The Oxford Handbook of Externalizing Spectrum Disorders [Illustrated] 9780199324675

Table of contents :
Cover
Series
The Oxford Handbook of Externalizing Spectrum Disorders
Copyright
Short Contents
Contents
Part One • Models of Externalizing Behavior
1. Overview of DSM Disruptive Behavior Disorders
2. Attention-Deficit/Hyperactivity Disorder: Similarities to and Differencesfrom Other Externalizing Disorders
3. Substance Use Disorders as Externalizing Outcomes
4. Self-Injury, Borderline Personality Development,and the Externalizing Spectrum
5. The Externalizing Spectrum of Personality and Psychopathology: AnEmpirical and Quantitative Alternative to DiscreteDisorder Approaches
6. The Developmental Psychopathology Perspective on Externalizing BehaviorDimensions and Externalizing Disorders
Part Two • Biological Vulnerabilities to ExternalizingSpectrum Disorders
7. Behavioral Genetics of the Externalizing Spectrum
8. Molecular Genetic Approaches to Studyingthe Externalizing Spectrum
9. Molecular Genetics of the Externalizing Spectrum
10. Temperament and Vulnerability to Externalizing Behavior
11. Midbrain Neural Mechanisms of Trait Impulsivity
12. Prefrontal and Anterior Cingulate Cortex Mechanisms of Impulsivity
13. Neural Mechanisms of Low Trait Anxiety and Riskfor Externalizing Behavior
14. Sex Differences in the Prevalence and Expressionof Externalizing Behavior
Part Three • Socialization Mechanisms of Externalizing Behavior
15. Child Maltreatment and Vulnerability to ExternalizingSpectrum Disorders
16. Coercive Family Processes in the Development of ExternalizingBehavior: Incorporating Neurobiology into Intervention Research
17. Friendship and Adolescent Problem Behavior: Deviancy Trainingand Coercive Joining as Dynamic Mediators
18. Neighborhood Risk and Development of Antisocial Behavior
19. Incarceration and Development of Delinquency
Part Four • Cognitive and Emotional Vulnerabilitiesto Externalizing Spectrum Disorders
20. Externalizing Behaviors and Attribution Biases
21. Callous-Unemotional Traits and the Development of ExternalizingSpectrum Disorders
22. Low Intelligence and Poor Executive Function as Vulnerabilitiesto Externalizing Behavior
Part Five • Other Vulnerabilities to ExternalizingSpectrum Disorders
23. Head Injury and Externalizing Behavior
24. Teratogen Exposure and Externalizing Behavior
Part Six • Externalizing Comorbidities
25. Externalizing and Internalizing Comorbidity
26. Comorbidity Among Externalizing Disorders
Part Seven • Conclusions and Future Directions
27. An Ontogenic Processes Model of Externalizing Psychopathology
Index

Citation preview

The Oxford Handbook of Externalizing Spectrum Disorders

O X F O R D L I B R A RY O F P S Y C H O L O G Y

Editor-in-Chief Peter E. Nathan Area Editors: Clinical Psychology David H. Barlow Cognitive Neuroscience Kevin N. Ochsner and Stephen M. Kosslyn Cognitive Psychology Daniel Reisberg Counseling Psychology Elizabeth M. Altmaier and Jo-Ida C. Hansen Developmental Psychology Philip David Zelazo Health Psychology Howard S. Friedman History of Psychology David B. Baker Methods and Measurement Todd D. Little Neuropsychology Kenneth M. Adams Organizational Psychology Steve W. J. Kozlowski Personality and Social Psychology Kay Deaux and Mark Snyder

OXFORD

L I B R A RY

OF

Editor in Chief

PSYCHOLOGY

peter e. nathan

The Oxford Handbook of Externalizing Spectrum Disorders Edited by Theodore P. Beauchaine Stephen P. Hinshaw

1

1 Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford New York Auckland Cape Town Dar es Salaam Hong Kong Karachi Kuala Lumpur Madrid Melbourne Mexico City Nairobi New Delhi Shanghai Taipei Toronto With offices in Argentina Austria Brazil Chile Czech Republic France Greece Guatemala Hungary Italy Japan Poland Portugal Singapore South Korea Switzerland Thailand Turkey Ukraine Vietnam Oxford is a registered trademark of Oxford University Press in the UK and certain other countries. Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016

© Oxford University Press 2016 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by license, or under terms agreed with the appropriate reproduction rights organization. Inquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above. You must not circulate this work in any other form and you must impose this same condition on any acquirer. Library of Congress Cataloging-in-Publication Data The Oxford handbook of externalizing spectrum disorders / edited by Theodore P. Beauchaine and Stephen P. Hinshaw.   p. ; cm. — (Oxford library of psychology) Handbook of externalizing spectrum disorders Externalizing spectrum disorders Includes bibliographical references and index. ISBN 978–0–19–932467–5 (alk. paper) I.  Beauchaine, Theodore P., editor.  II.  Hinshaw, Stephen P., editor.  III.  Title: Handbook of externalizing spectrum disorders.  IV.  Title: Externalizing spectrum disorders.  V.  Series: Oxford library of psychology. [DNLM:  1.  Child Development Disorders, Pervasive—psychology.  2.  Attention Deficit and Disruptive Behavior Disorders—psychology.  3.  Child Development Disorders, Pervasive—etiology.  4.  Child Development Disorders, Pervasive—genetics. WS 350.8.P4] RJ506.A9 618.92′85882—dc23 2015016204

9 8 7 6 5 4 3 2 1 Printed in the United States of America on acid-free paper

S H O RT CO N T E N T S

Oxford Library of Psychology  vii About the Editors  ix Contributors xi Table of Contents  xiii Preface xvii Chapters 1–502 Index 503

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O X F O R D L I B R A R Y O F   P S YC H O L O G Y

The Oxford Library of Psychology, a landmark series of handbooks, is published by Oxford University Press, one of the world’s oldest and most highly respected publishers, with a tradition of publishing significant books in psychology. The ambitious goal of the Oxford Library of Psychology is nothing less than to span a vibrant, wide-ranging field and, in so doing, to fill a clear market need. Encompassing a comprehensive set of handbooks, organized hierarchically, the Library incorporates volumes at different levels, each designed to meet a distinct need. At one level are a set of handbooks designed broadly to survey the major subfields of psychology; at another are numerous handbooks that cover important current focal research and scholarly areas of psychology in depth and detail. Planned as a reflection of the dynamism of psychology, the Library will grow and expand as psychology itself develops, thereby highlighting significant new research that will impact on the field. Adding to its accessibility and ease of use, the Library will be published in print and, later on, electronically. The Library surveys psychology’s principal subfields with a set of handbooks that capture the current status and future prospects of those major subdisciplines. This initial set includes handbooks of social and personality psychology, clinical psychology, counseling psychology, school psychology, educational psychology, industrial and organizational psychology, cognitive psychology, cognitive neuroscience, methods and measurements, history, neuropsychology, personality assessment, developmental psychology, and more. Each handbook undertakes to review one of psychology’s major subdisciplines with breadth, comprehensiveness, and exemplary scholarship. In addition to these broadly conceived volumes, the Library also includes a large number of handbooks designed to explore in depth more specialized areas of scholarship and research, such as stress, health and coping, anxiety and related disorders, cognitive development, or child and adolescent assessment. In contrast to the broad coverage of the subfield handbooks, each of these latter volumes focuses on an especially productive, more highly focused line of scholarship and research. Whether at the broadest or most specific level, however, all of the Library handbooks offer synthetic coverage that reviews and evaluates the relevant past and present research and anticipates research in the future. Each handbook in the Library includes introductory and concluding chapters written by its editor to provide a roadmap to the handbook’s table of contents and to offer informed anticipations of significant future developments in that field. An undertaking of this scope calls for handbook editors and chapter authors who are established scholars in the areas about which they write. Many of the vii

nation’s and world’s most productive and best-respected psychologists have agreed to edit Library handbooks or write authoritative chapters in their areas of expertise. For whom has the Oxford Library of Psychology been written? Because of its breadth, depth, and accessibility, the Library serves a diverse audience, including graduate students in psychology and their faculty mentors, scholars, researchers, and practitioners in psychology and related fields. Each will find in the Library the information they seek on the subfield or focal area of psychology in which they work or are interested. Befitting its commitment to accessibility, each handbook includes a comprehensive index, as well as extensive references to help guide research. And because the Library was designed from its inception as an online as well as a print resource, its structure and contents will be readily and rationally searchable online. Further, once the Library is released online, the handbooks will be regularly and thoroughly updated. In summary, the Oxford Library of Psychology will grow organically to provide a thoroughly informed perspective on the field of psychology, one that reflects both psychology’s dynamism and its increasing interdisciplinarity. Once published electronically, the Library is also destined to become a uniquely valuable interactive tool, with extended search and browsing capabilities. As you begin to consult this handbook, we sincerely hope you will share our enthusiasm for the more than 500-year tradition of Oxford University Press for excellence, innovation, and quality, as exemplified by the Oxford Library of Psychology. Peter E. Nathan Editor-in-Chief Oxford Library of Psychology

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oxford library of psychology

A B O U T T H E   E D I TO R S

Theodore P. Beauchaine Theodore P.  Beauchaine, PhD, is Professor of Psychology at The Ohio State University. He is past recipient of the American Psychological Association Distinguished Scientific Award for Early Career Contributions to Psychology and has published extensively on child and adult psychopathology. His research addresses the neural underpinnings and development of both behavioral impulsivity and emotion dysregulation in children, adolescents, and adults. Stephen P. Hinshaw Stephen P. Hinshaw, PhD, is Professor of Psychology at University of California, Berkeley. He is an internationally recognized research investigator of child and adolescent disorders, award-winning teacher, and author/co-author of more than 275 research articles and chapters plus 10 books. His most recent book, with Richard Scheffler, is The ADHD Explosion: Myths, Medication, Money, and Today’s Push for Performance.

ix

CO N T R I B U TO R S

Shaikh I. Ahmad University of California, Berkeley Laura A. Baker University of Southern California Theodore P. Beauchaine The Ohio State University Sytske Besemer University of California, Berkeley Natalie Castellanos-Ryan University of Montreal Dante Cicchetti University of Minnesota Philip J. Corr City University London Sheila E. Crowell University of Utah Devika Dhamija University of Southern California Thomas J. Dishion Arizona State University Deborah A. G. Drabick Temple University Colleen M. Ehatt University of North Carolina Jarrod M. Ellingson University of Missouri Robert Eme Illinois School of Professional Psychology, Argosy University Jens Foell Florida State University Bryanna Hahn Fox University of South Florida Paul J. Frick University of New Orleans Joan P. Gerring State University of New York Ian R. Gizer University of Missouri

