and requests that his parents have

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 697 him evaluated for ADHD. The mother arranges an appointment with a pediatrician. At best, the busy pediatrician administers a set of attention and impulse-control focused metrics (e.g., the Conners Behavior Rating Scales) to the mother and the teacher, and takes a history of specific symptoms and current presentation in a very brief—perhaps 15 minute—appointment. Based on all information collected, the child meets diagnostic criteria for ADHD, and a psychostimulant is prescribed. Yet there are dozens of potential pathophysiological processes that can result in these symptoms, many of which are related to developmental stressors and trauma. The child may have experienced intrauterine exposure to alcohol or other teratogens, in which case these symptoms are part of a more complex constellation of problems (see Chapter 9 [Doyle, Mattson, Fryer, & Crocker]). Alternatively, the mother may have experienced serious postpartum depression that reduced her capacity to be attuned and responsive in the first months of her infant’s life, resulting in dysregulated stress-responding and ADHD-like symptoms (see Beeghly et al., 2016). The child may have experienced trauma-related alterations in monoamine function and/or stress-responding following exposure to domestic violence, sexual abuse, physical abuse, and/or community violence (see above Beauchaine et al., 2011). Thus, the nature, timing, and severity of a host of adversities during development could result in the symptoms this child is demonstrating (Anda et al., 2006; Teicher & Samson, 2013). It is highly likely that primary informants (i.e., the child, parent, and teacher) are all unaware of relations between past experiences (e.g., domestic violence when the child was ages 4 to 6 years old, community violence, sensitizing distress of poverty), and current symptoms. Even when a clinic screens for some of these events, caregivers may be unwilling to report current traumatic experiences that underlie symptom expression (e.g., ongoing physical or emotional abuse). Furthermore, imagine a research project in which the pathophysiology of ADHD (or any other DSM-5 disorder that is affected by developmental trauma) is studied, and this child (and dozens more like him with similar developmental adversities and traumatic experiences) are recruited. Any specific pathophysiology will be drowned out in the complex noise of heterogeneous pathophysiologies of equifinal routes to problems with attention, impulse control, and behavior regulation. Similarly, an outcome study that examines effects of an intervention, whether behavioral or pharmacological, in which children are recruited based on DSM symptom clusters, will have mixed results. Indeed, the greater the number of heterogeneous, equifinal pathophysiologies to a disorder, the less robust any finding will be, and our ability to replicate will be much more difficult because the relative ratios of heterogeneous pathophysiologies in any sample will vary from study to study. The end result is a never-ending, tail chasing research process that is confounded by extensive comorbidities, creation of apparent subtypes of primary disorders, and inability to replicate findings. A brief examination of the literature on most child and adolescent DSM disorders bears this out. For years, few studies in our field that used DSM disorders as a primary differentiator even addressed

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developmental adversity, histories of attachment disruption, trauma-related experiences, or histories of resilience factors, which all influence neuropsychiatric phenotypes. Thus, the DSM, symptom-based model poses major obstacles for both basic and applied research. This illustrates the need for (a) widespread capacity building in health, mental health, and education about relations between trauma, adversity, and neuropsychiatric problems; (b) the need for clinical assessments to include more detailed histories of developmental adversities and resilience-related factors; and (c) the need for research focused on mechanisms to include detailed developmental histories of the nature, timing, and severity of adversities and potential buffering resilience-related factors, in order to create more homogeneous groups. Problem 2: A Single Disease Process May Have Heterogeneous Manifestations of Symptoms Dependent upon Factors Such as Sex, Developmental Timing, and Potentiating or Attenuating Conditions (Multifinality). When a diagnosis (a disorder or a disease) is connected to an underlying pathophysiology (mechanism) there can be many different clinical presentations (clusters of symptoms and physical signs) as a result (see also Beauchaine & Cicchetti, 2016; Beauchaine & McNulty, 2013). For example, coronary heart disease may not cause chest pain—it may cause numbing and tingling of the left arm or jaw. It may cause nausea. It may merely cause shortness of breath and exhaustion. Symptoms of coronary heart disease often manifest differently in women compared to men. Yet the treatment for coronary heart disease is determined by the extent and specific location of the blockage—not the symptom cluster. Even with the capacity to examine and measure mechanism-related biomarkers, the process of sorting and clustering into similar “diseases” and defining disease-targeting interventions is complex. As the previous sections of this chapter outline, dysregulation of key neural networks can lead to heterogeneous symptoms. Stress-related neural networks are so extensive, and so many factors play roles in their maturation, that regulation and ongoing neuroplasticity render intervention research very difficult. Development of individualized treatment interventions based on genotypes, phenotypes, and physiologies has emerged in other areas of medicine. Similar efforts are underway in psychiatry, but due to the complexity of development this is a daunting task. A maltreated 12-year-old child, for example, may have the self-regulation capacity of a neurotypical 3-year-old, the social skills of an infant, and cognitive capabilities of a 5-year-old. And, due to unique genetic, epigenetic, and developmental histories of each child, it is usually ineffective to apply a “one-size-fits-all” therapeutic approach (Ungar & Perry, 2012). The Neurosequential Model of Therapeutics© (NMT) is one approach to clinical problem solving that attempts to incorporate this complexity into a practical assessment and treatment planning process (Perry, 2006, 2009; Perry & Dobson, 2013). This assessment method examines and quantifies the timing, nature, and severity of adversity

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 699 and resilience-related experiences, as well as current functioning, across multiple functional domains (e.g., sensory motor, regulatory, relational, and cognitive). The NMT creates a matrix of both developmental experience and current functioning across multiple domains, which allows clinical teams to select and sequence therapeutic, educational, and enrichment interventions in a developmentally sensitive fashion. In this regard, the NMT is conceptually similar to the emerging Research Domain Criteria (RDoC) being developed by the National Institutes of Mental Health (NIMH; e.g., Insel et al., 2010).

RESEARCH DOMAIN CRITERIA Advancing research in developmental trauma, especially mechanism-focused research, will be impossible without addressing confounds of neurodevelopmental heterogeneity. Much larger sample sizes will be required for the multiple levels of analysis research that is required to truly address pathophysiological and other mechanisms related to developmental adversity, including genetic, epigenetic, neurochemical, neurophysiology, neural connectivity, neural networks and regions, individual emotional, social, cognitive and behavioral functioning, caregiver interactions, family composition and function, community strengths and vulnerabilities, and transgenerational cultural and historical factors. For research purposes, the RDoC have stepped away from the DSM-5 nosology to adopt a matrix approach to systematically gathering data across multiple levels of analysis for five key behavioral domains (negative valence system; positive valence systems; cognitive systems, social processes, and arousal/regulatory systems). Each domain has primary constructs and subconstructs (e.g., for social processes, social communication is a construct). There are multiple levels of analysis represented, spanning genes through paradigms (see Insel, T. (2013)). The RDoC model will greatly enhance research, and, ultimately, clinical work with children and adolescents who are affected by trauma and adversity. One potential weakness of the RDoC is an apparent benign neglect of the importance of developmental history of adversity and resilience-related experience (see Beauchaine & Cicchetti, 2016). A more intentioned focus on developmental history would add a crucial dimension to the existing matrix.

SYNTHESIS AND FUTURE DIRECTIONS The human brain is complex. The multiple dimensions of human development and functioning that can be examined and used to cluster individuals into groups is staggering. Efforts of the APA—via the DSM model—to perform this task for trauma-related neuropsychiatric presentations by creating meaningful clusters for further study have come up against the reality of this complexity. In all of the historical and academic descriptions of stress, trauma, and attachment related

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problems, mechanisms underlying symptoms were hypothesized. Whether mental health problems were conceptualized as caused by spirits of warriors killed in battle, Athena messing with your head, “mental weakness,” unresolved conflicts involving the id, classical conditioning and subsequent generalization of a specific conditioned response, some pathological process was “underneath” the expression of trauma and stress-related symptoms. As in all areas of science, mechanisms matter. Your understanding of the problem determines your solution. If you believe the problem is with gods, you appease the gods by whatever process you think will work, or the spirits of fallen warriors, or resolve internal conflict through an analysis; the point is that the intervention selected is intended to address the source of the problem. Current understandings of mental disorders recognize the brain as a major mediator of dysfunctions in emotional, behavioral, social, and cognitive functioning. Decades of quality academic work have observed, sorted, sifted, and analyzed symptoms and symptom clusters associated with adversity and trauma. There have been advances from this careful, deliberate work. Yet we are at an impasse. The complex and interactive effects of genetic, epigenetic, intrauterine, early perinatal experience, and ongoing neuroplasticity of key neural networks, including stress-mediating networks, all responsive to both good and bad experiences, collectively mean that human functioning in multiple domains is affected by myriad factors including caregiving, education, social milieus, and cultures, to name but a few. Advancing our capacity to understand trauma and stress-related problems (whether framed as DSM constructs or not) will require taxonomies and nosologies beyond mere clustering of symptoms (Chapter 2 [Beauchaine & Klein]). Adding another DSM diagnosis or two or five will not help much if at all, nor will adding ecophenotype qualifiers to the existing DSM. Major advances in this area will require more dramatic shifts in frame of reference. More developmental- and neuroscience-informed models are required.

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Perry, B. D. (2008). Child maltreatment: A neurodevelopmental perspective on the role of trauma and neglect in psychopathology. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Child and adolescent psychopathology (pp. 93–128). Hoboken, NJ: Wiley. Perry, B. D. (2009). Examining child maltreatment through a neurodevelopmental lens: Clinical applications of the Neurosequential Model of Therapeutics. Journal of Loss and Trauma, 14, 240–255. Perry, B. D., & Dobson, C. (2013). Application of the Neurosequential Model of Therapeutics (NMT) in maltreated children. In J. Ford & C. Courtois (Eds.), Treating complex traumatic stress disorders in children and adolescents (pp. 249–260). New York, NY: Guilford Press. Perry, B. D., Hambrick, E., & Perry, R. D (2015). A neurodevelopmental perspective and clinical challenges. In R. Fong & R. McCoy (Eds.), Transracial and intercountry adoptions (pp. 126–153). New York, NY: Columbia University Press. Perry, B. D., Pollard, R., Blakely, T., Baker, W., & Vigilante, D. (1995). Childhood trauma, the neurobiology of adaptation, and ‘use-dependent’ development of the brain: How “states” become “traits.” Infant Mental Health Journal, 16, 271–291. Perry, B. D., Stolk, J. M., Vantini, G., Guchhait, R. B., & U’Prichard, D. C. (1983). Strain differences in rat brain epinephrine synthesis: Regulation of alpha-adrenergic receptor number by epinephrine. Science, 221, 1297–1299. Porges, S. W. (2011). The Polyvagal Theory: Neurophysiological foundations of emotions, attachment, communication, and self-regulation. New York, NY: W. W. Norton. Rauch, S. L., & Drevets, W. C. (2009). Neuroimaging and neuroanatomy of stress-induced and fear circuitry disorders. In G. Andrews, D. S. Charney, P. J. Sirovatka, & D. A. Regier (Eds.), Stress-induced and fear circuitry disorders: Refining the research agenda for DSM-V (pp. 215–254). Arlington, VA: American Psychiatric Press. Read, J., Perry, B. D., Moskowitz, A., & Connolly, J. (2001). The contribution of early traumatic events to schizophrenia in some patients: A traumagenic neurodevelopmental model. Psychiatry, 64, 319–345. Read, J., Fosse, R., Moskowitz, A., & Perry, B. D. (2014). Traumagenic neurodevelopment model of psychosis revisited. Neuropsychiatry, 4, 1–15. Risch, N., Herrell, R., Lehner, T., Liang, K.-Y., Eaves, L., Hoh, J., . . . Merikangas, K. R. (2009). Interaction between the serotonin transporter gene (5-HTTLPR), stressful life events, and risk of depression: A meta-analysis. Journal of the American Medical Association, 301, 2462–2471. Saunders, B. E., & Adams, Z. W. (2014). Epidemiology of traumatic experiences in childhood. Child and Adolescent Psychiatric Clinics of North America, 23, 167–184. Schanberg, S., Evoniuk, G., & Kuhn, C. M. (1984) Tactile and nutritional aspects of maternal care: Specific regulators of neuroendocrine function and cellular development. Proceedings of the Society for Experimental Biology and Medicine, 175, 135–46. Selye, H. (1936). A syndrome produced by diverse nocuous agents. Nature, 196, 32. Smoller, J. W. (2015) The genetics of stress-related disorders: PTSD, depression, and anxiety disorders. Neuropsychopharmacology Reviews, 41, 297–319.

Trauma- and Stressor-Related Disorders in Infants, Children, and Adolescents 705 Teicher, M. H., & Samson, J. A. (2013). Childhood maltreatment and psychopathology: A case for ecophenotypic variants as clinically and neurobiologically distinct subtypes. American Journal of Psychiatry, 170, 1114–1133. Terr, L. (1983). Chowchilla revisited: The effects of psychic trauma four years after a school-bus kidnapping. American Journal of Psychiatry, 140, 1543–1550. Terr, L. (1991). Childhood traumas: An outline and overview. American Journal of Psychiatry, 148, 1–20. Tronick, E., & Perry B. (2015). Multiple levels of meaning-making: The first principles of changing meanings in development and therapy. In G. Marlock H. Weiss, C. Young, & M. Soth (Eds.), Handbook of body psychotherapy and somatic psychology (pp. 345–355). Berkeley, CA: North Atlantic Books. Tyrka, A. R., Price, L. H., Gelernter, J., Schepker, C., Anderson, G. M., & Carpenter, L. L. (2009). Interaction of childhood maltreatment with the corticotropinreleasing hormone receptor gene: Effects on hypothalamic-pituitary-adrenal axis reactivity. Biological Psychiatry, 66, 681–685. Tyrka, A. R., Price, L. H., Marsit, C., Walters, O. C., & Carpenter, L. L. (2012). Childhood adversity and epigenetic modulation of the leukocyte glucocorticoid receptor: Preliminary findings in healthy adults. PloS One, 7, 1. Ungar, M., & Perry, B. D. (2012). Trauma and resilience. In R. Alaggia & C. Vine (Eds.), Cruel but not unusual: Violence in Canadian families (pp. 119–143). Waterloo, Ontario, Canada: Wilfred Laurier University Press. van Cleve, T. C. (1972). The Emperor Frederick II of Hohenstufen, Immutator Mundi. New York, NY: Oxford University Press. van der Kolk, B. A. (2005). Developmental trauma disorder: Towards a rational diagnosis for children with complex trauma histories. Psychiatric Annals, 33, 401–408. van der Kolk, B., Pynoos, R. S, Cicchetti, D., Cloitre, M., D’Andrea, W., Ford, J. D., . . . Teicher, M. (2009). Proposal to include a developmental trauma disorder diagnosis for children and adolescents in DSM-V. Unpublished manuscript. Vantini, G., Perry, B. D., Hurst, J. H., Guchhait, R. B., Elston, R. C., U’Prichard, D. C., & Stolk, J. M. (1983). Genetic differences in phenylethanolamine N-methyltransferase activity in rats. Psychopharmacology Bulletin, 19, 616–619. Waterfield, R. (Trans.), & Dewald, C. (Ed.). (1998). The Histories by Herodotus. New York, NY: Oxford University Press. Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions. Journal of Experimental Psychology, 3, 1–14. Yehuda, R., Daskalakis, N. P., Bierer, L. M., Bader, H. N., Klengel, T., Holsboer, F., & Binder, E. B. (2015). Holocaust exposure induced intergenerational effects on FKBP5 methylation. Biological Psychiatry.

C H A P T E R 21

Bipolar Disorder JOSEPH C. BLADER, DONNA J. ROYBAL, COLIN L. SAUDER, AND GABRIELLE A. CARLSON

HISTORICAL CONTEXT

B

ipolar disorder (BPD) describes a pattern of cyclic mood disturbance that usually crystallizes by early adulthood and persists or recurs for decades. Severity and course vary among affected individuals, but overall it is a pernicious psychiatric illness that heightens risk for early mortality (Dutta et al., 2007; Khalsa et al., 2008). It is a leading cause of disability worldwide (Merikangas et al., 2011; World Health Organization, 2002).

Bipolar Disorder as a Diagnostic Entity Recognition of mood disturbance characterized by oscillations between extremes of melancholy and excitement was articulated early in Western medical writings. Aretaeus of Cappadocia, a major figure in the history of medicine who is thought to have practiced in Alexandria and Rome during the first to second centuries, often receives credit for the first description of bipolar episodes as a single illness. In more modern times, Baillarger’s (1809–1891) and Falret’s (1794–1870) descriptions of a cyclical disturbance of depression and manic excitement established the entity in the emerging discipline of psychiatry (Angst & Marneros, 2001; Sedler, 1983). Emil Kraepelin (1856–1926) elaborated on these accounts, providing the designation manic-depressive insanity (Kraepelin, 1921). His goal was to refine the broader concept of psychosis by distinguishing forms of mania and depression as fundamental disturbances of mood, from dementia praecox as a fundamental disturbance of cognition. The latter category, as amended by Eugen Bleuler (1857–1939), was the forerunner of today’s concept of schizophrenia. Early efforts to systematize psychiatric diagnosis reflect this scheme. Both the first and second editions of the 706

Bipolar Disorder 707 DSM included manic-depressive illness and schizophrenia in sections on psychosis (American Psychiatric Association, 1952, 1968). The metaphor of manic and depressive states as “poles” was first articulated by Karl Leonhard in the 1950s (Leonhard & Beckmann, 1999), and the term bipolar disorder was incorporated into DSM-III in 1980. The DSM-III also abandoned psychoses and neuroses as superordinate categories of disorders, and a new “Mood Disorders” category appeared, which included various forms of both bipolar disorder and unipolar depression. The DSM-5 includes separate chapters for “Bipolar and Related Disorders” and “Depressive Disorders” (issues are reviewed by Goldberg, Andrews, & Hobbs, 2009).

Bipolar Disorder in Children and Adolescents Our understanding of bipolar disorder among youth began with attempts to identify children and adolescents who exhibited versions of the manic-depressive condition Kraepelin described. Child psychiatrists in the 1920s and 1930s concluded that Kraepelin’s description of manic-depression occurred among youth, but was very rare, and appeared mostly among adolescents. In the 1950s, papers on youth manic-depression confirmed that the condition was indeed rare, with depression predominating. In a review from the time, Anthony & Scott (1960) concluded that manic-depression was exceptionally rare before age 11 (see Carlson & Glovinsky, 2009 for a review). The efficacy of lithium salts in treating acute mania was established in the early 1950s, and its prophylactic value in preventing relapse was confirmed later. These developments motivated a search for a symptom constellation in younger children that might be lithium-responsive. A review of 211 published studies and case reports (Youngerman & Canino, 1978) of use of lithium among children and adolescents uncovered 46 accounts with enough detail to adequately characterize patients and their responses. Among these, 22 cases included children, only two of whom had manic-depression. Among 24 cases involving adolescents, 9 had manic-depression and 13 had other mood disorders (the remaining two had other conditions). Response to lithium was poor among those without classic bipolar disorder. Defining the boundaries of bipolar disorder has been a persistent controversial issue in both adult and child/adolescent psychiatry. In the adult area, some investigators express concern that possible indicators of hypomania (such as affective lability, racing thoughts, agitation, and impulsive behavior) are often overlooked among adult patients with depressive disorders, which in turn delays appropriate treatment and underestimates the prevalence of bipolar disorder (Angst et al., 2013; Phillips & Kupfer, 2013). In the child and adolescent literature, the applicability of bipolar disorder to youth has been a major point of debate for about the past 20 years. During this time, rates of bipolar disorder diagnoses among youth in clinical settings have risen dramatically

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(Blader & Carlson, 2007; Moreno et al., 2007). Increased recognition that bipolar disorders can develop in mid- to late adolescence may have contributed to this trend for older youth (Kessler, Chiu, Demler, Merikangas, & Walters, 2005). The more controversial issue is whether bipolar disorder is an appropriate diagnosis for children who display behavioral and emotional volatility, aggression, poor impulse control, and irritability. These problems do not occur in discrete episodes, nor do they represent a change from the child’s “usual” level of functioning. The majority of these youth fulfill diagnostic criteria for attention-deficit/hyperactivity disorder (ADHD) and disruptive behavior disorders (oppositional defiant disorder [ODD], conduct disorder [CD]). However, some investigators and clinicians feel that these diagnoses do not adequately convey the degree of affective disturbance that these children’s rageful outbursts and irritability signify. Thinking of them as manifestations of bipolar illness thus had some appeal as a means of focusing on affective disturbance. A bipolar disorder diagnosis would also “cover” the high activity level, impatient, inattention and other features of ADHD. But the viewpoint that there actually are early indicators of bipolar disorder, as the next section details, requires significant shifts in traditional concepts of this diagnosis to accommodate important differences in symptoms, course, and long-term outcomes. A new syndrome within the DSM-5 Depressive Disorders category, disruptive mood dysregulation disorder (DMDD), is intended as an alternative diagnosis that captures those who exhibit nonepisodic, sustained irritability and rageful outbursts. This diagnosis may curb what some view as overdiagnosis of bipolar disorder among youth. The effects of this new category (and of other changes in diagnostic convention) on clinical practice and research are at this time unknown. For the time being, developmental psychopathologists should approach the current literature with close attention to how patient characteristics are defined.

Diagnostic Issues and DSM-5 Criteria Primary diagnoses defined in the Bipolar and Related Disorders section of DSM-5 are bipolar I disorder, bipolar II disorder, and cyclothymic disorder. Two other bipolar disorder diagnoses are intended for those who display elevated, expansive, or irritable mood as a consequence of (1) exposure to intoxicating substances or medications or (2) other medical conditions. The DSM-5 also introduced other specified bipolar and related disorder, which allows clinicians to convey that a patient has significant symptoms, yet does not fulfill criteria for any of the disorders in this section. Clinicians specify reasons that a patient does not meet full criteria (insufficient duration, number of symptoms short of those required, etc.). Another diagnosis, unspecified bipolar and related disorder, is to be used in similar circumstances, but clinicians either opt not to indicate the reason the clinical picture is subsyndromal, or there is insufficient information.

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EPISODES Diagnoses of bipolar disorders are anchored by definitions of three types of episodes: manic, hypomanic, and major depressive. In all instances, an episode refers to a distinct period of persistent mood disturbance that is a change from one’s usual behavior or functioning.

Manic Episode During a manic episode, an individual experiences elevated, expansive, or irritable mood to an abnormal degree, accompanied by increased goal-directed activity or energy. The requirement for increased activity or energy is newly introduced in the DSM-5. This state must predominate most of the time for seven days, unless hospitalization is required before then. The episode is further defined by additional specific symptoms that represent a significant change from a person’s usual behavior: (a) inflated self-esteem or grandiosity; (b) decreased need for sleep; (c) increased talkativeness or pressured speech; (d) racing thoughts or flitting from one idea to another that share only superficial associations (“flight of ideas”); (e) increased distractibility; (f) heightened purposeful activity or psychomotor agitation; and (g) excessive, usually impulsive involvement in activities that are pleasurable or appear to have the prospect of high reward but their pursuit poses a strong likelihood of disadvantageous consequences. At least three of these symptoms must accompany the abnormally “high” mood state, or if the mood disturbance is high irritability, four are necessary. The “four-for-irritability” requirement is intended to compensate for the fact that irritability is nonspecific for mania. These symptoms must cause significant functional impairment to constitute a manic episode. Common harmful behaviors during manic episodes include belligerence, drug abuse, promiscuity, plundering financial resources, gambling, and impulsive unannounced journeys that leave others worried about the person’s whereabouts. Poor judgment and irresponsibility endanger occupational and family roles, and thus the well-being of dependents and co-workers. When people who interact with the patient seem perplexed or do not reciprocate enthusiasm, they risk being perceived as obstructionist and ungrateful. Social strife and rage are common. Impatience with others’ shortcomings or uncooperativeness, and the inevitable frustration with unattained goals and desires, can manifest as irritability and embitterment. Because insight and judgment are often compromised during manic episodes, even without psychosis, affected individuals may be oblivious to the magnitude of their impairment and hazardous behaviors. In fact, patients often enjoy their near-boundless energy, enthusiasm, and high self-regard, especially during early phases of manic episodes and especially in contrast to the despair encountered during past episodes of severe depression. Creativity, charisma, and generosity that may be uncharacteristic of the person at other times may be observed.