Leila Glass San Diego State University Farrah N. Golmaryami University of New Orleans Diana M. Graham San Diego State University Ashley Hampton Shields Temple University David J. Hawes University of Sydney Stephen P. Hinshaw University of California, Berkeley Anne-Marie R. Iselin University of North Carolina Wesley G. Jennings University of South Florida Adam Johns Sydney Children's Hospital Erin A. Kaufman University of Utah Hanjoe Kim Arizona State University Fernanda Valle Krieger Universidade de São Paulo, Brazil Robert F. Krueger University of Minnesota Florence Levy University of New South Wales Sarah N. Mattson San Diego State University Neil McNaughton University of Otago Allison A. McVey University of North Carolina Joseph Murray Department of Psychiatry, University of Cambridge Molly A. Nikolas University of Iowa xi

Jacqueline M. Otto University of Missouri Sophie Parent University of Montreal Christopher J. Patrick Florida State University Michelle Pinsonneault University of Montreal Jean R. Séguin University of Montreal Tiffany M. Shader The Ohio State University James Snyder Department of Psychology Wichita State University Elizabeth Steinberg Temple University Stephanie D. Stepp University of Pittsburgh Argyris Stringaris King’s College London

xii Contributors

Jennifer L. Tackett University of Houston Jenn-Yun Tein Arizona State University Eric Thibodeau University of Minnesota Catherine Tuvblad Örebro University and University of Southern California Adrienne VanZomeren-Dohm University of Minnesota Roma A. Vasa Johns Hopkins University Noah C. Venables Florida State University Darrell A. Worthy Texas A & M University Xiaoyenan Xu University of Minnesota Aimee R. Zisner The Ohio State University

CONTENTS

Preface  xvii

Part One  •  Models of Externalizing Behavior 1. Overview of DSM Disruptive Behavior Disorders  3 Deborah A. G. Drabick, Elizabeth Steinberg, and Ashley Hampton Shields 2. Attention-Deficit/Hyperactivity Disorder: Similarities to and Differences from Other Externalizing Disorders  19 Shaikh I. Ahmad and Stephen P. Hinshaw 3. Substance Use Disorders as Externalizing Outcomes  38 Christopher J. Patrick, Jens Foell, Noah C. Venables, and Darrell A. Worthy 4. Self-Injury, Borderline Personality Development, and the Externalizing Spectrum  61 Erin A. Kaufman, Sheila E. Crowell, and Stephanie D. Stepp 5. The Externalizing Spectrum of Personality and Psychopathology: An Empirical and Quantitative Alternative to Discrete Disorder Approaches  79 Robert F. Krueger and Jennifer L. Tackett 6. The Developmental Psychopathology Perspective on Externalizing Behavior Dimensions and Externalizing Disorders  90 Stephen P. Hinshaw and Theodore P. Beauchaine

Part Two  • Biological Vulnerabilities to Externalizing Spectrum Disorders 7. Behavioral Genetics of the Externalizing Spectrum  105 Devika Dhamija, Catherine Tuvblad, and Laura A. Baker 8. Molecular Genetic Approaches to Studying the Externalizing Spectrum  125 Ian R. Gizer, Jacqueline M. Otto, and Jarrod M. Ellingson 9. Molecular Genetics of the Externalizing Spectrum  149 Ian R. Gizer, Jacqueline M. Otto, and Jarrod M. Ellingson 10. Temperament and Vulnerability to Externalizing Behavior  170 Fernanda Valle Krieger and Argyris Stringaris 11. Midbrain Neural Mechanisms of Trait Impulsivity  184 Aimee R. Zisner and Theodore P. Beauchaine 12. Prefrontal and Anterior Cingulate Cortex Mechanisms of Impulsivity  201 Natalie Castellanos-Ryan and Jean R. Séguin xiii

13. Neural Mechanisms of Low Trait Anxiety and Risk for Externalizing Behavior  220 Philip J. Corr and Neil McNaughton 14. Sex Differences in the Prevalence and Expression of Externalizing Behavior  239 Robert Eme

Part Three  •  Socialization Mechanisms of Externalizing Behavior 15. Child Maltreatment and Vulnerability to Externalizing Spectrum Disorders  267 Adrienne VanZomeren-Dohm, Xiaoyenan Xu, Eric Thibodeau, and Dante Cicchetti 16. Coercive Family Processes in the Development of Externalizing Behavior: Incorporating Neurobiology into Intervention Research  286 James Snyder 17. Friendship and Adolescent Problem Behavior: Deviancy Training and Coercive Joining as Dynamic Mediators  303 Thomas J. Dishion, Hanjoe Kim, and Jenn-Yun Tein 18. Neighborhood Risk and Development of Antisocial Behavior  313 Wesley G. Jennings and Bryanna Hahn Fox 19. Incarceration and Development of Delinquency  323 Sytske Besemer and Joseph Murray

Part Four  • Cognitive and Emotional Vulnerabilities to Externalizing Spectrum Disorders 20. Externalizing Behaviors and Attribution Biases  347 Anne-Marie R. Iselin, Allison A. McVey, and Colleen M. Ehatt 21. Callous-Unemotional Traits and the Development of Externalizing Spectrum Disorders  360 Farrah N. Golmaryami and Paul J. Frick 22. Low Intelligence and Poor Executive Function as Vulnerabilities to Externalizing Behavior  375 Michelle Pinsonneault, Sophie Parent, Natalie Castellanos-Ryan, and Jean R. Séguin

Part Five  • Other Vulnerabilities to Externalizing Spectrum Disorders 23. Head Injury and Externalizing Behavior  403 Joan P. Gerring and Roma A. Vasa 24. Teratogen Exposure and Externalizing Behavior  416 Diana M. Graham, Leila Glass, and Sarah N. Mattson

xiv contents

Part Six  •  Externalizing Comorbidities 25. Externalizing and Internalizing Comorbidity  443 Florence Levy, David J. Hawes, and Adam Johns 26. Comorbidity Among Externalizing Disorders  461 Molly A. Nikolas

Part Seven  •  Conclusions and Future Directions 27. An Ontogenic Processes Model of Externalizing Psychopathology  485 Theodore P. Beauchaine, Tiffany M. Shader, and Stephen P. Hinshaw Index  503

contents

xv

PREFACE

Since publication of Robins’s (1966) landmark text almost 60 years ago, we have known that antisocial adult males usually follow a developmental trajectory that begins as early as preschool with hyperactivity/impulsivity and oppositionality, followed by conduct problems in middle school, delinquency and substance use in high school, and incarceration and recidivism by late adolescence and adulthood (see also Loeber & Hay, 1997; Moffitt, 1993). Using the prevailing classification system for psychopathology, the Diagnostic and Statistical Manual of Mental Disorders, 5th edition (DSM-5; American Psychiatric Association [APA], 2013), many of these males could be diagnosed with six psychiatric disorders over the course of their development, including attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), intermittent explosive disorder (IED), conduct disorder (CD), a substance-related/addictive disorder, and, finally, antisocial personality disorder (ASPD; Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013; Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008; Patrick, this volume). Although understudied until recently, girls who are reared in the same families as externalizing males—and therefore share genetic susceptibility and environmental risk—also suffer from difficulties with behavioral impulsivity very early in life and are vulnerable to subsequent depression, self-inflicted injury, and borderline personality development as they mature (Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009; Crowell, Beauchaine, & Linehan, 2009; Hinshaw et al., 2012). In addition to these heterotypically continuous patterns of psychopathology across development, concurrent comorbidity rates among externalizing syndromes are extremely high (see Krueger & Tackett, this volume; Nikolas, this volume). As outlined in chapters to follow, accumulating evidence points toward both (1) shared genetic and neural mechanisms of heterotypic continuity and concurrent comorbidity (Gizer, Otto, & Ellingson, this volume; Zisner & Beauchaine, this volume), and (2) interactions between biological vulnerabilities and environmental risk factors in shaping the ultimate expression of externalizing behavior (Beauchaine, Shader, & Hinshaw, this volume; Krieger, & Stringaris, this volume). According to this transactional perspective, externalizing disorders are more accurately viewed as a spectrum of related behavioral syndromes than as discrete diagnostic categories. Traditionally, it has been assumed that different externalizing syndromes reflect distinct disorders with separate etiologies (Kopp & Beauchaine, 2007). This is implied by the DSM (APA, 2013), which treats comorbidity as a differential xvii

diagnostic concern. The assumption that different externalizing syndromes are diagnostically distinct has resulted in the development of largely separate literatures for ADHD, ODD, CD, substance-related/addictive disorders, and ASPD. Basic scientists and clinicians tend to study and treat, respectively, single, traditionally defined disorders despite clear etiological underpinnings across the externalizing spectrum (Beauchaine et al., 2008). One consequence of this fractionated approach to research and practice is the evolution of distinct treatment approaches for each behavioral syndrome. Psychostimulants are often the front-line treatment for ADHD (MTA Cooperative Group, 1999), behavioral and multisystemic interventions are preferred for CD (e.g., Nock, 2003), and motivational techniques are often favored for substance-related and addictive disorders (e.g., Masterman & Kelly, 2003). Thus, many empirically supported treatments target specific diagnostic syndromes and are limited in their capacity to address concurrent comorbidities, thus rendering them less effective than they ultimately should be (see, e.g., Conrod & Stewart, 2005). Although treatment development is not the major focus of this volume, we assume that a comprehensive understanding of etiology and pathophysiology is a prerequisite of maximally effective interventions (Preskorn & Baker, 2002). Thus, in editing the volume, we recruited world-renowned experts and asked them to address questions about etiological and pathophysiological commonalities across the externalizing spectrum. Although traditional externalizing disorders are defined in the chapter by Drabick, Steinberg, and Hampton, the following chapter by Ahmad and Hinshaw immediately departs from the DSM and instead presents ADHD from a developmental psychopathology perspective in which individual-level vulnerabilities interact with contextual risk factors over time, resulting in development of more severe externalizing behaviors for those who are exposed to significant risk and adversity but not for those who are reared in protective environments. Although about half of the remaining chapters focus more heavily on individual-level vulnerabilities (e.g., Baker, this volume; Corr & McNaughton, this volume; Eme, this volume; Gerring & Vasa, this volume; Golmaryami & Frick, this volume; Iselin, McVey, Ehatt; this volume; Pinsonneault, Parent, Castellanos-Ryan, & Séguin, this volume; Séguin & Parent, this volume), whereas the other half focus more heavily on environmental adversity and risk (e.g., Besemer & Murray, this volume; Graham, Glass, & Mattson, this volume; Jennings & Hahn Fox, this volume; Snyder, this volume; VanZomeren-Dohm, Xu, Thibodeau, & Cicchetti, this volume), all treat externalizing behaviors as a spectrum of behavioral syndromes with common etiological mechanisms. An externalizing spectrum is consistent with both the developmental psychopathology perspective (Hinshaw & Beauchaine, this volume), which is transactional by nature, and with the philosophy that undergirds the Research Domain Criteria (RDoC), currently being developed and used by the National Institute of Mental Health (e.g., Insel et al., 2010; Sanislow et al., 2010). Explicit objectives of RDoC are to map primary dimensions of behavior, such as trait impulsivity, and to identify their biobehavioral substrates, from genes to behavior. For xviii preface