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A significant subgroup of patients experience psychotic features during manic episodes (Kennedy et al., 2005; Müller-Oerlinghausen, Berghöfer, & Bauer, 2002). These often present as delusions that aggrandize one’s abilities and/or identity (e.g., of being an important official or historical person, spiritual messenger, of royal heritage). Hallucinations that reinforce one’s elevated status (e.g., special messages from powerful or famous people or spirits) may occur. Paranoid or persecutory delusions focus on dangers associated with having a special mission, such as being the target of the envious or “the enemy” (Keck et al., 2003). Generally, ideas expressed are intelligible, even if absurd and too rapid to follow easily. That is, they lack the incoherence or apparent meaninglessness of formal thought disorder, which is common to other psychotic illnesses such as schizophrenia and schizoaffective disorder. Psychotic features during a manic episode that emphasize one’s importance or powers are termed mood-congruent. Those that do not have such a clear relationship to inflated worth (e.g., belief that one really deserves punishment, that one’s mind is being controlled by others) are considered mood-incongruent.

Hypomanic Episode A hypomanic episode is defined by the same criteria as a manic episode. The two types of episodes differ by their required minimum duration and degree of impairment. A hypomanic episode lasts at least four consecutive days. In DSM-5, there is no minimum severity requirement for hypomania; however, the degree of impairment must not rise to that of a manic episode (i.e., impairment is not marked, hospitalization is not needed, psychotic features are absent). Although hypomania represents a significant change from one’s usual level of functioning, it is often not perceived as problematic by affected individuals ( Judd & Akiskal, 2003). When patients have been depressed for a long time and clinicians are unfamiliar with their premorbid functioning, there may be concern about emerging hypomania when the change from a depressed state is pronounced. For those who have experienced prior manic episodes, family members are often exquisitely attuned to and concerned by mood shifts that tend toward hypomania for fear that they may portend mania.

Major Depressive Episode The criteria for a major depressive episode in the context of bipolar illness are the same as those for unipolar depression (see Chapter 18 [Klein, Goldstein, & Finsaas]). As described below, when a major depressive episode manifests in a person with a history of mania or hypomania, the applicable diagnosis is bipolar disorder, current episode depressed. Depressive symptoms among those with bipolar disorder often include those that constitute “atypical features” in major depression and persistent depressive disorder (Singh & Williams, 2006). Mood incongruent reactivity (brightening when pleasant

Bipolar Disorder 711 things occur) distinguishes this presentation. Other features include weight gain, increased appetite, hypersomnia, a sense of heaviness in one’s limbs (“leaden paralysis”), and a trait-like sensitivity to interpersonal rejection, which affects one’s social functioning. This subtype derives from the concept of “atypical depression” (West, 1959). The atypicality of these features does not indicate rarity or low prevalence (it may be the most prevalent subtype of depressive illness in outpatient settings), but rather their difference from depressive symptoms such as unrelenting sadness, insomnia, and diminished appetite (Thase, 2007). The DSM-III and DSM-IV included a mixed episode specifier when criteria for both major depression and mania were met during the same period. This was a difficult threshold to fulfill. Concern that mixed-episode criteria were too restrictive reflected the fact that many patients display an admixture of negative affect and heightened behavioral and/or cognitive activation seen in mania, but they do not meet criteria for both major depression and mania simultaneously (Bauer, Simon, Ludman, & Unützer, 2005; Dilsaver, Benazzi, & Akiskal, 2005; Goldberg, Perlis, et al., 2009; Maj, Pirozzi, Magliano, & Bartoli, 2003; Sato, Bottlender, Kleindienst, & Möller, 2002). For instance, a severely depressed person may experience racing thoughts about his or her worthlessness and exhibit agitated reckless behavior. Or a person with the high activity and drive characteristic of mania may express simultaneous despair and suicidal ideation. It is also not uncommon to observe dramatic changes in mood state within a brief period of time (mood lability). A person may, even during the same interview, appear exuberant and elated at first but then despondent and tearful. In these cases, though, the person only fulfills criteria for one type of episode although symptoms of the other type are present. However, the presence of manic symptoms during a depressive episode is associated with a higher likelihood of developing bipolar disorder, even when the manic symptoms are subthreshold. This made it desirable to establish diagnostic terms that indicate the presence of manic symptoms within depressive episodes, and of depressive symptoms within manic episodes, which required a less stringent approach than the DSM-IV mixed-episode requirement of meeting full criteria for both episode types. An important practical consequence of misdiagnosing bipolar disorders as unipolar depression is the potential for certain types of antidepressants, particularly serotonin selective reuptake inhibitors (SSRIs), to catalyze a switch to mania (Goldberg & Truman, 2003; Tondo, Vazquez, & Baldessarini, 2010). Accordingly, the DSM-5 suggests that clinicians identify the episode for which the patient meets full syndrome criteria and indicate the presence of three or more symptoms from the other subsyndromal episode type by using the specifier with mixed features. If a patient simultaneously fulfills criteria for a manic episode and a major depressive episode, then it is considered a manic episode with mixed features. A person who experiences a major depressive episode and has no prior manic or hypomanic episodes, but who shows three manic symptoms, is diagnosed as having major depressive episode with mixed features.

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Among those with a bipolar disorder, psychotic features during major depressive episodes often involve, as in unipolar depression, mood-congruent hallucinations or delusions of a self-deprecatory nature (voices telling one how awful he or she is, feelings that one’s body is rotting, etc.). Psychotic symptoms that reflect, for instance, inflated self-worth during a major depressive episode are considered mood incongruent.

SPECIFIC BIPOLAR DISORDER DIAGNOSES Compared to most other psychiatric diagnoses, assigning diagnoses for bipolar disorder is complicated because decision rules rely heavily on lifetime history of specific types of episodes. Despite separation of depressive and bipolar disorders in the DSM-5, lifetime depressive symptoms remain important in conceptualization of most bipolar disorders and the specific diagnoses that one applies.

Bipolar I Disorder If a person has ever experienced a manic episode, he or she is diagnosed with bipolar I disorder. Although bipolar I disorder can therefore be diagnosed in someone who has no prior history of a major depressive episode, the assumption is that the majority of those who experience a manic episode first will have a major depressive episode later. The very term bipolar disorder for these individuals, rather than the abandoned concept of unipolar mania, implies this expectation. Nevertheless, there appear to be a sizable number of adolescents with a chronic history of mood disturbance who have met criteria only for mania (Merikangas et al., 2012). The long-term course of these individuals into adulthood is not yet known. Following specification of bipolar I disorder, clinicians indicate the episode that presents currently or most recently (e.g., “Bipolar I disorder, current episode . . . ” followed by “manic,” “hypomanic,” or “depressed”). Unless the current or most recent episode is hypomanic, one also adds specifiers for severity (“mild,” “moderate,” “severe,” “with psychotic features”). If the patient is in full or partial remission, this status is also indicated, and replaces the severity specifier. Two examples are as follows: “Bipolar I disorder, current episode depressed, severe” and “Bipolar I disorder, most recent episode manic, in partial remission.”

Bipolar II Disorder Bipolar II disorder is the diagnosis applied when an individual experiences either (a) an episode of hypomania with a prior history of one or more major depressive episodes, or (b) an episode of major depression with a prior history of hypomania. Importantly, any history of manic episodes precludes the bipolar II disorder diagnosis and bipolar I disorder applies instead.

Bipolar Disorder 713 In the DSM-5, a person who appears to experience a hypomanic episode without a clear history of major depressive illness receives the diagnosis of other specified bipolar disorder, hypomanic episode without prior major depressive episode.

Cyclothymic Disorder The diagnosis of cyclothymic disorder indicates a relatively sustained period (at least 2 years for adults, 1 year for children and adolescents) of mood disturbances that feature hypomanic symptoms and depressive symptoms that have never fulfilled criteria for a hypomanic, manic, or major depressive episode.

PROBLEMS WITH DIAGNOSIS OF BIPOLAR DISORDER AMONG YOUTH Bipolar disorder epitomizes the difficulties encountered when one takes a psychopathological entity defined by symptom descriptions observed in one population—in this case early- to midlife adults—and applies it to another population—in this case children. The same underlying disturbance that produces observed symptoms among mature individuals might produce different behavioral manifestations among youth. Conversely, similar behavioral abnormalities may reflect different underlying etiologies, and similar symptomatic behaviors across age groups may have different likelihoods of signifying a particular disorder. These developmental considerations are important to differential diagnosis of bipolar disorders among youth.

Symptom Differences and Confounding Comorbidities In the context of defining mania, elevated mood (sometimes also called euphoria or elation) is an uncharacteristically exaggerated feeling of well-being that the person may describe as feeling “high,” “ecstatic,” or “on top of the world.” In contrast, expansiveness refers to lack of restraint in expressing one’s feelings, frequently with an overvaluation of one’s significance or importance. Adolescents are better equipped linguistically and experientially to articulate these mood states than children. It is important, especially among adolescents and adults, to evaluate possible substance intoxication or medications as causes of these mood states. Among children, mood is usually inferred from behavior. Hence, terms such as silly and giddy are used to describe a child’s euphoria. However, it is difficult to determine when and if such behaviors indicate elevated mood in the sense that mania requires. Children may seek attention with silly antics for a variety of nonmanic reasons or may be susceptible to extreme emotional displays in high-stimulation situations such as parties or family gatherings (Carlson & Meyer, 2006). A bipolar disorder–related abnormality is therefore more likely when the behavior is highly

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incongruent with the situation. Relatedly, although an abrupt and sustained change in demeanor from sullen or reserved to euphoric or expansive may suggest mania, it is less clear when one observes elevated mood in a typically excitable person. Euphoria and elation were once thought to be somewhat specific to mania (Geller et al., 2002). However, epidemiological research indicates high prevalence of brief periods of such mood states among typically developing adolescents, based on both parent report (12.7%) and youth self-report (28%) (Stringaris et al., 2010; Stringaris, Stahl, Santosh, & Goodman, 2011). Extreme irritability manifests in a number of other disorders, including unipolar depression, ADHD, and conduct disorder. There is some evidence, however, that adolescents are susceptible to a normative, developmentally specific form of hyperemotionality, which may magnify periods of irritability (Arnett, 1999; Casey, Getz, & Galvan, 2008; Casey, Jones, & Somerville, 2011). These features are prevalent among those with ODD, because half of the symptom descriptors for this disorder involve displays of irritability and hostility. In ODD, however, irritability typically represents chronic, trait-like characteristics, as opposed to significant change from typical functioning that hypomania and mania indicate. Longitudinal studies of youth with high ratings of irritability suggest weak specificity to bipolar disorder, as many show heightened risk for anxiety and mood disorders but not BPD (Stringaris, et al., 2011). Grandiosity, or inflated appraisal of one’s worth, power, knowledge, importance, or identity, is easily identified in adults with mania. Among adolescents, grandiose claims tend to reflect adolescent preoccupations. One girl, who believed she was a pop music idol, spent an hour picking out items in a clothing boutique. When it was her turn in line to pay, she was content to step aside and let others ahead because, she explained, she was waiting for her manager to come and complete the purchase. Among children, inflated self-esteem may be hard to differentiate from excessive bragging to peers or simple immaturity. For instance, a child may interpret a compliment “you swim like a fish” to mean that he can swim across the ocean (Carlson & Meyer, 2006). Even among adolescents, who may feel that they can achieve greatness without finishing high school or are popular without evidence to substantiate it, distinguishing truly inflated self-esteem from a defensive stance requires clinical skill (Harrington & Myatt, 2003). Decreased need for sleep is relatively straightforward to establish as a symptom when sleep time is replaced with energetic pursuit of activities and daytime fatigue is absent. Among children, one has to distinguish true decreased need for sleep from (a) highly prevalent bedtime struggles, especially among children with behavioral difficulties; and (b) true insomnia and night waking that typically occur among those with anxiety disorders (Blader, Koplewicz, Abikoff, & Foley, 1997). Those with true decreased need for sleep do not experience daytime fatigue. This distinction can be accomplished by asking how tired the child is during the day, whether he or she is difficult to rouse following late sleep onset and/or tends to rise later on nonschool days.