example, as outlined by Gizer, Otto, and Ellingson (this volume), several genes that affect dopamine (DA) neurotransmission are implicated in trait impulsivity. These genes appear to confer less DA reactivity in neural structures (e.g., nucleus accumbens, caudate) that are implicated in reward processing and extinction of previously learned behaviors (Zisner & Beauchaine, this volume). Under-reactive DA responding is expressed as a behavioral propensity to reward-seeking and trait impulsivity. Importantly, this impulsivity underlies disorders across the externalizing spectrum (Beauchaine, Shader, & Hinshaw, this volume; Krueger & Tackett, this volume) yet is also observed in other forms of psychopathology not traditionally classified as externalizing, such as borderline personality (see Kaufman, Crowell, & Stepp, this volume). Identifying traits that cut across traditional diagnostic boundaries is a core objective of both the developmental psychopathology perspective (Levy, Hawes, & Johns, this volume) and of RDoC (Beauchaine et al., 2013). Major assumptions of this approach are that it will (1) identify more genetically homogenous samples for psychiatric genetics studies, an area of research that has been hampered by the current approach of mapping genes onto behavioral syndromes that arise from heterogeneous causes (Gizer, Otto, & Ellingson, this volume); (2) improve construct validity over the DSM; and (3) result in better treatments because our understanding of etiology will necessarily improve, thus providing for better matching of patients to interventions. Following from the developmental psychopathology perspective, and given the heavy research emphasis on the RDoC initiative, the Oxford Handbook of Externalizing Spectrum Disorders is, we believe, a timely contribution to the literature. Each one of the authors is a chosen expert in the field, and each presents cutting-edge research on externalizing syndromes, including their continuities, comorbidities, and etiological underpinnings. We invite readers to challenge their assumptions about externalizing disorders as discrete diagnostic entities, and to consider how externalizing behavior develops across the life-span, as vulnerable individuals confront environmental risk and adversity. References

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders, 5th ed. Washington, DC: Author. Robins, L. N. (1966). Deviant children grown up. Baltimore, MD: Williams & Wilkins. Beauchaine, T. P., Hinshaw, S. P., & Pang, K. L. (2010). Comorbidity of attention-deficit/hyperactivity disorder and early-onset conduct disorder: Biological, environmental, and developmental mechanisms. Clinical Psychology: Science and Practice, 17, 327–336. Beauchaine, T. P., Klein, D. N., Crowell, S. E., Derbidge, C., & Gatzke-Kopp, L. M. (2009). Multifinality in the development of personality disorders: A biology × sex × environment interaction model of antisocial and borderline traits. Development and Psychopathology, 21, 735–770. Beauchaine, T. P., Klein, D. N., Erickson, N. L., & Norris, A. L. (2013). Developmental psychopathology and the Diagnostic and Statistical Manual of Mental Disorders. In T. P. Beauchaine & S. P. Hinshaw (Eds.), Child and adolescent psychopathology (2nd ed., pp. 29–110). 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. Conrod, P. J., & Stewart, S. H. (2005). A critical look at dual focused cognitive-behavioral treatments for comorbid substance use and psychiatric disorders: Strengths, limitations, and future directions. Journal of Cognitive Psychotherapy, 19, 261–284. Crowell, S. E., Beauchaine, T. P., & Linehan, M. (2009). A biosocial developmental model of borderline personality: Elaborating and extending Linehan’s theory. Psychological Bulletin, 135, 495–510.

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Hinshaw, S. P., Owens, E. B., Zalecki, C., Huggins, S. P., Montenegro-Nevado, A. J., Schrodek, E., & Swanson, E. N. (2012). Prospective follow-up of girls with attention-deficit/hyperactivity disorder into early adulthood: Continuing impairment includes elevated risk for suicide attempts and self-injury. Journal of Consulting and Clinical Psychology, 80, 1041–1051. Insel., T. R., Cuthbert, B. N., Garvey, M. A., Heinssen, R. K., Pine, D. S., Quinn, K. J., … Wang, P. S. (2010). Research Domain Criteria (RDoC): Toward a new classification framework for research on mental disorders. American Journal of Psychiatry, 167, 748–751. Kopp, L. M., & 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. Loeber, R., & Hay, D. (1997). Key issues in the development of aggression and violence from childhood to early adulthood. Annual Review of Psychology, 48, 371–410. Masterman, P. W., & Kelly, A. B. (2003). Reaching adolescents who drink harmfully: Fitting intervention to developmental reality. Journal of Substance Abuse Treatment, 24, 347–355. Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701. MTA Cooperative Group. (1999). A 14-month randomized clinical trial of treatment strategies for attention-deficit/hyperactivity disorder. Archives of General Psychiatry, 56, 1073–1086. Nock, M. K. (2003). Progress review of the psychosocial treatment of child conduct problems. Clinical Psychology Science and Practice, 10, 1–28. Preskorn, S. H., & Baker, B. (2002). The overlap of DSM-IV syndromes: Potential implications for the practice of polypsychopharmacology, psychiatric drug development, and the human genome project. Journal of Psychiatric Practice, 8, 170–177. 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.

xx preface

PA RT

1

Models of Externalizing Behavior

CH A PT E R

1

Overview of DSM Disruptive Behavior Disorders

Deborah A. G. Drabick, Elizabeth Steinberg, and Ashley Hampton Shields

Abstract This chapter focuses on the historical and current approaches adopted in the American Psychiatric Association’s Diagnostic and Statistical Manual of Mental Disorders (DSM) to six mental health conditions: attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, substance use disorder (alcoholism and drug addiction), antisocial personality disorder, and disruptive mood dysregulation disorder. The historical approaches are considered chronologically, from the first edition of the DSM (DSM-I) in 1952 to the fourth edition (and text revision; DSM-IV) in 2000. Developmental considerations, onset, and interrelations among disorders are then discussed. The chapter also examines controversies among the diagnostic categories across versions of the DSM before concluding with an assessment of several directions for future research. Key Words:  American Psychiatric Association, DSM, mental health conditions, attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, substance use disorder, antisocial personality disorder, disruptive mood dysregulation disorder

Introduction

In this chapter, we provide an overview of historical and current approaches taken in the Diagnostic and Statistical Manual of Mental Disorders (DSM) to the following mental health conditions:  attention-deficit/hyperactivity disorder (ADHD), disruptive mood dysregulation disorder (DMDD), oppositional defiant disorder (ODD), conduct disorder (CD), substance use disorder (SUD), and antisocial personality disorder (ASPD). Following consideration of historical and current diagnostic conceptualizations of these conditions, we discuss developmental considerations, onset, and interrelations among disorders. Despite changes to these diagnostic categories across versions of the DSM, controversies among these categories remain. We finish the chapter with a presentation of some of these controversies, and we discuss opportunities for future research.

Historical Context

In this section, we describe historical approaches to ADHD, ODD, CD, SUD, and ASPD chronologically, from the first instantiation of the DSM (DSM-I; American Psychiatric Association, 1952) through the fourth edition (and text revision) of the DSM (DSM-IV-TR; APA, 2000). Disruptive mood dysregulation disorder was first introduced in the fifth edition of the DSM (DSM-5; APA, 2013) and thus is included in the section that addresses the current DSM approach to classifying each of these conditions. In addition, because well-operationalized diagnostic criteria were not formally introduced until DSM-III (APA, 1980), most of our discussion focuses on description of diagnostic categories from more recent decades. Last, because diagnostic criteria for ADHD, ODD, CD, SUD, and ASPD did not change from DSM-IV to DSM-IV-TR, we do not consider DSM-IV-TR criteria in this chapter.

3

ADHD

The concept of hyperactivity was first included in the second edition of the DSM (DSM-II; APA, 1968) within the diagnosis of “hyperkinetic reaction of childhood.” This disorder was defined using a simple statement:  namely, that it was characterized by overactivity, restlessness, distractibility, and short attention span. The disorder was specific to young children and was expected to diminish by adolescence. Without formal operational criteria, low diagnostic reliability was reported for this condition (McBurnett, Lahey, & Pfiffner, 1993; Spitzer, Williams, & Skodol, 1980). In addition, continued research efforts highlighted the need to consider the attention deficit and impulsivity features of the disorder rather than just hyperactivity (Douglas, 1972). In the DSM-III (1980), hyperkinetic reaction was renamed “attention deficit disorder (ADD) (with or without hyperactivity),” and formal criteria were operationalized. Hyperactivity was no longer an essential diagnostic criterion for the disorder, which was a departure from the ICD-9 approach that continued to focus on hyperactivity (Conners, 2003). Fourteen symptoms were arranged into three groups:  inattention (five symptoms), impulsivity (five symptoms), and hyperactivity (four symptoms). In addition to providing more specific symptom criteria, the DSM-III approach provided a cutoff score for the number of symptoms required for each subgroup of symptoms. Specifically, at least three symptoms of inattention, at least three symptoms of impulsivity, and at least two symptoms of hyperactivity were required to receive a diagnosis of ADD with hyperactivity. Furthermore, the DSM-III added guidelines regarding age of onset and duration of symptoms and exclusion criteria for other childhood psychiatric conditions. Although these symptoms were derived empirically through rating scales and a field trial (Barkley, 2006), the lack of research supporting these subtypes made their creation and inclusion controversial. The DSM-III-R (APA, 1987) aimed to address the lack of empirical validation of subtypes described in DSM-III by removing them and renaming the disorder “attention deficit-hyperactivity disorder (ADHD).” As opposed to the three subgroups, symptoms were combined into a single list of 14 criteria, any eight of which were sufficient for a diagnosis of ADHD. Along with ODD and CD (described later), ADHD was included in the disruptive behavior disorders of childhood section of the DSM-III-R. Although the DSM-III syndrome 4 Overview

of ADD without hyperactivity was not included in DSM-III-R, a separate category of undifferentiated attention deficit disorder (UADD) was added and described as ADD not specified by ADHD criteria, including attention deficits without significant hyperactivity. Nevertheless, because UADD had no diagnostic criteria, it was a heterogeneous category with low reliability and predictive utility. In the DSM-IV (APA, 1994), diagnostic categorization of ADHD changed yet again. Based on research documenting differential correlates for ADHD with and without hyperactivity (e.g., academic difficulties, aggression, peer rejection; Barkley, 2006) and results of a field trial (Lahey et  al., 1994), three subtypes were created. These subtypes were predominantly inattentive, predominantly hyperactive-impulsive, and combined (i.e., meeting criteria for both the inattentive and hyperactive-impulsive subtypes), thus allowing again for diagnosis of a (relatively) purely inattentive form of ADHD. Criteria for DSM-IV (and DSM-IV-TR) included six or more symptoms of inattention from a list of nine symptoms and/or six or more symptoms of hyperactivity-impulsivity from a list of nine symptoms. To meet criteria, symptoms must have persisted for at least 6 months to a degree that is maladaptive and inconsistent with developmental level. Furthermore, symptoms must be present before the age of 7 years. Finally, impairment should be present in two or more settings, such as social, academic, or occupational functioning. Thus, with each revision of the DSM, operationalization of ADHD involved further refinement of symptoms related to inattention and hyperactivity-impulsivity, clarification of expected age of onset and duration criteria, expectation of impairment across settings, and (with the exception of DSM-III-R) specification of ADHD subtypes.