Bipolar Disorder 715 Increased talkativeness and flight of ideas are more strongly suggestive of BPD when a person is ordinarily reserved and shy outside of manic/hypomanic episodes. Given the high comorbidity with ADHD among children with BPD, using these behaviors to identify a manic episode superimposed on preexisting ADHD is typically futile. The picture is more congruent with BPD, however, when a child spontaneously reports that his or her brain is on “overdrive” to a degree that is uncomfortable (e.g., Goodwin, Jamison, & Ghaemi, 2007, p. 189). Similarly, distractibility is also a symptom/feature of other disorders, such as ADHD, which designates the problem as “impaired ability to concentrate.” Moreover, distractibility is also characteristic of depression. Thus, unless it is clear that one’s focus and attention has undergone marked changes, distractibility contributes little to differential diagnosis. Increases in goal-directed activity among older individuals with BPD generally have a focus and purpose, albeit of a rather grandiose or impractical nature, as noted earlier. Some children with BPD get excited about a project they intend to start or an invention they claim will reap millions of dollars. Many children, of course, ruminate or talk about such plans, but acting on these ideas may be more suggestive of BPD. One youngster snuck out of his home to spend most of the night in the 24-hour photocopying store preparing brochures. This type of activity must be differentiated from the more aimless but energetic high-activity level intrinsic to ADHD or the intense, even obsessional interests of some children on the high-functioning autistic spectrum and those with obsessive-compulsive disorder. The term psychomotor agitation has been rightly criticized for its ambiguousness (Day, 1999) but nonetheless conveys discomfort (pacing, hang-wringing, repositioning, or frantically distraught inability to settle) that should be distinguished from restlessness observed among children with ADHD. The former persists and is experienced as unpleasant, whereas the latter is relieved as soon as a child is permitted to resume enjoyment of his or her frenetic activity level. Excessive involvement in pleasurable activities better conveys a BPD-specific abnormality for people who in the past have shown normal self-restraint and a characteristically cautious, even avoidant approach to new ventures. Among the majority of children who are considered for a BPD diagnosis, however, impulse-control deficits have often been present since early in life, making differentiation from ADHD problematic. Hypersexuality among children and adolescents may include frequent masturbation, often with little regard for privacy; unusual sexual inquisitiveness and preoccupation; or grabbing for others’ genitals or breasts. However, these behaviors among children always require further consideration of possible sexual abuse and/or inappropriate exposure to pornography or others’ sexual behavior. Intense sexual behavior or interest may be a manifestation of mania, but mania certainly does not rule out maltreatment. Moreover, histories of sexual abuse portend worse outcomes among those with BPD (Leverich et al., 2002). Psychotic symptoms among children can be difficult to differentiate from the magical and unrealistic thinking, active imagination, cognitive immaturity, exaggeration,

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and imaginary friends common to this age group. In some controversial instances, behavior that would not be intrinsically psychotic among adults is interpreted as such among children. For instance, in one clinical trial, 77.1% of 6- to 15-year-olds with bipolar disorder reported psychotic features (Geller et al., 2012). Psychotic grandiosity was inferred when, in one example, a youngster wrote to his principal demanding the dismissal of a specific teacher. Although perhaps insolent, there was no indication the child believed he was, say, a government official with such powers. Unfortunately, such misinterpretations of psychosis may contribute to overdiagnosis among children.

Onset of Mood Disturbance When a behavioral abnormality first appears as a clear departure from one’s usual level of functioning, it is easy to conclude that an untoward change is underway. BPD is characterized by such changes. Imagine a 25-year-old, reserved and timid woman who rapidly turns gregarious, uses coarse language, adopts an unconventional appearance, invades others’ personal space, is sexually preoccupied, suddenly gets on a bus to New York to start a modeling career, and is hospitalized because she was belligerent at a modeling agency where she appeared, insisting that she had an appointment. Chances are good she is experiencing a manic episode. In contrast, suppose that for most of her life, and possibly worsening in adolescence, she was highly extraverted, had poor boundaries, was impulsive, irresponsible, flamboyant in appearance, demanding, argumentative, and sulky—but was generally likeable and had never received mental health care. Her sudden departure, combativeness, and possible delusions still need psychiatric attention, but when did her difficulties first begin? Were the preceding years a prodromal phase? Or did personality traits that coalesced in adolescence establish higher risk for bipolar illness? There are no clear answers to these questions, despite their importance to a developmental perspective on BPD. For children, the situation is further complicated because, as we have seen, many behavioral disturbances that might otherwise signify BPD in an older person, such as irritability, talkativeness, and distractibility, are frequent early in life. Thus, there may be no acute break, or clear onset of psychopathology, as often observed among older individuals. When symptoms comprise only those that are nonspecific to BPD (irritability, impulsiveness, poor judgment in fulfillment of one’s desires), it is particularly controversial whether BPD without a clear onset is a valid diagnosis.

Persistent Versus Transient Mood Disturbance Some children do show persistently irritable or angry mood that changes only minimally with positive events (Blader et al., in press; Roy et al., 2013). It is far more

Bipolar Disorder 717 common, however, that children who manifest with significant irritability show heightened emotional reactivity only when provoked, mild as that provocation may seem. When things are going their way they are euthymic. Their brittle frustration tolerance, however, leads to frequent but relatively short-lived upsets, even meltdowns, followed by return to a normal mood state. Similarly, momentary elation may be brought on by exciting or stimulating events, so the reaction is not qualitatively inappropriate although its expression may be excessive for the context. Several deviations in development are related to momentary emotion dysregulation. For instance, temperament studies often distinguish an affective tone factor (i.e., sustained mood) that is distinct from more momentary negative reactivity (Muris & Ollendick, 2005; Sanson & Prior, 1999). The latter is common in ADHD (Melnick & Hinshaw, 2000), especially when comorbid with ODD/CD. This pattern often remits with treatment directed primarily toward ADHD (Blader, Pliszka, Jensen, Schooler, & Kafantaris, 2010; Blader et al., in press). Some have suggested that among youth, very brief periods of emotional volatility, occurring as often as several times per day, punctuated by euthymic mood states, may indicate a bipolar disorder presentation called ultradian cycling (e.g., Geller, Tillman, & Bolhofner, 2007; Kramlinger & Post, 1996). Such patterns of mood shifts among those with established bipolar disorder mandate more data for both scientific and practical clinical management purposes. However, strong reliance on atypical mood variations of this sort to identify bipolar disorder leads to over diagnosis among both children (Leibenluft, 2011) and adults (Goldberg et al., 2008; Zimmerman, Ruggero, Chelminski, & Young, 2010).

PREVALENCE The point prevalence of BPD among adults in the United States is generally agreed to be about 1% to 1.5%, with lifetime prevalence of disorders in the BPD spectrum around 4.5% (Kessler et al., 2006; Merikangas et al., 2007). Worldwide estimates of point and lifetime prevalences are similar, 0.6% for bipolar I disorder, 0.4% for bipolar II, 1.4% for subthreshold but impairing bipolar disorder, and 2.4% for other presentations in the bipolar spectrum (Merikangas et al., 2011). Recent epidemiological studies in the United States show a lifetime prevalence of 2.9% among adolescents for bipolar I or II disorder combined (Merikangas et al., 2010). Prevalence increases with age during adolescence. Fully 89.7% of adolescents with these disorders were classified as manifesting “severe” impairment. In addition, the prevalence of mania unaccompanied by a history of major depression among adolescents is estimated to be additional 1.7% (Merikangas et al., 2012). Despite increased application of BPD diagnoses to young people in U.S. clinical settings (Blader & Carlson, 2007; Harpaz-Rotem, Leslie, Martin, & Rosenheck, 2005;

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Moreno et al., 2007), there is no evidence that the rate of affected individuals, when applying the same diagnostic criteria, is rising over time (Van Meter, Moreira, & Youngstrom, 2011). Rates of epidemiologically determined BPD among youth in the United States are similar to those in other countries (Van Meter et al., 2011). However, non-U.S. health providers do not show the same increase in clinical use of these diagnoses as their U.S. counterparts (Meyer, Koßmann-Böhm, & Schlottke, 2004; Soutullo et al., 2005).

ETIOLOGY The etiology of bipolar disorder remains unknown. However, research on factors that heighten risk for the disorder may hold clues to why and how it develops in some individuals but not others.

Vulnerabilities and Risk Factors Clinical risk factors are symptoms that portend increased likelihood that one will develop a target disorder later. Depression. Patients who develop bipolar disorder often experience depression as their first episode (Beesdo et al., 2009; Chengappa et al., 2003; Nadkarni & Fristad, 2010). Given very different treatments for bipolar versus unipolar depressions, and given that SSRIs can precipitate manic episodes among those who are vulnerable to BPD but have yet to experience mania (see above), predicting who among depressed young people will develop a bipolar course becomes important in deciding treatment. Some relevant risk factors include precipitous onset of the depressive episode, psychotic features, manic symptoms below threshold for bipolar disorder diagnosis, family history of mood disorder, and susceptibility to hypomania with antidepressant treatment (Beesdo et al., 2009; Fiedorowicz et al., 2011; Strober & Carlson, 1982a; Zimmermann et al., 2008; Zimmermann et al., 2009). The conversion rate of unipolar depression to bipolar disorder (switching) varies across studies partly as a function of recruitment strategy and length of follow-up. For example, a community epidemiological sample reported a switch rate of 4% over 7 years for those identified at baseline with MDD (Beesdo et al., 2009). The incidence of switching rose to 20% in clinical samples (Fiedorowicz et al., 2011), and 40.5% with long-term follow-up of an inpatient cohort (Goldberg, Harrow, & Whiteside, 2001). Clinical samples show a switch rate from childhood MDD to bipolar I or II disorder of 6% of child outpatients over 10 years (Weissman et al., 1999), 19% of adolescent outpatients over 7 years (Rao et al., 1995), and 20% of adolescent inpatients over 3 to 4 years (Strober & Carlson, 1982b).

Bipolar Disorder 719 Subthreshold Bipolar Disorder. There is mounting evidence that some young patients who experience manic symptoms for fewer than the four days required for hypomania are indeed vulnerable to later development of bipolar I and II disorders (Axelson et al., 2011). Conversion to bipolar I or bipolar II disorder among those diagnosed with DSM-IV bipolar disorder not otherwise specified (NOS) occurred for 45% of 7- to 17-year-olds within 5 years, with median time to conversion from study entry of 58 weeks across disorders (Axelson et al., 2011). Positive family history was the only predictor. Other samples corroborate the high impairment of young people who meet manic episode criteria except for duration (Stringaris, Santosh, Leibenluft, & Goodman, 2010). In community samples, brief but memorable and distinct periods of elation are associated with higher risk for psychopathology, although not bipolar disorder specifically (Stringaris et al., 2011). Neurodevelopmental Antecedents. Some findings suggest that perinatal events are associated with bipolar disorder in childhood. For instance, obstetric complications are more prevalent among children later diagnosed with bipolar disorder than controls (Pavuluri, Henry, Nadimpalli, O’Connor, & Sweeney, 2006). Studies involving adults do not indicate a major influence of perinatal problems, although wide variation in definitions of such complications precludes a firm conclusion (Scott, McNeill, Cavanagh, Cannon, & Murray, 2006). A study of 11- to 18-year-old psychiatric inpatients compared the neurodevelopmental status of youth (a) with bipolar disorder or unipolar depression with psychotic features to (b) patients who were diagnosed with nonpsychotic unipolar depressive disorder (Sigurdsson, Fombonne, Sayal, & Checkley, 1999). The former group had higher rates of premorbid language, motor, and social developmental problems, leading the authors to conclude they predispose more strongly to bipolar disorder than to depression. There were no significant differences in perinatal complications. Disturbances of the Sleep-Wake Cycle. There are some data to suggest that circadian rhythms governing the sleep-wake cycle may be relevant to the development of bipolar disorder and its exacerbations. It has been known for many years that sleep deprivation in some individuals with major depression provides temporary symptomatic relief but in a number of cases elicits hypomania (Giedke & Schwarzler, 2002). Sleep deprivation in bipolar disorder may also instigate mania. Regularizing sleep/wake times is therefore an important component of relapse prevention (Frank et al., 2008; Fristad, 2006; Harvey, 2008; Miklowitz et al., 2011). Moreover, circadian irregularity may have etiological significance for bipolar disorder (Harvey, Mullin, & Hinshaw, 2006; Jones, 2001; Nievergelt et al., 2006). Cognitive Vulnerabilities. Impaired response inhibition and other executive function deficits are often observed among those who are vulnerable to bipolar