ODD

ODD was first included as a separate diagnosis in the DSM-III (APA, 1980). To meet criteria, youth had to exhibit two out of five behaviors (violations of minor rules, temper tantrums, argumentativeness, provocative behavior, stubbornness) for at least 6  months with onset prior to age 3  years. A  hierarchical exclusion rule involving CD was included, in that ODD could not be diagnosed in the presence of CD. This criterion was based on the facts that ODD is often a developmental precursor to CD, that ODD and CD share numerous risk factors, and the disorders are closely related in taxonomic and developmental terms (see Chapter  28;

Beauchaine, Hinshaw, & Pang, 2010; Burke, Waldman, & Lahey, 2010). Nevertheless, although most youth with CD previously met diagnostic criteria for ODD, children with ODD do not necessarily have CD or progress to CD (Beauchaine & McNulty, 2013). The DSM-III-R (APA, 1987) expanded criteria to include nine behaviors, of which five had to be present for at least 6 months. These nine symptoms included loses temper, argues with adults, actively defies or refuses adult requests or rules, deliberately annoys others, blames others for his or her own mistakes, touchy or easily annoyed, angry and resentful, spiteful or vindictive, and swears or uses obscene language. The requirement of onset prior to age 3 was removed, and the word “often” was added to each symptom criterion, along with the statement that the behaviors had to occur more frequently than is typical for youth of comparable mental age. The hierarchical exclusion rule indicating that ODD could not be diagnosed if criteria for CD were met was maintained; moreover, it was noted that the behaviors could not occur exclusively in the context of a mood episode or psychotic disorder. The DSM-IV (APA, 1994) eliminated the symptom related to swearing, reduced the required number of symptoms from five to four from the list of the remaining eight, maintained the 6-month duration criterion and hierarchical exclusion criterion with CD, added an impairment criterion, and expanded the “often” descriptor to indicate that a criterion would only be met if it occurred more frequently than observed among individuals of comparable age and developmental level. Thus, similar to ADHD, successive versions of the DSM expanded and then refined ODD symptoms, including adding the word “often” to criteria and highlighting that determining whether criteria are met must take into account typical developmental levels of ODD symptoms. Other notable changes were inclusion of a hierarchical exclusion criterion for CD (i.e., if youth meet criteria for CD, ODD cannot be diagnosed) and omission of the age of onset requirement.

CD

The earliest reference in the DSM to symptoms consistent with CD was in DSM-I (APA, 1952), which included adjustment reaction of childhood within the personality disorders category. This subcategory included conduct disturbance, which was defined as a transient reaction that manifests as a disturbance in social conduct

or behavior. Symptomatic manifestations in this category included truancy, stealing, destructiveness, cruelty, sexual offenses, and alcohol use. The DSM-II (APA, 1968) expanded to seven categories for behavior disorders of childhood and adolescence, three of which were relevant to CD:  runaway reaction, unsocialized aggressive reaction, and group delinquent reaction. Similar to ODD, CD was first listed as a separate condition and with operationalized criteria in the DSM-III (APA, 1980). The CD category included subtypes that differed in terms of whether behaviors were socialized/undersocialized and aggressive/ nonaggressive, resulting in five subtypes: socialized nonaggressive, socialized aggressive, undersocialized aggressive, undersocialized nonaggressive, and atypical. The undersocialized component included five items related to failure to establish a normal degree of affection, empathy, or bond with others as evidenced by no more than one of five behavioral criteria that are indicative of appropriate social attachment (e.g., peer relationships, extends self for others, feels guilt or remorse, avoids blaming others, and shows concern for others’ welfare). The nonaggressive component involved a repetitive and persistent pattern in which either basic rights of others or major developmentally appropriate societal norms or rules were violated (e.g., truancy, substance abuse, running away, serious lying, stealing without confronting a victim), whereas aggression involved aggression toward people and/ or animals. Despite this subtyping approach, only one symptom was required, with a duration of at least 6 months. Some links were made among categories from the DSM-II to the DSM-III as well. For example, it was suggested that DSM-II runaway reaction was broadened to become DSM-III undersocialized CD, nonaggressive type; DSM-II unsocialized aggressive reaction became DSM-III undersocialized CD, aggressive type; and group delinquent reaction from DSM-II became DSM-III socialized CD. The DSM-III-R (APA, 1987) omitted symptoms related to disobedience, substance abuse, and blaming others from the CD category, reducing the total number of symptoms to 13. As with the DSM-III, the disturbance of conduct was required to last at least 6  months. However, the required number of symptoms was increased from one to three, and the subtyping approach was modified to include three subtypes:  group type, solitary aggressive type, and undifferentiated type. The DSM-III-R solitary aggressive type corresponded roughly to

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the DSM-III undersocialized aggressive type and involved predominantly aggressive behavior initiated by the person. The group type was indicated when CD behaviors occurred primarily within a group activity with peers and corresponded roughly to the DSM-III socialized nonaggressive type, although physical aggression could be present in the DSM-III-R version of CD. The undifferentiated type was a residual group, selected if CD behaviors did not occur either predominantly alone or with peers. The DSM-IV (APA, 1994) added two symptoms to CD (i.e., bullying, staying out late), modified the lying criterion, and changed the duration criteria. As in the DSM-III-R, three criteria were required; however, duration criteria now indicated that at least three criteria should be met within the past 12 months, with at least one in the past 6 months. In addition, an age-of-onset specifier was added, which included a childhood-onset type (at least one symptom prior to age 10 years), an adolescent-onset type (absence of any symptom prior to age 10), and an unspecified onset. The addition of these specifiers to DSM-IV stemmed from multiple lines of inquiry. Specifically, research examining childhoodand adolescent-onset CD indicates that compared to individuals with adolescent-onset CD, individuals with childhood-onset CD exhibit (a)  higher levels of aggressive and antisocial behavior; (b)  a more persistent course of CD; (c) more cognitive, verbal, and neuropsychological deficits; (d)  higher levels of familial risk factors; (e)  different patterns of comorbid conditions; (f ) greater impairment in occupational and interpersonal functioning across developmental periods; and (g) higher levels of antisocial and substance-abusing behaviors in adulthood (Connor, Ford, Albert, & Doerfler, 2007; Dandreaux & Frick, 2009; Frick & Viding, 2009; Lahey, Waldman, & McBurnett, 1999; Loeber, Burke, & Pardini, 2009; Moffitt, 1993; Moffitt & Caspi, 2001; Moffitt, Caspi, Harrington, & Milne, 2002; Odgers, Milne, Caspi, Crump, Poulton, & Moffitt, 2007; Patterson, DeBaryshe, & Ramsey, 1989). In sum, refinement and greater specification of CD symptoms occurred over time across revisions of the DSM. Although different subtypes were used throughout various DSM instantiations, the childhood- and adolescent-onset types have received the most empirical support and evidenced the best predictive validity. They have thus been retained in the DSM-5 (Frick & Nigg, 2012; Moffitt et al., 2008), presented in greater detail later. 6 Overview

SUDs

The first edition of the DSM (APA, 1952) classified addiction as one of four subcategories of “sociopathic personality disturbance,” grouped with antisocial reaction, dissocial reaction, and sexual deviations (Oldham, 2005). Within this sociopathic personality disturbance/addiction subcategory, there were two substance use-related diagnoses—alcoholism and drug addiction. Different classes of drugs were not specified for the drug addiction diagnosis, and no specific criteria were described to facilitate assigning these diagnoses. In the DSM-II (APA, 1968), alcoholism and drug dependence were categorized as part of a group of “certain other nonpsychotic mental disorders,” along with sexual deviations. Although the DSM-II continued to lack operational criteria for alcoholism or drug dependence (or any other disorders, as well), it included some definitions. Alcoholism was defined as a category for individuals whose alcohol intake is great enough to damage their physical health or their personal or social functioning (APA, 1968). Within the alcoholism category, there were four possible diagnoses: episodic excessive drinking (for individuals with alcoholism who become intoxicated as frequently as four times a year); habitual excessive drinking (for individuals with alcoholism who become intoxicated more than 12 times a year or who are noticeably under the influence of alcohol more than once per week); alcohol addiction (when there is evidence that the individual is dependent on alcohol, such as withdrawal symptoms and/or the inability to go 1 day without drinking); and other (and unspecified) alcoholism. Drug dependence in the DSM-II was designated for individuals who are addicted to or dependent on drugs other than alcohol, tobacco, caffeine, or medically prescribed drugs. A diagnosis of drug dependence required evidence of habitual use or a clear sense of need for the drug (APA, 1968). The DSM-II recognized several classes of drugs, including barbiturates and other sedative-hypnotics, cannabis, cocaine, hallucinogens, and opioids. The DSM-III (APA, 1980) brought many changes to categorization of addiction, including a new category: SUDs. Abuse/dependence on each class of drugs was considered a separate disorder. Classes of drugs included alcohol, amphetamines, barbiturates and other sedative-hypnotics, cannabis, cocaine, hallucinogens, opioids, phencyclidine, and tobacco, as well as other substances and combinations of substances. The DSM-III also introduced

two classifications of SUD (abuse and dependence), both of which were defined as a pattern of pathological use causing impairment in social or occupational functioning. Substance abuse also included a minimum duration of 1  month, whereas substance dependence included evidence of tolerance or withdrawal (APA, 1980). Course specifiers also were included in DSM-III. Thus, a diagnosis of substance abuse or dependence could be specified as continuous, episodic, in remission, or unspecified. For the DSM-III-R (APA, 1987), theoretical rationale was used in developing criteria for SUDs (Rounsaville, Spitzer, & Williams, 1986). This rationale was based on a construct referred to as alcohol dependence syndrome (ADS; Edwards & Gross, 1976; World Health Organization [WHO], 1981), which was broadened to apply to other substances (Kosten, Rounsaville, Babor, Spitzer, & Williams, 1987). ADS differentiated between psychological and physiological processes of substance dependence and consequences of substance use (Edwards, 1986). Consistent with ADS, a diagnosis of dependence required three of the following symptoms:  tolerance, withdrawal, substance use to relieve or avoid withdrawal, persistent desire or unsuccessful efforts to reduce substance use, substance use in larger amounts or over a longer period than intended, neglect of activities, excessive time spent in substance-related activity, inability to fulfill roles, hazardous use, or continued use despite problems (APA, 1987). Criteria for substance abuse required that an individual either continued to use a substance despite knowledge of having a persistent or recurrent social, occupational, psychological, or physical problem caused or exacerbated by use and/ or using in situations where use is physically hazardous (APA, 1987). Substance abuse and dependence were referred to as substance-related disorders in the DSM-IV (APA, 1994). Criteria for abuse and dependence were similar to those described in the DSM-III-R. However, for substance dependence, DSM-III-R criteria for withdrawal (i.e., withdrawal symptoms, substance use to relieve or avoid withdrawal) were combined into a single item, and inability to fulfill roles and hazardous use were removed. Even so, for dependence, an individual still had to meet three criteria from the remaining areas of concern, as in DSM-III-R. For substance abuse, one of the following four criteria was required for a diagnosis: recurrent use of the substance resulting in a failure to fulfill major role obligations at work, school, or home; recurrent substance use in physically

hazardous situations; recurrent substance-related legal problems; and continued substance use despite persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of the substance. The DSM-IV also added inhalants as a drug class. In summary, successive versions of the DSM modified diagnostic criteria related to substance use to clarify constructs associated with substance abuse and dependence and to link these criteria to a variety of substances. Although considered separate disorders, abuse and dependence were distinguished as two hierarchical disorders, with abuse considered to be a mild or early phase of a substance-related disorder, and dependence conceptualized as a more severe manifestation of substance use (Hasin et al., 2013).

ASPD

The roots of ASPD can be traced to the first version of the DSM (APA, 1952). The DSM-I included “sociopathic personality disturbance,” which consisted of four subcategories:  antisocial reaction, dissocial reaction, sexual deviations, and addiction (described in the preceding SUDs section; Oldham, 2005). Antisocial reaction was diagnosed when individuals were aggressive, criminally deviant, and repeatedly violated societal laws and norms (Patrick, 2007). Sociopathic personality disturbance was removed from the DSM-II (Oldham, 2005), and sexual deviations, addiction, and antisocial or delinquent personality types were categorized as “Personality Disorders and Certain Other Non-Psychotic Mental Disorders.” Antisocial personality was conceptually similar to psychopathy (Cleckley, 1941), and individuals with this diagnosis were described as selfish, callous, irresponsible, unable to feel guilt, incapable of loyalty, and having weak socialization (Patrick, 2007). The DSM-III (APA, 1980) introduced not only observable, measurable, and behavior-oriented diagnostic criteria, but also multiaxial diagnosis, in which personality disorders were classified on Axis II to reflect expectations that these disorders were persistent and largely untreatable, with developmental roots in late childhood and adolescence (see Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009; Oldham, 2005). Criteria for DSM-III ASPD were influenced heavily by research by Lee Robins (1968). Although Robins based her investigation on Cleckley’s work (1941), her findings indicated that adults with and without sociopathy did not differ on items related to lack of guilt, remorse, and shame, so these traits were discarded

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in further work by Feighner and colleagues (1972) and by Spitzer, Endicott, and Robins (1978). Instead, criteria for ASPD in the DSM-III focused on behavioral indicators of deviance in childhood and adulthood, including truancy, delinquency, stealing, vandalism, irresponsibility, aggression, impulsivity, recklessness, and lying (Patrick, 2007). To meet diagnostic criteria, at least three antisocial behaviors before age 15 were required. Nevertheless, the emphasis on behavioral indicators for ASPD and exclusion of characteristics associated with psychopathy led to controversy (Frances, 1980; Hare, 1983, 1996; Millon, 1981). Formulators of the DSM-III-R (APA, 1987) responded to these criticisms by adding lack of remorse as a criterion for ASPD. Following the DSM-IV field trials, parental irresponsibility and failure to sustain monogamous relationships were removed from the ASPD criteria, and a single item (irresponsibility) was created by combining employment-related and financial irresponsibility to simplify diagnosis. The field trial also investigated the utility of adding criteria that represented affective-interpersonal features of psychopathy, such as lacking empathy, inflated self-appraisal, and glib/superficial interactive style. However, findings provided only partial support for amending criteria to include such features of psychopathy, and additional criteria therefore were not included (Widiger et  al., 1996). This omission resulted in continued criticism of the validity of ASPD (e.g., Hare & Hart, 1995). Despite the decision to exclude affective-interpersonal features of psychopathy in the DSM-IV, the text listed alternative names for ASPD, including psychopathy, sociopathy, and dissocial personality disorder. Thus, behavioral criteria for ASPD and evidence for these behaviors prior to age 15 were clarified in the DSM over time; however, debate continues regarding the inclusion of features related to the affective-interpersonal components of psychopathy in the ASPD diagnostic category.

Current State of the Science ADHD

The DSM-5 (APA, 2013) defines ADHD as a persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development. Specific criteria for ADHD have not been changed from DSM-IV to DSM-5. Thus, there are still nine symptoms of inattention and nine symptoms of hyperactivity/impulsivity (six for hyperactivity and three for impulsivity). 8 Overview

However, rather than framing these symptom clusters in terms of subtypes as in previous versions of the DSM, they are divided into three “presentations,” including predominantly inattentive, predominantly hyperactive-impulsive, and combined. This modification was made because the DSM-IV subtypes did not reliably identify subgroups with sufficient long-term stability to justify classification of distinct forms of the disorder (Willcutt et al., 2012). However, given evidence that ADHD symptoms may persist through adulthood and studies of specificity and reliability for the number of required symptoms, formulators of the DSM-5 decreased the number of criteria required for older adolescents and adults to five symptoms per domain and expanded symptom descriptions to provide examples of developmentally relevant presentations. For example, the symptom “often loses things necessary for tasks and activities” was expanded to include school materials, pencils, books, tools, wallets, keys, paperwork, eyeglasses, and mobile telephones. In addition, for the criterion “Is often ‘on the go,’ acting as if ‘driven by a motor,’” examples include “is unable to be or uncomfortable being still for extended time, as in restaurants, meetings;" and “may be experienced by others as being restless or difficult to keep up with.” To receive a diagnosis of ADHD, children must have at least six symptoms from either (or both) the inattention or the hyperactivity/impulsivity criteria lists. The inattention criteria list is as follows: (1) often fails to give close attention to details or makes careless mistakes in schoolwork, at work, or with other activities; (2) often has trouble holding attention on tasks or play activities; (3)  often does not seem to listen when spoken to directly; (4)  often does not follow through on instructions and fails to finish schoolwork, chores, or duties in the workplace (e.g., loses focus, side-tracked); (5) often has trouble organizing tasks and activities; (6) often avoids, dislikes, or is reluctant to do tasks that require mental effort over a long period of time (such as schoolwork or homework); (7) often loses things necessary for tasks and activities (e.g., school materials, pencils, books, tools, wallets, keys, paperwork, eyeglasses, mobile telephones); (8)  is often easily distracted; and (9) is often forgetful in daily activities. The hyperactivity and impulsivity criteria list is as follows: (1) often fidgets with or taps hands or feet, or squirms in seat; (2) often leaves seat in situations when remaining seated is expected; (3) often runs about or climbs in situations where it is not appropriate (adolescents or adults may be limited to feeling restless); (4) often unable to play or take

part in leisure activities quietly; (5) is often “on the go” acting as if “driven by a motor;” (6) often talks excessively; (7) often blurts out an answer before a question has been completed; (8) often has trouble waiting his or her turn; and (9) often interrupts or intrudes on others (e.g., butts into conversations or games) (APA, 2013). As with the DSM-IV-TR, several inattentive or hyperactive-impulsive symptoms must be present in two or more settings (e.g., home, school, work; with friends, relatives; in other activities), and there must be clear evidence that symptoms interfere with or reduce the quality of social, academic, or occupational functioning. Another modification to the DSM-5 is a change to the age of onset criterion. Specifically, several ADHD symptoms must be present prior to 12 years, compared to 7 years as in the DSM-IV-TR. A large body of research demonstrates that there is no clinical difference between children identified as having symptoms by 7 years old versus later in development in terms of course, severity, outcome, or treatment response. In addition, the autism exclusion criterion was removed. Finally, ADHD now is classified in the Neurodevelopmental Disorders section of the DSM-5, which also includes intellectual disabilities, communication disorders, autism spectrum disorder, specific learning disorder, and motor disorders, rather than in the Disruptive Behavior Disorders section. This change stems in part from research indicating that individuals with ADHD exhibit dysfunctional responding across different neural networks; however, this move is also intended to facilitate earlier identification of youth with ADHD, improve assessment, increase access to intervention, and spur research designed to disambiguate learning disabilities and inattention on academic outcomes (see Beauchaine & Hayden, in press). Nevertheless, these issues apply to other externalizing disorders (e.g., CD); thus, moving ADHD from the Disruptive Behavior Disorders section obfuscates relations among ADHD and the other disruptive behaviors in terms of etiological processes, correlates, course, and comorbidity (see Beauchaine & Hayden, in press; Beauchaine & McNulty, 2013).