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disorder risk based on family history (Bora, Yucel, & Pantelis, 2009; Hasler, Drevets, Gould, Gottesman, & Manji, 2006). However, other studies demonstrate problems with attention only among patients, not among relatives (Clark, Kempton, Scarna, Grasby, & Goodwin, 2005; Clark, Sarna, & Goodwin, 2005). Functional deficits related to attention and inhibitory control often implicate the dorsolateral prefontal cortex (DLPFC) and the ventrolateral prefrontal cortex (VLPFC) in ADHD (Halperin & Schulz, 2006; Pliszka et al., 2006). Some research suggests that functions subserved by the VLPFC are specifically deficient in patients with BPD and their relatives (Frangou, Haldane, Roddy, & Kumari, 2005). Well-known cognitive features of depression include a tendency to exaggerate and dwell on misfortunes and one’s perceived shortcomings and to face the future with hopelessness and dread (Beck, 2008). However, cognitive biases for mania have yet to be confirmed (Alloy et al., 2005). Current efforts to evaluate overly positive self-related cognitions and individual’s attributions for them may advance this area (Jones, Mansell, & Waller, 2006).

Genetic Vulnerabilities Liability to development of bipolar disorder increases among those who have affected biological relatives, and risk is proportional to degree of relatedness (Craddock & Sklar, 2013). A large Swedish health registry-based study indicated that having a sibling with bipolar disorder increases risk about eightfold, whereas offspring of those with BPD are at 6.4 times the risk. Half-siblings have elevated but smaller risk (Lichtenstein et al., 2009). Rates of mania and bipolar I disorder among offspring of those with bipolar disorder who have been followed into adulthood vary from 2%–7%. Rates of depression and other psychopathology are even higher, however (Duffy, Alda, Hajek, & Grof, 2009; Egeland et al., 2012; Mesman, Nolen, Reichart, Wals, & Hillegers, 2013; Meyer et al., 2004; Shaw, Egeland, Endicott, Allen, & Hostetter, 2005) Studies have examined whether bipolar disorder shares genetic risk with other psychiatric conditions. Evidence from both genetic epidemiological studies (i.e., the relationship between the presence of disorders and familial relatedness) and molecular genetics research show common genetic vulnerability to bipolar disorder and schizophrenia (Cross-Disorder Phenotype Group of the Psychiatric GWAS Consortium et al., 2009; Lichtenstein et al., 2009; Van Snellenberg & de Candia, 2009). In fact, bipolar, major depressive, and schizophrenia spectrum disorders share a number of genetic risk loci, with the bipolar-schizophrenia association being especially strong (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). Genome-wide association studies have identified numerous single-nucleotide polymorphisms (SNP) that are associated with bipolar disorder, but vulnerability

Bipolar Disorder 721 attributable to any single gene is quite small, and replications have proved elusive (Craddock & Sklar, 2013). Technological advances in the speed and accessibility of full genome sequencing, computational capacity, and merging of datasets to yield large samples have enabled examination of cumulative effects of common risk variants. This approach has helped to identify patterns of common gene variants associated with psychiatric disorders that converge on related biological processes. These approaches set the stage for identifying genetic variants associated with mood instability. It is noteworthy that ADHD does not seem to share genetic associations with bipolar disorders (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013), however this conclusion is tentative due the small sample size of those with ADHD.

Experiential and Environmental Risk Factors It is difficult to judge to what extent stressful life experiences contribute to the onset of BPD because (a) retrospective reports carry potential biases toward overidentification of events, as patients and families strive to understand the course of illness; (b) many stressful events can arise as a consequence of behavior changes caused by the incipient disorder; and (c) few studies include comparison groups. In any case, there are more data on associations between life events and the course of bipolar disorder than between life events and onset. Efforts have been made to distinguish between stressful life events that are dependent versus independent of patients’ behavior. One investigation revealed higher rates of independent stressful events in families of youth with bipolar disorder than in families of children with uncomplicated ADHD (Tillman et al., 2003). Retrospective self-reports indicate markedly elevated rates of child maltreatment among adults with mood and personality disorders, especially women (MacMillan et al., 2001). Among adults and adolescents with BPD, several studies report high prevalence of severe childhood trauma, which is associated with a pernicious course of illness (e.g., early onset, fewer remissions, suicidality) (Garno, Goldberg, Ramirez, & Ritzler, 2005; Neria et al., 2008; Romero et al., 2009). Studies of Gene × Environment (G×E) interactions that affect the onset and/or course of bipolar illness are limited. Effects of stressful life events on depression severity are moderated by allelic variation in the brain-derived neurotrophic factor (BDNF) gene among adults with BPD (Hosang et al., 2010). Effects of G×E interactions on manic episodes are underexamined. As noted above, certain psychotropic medications—particularly SSRIs—can elicit manic-like episodes among some individuals. Conversion from depression to mania that first emerges during treatment with antidepressants is referred to as medication-induced switching. If mood disturbance persists after the pharmacological agent is discontinued, the episode counts toward a diagnosis of bipolar disorder.

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If it remits, the mood change may be diagnosed as substance/medication-induced bipolar disorder. Some contend that those who switch have vulnerability to bipolar disorder and that the medication simply hastens onset of illness. However, this issue remains unsettled (Bauer et al., 2006). Among children, SSRIs can induce behavioral disinhibition, but there is no evidence that this is related to bipolar disorder vulnerability (Safer & Zito, 2006; Walkup & Labellarte, 2001). Another concern is that children with ADHD and explosive, aggressive behavior may have an underlying bipolar disorder diathesis that stimulant medications may trigger. Treatment and observational studies do not support this notion (Blader et al., 2010; Blader et al., in press; Carlson & Mick, 2003).

PATHOGENESIS AND PATHOPHYSIOLOGY Despite hints about potential etiological factors in the development of bipolar disorder, its exact pathophysiology remains unknown. In this section, we selectively summarize current approaches to understanding the neurobiology of bipolar disorders at two levels of analysis: small-scale molecular effects on neural signaling, and larger-scale brain regions and functional networks.

Molecular Physiology When different pharmacological agents affect an illness, one approach is to examine molecular effects that these compounds may share to infer clues about underlying pathogenesis. However, as with most psychiatric conditions, bipolar disorder treatments are chiefly symptom-modifying rather than disease-modifying. Therapies that help to reduce symptoms but do not reverse pathophysiological processes may exert their effects through pathways unrelated to etiology. With this caveat in mind, some potential mechanisms of action for antimania drugs illustrate recent work on the pathogenesis of bipolar disorder. It has been known for some time that lithium inhibits glycogen synthase kinase-3𝛽 (GSK-3b). This kinase affects signaling of proteins involved in neuronal growth, neuronal death (apoptosis), and synaptic plasticity. Curtailment of GSK-3b activity is believed to improve neuronal integrity. Other drugs that are effective in treating mania share this property (Eickholt et al., 2005). This finding has spurred interest in pathways that involve GSK-3b as perhaps influential in bipolar disorder (Einat & Manji, 2006; Soeiro-de-Souza et al., 2012). Several neurotransmitters have receptors in the Gq family that activate phospholipase C, which sets in motion metabolism of the second-messengers diaglycerol and inositol triphosphate (IP3 ). IP3 is important in regulation of intracellular calcium ions (Ca2+ ), which are also major second-messengers. Broadly speaking, higher free intracellular Ca2+ makes neurons more prone to excitation/firing through a number of mechanisms. These include stimulating a number of protein kinases that affect other ion channels, direct action of Ca2+ on those channels, and, in axon terminals,

Bipolar Disorder 723 facilitating vesicular release of neurotransmitter. Lithium and other antimanic drugs share the capacity to inhibit free inositol, leading to less Ca2+ release from internal stores. This undergirds the inositol-depletion hypothesis. It remains open to debate whether these inositol-related intracellular cascades may be dysregulated in bipolar disorder (Harwood, 2005; Silverstone et al., 2002). Genome-wide association studies (GWAS) suggest certain vulnerability loci that are associated with bipolar disorder. If these genetic loci have known physiologic functions, such functions may be related to etiology. As noted above, genes involved in voltage-gated Ca2+ have attracted attention in this context. The strongest evidence to date for Ca2+ involvement identifies the CACNA1C gene, which codes for L-type Ca2+ channels in neurons (Casamassima et al., 2010; Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). These channels remain open for a long period (hence the “L” designation), and are thought to mediate activity-dependent changes in gene expression. This vulnerability allele is also more prevalent among those with unipolar depression, schizophrenia, and substance-use disorders (Green et al., 2010). GWAS suggest other potential pathophysiologic pathways, though none yet specific to bipolar disorder. One involves transmembrane proteins called teneurins, which affect gene transcription via a vulnerability allele in the ODZ4 gene (Psychiatric GWAS Consortium Bipolar Disorder Working Group, 2011). Neurocan is a protein involved in cell adhesion and migration. Allelic variation in the encoding NCAN gene is associated with several psychiatric disorders, including bipolar illness. Interestingly, mouse knockout NCAN models may produce an animal homologue of mania (Miro et al., 2012). Among humans, the same NCAN variant is associated with diminished cognitive performance (Raum et al., 2014) and reduced volume in limbic areas (Dannlowski et al., 2015).

Neural Systems Cross-sectional case-control studies comparing those with illness versus those without are the predominant method for studying neural correlates of bipolar disorder. Besides the usual constraints of this approach for illuminating causality in any illness, some features of bipolar disorder compel additional cautions in interpretation. Those with bipolar illness usually have, by definition, histories of depression, and comorbidity with other disorders is high. Differences between affected cases and healthy controls may therefore be confounded by these other forms of psychopathology, unless care is taken to include groups with unipolar depression. But even then, there is a chance of misclassifying those with unipolar depression because some who are on the bipolar spectrum will not yet have experienced mania or hypomania. This problem is especially likely for studies involving young people, who have yet to move into an age range where onset of bipolar disorder is most likely. Longitudinal cohort studies can surmount some of these concerns, but are difficult, expensive, and time consuming to complete.