DMDD

DMDD was first included in the DSM-5 and is a condition in which a child displays chronic irritability and severe behavioral outbursts. Concerns had arisen that youth with chronic irritability and anger outbursts increasingly were diagnosed with bipolar disorder even though these youth were at low risk

for bipolar disorder over time; thus, DMDD was added to address concerns about possible overdiagnosis and treatment of bipolar disorder among children (Axelson, 2013; Ryan, 2013). DMDD consequently is intended to be a diagnosis for youth whose behavioral outbursts or “rages” do not fit into ADHD or ODD and have received the diagnosis of bipolar disorder (Margulies, Weintraub, Basile, Grover, & Carlson, 2012). Although the symptoms are relatively common, the diagnosis is rare when the frequency, duration, and cross-contextual criteria are applied (Copeland, Angold, Costello, & Egger, 2013). The empirical basis of DMDD is actually based on studies of “severe mood dysregulation” (e.g., Leibenluft, 2011), which is a related, although not identical, syndrome (Axelson, 2013). Severe mood dysregulation describes children who exhibit nonepisodic irritability and hyperarousal symptoms characteristic of mania but who lack well-demarcated periods of elevated or irritable mood characteristic of bipolar disorder (Leibenluft, 2011). Little research to date has examined DMDD, making its addition in the DSM-5 contentious (Ryan, 2013). In a large-scale examination of DMDD, Copeland et al. (2013) used existing data from three large epidemiological samples of children aged 2–5  years and 9–17  years (N  =  3,258) who had been assessed using structured diagnostic instruments to examine DMDD as defined by the DSM-5. To characterize DMDD, items evaluating anger outbursts and irritable or angry mood were considered. Although there is conceptual overlap and although DMDD criteria are based on those for severe mood dysregulation, only 39% of youth with severe mood dysregulation also met criteria for DMDD. Thus, it is unclear how well findings related to severe mood dysregulation generalize to DMDD (Axelson, 2013). In addition, the DSM-5 field trials reported low test–retest reliability of DMDD (κ  =  0.25; Axelson, 2013; Regier et  al., 2013; Ryan, 2013). However, using data from large-scale epidemiological studies, Copeland et al. (2013) determined that DMDD meets typical standards for psychiatric “caseness,” given evidence that youth with DMDD exhibit elevated levels of comorbidity (especially with depressive disorders and ODD), social impairment, school suspensions, and service use. To meet criteria for DMDD, a child must (1)  exhibit persistent irritability and severe outbursts, which may manifest verbally and/or behaviorally (e.g., physical aggression toward people

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or property) and are grossly out of proportion in intensity or duration to the situation or provocation (Criterion A). These temper outbursts are (2) inconsistent with developmental level (Criterion B) and (3) occur, on average, three or more times per week (Criterion C). Additionally, (4) the child’s mood between these outbursts must be consistently angry and irritable most of the day and nearly every day, observable by parents, teachers, and peers (Criterion D). These symptoms must be (5)  present for 12 or more months, and, during this period, the child must not have gone 3 or more consecutive months without symptoms (Criterion E); and these symptoms must be (6) present in at least two settings (at home, at school, or with peers) and the symptoms must be severe in at least one of these settings (Criterion F). Last, (7)  the diagnosis of DMDD should not be made before age 6 or after age 18 (Criterion G), and (8) symptom onset must be before age 10 years (Criterion H). Criteria further specify that there should never have been a distinct period lasting more than 1 day during which full symptom criteria, except duration, for a manic or hypomanic episode have been met. Outbursts or elevated moods that continue for longer than a few hours or for days are more likely to be signs of mania, a rule-out for DMDD. Finally, the diagnosis cannot coexist with ODD, intermittent explosive disorder, or bipolar disorder (APA, 2013). In terms of differential diagnosis, symptoms of ODD and bipolar disorder overlap conceptually with symptoms of DMDD. Compared to ODD, however, the symptom threshold for DMDD is higher. Indeed, temperamental outbursts with DMDD are so severe that they require clinical attention and cause severe impairment. To avoid artificial comorbidity with ODD, children who meet criteria for both ODD and DMDD should be diagnosed only with DMDD, as noted earlier. Relative to the episodic nature of bipolar disorder, DMDD is more chronic, and children with DMDD are not expected to develop adult bipolar disorder. Thus, if an individual has ever experienced a manic or hypomanic episode, the diagnosis of DMDD should not be assigned (see criteria listed in the preceding paragraphs). Further research on DMDD is needed to understand this diagnostic construct, course, correlates, overlap with other disorders, impairment, and treatment response.

ODD

As noted, earlier instantiations of the DSM included a hierarchical exclusion criterion for CD 10 Overview

given that ODD is often (but not always) a developmental precursor to CD, that ODD and CD share numerous risk and etiological factors, and that the disorders are closely related in taxonomic and developmental terms (Beauchaine et al., 2010; Burke et al., 2010). However, the hierarchical exclusion criterion was removed in DSM-5 because it masked high rates of overlap between ODD and CD, particularly during childhood (Maughan, Rowe, Messer, Goodman, & Meltzer, 2004). Furthermore, although most youth with CD previously met diagnostic criteria for ODD, children with ODD do not necessarily have CD or progress to CD (Beauchaine & McNulty, 2013). Last, ODD evidences predictive utility beyond what is attributable to co-occurring conditions, indicating that it is important to assign an ODD diagnosis even if CD criteria are met. In the DSM-5, ODD symptoms are organized into angry/irritable mood (symptoms 1–3), argumentative/defiant behavior (symptoms 4–7), and vindictiveness (symptom 8). Specific wording for these symptoms is as follows:  (1)  often loses temper; (2)  is often touchy or easily annoyed; (3)  is often angry and resentful; (4)  often argues with authority figures or, for children and adults, with adults; (5)  often actively defies or refuses to comply with requests from authority figures or with rules; (6) often deliberately annoys others; (7) often blames others for his or her mistakes or misbehavior; and (8) has been spiteful or vindictive at least twice within the past 6 months (APA, 2013). The rationale for considering ODD symptoms separately stems from research suggesting that “emotional” symptoms of ODD (loses temper, is touchy or easily annoyed, and is angry or resentful) are differentially predictive of mood and anxiety disorders, whereas the “behavioral” symptoms of ODD (e.g., argues, defies, annoys) are associated with ADHD and CD. However, research that informs this distinction provides mixed results based on the way that the emotional and behavioral symptoms are operationalized, specific items attributed to each factor, and how symptom clusters are created (e.g., a priori, using factor analysis; Drabick & Gadow, 2012). The DSM-5 also includes a note clarifying how to distinguish whether persistence and frequency of these behaviors are within normal limits or symptomatic (i.e., further operationalization of “often”). For all individuals, the symptom “spiteful and vindictive” should occur at least twice in the past 6  months. All other ODD symptoms must occur

on most days for children under 5 years and at least once per week for individuals 5 years or older. Finally, the DSM-5 includes a severity specifier related to the number of settings in which symptoms are exhibited (mild = 1 setting, moderate = 2 settings, severe = 3 or more settings). This pervasiveness criterion stems from research indicating that youth who exhibit ODD according to multiple informants have higher levels of impairment and comorbid conditions than do youth who meet criteria for ODD based on one informant and that the number of settings (home, school, peers) in which ODD symptoms are present predicts problems in adjustment, controlling for the number of ODD symptoms (Drabick, Gadow, & Loney, 2007; Frick & Nigg, 2012).

CD

All 15 symptoms of CD in the DSM-IV and DSM-IV-TR remain in DSM-5. Duration criteria (three symptoms in the past 12  months, with at least one in the past 6 months) are also unchanged, and the distinction between childhood-onset and adolescent-limited CD is retained. Criteria include aggression to people and animals (symptoms 1–7), destruction of property (symptoms 8–9), deceitfulness or theft (symptoms 10–12), and serious violations of rules (symptoms 13–15). Specific wording for these symptoms is as follows: (1) often bullies, threatens, or intimidates others; (2)  often initiates physical fights; (3) has used a weapon that can cause serious physical harm to others (e.g., a bat, brick, broken bottle, knife, gun); (4) has been physically cruel to people; (5) has been physically cruel to animals; (6) has stolen while confronting a victim (e.g., mugging, purse snatching, extortion, armed robbery); (7) has forced someone into sexual activity; (8) has deliberately engaged in fire setting with the intention of causing serious damage; (9) has deliberately destroyed others’ property (other than by fire setting); (10) has broken into someone else’s house, building, or car; (11) often lies to obtain goods or favors or to avoid obligations (i.e., “cons” others); (12) has stolen items of nontrivial value without confronting a victim (e.g., shoplifting, but without breaking and entering; forgery); (13) often stays out at night despite parental prohibitions, beginning before age 13  years; (14) has run away from home overnight at least twice while living in the parental or parental surrogate home, or once without returning for a lengthy period; and (15) is often truant from school, beginning before age 13 years (APA, 2013).

There has been increasing recognition that not all youth with childhood-onset CD exhibit a life-course-persistent pathway and that some desist over time (Frick & Nigg, 2012; Moffitt et  al., 2008). Although a childhood-limited subtype was not added to the DSM-5 to characterize such youth, the DSM-5 adds the specifier, “with limited prosocial emotions,” which may help to distinguish youth with childhood-onset CD who evidence a more persistent course from those who desist over time. This specifier is based on research examining youth with CD with and without callous-unemotional (CU) traits, which indicates that youth with and without CU traits differ in terms of CD symptom severity, course, neurobiological and psychosocial risk factors and correlates, and in response to contextual factors and interventions (see Chapter 22; Frick & White, 2008; and Frick, Ray, Thornton, & Kahn, 2014, for a review). This specifier requires at least two characteristics over at least 12 months, including lack of remorse or guilt, lack of empathy or callous behaviors, lack of concern about performance, and shallow or deficient affect. Such symptoms must manifest across relationships and settings.