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Another challenge that episodic disorders pose for studying neural processes is whether findings reflect the person’s state at the time of data acquisition or a more persistent trait. If a sample of individuals with bipolar disorder contain some who are euthymic, some who are depressed, and others who are manic, it is difficult to isolate effects of current status (state) from overall effects of lifetime history of the disorder (trait; e.g., Hulvershorn et al., 2012; Liu et al., 2012). The stronger test would involve repeated assessments in different illness episodes, which is seldom practical. With these caveats in mind, there are some well-studied topics. The amygdala is a brain structure in the medial anterior portion of each temporal lobe that adjoins the hippocampus. Its role in motivation, emotion encoding, and response to threat has been described thoroughly, and it is a natural focus of interest for mood and anxiety disorders. Reduced amygdala volumes have been reported among the first-degree relatives, including children, of adults with bipolar disorder (Chang et al., 2005; Hajek, Carrey, & Alda, 2005). Among patients themselves, reduced amygdala volumes are observed in bipolar disorder, yet such findings are not specific to BPD, as they apply to other disorders characterized by affective instability (Blumberg et al., 2005; Rosso et al., 2005; Szeszko et al., 1999). Patients with bipolar disorder who receive pharmacotherapy may display amygdala enlargement, whereas untreated patients show decreased volumes relative to healthy controls (Savitz et al., 2010). In one of the few longitudinal studies of adolescents, a sample hospitalized for a first manic/mixed episode showed amygdalar volume decreases relative to ADHD and healthy comparison groups. These differences were progressive at 12-month follow-up. In addition, baseline amygdala volumes were associated with extent of recovery (Bitter, Mills, Adler, Strakowski, & DelBello, 2011) Functional neuroimaging studies show increased amygdala activity to negative-emotion induction among adults with bipolar disorder compared with controls (Altshuler et al., 2005). This finding may be absent when mood is elevated (Hulvershorn et al., 2012). A comparison of depressed adults with either bipolar disorder or unipolar depression showed group differences in temporal cortical activity. Activation was greater for bipolar patients to mood-congruent (i.e., sad) faces, whereas other depressed patients showed greater activation to moodincongruent (angry, fearful, happy) faces (Fournier, Keener, Almeida, Kronhaus, & Phillips, 2013). Prefrontal cortical areas may be relatively underactive, suggesting less “top-down” control over emotional reactivity in those with bipolar disorder (Chepenik et al., 2010; Foland et al., 2008; Townsend et al., 2012). Numerous fMRI studies suggest just such “hypofrontality” in adults (Vargas, Lopez-Jaramillo, & Vieta, 2013) and adolescents (Blumberg et al., 2003). However, such findings are observed in many psychiatric disorders. Another brain region of interest for bipolar disorder is the cingulate gyrus, a long rostral-caudal band of cortex that integrates components of the limbic system. The anterior cingulate cortex in particular is involved in regulating

Bipolar Disorder 725 affect, self-monitoring, and evaluating changes in external reward contingencies. Reductions in volume of the anterior cingulate have been reported among patients with BPD and their unaffected close relatives. Cortical thickness specifically, rather than overall regional volume, is less in patients than controls (Fornito et al., 2008). Among youth, the possibility that decreased volumes in more ventral portions of the cingulate (subgenual) accompany illness onset rather than being evident beforehand suggests some avenues for understanding developmental consequences of symptom expression (Gogtay et al., 2007). Neurofunctional models for bipolar disorder that integrate these lines of research focus on the interplay between processes and neural circuits that (a) underlie affective arousal and “automatic” emotion regulation and (b) those that underlie cognitive control and volitional factors (Phillips, Ladouceur, & Drevets, 2008; Strakowski et al., 2012). This framework, in broad terms, mirrors more general models of the neural substrates emotion regulation (Beauchaine, 2015; Ochsner & Gross, 2014).

Developmental Progression Adolescent-onset bipolar disorder demonstrates a particularly insidious course (Birmaher et al., 2014; Lewinsohn, Seeley, & Klein, 2003; Merikangas et al., 2010), with higher rates of serial hospitalizations, substance abuse, attempted and completed suicides, lower response to lithium and divalproex, and worse interepisode functioning than adult-onset bipolar disorder (Birmaher et al., 2006; Goldstein et al., 2005; Wilcox & Anthony, 2004). In the short term, many youth seem to recover from the functional nadir of their index episode, but subsequent exacerbations and relapse are common (Birmaher et al., 2006). In addition, 20% to 25% of adolescents diagnosed with bipolar II or bipolar NOS progress to fulfill criteria for a more severe form of the disorder (i.e., bipolar I) (Axelson et al., 2011). Most adolescents with bipolar, I, II, or NOS disorders followed between 4 and 8 years show stability in the time they spend in a euthymic mood state, with only 19.1% showing dramatic improvement from significant baseline mood disorder (Birmaher et al., 2014). In this same study, a more benign course was observed among 24% of participants who were the least impaired at baseline. It remains unclear, however, whether this subset experienced the same disorder, or simply overlapping, less severe symptoms. Besides baseline severity, risk for adverse outcomes rises with earlier onset, presence of psychotic features, mixed manic and depressive features, a history of maltreatment, and low socioeconomic resources (Birmaher et al., 2006; Birmaher et al., 2014). Results from longitudinal clinical studies with children, which are fewer and involve smaller samples, also show continuity of impairment, whereas specific diagnoses at follow-up are more variable. Among 15 boys with ADHD who also met criteria for mania, only one was diagnosed with mania 6 years later (Hazell, Carr, Lewin, & Sly, 2003), although all continued to display marked functional impairment. Other longitudinal studies (Biederman et al., 2004; Geller, Tillman,

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Bolhofner, & Zimerman, 2008) continue to show high rates of ADHD and mania from 4 to 8 years after baseline assessments. Most information on the long-term course of bipolar disorder comes from clinical groups of affected adults, for whom there is a selection bias toward chronic illness. However, analyses of community epidemiological surveys raise the prospect that some individuals may achieve syndromal remission by mid-adulthood (Cicero, Epler, & Sher, 2009). This finding awaits clarification in extended longitudinal studies. In clinical samples, depressive episodes become more frequent and longer with age (Kupka et al., 2005; Suppes, Brown, Schuh, Baker, & Tohen, 2005). In the best of cases, functioning between episodes of mood disturbance is quite good (full interepisode recovery) and one enjoys social and occupational success. The likelihood of good outcomes increases with a stable, tolerant family and a social milieu that buffers a person from consequences of illness and from stresses that would otherwise aggravate it. At the same time, this interpersonal environment must support treatment adherence and lifestyle practices that may forestall relapse (Alloy et al., 2005; Johnson, Lundströem, Åberg-Wistedt, & Mathé, 2003). Less fortunate individuals experience an unstable, often unremitting course. Although depression may come to predominate the clinical presentation, outcomes for those with bipolar disorder are more often worse than for unipolar depression (Goldberg & Harrow, 2011). They may drift downward socially as interpersonal and occupational functioning become increasingly erratic and inadequate. Interepisode recovery is incomplete in such cases. Sources of social support may become either alienated or actively reject the increasingly irascible patient. Legal entanglements are common complications. Civil liabilities arise from failure to meet financial and/or family obligations. Criminal activity may stem from explosiveness, belligerence, and violence, sometimes impelled by psychotic delusions but often also from impatience and anger with others who thwart the person’s immediate attainment of some, often peculiar, objectives. Over time, a sizable proportion of individuals become permanently disabled ( Judd et al., 2005; Morgan, Mitchell, & Jablensky, 2005). Social marginalization and loneliness are common outcomes that exacerbate the illness. Risk for suicide and age-adjusted mortality from all causes is high, particularly among those with earlier onset and high impulsivity (Dilsaver et al., 1997; Fiedorowicz et al., 2009; Garno et al., 2005; Novick, Swartz, & Frank, 2010; Ösby, Brandt, Correia, Ekbom, & Sparén, 2001). In the largest population-based study conducted to date, women and men with bipolar disorder exhibited suicide rates (after adjusting for age and sociodemographic factors) that are 10.4 and 8.1 times those observed in the general population, respectively (Crump, Sundquist, Winkleby, & Sundquist, 2013). Adjustment for substance use disorders reduces these estimates to 4.7 for women and 5.1 for men, reflecting the large effect of comorbid substance use on suicide risk among those with bipolar disorder.

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SEX DIFFERENCES Epidemiological studies of bipolar illness among adults show similar rates for both sexes (Grant et al., 2005; Merikangas et al., 2007), which also seems to be the case among adolescents (Johnson, Cohen, & Brook, 2000; Lewinsohn et al., 2003). Males are often more prevalent in research samples of children diagnosed with bipolar disorder (Biederman et al., 2005; Findling et al., 2010; Geller, Tillman, Craney, & Bolhofner, 2004), as well as those admitted to inpatient care (Blader & Carlson, 2007). The sex difference may reflect the high prevalence of ADHD, which is more prevalent among males (Geller et al., 2012).

COMORBIDITIES ADHD is the leading comorbidity reported among children diagnosed with bipolar disorder (e.g., Arnold et al., 2011; Biederman et al., 2005; Carlson, 1998; Pavuluri, Birmaher, & Naylor, 2005). Although variable definitions of bipolar disorder and methods of ascertainment affect estimates, comorbidity with ADHD ranges from 60% to 95% among children (Carlson & Klein, 2014). Among adolescents, rates of comorbid ADHD drop as more precipitous onset of bipolar symptoms becomes more prevalent (e.g., Birmaher et al., 2009; Kafantaris, Coletti, Dicker, Padula, & Pollack, 1998). Retrospective studies of adults with bipolar disorder suggest lower, but not negligible rates of premorbid ADHD, generally between 10% and 20% (Bernardi, Cortese, Solanto, Hollander, & Pallanti, 2010; Perlis et al., 2004; Sachs, Baldassano, Truman, & Guille, 2000). However, prospective studies do not show that ADHD confers high risk for bipolar disorder, although offspring of affected individuals do exhibit higher rates of inattention, anxiety, and depressive symptoms (Axelson et al., 2015; Duffy, 2012). Substance abuse is common among adolescents and adults with bipolar disorder, and it is the most prevalent secondary diagnosis among inpatients (Blader, 2011). Community and clinical outpatient samples also exhibit high rates of substance abuse, especially among adolescents and young adults (Kessler, Berglund, et al., 2005; Kessler, Chiu, et al., 2005). Alcohol and drug abuse worsen course of illness and increase risk of self-injurious behaviors and of death from external causes (Blader, 2011; Cardoso et al., 2008; Friedman et al., 2005; Goldberg, 2010; Goldberg, Garno, Leon, Kocsis, & Portera, 1999; Goldstein et al., 2005; Grant et al., 2005; Keck et al., 1998; Lewinsohn et al., 2003). Higher rates of substance abuse among adolescents with BPD may be mediated in part by comorbid conduct disorder (Carlson, Bromet, & Jandorf, 1998). Prevalence estimates of comorbid anxiety disorders vary considerably in child bipolar disorder. One research group (Dickstein et al., 2005) found that 77% of children who met narrow criteria for bipolar I or II disorder had at least one comorbid anxiety disorder. However, children who were culled from psychiatric clinic attendees and showed signs of mania did not have increased rates of anxiety disorder

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relative to other patients, although when present anxiety was more severe (Findling et al., 2010).

CULTURAL CONSIDERATIONS Mood disorders may be more prevalent among Hispanic adolescents compared with non-Hispanic White adolescents in the United States (Merikangas et al., 2010). The lifetime prevalence of mania/hypomania without history of major depression may also be higher among Hispanic and African American youth than among non-Hispanic White youth (Merikangas et al., 2012) However, there may be racial biases in diagnostic practices in clinical settings. African American individuals, especially men, are less likely to receive a diagnosis of bipolar disorder and more likely to receive a diagnosis of schizophrenia or schizoaffective disorder than White patients (Kilbourne et al., 2005; Strakowski et al., 2003). Similar findings have emerged among adolescents (DelBello, Lopez-Larson, Soutullo, & Strakowski, 2001). There may be a trend to attribute higher rates of psychotic features to African American patients, both adults and adolescents (Patel, Delbello, & Strakowski, 2006). The extent to which nonracial cultural factors affect symptom expression in bipolar disorder is an interesting issue about which there is little empirical literature.

RESEARCH DOMAIN CRITERIA To date, there are few accounts of the pathophysiology of bipolar disorder based on neural systems outlined by RDoC. Developing such an account poses formidable challenges. It seems likely that several of the posited domains in the RDoC framework will be implicated in bipolar disorder. Constructs from both the negative valence and positive valence systems comport (a) with bipolar disorder behavioral phenotypes and (b) with neuroimaging findings that compare affected individuals with controls. Affect-relevant RDoC constructs for bipolar disorder include loss (where most depression-related phenomena are situated), sustained threat, frustrative nonreward, approach motivation, and reward learning. Various components of cognitive systems (cognitive control, working memory), social processes, and arousal systems domains also seem to bear on the wide-ranging manifestations of bipolar disorder. Complicating matters further, a complete account of the underlying mechanisms needs to explain the episodic and often cyclic nature of the disorder over time. Some recent studies on the pathophysiology of bipolar disorders have integrated RDoC elements. In an adolescent clinical sample, fMRI-measured BOLD signal from left medial prefrontal cortex when experiencing reward was better correlated with a continuous measure of mania symptoms than specific categorical diagnosis (Bebko et al., 2014). A comparison of heart-rate variability between adults with bipolar disorder, unipolar depression, and healthy controls showed that the bipolar group had larger within-individual variation in HRV over 6 days of

Bipolar Disorder 729 ambulatory monitoring, which supports HRVs as a maker of the positive emotional valence system (Gruber, Mennin, Fields, Purcell, & Murray, 2015).