SUD

Prior to the DSM-5 (APA, 2013), criteria for alcohol and other drug use disorders were separated into dependence and abuse (see earlier discussion). In the DSM-5, criteria for dependence and abuse are consolidated into a single, overarching criterion set entitled “substance use disorder,” with specific disorders diagnosed based on the substance for which an individual meets criteria (e.g., cannabis use disorder, alcohol use disorder). This change reflects a shift from the DSM-IV biaxial concept (i.e., separation of dependence from social and interpersonal consequences of use) to an approach that omits any hierarchical relation between dependence and abuse (Hasin et al., 2013). Indeed, in the DSM-IV, abuse could not be diagnosed if criteria for dependence were met. This exclusion criterion led to a potential assumption that abuse was less severe than dependence, in spite of clinically severe problems indicated by several of the criteria (e.g., recurrently using substances in harmful situations). Thus, substance abuse and dependence were combined into a single SUD category in DSM-5. Criteria for SUD are identical to DSM-IV dependence and abuse criteria (but combined in a single diagnosis), with two exceptions. The symptom of recurrent legal problems, previously a criterion for substance abuse, is no longer included, and craving

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or a strong desire or urge to use a substance has been added. To be diagnosed with an SUD, two or more criteria must be met, compared to one or more criteria for abuse and three or more criteria for dependence in the DSM-IV. The 11 criteria included in DSM-5 are linked to specific substances, but the general symptom wording is as follows:  (1)  the substance is often taken in larger amounts or over a longer period than was intended; (2) there is a persistent desire or unsuccessful efforts to cut down or control use of the substance; (3) a great deal of time is spent in activities necessary to obtain the substance, use the substance, or recover from its effects; (4) craving, or a strong desire or urge to use the substance; (5) recurrent use of the substance resulting in a failure to fulfill major role obligations at work, school, or home; (6) continued use of the substance despite having persistent or recurrent social or interpersonal problems caused or exacerbated by the effects of the substance; (7) important social, occupational, or recreational activities are given up or reduced because of use of the substance; (8) recurrent use of the substance in situations in which is it physically hazardous; (9) use of the substance is continued despite knowledge of having a persistent or recurrent physical or psychological problem that is likely to have been caused or exacerbated by the substance; (10) tolerance, as defined by either of the following: (a) a need for markedly increased amounts of the substance to achieve intoxication or desired effect, (b) a markedly diminished effect with continued use of the same amount of the substance; and (11) withdrawal, as manifested by either of the following:  (a)  the characteristic withdrawal syndrome for the substance, (b) the substance (or a closely related substance) is taken to relieve or avoid withdrawal symptoms (APA, 2013). Severity in the DSM-5 is determined by the number of criteria endorsed. A  mild disorder is indicated by 2–3 criteria, a moderate disorder by 4–5 criteria, and a severe disorder by 6 or more criteria. Specifiers include “in early remission,” “in sustained remission,” “on maintenance therapy,” and “in a controlled environment.” Criteria are also provided for intoxication, withdrawal, substance/ medication-induced disorders, and unspecified substance-induced disorders.

in the DSM-5 in an effort to remove boundaries imposed by this framework between personality disorders and other psychiatric disorders. As a result, personality disorders, including ASPD, are combined with all mental and other medical diagnoses. Initial plans for the DSM-5 proposed significant changes to personality disorder types and criteria, with hopes of improving the utility of diagnoses and addressing concerns related to comorbidity, predictive and convergent validity, and heterogeneity within personality disorder categories (see Beauchaine et al., 2009; Skodol et al., 2013). However, this model was deemed too complex for clinical practice, and, instead, the proposed revisions are included in a separate chapter in Section III of the DSM-5 (APA, 2013). This alternative hybrid dimensional-categorical model features evaluation of impairments in personality functioning, as well as five broad areas of pathological personality traits (APA, 2013; Skodol et al., 2011). Because this hybrid model is classified as an “area for further study” and was not implemented in DSM-5, criteria for ASPD remain highly similar to those in the DSM-IV (and DSM-IV-TR). To meet criteria for ASPD, an individual must exhibit a pervasive pattern of disregard for and violation of the rights of others occurring since age 15 years, as indicated by three or more of the following: (1) failure to conform to social norms with respect to lawful behaviors, as indicated by repeatedly performing acts that are grounds for arrest; (2)  deception, as indicated by repeatedly lying, use of aliases, or conning others for personal profit or pleasure; (3) impulsivity or failure to plan ahead; (4)  irritability and aggressiveness, as indicated by repeated physical fights or assaults; (5)  reckless disregard for safety of self or others; (6)  consistent irresponsibility, as indicated by repeated failure to sustain consistent work behavior or honor financial obligations; or (7)  lack of remorse, as indicated by being indifferent to or rationalizing having hurt, mistreated, or stolen from another (APA, 2013). An ASPD diagnosis can be rendered only to individuals 18 years of age or older who evidenced CD prior to age 15 years and whose antisocial behavior did not solely occur during the course of schizophrenia or a manic episode.

ASPD

Developmental Considerations

Multiaxial diagnosis, whereby personality disorders were placed on Axis II, was abandoned 12 Overview

As with any condition examined among youth, it is important to consider both typical and atypical

development in determining whether behavior is aberrant. Some behaviors characteristic of the syndromes considered in this chapter are normative during different developmental periods (e.g., inattention, temper tantrums, and oppositional behavior among toddlers; defiance and substance use among adolescents; Drabick, 2009; Steinberg, 2008). Thus, many of the disorders include (a) age of onset criteria and (b) explicit statements to compare individuals to others of the same developmental level to determine whether behaviors fall outside of expected ranges. A  second developmental concern is whether age of onset is important to consider (Frick & Nigg, 2012). In the case of CD, age of onset (childhood vs. adolescence) is related to different courses, correlates, chronicity, and treatment responses, and thus has important clinical and predictive utility (Connor et al., 2007; Drabick, 2009; Frick & Viding, 2009; Lahey et al., 1999; Loeber et al., 2009; Moffitt, 1993; Moffitt & Caspi, 2001; Moffitt et  al., 2002, 2008; Odgers et  al., 2007; Patterson et  al., 1989). Similarly, recognition of ADHD symptoms that emerge before age 12 facilitates differential diagnosis and treatment planning. The utility of the age of onset criteria for DMDD requires further study, although these criteria are in place to facilitate identification of youth at risk for a more severe and chronic course and to differentiate these youth from typically developing youth who may exhibit significant tantrums during particular developmental periods. A third developmental consideration involves whether symptoms can be applied across developmental periods to reflect expected changes in tasks and expectations. With regard to ADHD, provision of additional developmentally appropriate examples for several symptoms (e.g., the symptom involving losing things now includes keys, wallets, and mobile telephones) and changes to the diagnostic threshold (i.e., five rather than six symptoms per domain for late adolescents and adults) are consistent with this need to facilitate application of a diagnostic category across development. The addition of developmentally appropriate examples and change in the diagnostic threshold for ADHD were informed by research indicating that although hyperactivity symptoms decline over time, ADHD-related impairments often continue into adulthood (Barkley, Fischer, Smallish, & Fletcher, 2002; Davidson, 2008), and thus criteria should account for potential adult presentations. A fourth developmental issue involves relations among conditions over time. It may be that there is

a common underlying process that underpins many externalizing conditions, but the behaviors manifested change over time in accordance with developmental changes and opportunities for exhibiting externalizing behaviors (Chapter  28; Beauchaine et al., 2010; Beauchaine & McNulty, 2013; Frick & Nigg, 2012). An alternative but complementary possibility is that correlates or sequelae of one condition may confer risk for another and thus facilitate successive or concurrent comorbidity among disorders (Drabick, Beauchaine, Gadow, Carlson, & Bromet, 2006). For example, youth with ADHD often experience interpersonal and academic difficulties (Hinshaw & Lee, 2003), which may confer risk for ODD. Youth with ADHD and ODD may be rejected by typically developing peers and thus be more likely to select and/or to be socialized by deviant peers, thus increasing risk for CD and substance use. Although discussion of possible mechanisms or mediators of relations among these conditions is beyond the scope of this chapter, it is likely that both common vulnerabilities related to externalizing conditions and shared risk processes account for disorder co-occurrence over time. Based on the typical age of onset of conditions considered in this chapter, we can suggest some potential developmental pathways among conditions. Symptoms of ADHD often emerge in preschool; thus, ADHD tends to precede other externalizing conditions. Onset of DMDD and ODD generally occurs prior to age 10 (retrospective self-reported onset of ODD begins at age 4 and increases steadily into adolescence; Nock, Kazdin, Hiripi, & Kessler, 2007), indicating that these conditions would be likely to follow ADHD. The course of DMDD is not characterized well at this point; thus, it is difficult to speculate about comorbidity of DMDD with externalizing conditions over time. However, it appears that conversion to bipolar disorder among youth with DMDD is low; youth with DMDD who subsequently meet criteria for other conditions are more likely to exhibit depressive or anxiety disorders (APA, 2013; Copeland et  al., 2013). Childhood-onset CD requires at least one symptom prior to age 10; thus, this CD subtype can occur with these other conditions, now that the CD hierarchical exclusion criterion has been removed for ODD in DSM-5. In particular, the behavioral symptoms of ODD are expected to be associated with CD (Burke et  al., 2010; Drabick & Gadow, 2012; Frick & Nigg, 2012). Antisocial personality disorder cannot be diagnosed prior to age 18 and requires that individuals

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meet criteria for CD prior to age 15. Accordingly, continuity between CD and ASPD is expected. However, it is unclear whether ASPD is more likely to be continuous with CD when the latter is exhibited with or without limited prosocial emotions. Given that CU traits and this specifier are expected to identify youth with a more persistent and pernicious course, it is likely that continuity will be associated with youth with CD with limited prosocial emotions, but further research is necessary. Substance use symptoms generally emerge in adolescence, with risk of onset steadily increasing from ages 10 to 18 then declining thereafter (Dodge et  al., 2008). Thus, substance use symptoms are likely to overlap with both CD subtypes. However, the median age of onset of SUDs is around age 20 (Kessler et  al., 2005). Thus, comorbidity among SUD and ASPD is especially likely, with significant substance use levels expected to co-occur with or follow CD onset.