THEORETICAL SYNTHESIS AND FUTURE DIRECTIONS Severe behavioral dyscontrol, occasioned by affective instability, regardless of specific bipolar diagnosis, most often has a chronic course and confers risk for impairments that entail grave personal, occupational, and familial misfortune. The challenge for developmental psychopathology is to better elucidate likely multifinal ways that these difficulties develop early in life, which would facilitate development of appropriate interventions. The signature features and course of bipolar disorder are well characterized; the frequent development of this condition in middle to late adolescence makes it an important focus for developmental psychopathologists. Nevertheless, current understanding of mental illness remains grounded principally in a descriptive approach to symptom co-occurrence and patterns of onsets and course (see Chapter 2 [Beauchaine]). Improved grasp of the underlying mechanisms of disorder has been a core goal for decades, but difficulties in observing the brain in action, and the ephemeral nature of behavior itself make progress reliant on incremental advances in technology and conceptual approaches. More detailed accounts of the genetic and neural underpinnings of impulse control, emotional states, affect regulation, and social adaptation, as well as the diverse ways that these areas can incur dysfunction, will mark pivotal advances in science. It is also imperative that a developmental framework inform acquisition and application of this knowledge.

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C H A P T E R 22

Autism Spectrum Disorder SUSAN FAJA AND GERALDINE DAWSON

A

utism spectrum disorder (ASD) is a group of neurodevelopmental disorders that are characterized by impairments in social and communication behavior and by a restricted range of activities and interests. Recent advances have helped explain some causes of ASD, which include both genetic vulnerabilities and environmental risk factors. Effective treatments for reducing core and associated symptoms are being developed. In this chapter, we review current findings regarding genetic vulnerabilities, environmental risk and protective factors, and early brain and behavioral development. Overall, we provide a perspective that offers hope for improved outcomes for many individuals with ASD.

HISTORICAL CONTEXT The term autism was introduced by Eugen Bleuler (1950), who used it to describe individuals with schizophrenia who had difficulties with reality testing. Autism was first distinguished from schizophrenia by Leo Kanner (1943), who described a clinical syndrome characterized by behaviors that gave rise to modern diagnostic criteria, including lack of social reciprocity and emotional awareness, delays in communication, atypical use of language, and repetitive interests and behaviors. Around the same time, Hans Asperger described a related syndrome characterized by social aversion, limited expressions of affect, and difficulties with conversation, often in individuals with high intelligence (Frith, 1991). Asperger’s syndrome was also associated with both need for sameness and preoccupation with circumscribed interests. Asperger described affected individuals as “little professors,” with intense interests and the verbal ability to provide lengthy descriptions of these interests. 745

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TERMINOLOGICAL AND CONCEPTUAL ISSUES ASD in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) includes two core symptom domains: social communication impairments and restricted and repetitive patterns of behavior and interests (American Psychiatric Association, 2013). ASD is extremely heterogeneous in its presentation, and DSM-5 diagnostic criteria reflect the need to account for a wide range of developmental levels and to use a dimensional rather than categorical approach to symptoms (Rutter, 2011). A diagnosis of ASD requires each of three social communication symptoms: deficits in social-emotional reciprocity; deficits in nonverbal communicative behaviors used for social interaction; and deficits in developing, maintaining, and understanding relationships. Importantly, these symptoms may be associated with somewhat different behaviors across individuals and throughout development. In addition, at least two of four possible symptoms of restricted or repetitive behavior or interests must be present at some point during development. Thus, autism varies in severity, and individuals with different combinations of symptoms may meet diagnostic criteria. Indeed, the DSM-5 diagnostic criteria provide a variety of examples of possible behaviors for each symptom and describe three severity levels ranging from requiring support to requiring very substantial support. Symptoms of ASD must be present early in development. They are often detected during toddlerhood or preschool, although they may not raise concern until social demands exceed a child’s capabilities. For children without comorbid language and cognitive delays, this recognition may occur only after they enter school and must adhere to social conventions of the classroom and increased social demands of peer interactions. In many cases, reliable diagnosis may be made as early as 24 months (Lord et al., 2006) or even earlier. Indeed, infants at risk for autism are being identified with good reliability at increasingly younger ages (Guthrie, Swineford, Nottke, & Wetherby, 2013; Ozonoff et al., 2015). Yet many children continue to go undiagnosed until preschool (Coonrod & Stone, 2004) or later (Shattuck et al., 2009), even though screening tools exist for infants (e.g., the First Year Inventory; Reznick, Baranek, Reavis, Watson, & Crais, 2007) and toddlers (e.g., Modified-Checklist for Autism in Toddlers; Robins, Fein, Barton, & Green, 2001). Fortunately, delays in diagnosis are being reduced with changing clinical practices but continue to occur more often among children of low socioeconomic status, children with milder symptoms, and those with high IQs (Mazurek et al., 2014). Delays result in late entry into early intervention programs, which improve prognosis.

Comorbidities ASD is associated with several comorbid conditions. Most commonly, ASD is accompanied by developmental delay/intellectual disability, even though a significant portion of individuals with ASD have average to above average intelligence. Approximately 31% of children with ASD have cognitive impairment (IQ ≤ 70;

Autism Spectrum Disorder 747 Autism and Developmental Disabilities Monitoring [ADDM], 2014). Girls with an ASD exhibit higher rates of intellectual disability than boys (Centers for Disease Control, 2007; Van Wijngaarden-Cremers et al., 2014). Given the rate of children who have both ASD and intellectual disability, it is important to consider a child’s developmental level and typical developmental milestones when making a diagnosis. A wide range of medical comorbidities—including epilepsy, sleep disorders, gastrointestinal disorders, and various psychiatric conditions—is associated with ASD (Kohane et al., 2012). Sleep disruptions affect 50% to 80% of children with ASD (see Richdale & Schreck, 2009, for review). Gastrointestinal disorders affect between 9% and 70% of children with ASD, clearly a huge range (see Buie et al., 2010, for review). Common psychiatric comorbidities include attention-deficit/hyperactivity disorder, anxiety disorders (specific phobia, obsessive compulsive disorder, social anxiety disorder), and depression (Leyfer et al., 2006; Simonoff et al., 2008). Prevalence rates of seizures range from 5% to 39% (Ballaban-Gil & Tuchman, 2000; Tidmarsh & Volkmar, 2003), with an increasing risk for seizures with age.

Socioeconomic Considerations Autism is observed throughout the world and across cultures (e.g., Kim et al., 2011; Randall et al., 2015), and affects individuals regardless of socioeconomic status (SES; Fombonne, 1999, 2003). However, SES is associated with age of diagnosis, such that less educated families and families in the Medicaid system tend to receive later diagnoses (Fountain, King, & Bearman, 2011; Mandell et al., 2010). Furthermore, racial and ethnic disparities persist in identification of ASD, particularly among Black and Hispanic children, even though ASD can and should be diagnosed reliably at much younger ages (ADDM, 2014; Mandell et al., 2009).

PREVALENCE Once believed to be a rare disorder, it is now estimated that ASD affects approximately 1 in 68 children in the United States (ADDM, 2014). Approximately 3.5 million individuals are affected in the United States, with an annual societal cost of more than $61 billion for supporting children and $175 billion for supporting adults (Buescher, Cidav, Knapp, & Mandell, 2014). Lifetime costs of supporting an individual with ASD and intellectual disability are approximately $2.44 million, compared with $1.43 million for an individual with an ASD without intellectual disability (Buescher et al., 2014). Data from a large sample of children, ages 3 to 17 years, suggest a nearly fourfold increase in the prevalence of ASD since 1997—the largest relative increase among developmental disabilities (Boyle et al., 2011). Changes in prevalence result at least in part from broadened diagnostic criteria with revisions to the DSM, methodological differences in prevalence research, and increasing awareness and use of ASD diagnoses. The effect of increasing awareness

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is difficult to quantify, but epidemiological data (Hertz-Picciotto & Delwiche, 2009; King & Bearman, 2009) suggest that historical changes in diagnostic criteria, diagnostic substitution, inclusion of milder cases, and an earlier age at diagnosis do not fully account for the increase in prevalence, leaving a substantial portion of the increased rate unexplained. It is a major challenge to understand what these factors may be. Autism affects males more often than females, with a sex ratio of 4.5 to 1 (ADDM, 2014). Symptom profiles are slightly different. Though boys and girls are roughly equivalent in the social domain, females have less severe restricted and repetitive behaviors (Van Wijngaarden-Cremers et al., 2014), possibly due to different underlying etiology for females.

ETIOLOGIC FORMULATIONS ASD is now recognized as comprising multiple conditions with etiologies involving different combinations of genetic vulnerabilities and environmental risk factors (exemplifying equifinality, see Chapter 1 [Hinshaw]). The emergence and severity of ASD symptoms can be understood with a developmental psychopathology framework that considers the operation of transactional processes that eventuate in atypical early brain development and atypical engagement with the environment. Altered interaction patterns between the child and his/her environment are hypothesized to disrupt critical inputs, adversely affecting brain circuitry development during early sensitive periods. The final outcome is symptom expression, as illustrated in Figure 22.1. Thus, abnormal social interactions mediate the effects of early susceptibilities on later outcomes. As a result, there is not a one-to-one correspondence between genetic vulnerabilities or environmental risk factors and the occurrence of ASD. Rather, children follow varied developmental pathways. Although diversion from these pathways may be possible, canalization constrains the magnitude and quality of such changes such that, “the longer an individual

Vulnerabilities

Genetic vulnerability Environmental risk factors

Figure 22.1

Risk processes

Outcome

Altered neural circuitry Full ASD Altered patterns of interaction with the environment

Broader autism phenotype

Experience-based risk processes in autism.

Autism Spectrum Disorder 749 continues along a maladaptive ontogenetic pathway, the more difficult it is to reclaim a normal developmental trajectory” (Cicchetti & Cohen, 1995, p. 7). Accordingly, the earlier that risk for autism is detected and intervention begins, the greater the likelihood that intervention will alter abnormal developmental trajectories.

Genetics and Heritability Twin studies, sibling concordance rates, and evidence of subthreshold symptoms among first-degree relatives provide strong evidence for genetic vulnerability to ASD. Groundbreaking twin studies (e.g., Bailey et al., 1995; Folstein & Rutter, 1977) suggested extremely high heritabilities for ASD (e.g., 90%; Bailey et al., 1995) based on concordance rates for monozygotic (MZ) versus dizygotic (DZ) twin pairs. However, these studies were limited by small sample sizes and possible ascertainment biases. A large population-based sample with research-reliable diagnostic measures, estimated ASD heritability of 38% (95% confidence interval: 14%–67%), whereas shared environmental factors contributed 58% (CI: 30%–80%; Hallmayer et al., 2011). This study confirms significant heritability for ASD, albeit of much lower effect size than previously reported. The findings also indicate an important role of shared environmental risk factors. However, a recent meta-analysis that accounted for methodological limitations of previous studies indicated heritability coefficients for ASD between 64% and 91%, with shared environmental effects ranging from 7% to 35% (Tick, Bolton, Happé, Rutter, & Rijsdijk, 2015). The authors conclude that previous reports with significant shared environmental influences may be due to overinclusion of concordant DZ twins. Sibling recurrence rates (i.e., the rate of diagnosis for a younger sibling given an older sibling with ASD) are estimated to be about 19% (Ozonoff et al., 2011)—a rate much higher than the general population. Following a large cohort of infant siblings prospectively allowed for recurrence estimates that are more robust to methodological limitations. Specifically, Ozonoff included families who had a second child before receiving a diagnosis for their older child, which may have affected their likelihood of reproducing (i.e., stoppage) and enrolled younger siblings before concerns were noted, which prevented overselection of families with ASD recurrence. Recurrence risk for ASD in a third sibling was significantly higher when there were two affected siblings in a family than in families with one affected child (Ozonoff et al., 2011), suggesting increased genetic vulnerability in some families. Finally, recurrence patterns differ depending on the sex of the siblings with a nearly threefold increase for younger male versus female siblings (Ozonoff et al., 2011), suggesting protective mechanisms for females and sex differences in ASD vulnerability (Ozonoff et al., 2011; Werling & Geschwind, 2015). Finally, subthreshold symptoms, potentially indicating a “broad phenotype” of ASD, include difficulties such as social dysfunction and isolation, language delays, and atypical (i.e., ASD-like) communication. Such features are observed among 4% to 20% of siblings who do not meet criteria for an ASD (Bolton et al., 1994;

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Constantino et al., 2010; Piven et al., 1990) and some parents (Bailey et al., 1995; Folstein & Rutter, 1977; Losh et al., 2009). Several genome-wide linkage studies of ASD have been published (Cantor et al., 2005; Lamb et al., 2005; McCauley et al., 2005; Morrow et al., 2008; Schellenberg et al., 2006; Szatmari et al., 2007; Wang et al., 2009; Werling, Lowe, Luo, Cantor & Geschwind, 2014), although no single region has been consistently associated specifically with autism. Reducing the heterogeneity of samples based on certain features of probands, such as sex and language acquisition, increases linkage signals (e.g., Schellenberg et al., 2006; Werling et al., 2014), a common finding in psychiatric genetics (see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]). Although the mode of inheritance in autism is not understood fully, recent research suggests that multiple alleles confer vulnerability. In some cases, rare genetic variations, especially de novo mutations, with high probability of affecting the phenotype (i.e., high penetrance) are involved. In other cases, genetic variations appear to be more common. Although common variants individually have small effects (Abrahams & Geschwind, 2008), recent studies suggest that common variants in combination have substantial effects on genetic vulnerability to ASD (Gaugler et al., 2014). Based on a Swedish epidemiologic sample and results from other studies, Gaugler et al. suggest that autism heritability is about 52.4%, with most due to common variation. De novo mutations are believed to contribute substantially to individual liability, but their relative contribution to ASD vulnerability (2.6%) is modest. Common variants that have been implicated include GABRB3, a gene for a GABA receptor; OXTR, a gene coding for an oxytocin receptor; RELN, reelin, SLC6A4, a serotonin transporter allele; GRIN2B, an allele related to the NMDA receptor; AVPR1A, a gene linked to a vasopressin receptor, EN2; ITGB3; MET, a gene associated with a growth factor; and CNTNAP2 (see Yoo, 2015 for review). These genes are implicated in a variety of functions including neural signaling, growth and migration, immune function, gastrointestinal repair, and language. Technological advances now allow for genome-wide association studies of single nucleotide polymorphisms (SNP) and their role as common variants in genetic vulnerability to ASD. SNPs involve changes at one base pair of DNA. In contrast to linkage studies, genome-wide associations are nontargeted investigations. Several have been conducted for ASD (Anney et al., 2010; Anney et al., 2012; Chang, Pauls, Lange, Sasanfar & Santangelo, 2013; Chaste et al., 2015; Connolly, Glessner, & Hakonarson, 2012; Liu et al., 2015; Ma et al., 2009; Wang et al., 2009; Weiss et al., 2009). Results implicate two regions on chromosome 5p, and regions on 7q and 20p, including those that encode for the neural adhesion molecules cadherin 9 and 10 (CDH9 and CDH10), and MACROD2. However, more studies are needed with larger samples to resolve conflicting results. Recent investigations of autism genetic susceptibility have also examined the role of rare mutations. Initially, single-gene disorders were associated with increased risk for autism or expression of an autistic-like phenotype, including

Autism Spectrum Disorder 751 Fragile X syndrome, Rett syndrome, Angelman syndrome, and tuberous sclerosis (see Moss & Howlin, 2009; Veenstra-VanderWeele & Cook, 2004, for reviews). One factor underlying this association may be the presence of intellectual disability, although the severity of intellectual impairment is inconsistent across genetic syndromes. This fact suggests that ASD-like phenotypes are not accounted for by intellectual disability alone (Moss & Howlin, 2009). Although there appears to be overlap in features, careful clinical examination of cases also suggests subtle differences across syndromes, which may indicate targets for defining more homogeneous phenotypes (Moss & Howlin, 2009). Single-gene disorders have already provided important clues for identifying drug targets. Mouse models of single-gene disorders associated with ASD, such as tuberous sclerosis, Fragile X, and Rett syndrome, illustrate the use of targeted pharmacological agents to reverse behavioral, biochemical, and electrophysiological phenotypes, even among adult animals (Silva & Ehninger, 2009). Such research may pave the way for translational work among humans. Targeted sequencing focused on the X chromosome of affected females has identified rare point mutations in single genes, including the neuroligin 3 and neuroligin 4 genes (Jamain et al., 2003) and the SHANK3 gene (Durand et al., 2007), although these mutations fall among rare causes of autism. Findings with NLGN4 have been confirmed in one linkage study (Laumonnier et al., 2004). The role of these genes in encoding proteins that influence postsynaptic density is consistent with other findings implicating a variety of genetic influences acting at the synapse in ASD, particularly a pathway involved in experience-dependent stabilization of synaptic connections in the first three years of life (Bourgeron, 2009). In the past several years, assessment of rare variants with single-base resolution has provided additional information about patterns of genetic vulnerability to ASD. Whole exome sequencing (WES) can detect single nucleotide variants (SNVs), which are rare variations in the DNA sequence. The exome is the portion of the genome that contains coding sequences. Four large WES studies (Iossifov et al., 2012; Neale et al., 2012; O’Roak et al., 2011; O’Roak et al., 2012; Sanders et al., 2012) indicate that de novo loss of function (LoF) is higher among families with ASD than among control families. These studies identified six genes, CHD8, DYRK1A, GRIN2B, KATNAL2, POGZ, and SCN2A, which had de novo events with high penetrance and impacted a range of functions. This led to targeted resequencing of CHD8 using a genotype-first approach and identified a rare subtype of ASD in a subset of 15 individuals who displayed macrocephaly, distinct facial features, and gastrointestinal disruptions (Bernier et al., 2014). To date, two studies have used whole genome sequencing (WGS; see Chapter 3 [Beauchaine, Gatzke-Kopp, & Gizer]) with relatively large samples. One study revealed differences in de novo and rare SNVs among a substantial number of sibling pairs (Yuen et al., 2015). Siblings with discordant mutations also had more symptom variability. The second study demonstrated disruptions in noncoding DNA beyond that contained in the exome, with a higher rate of disruption among children affected by ASD than among

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nonaffected family members (Turner et al., 2016). This set of findings suggests that smaller mutations in regulatory DNA may confer additional genetic vulnerability to ASD. Finally, the role of copy number variations (CNV), which are inherited or de novo structural gains (duplications) or losses (deletions) in the genome between about 50 base pairs and 1 million base pairs (Girirajan, Campbell, & Eichler, 2011), has been explored in genetic vulnerability to ASD. CNVs are found in about 10% of simplex (one affected sibling) and 2% of multiplex (two affected siblings) cases of ASD (Sebat et al., 2007; but see Pinto et al., 2010), which suggests different genetic structural mechanisms for familial versus “sporadic” cases of ASD. CNVs may be highly penetrant or may work in combination with common variants to “push” individuals over the threshold of genetic susceptibility for ASD. It should be noted that CNVs are also found in unaffected family members, and, to a lesser extent, members of the population at large (Girirajan et al., 2011), so their effects are not always clinically significant. Some CNVs occur more frequently at specific genetic loci and may overlap with regions already associated with ASD and intellectual disability (Cook & Scherer, 2008; Guilmatre et al., 2009; Pinto et al., 2010). In genome-wide studies, CNVs have been found at 1q, 7q, 15q, 16p, and 22q (Bucan et al., 2009; Glessner et al., 2009; Sanders et al., 2011), disrupting groups of genes involved in neuronal cell adhesion, proliferation, projection and motility, and GTPase/Ras signaling (Glessner et al., 2009; Pinto et al., 2010). CNVs have also been implicated in disrupting genes involved in ubiquitination, which alters protein function and targets proteins for degradation (Glessner et al., 2009). Given the complexity of genetic risk for ASD, with substantial (though variable) heritability rates and estimates of 400–1000 possible genes conferring risk via de novo mutations (see Geschwind & State, 2015 for review), it is important to examine convergence in pathways that these genes signal. One approach to integrating multiple genetic vulnerabilities, including rare and de novo variations, and common variants with small effect, is to examine the possibility that candidates contribute to a functional network (see Peça, Ting, & Feng, 2011, for an illustration of such a synaptic network and Sahin & Sur, 2015, for an illustration of molecular pathways involved in transcriptional control, chromatin remodeling in the nucleus, protein synthesis and synaptic structure). An application of this approach identified clusters of CNVs in a functional network of loci involved in synapse development, axon targeting, and neuron motility (Gilman et al., 2011). Many of the genes implicated were related to cell-adhesion and scaffolding at the synapse, as well as regulating protein synthesis. In summary, there are a number of genetic vulnerabilities to ASD, yet the role of susceptibility genes is complex. Indeed, even among siblings in the same family, about 70% of affected siblings carry different ASD-relevant de novo and rare inherited mutations, which highlights the genetic heterogeneity of the disorder (Yuen et al., 2015). Evidence thus far indicates that multiple genetic effects interact to increase susceptibility to ASD by influencing gene expression and/or encoding

Autism Spectrum Disorder 753 functional changes in proteins that are part of complex regulatory networks. As we discuss in more detail below, expression and effects of many genes are influenced by environmental factors, offering hope that early intervention can alter genetic expression, brain development, and behavioral outcomes.

Environmental Risk Factors Identical twin concordance rates of less than 100%, along with a more than 600% increase in ASD prevalence in recent decades (some of which may reflect an actual increase; see above), suggest that environmental factors are also involved. Recent studies have identified a number of potential environmental risk factors, including exposure to some air pollutants, metals, and pesticides (Kalkbrenner, Schmidt, & Penlesky, 2014); maternal infection (Atladóttir et al, 2010); and use of certain medications (e.g., SSRIs) during pregnancy (Man et al., 2015). Other factors associated with ASD include extreme prematurity (Guy et al., 2015) and short interpregnancy interval (Cheslack-Postava et al., 2011). Exposure to teratogens such as thalidomide and valproic acid (Depakote) during pregnancy is also associated with increased risk of developing ASD (Moore et al., 2000; Rasalam et al., 2005; Strömland, Nordin, Miller, Akerstrom, & Gillberg, 1994). Perinatal and neonatal factors including uterine bleeding, abnormal presentation, fetal distress, low Apgar scores, feeding difficulties, hyperbilirubinemia, and low birth weight are related to increased risk (Gardener, Spiegelman, & Buka, 2011; Losh, Esserman, Ankarsater, Sullivan, & Lichtenstein, 2012; Pinto-Martin et al., 2011). Critically, the effects of environmental influences are likely to span pregnancy through the first year of life, given the time course of onset of ASD and the pattern of brain development observed. Yet, it is possible that these perinatal and neonatal conditions do not play a causal role but are instead correlates of fetal abnormalities or genetic factors (Bolton et al., 1997). Involvement of vaccinations during the first years of life, especially the measles-mumps-rubella (MMR) vaccination, has been hypothesized as an environmental risk factor. However, a review of the effects of MMR across studies including over one million children failed to uncover any association between the MMR vaccine and ASD (Taylor, Swerdfeger, & Eslick, 2014). Furthermore, the largest study ever conducted of the MMR vaccine among 95,727 children with increased genetic vulnerability to ASD (i.e., younger siblings of children with ASD) found no evidence of increased risk related to MMR vaccination (Jain et al., 2015). Thimerosal, a preservative containing ethyl mercury that was added to many vaccines, was also examined and no evidence of increased risk was found (Taylor et al., 2014). Advanced parental age (i.e., mothers older than 35–40 years, fathers older than 50–55 years) is associated consistently with increased risk of ASD (Croen, Najjar, Fireman, & Grether, 2007; Sandin et al., 2012; Sandin et al., 2015; Shelton, Tancredi, Hertz-Picciotto, 2010). Younger maternal age (