Controversies

Modifications to diagnostic criteria often bring about controversy. In terms of ADHD, the change in the age of onset to 12 was predicated on lack of validity of the DSM-IV age of onset of 7 years. In addition, lowering the diagnostic threshold to five symptoms for adults was based both on concerns regarding whether criteria could account for developmental changes in ADHD presentations and research considering alternative thresholds (Frick & Nigg, 2012). Nevertheless, these changes will in all likelihood lead to increases in the number of people who meet diagnostic criteria for ADHD. Although this shift may allow more people to receive treatment, it raises the possibility of overdiagnosis and, consequently, concerns about elevated rates of medication use (Hinshaw & Scheffler, 2014). Given that it is new, DMDD carries with it many controversies typically associated with new categories that have been subjected to limited research, including concerns about predictive validity, stability, differential diagnosis, construct validity (i.e., given its basis in severe mood dysregulation), comorbidity and related exclusion criteria, justification and validity of age of onset and frequency criteria, developmental course, treatment implications, and more (Axelson, 2013; Ryan, 2013; Wakefield, 2013). For ODD, numerous changes were made in the DSM-5 with potential for associated concerns. For example, findings are mixed regarding the predictive utility of emotional versus behavioral symptoms of 14 Overview

ODD, although changes to the DSM-5 provide a consistent framework for division of symptoms into subgroups and thus a foundation for future research. In addition, DSM-5 provides the first operationalization of “often” for ODD symptoms, which is defined as occurring on most days for children under 5 years and at least once per week for individuals 5 years or older. The symptom “spiteful and vindictive” is the only exception, and this symptom should occur at least twice in the past 6  months. These specifications require further examination to determine whether they can increase reliability and whether the frequency anchors represent valid comparisons to youth across developmental periods. Given that ODD predicts maladjustment over-and-above prediction afforded by CD when the conditions co-occur, the hierarchical exclusion criterion for CD was omitted. However, because ODD is often a developmental precursor to CD, its omission reflects a change in conceptualization of ODD as a relatively benign condition to a disorder that can be quite impairing and confer significant risk independent of co-occurring conditions. Finally, the decision to consider pervasiveness of ODD based on the number of areas in which an individual experiences impairment is also new to the DSM-5 and reflects that the presence of ODD behaviors across settings and relationships is likely to indicate a more severe and impairing condition, although future research is necessary to validate severity criteria. With regard to CD, the addition of the specifier “with limited prosocial emotions,” although grounded in research involving CU traits, similarly will require further research to determine its utility, particularly in differentiating youth with childhood-onset CD who differ in the persistence of CD symptoms (Frick & Nigg, 2012; Moffitt et al., 2008). For substance use diagnoses, concerns have emerged regarding the two-symptom threshold for adolescents, some of whom could have met criteria for substance abuse in DSM-IV or DSM-IV-TR but no longer in DSM-5, which could influence eligibility for services. Nevertheless, given that some substance use behaviors are normative among adolescents (Dodge et al., 2008; Steinberg, 2008), it is possible that the revised symptom threshold will facilitate identification of youth who exhibit atypical or problematic levels of substance use. This change from a biaxial model (i.e., from substance abuse and dependence as separate diagnoses to one diagnostic category) also has raised concerns over increased heterogeneity and excessive variability in

symptom severity among individuals who meet criteria for SUD, as well as potential effects on prevalence rates (Hasin et al., 2013). For ASPD, a controversy that has held across various editions of the DSM involves lack of recognition of psychopathy (Strickland, Drislane, Lucy, Krueger, & Patrick, 2013), even with inclusion of the limited prosocial emotions specifier in the CD category, which is based on research indicating differential course, severity, correlates, and treatment response among individuals who arguably exhibit the affective-interpersonal deficits associated with psychopathy (see Chapter  22). The alternative hybrid model for personality disorders in the DSM-5 also has generated a great deal of controversy. However, related concerns are not specific to ASPD, and future research that addresses personality disorders more broadly is thus necessary before conclusions can be drawn regarding this approach for ASPD.

Research Agenda and Future Directions

Suggestions for future research follow readily from current controversies and from changes to the DSM. For ADHD, future research is necessary to determine whether diagnostic criteria are appropriate for individuals in developmental periods that have received relatively less research attention, namely, preschool-aged children and adults. Additional examples for ADHD symptoms and changes to the threshold for ADHD among adults also require further examination to determine their predictive and clinical validities. Omission of ADHD subtypes in DSM-5 reflects a lack of stability of these subtypes, although symptoms of ADHD remain almost unchanged from DSM-IV to DSM-5. The current approach considers the individual’s current presentation of ADHD symptoms. Prospective research that considers symptom profiles (e.g., using person-centered analyses) and the stability and transitions among these profiles could inform our understanding of course and changes in symptom presentation over time. Such research could determine whether this shift in conceptualization of ADHD leads to increased predictive and clinical validity of the disorder and whether these changes account adequately for expected developmental changes in frequency and type of symptoms over time. Given that it is new to the DSM-5, extensive research on risk factors, correlates, developmental pathways, co-occurring conditions, treatment response, and reliability and validity of DMDD

is sorely needed. Although DMDD was linked to research involving severe mood dysregulation (Leibenluft, 2011), the DSM-5 field trial indicated that DMDD and severe mood dysregulation did not overlap considerably (Copeland et  al., 2013). This, of course, limits the generalizability of such work to DMDD. Consideration of multiple epidemiological samples and the resulting very large sample for the field trial provided a good foundation for key aspects of this disorder (e.g., prevalence rates, comorbidity, impairment, service use). Nevertheless, there is room for much additional work to provide further validation of the disorder. There are several directions for future research on ODD. First, we should continue to evaluate concurrent and predictive utility of the disorder, independent of co-occurring conditions (e.g., ADHD, CD), especially now that the CD exclusion criterion has been abandoned. Second, future research should continue to examine the predictive validity of ODD symptom subgroups using the DSM-5 approach to separating symptoms. Previous findings have been mixed in this regard, perhaps because of differences in methodological approaches (e.g., symptom subgroups considered, strategies for identifying symptom subgroups, categorical vs. dimensional approaches; Burke et  al., 2010; Drabick & Gadow, 2012). Third, greater operationalization of severity and pervasiveness criteria was included in the DSM-5, and effects on reliability and validity for ODD should be evaluated in future work. For CD, continued consideration of the limited prosocial emotions specifier is an important direction for future research. Two issues that were not addressed with the DSM-5 but that have been raised in discussions related to the DSM approach to CD involve (a) the potential utility of a childhood-limited subgroup and (b) whether changes to the CD diagnosis should be made to better accommodate girls. As mentioned, there is heterogeneity among youth with childhood-onset CD in that some exhibit a more persistent path of antisocial behavior, whereas others are more likely to desist. Although the former group may exhibit higher levels of CU traits (and meet criteria for the limited prosocial emotions specifier), children who desist are not well-characterized, suggesting that future research is necessary to better understand them. For example, do these youth display more frequent versions of typical behavior problems (e.g., bullying) or qualitatively distinct behaviors (e.g., stealing while confronting a victim)? What factors are linked to desistance? What long-term outcomes are expected among these

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individuals (findings are mixed in this regard; see Moffitt et  al., 2008)? To date, although discussion continues regarding whether different criteria, symptom thresholds, duration, or some combination would better characterize CD behaviors among girls, data do not support modifying diagnostic criteria for CD based on sex. However, continued research that considers sex differences in CD relevant to these diagnostic features could inform future versions of the DSM, as well as etiological and intervention models for CD (see Chapter 15). Changes to the SUD category also require future investigators to examine their predictive validity, reliability, and clinical validity. For example, the combination of symptoms associated previously with dependence and abuse increases the likelihood of heterogeneity within the diagnosis and raises potential concerns over changing prevalence rates and identification of individuals who may be subthreshold but nevertheless experience impairment (e.g., adolescents). Future research should evaluate the validity of the current symptom threshold across developmental periods and examine potential subtypes of symptoms that might have differential predictive or clinical utility to decrease heterogeneity within this diagnostic category. A final area for future research involves considering the relations among behavioral addictions (e.g., gambling, which is included in DSM-5) with SUDs, given the potential for shared etiological and risk factors, as well as potential comorbidity. Finally, despite the diagnostic link between CD and ASPD, future research should evaluate continuity from CD to ASPD, taking into consideration age of onset for CD as well as the presence of limited prosocial emotions. The role of affective-interpersonal deficits among individuals with ASPD requires further clarification, given evidence of different etiologies, correlates, trajectories, and outcomes for affective-interpersonal and aggressive-impulsive deficits (Skodol et  al., 2011, 2013). The hybrid approach to personality disorders proposed for DSM-5 could lend itself to further evaluation of these features from a dimensional perspective, thereby informing our understanding of potentially different pathways from CD to ASPD.

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Hare, R. D., & Hart, S. D. (1995). A comment on the DSM-IV antisocial personality disorder field trial. In W. J.  Livesley (Ed.), The DSM-IV personality disorders (pp.  127-134). New York: Guilford. Hasin, D.  S., O’Brien, C.  P., Auriacombe, M., Borges, G., Bucholz, K., Budney, A., … & Grant, B. F. (2013). DSM-5 criteria for substance use disorders:  Recommendations and rationale. American Journal of Psychiatry, 170, 834–851. Hinshaw, S. P., & Lee, S. S. (2003). Conduct and oppositional defiant disorders. In E. J. Mash & R. A. Barkley (Eds.), Child psychopathology (2nd ed., pp. 144–198). New York: Guilford. Hinshaw, S.  P., & Scheffler, R.  M. (2014). The ADHD explosion:  Myths, medication, money, and today’s push for performance. New York: Oxford University Press. Kessler, R.  C., Berglund, P., Demler, O., Jin, R., Merikangas, K.  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. Kosten, T.  R., Rounsaville, B.  J., Babor, T.  F., Spitzer, R.  L., & Williams, J.  B. (1987). Substance-use disorders in DSM-III-R:  Evidence for the dependence syndrome across different psychoactive substances. British Journal of Psychiatry, 151, 834–843. Lahey, B.  B., Applegate, B., McBurnett, K., Biederman, J., Greenhill, L., Hynd, G. W., … & Richters, J. (1994). DSM-IV field trials for attention deficit hyperactivity disorder in children and adolescents. American Journal of Psychiatry, 151, 1673–1685. Lahey, B.  B., Waldman, I.  D., & McBurnett, K. (1999). Annotation:  The development of antisocial behavior:  An integrative causal model. Journal of Child Psychology and Psychiatry, 40, 669–682. Leibenluft, E. (2011). Severe mood dysregulation, irritability, and the diagnostic boundaries of bipolar disorder in youths. American Journal of Psychiatry, 168, 129–142. Loeber, R., Burke, J. D., & Pardini, D. A. (2009). Perspectives on oppositional defiant disorder, conduct disorder, and psychopathic features. Journal of Child Psychology and Psychiatry, 50, 133–142. Margulies, D.  M., Weintraub, S., Basile, J., Grover, P.  J., & Carlson, G. A. (2012). Will disruptive mood dysregulation disorder reduce false diagnosis of bipolar disorder in children? Bipolar Disorders, 14, 488–496. Maughan, B., Rowe, R., Messer, J., Goodman, R., & Meltzer, H. (2004). Conduct disorder and oppositional defiant disorder in a national sample: Developmental epidemiology. Journal of Child Psychology and Psychiatry, 45, 609–621. McBurnett, K., Lahey, B. B., & Pfiffner, L. J. (1993). Diagnosis of attention deficit disorders in DSM-IV:  Scientific basis and implications for education. Exceptional Children, 60, 108–117. Millon, T. (1981). Disorders of personality:  DSM-III, Axis II. New York: Wiley. Moffitt, T.  E. (1993). Life-course persistent and adolescence-limited antisocial behavior:  A  developmental taxonomy. Psychological Review, 100, 674–701. Moffitt, T.  E., Arseneault, L., Jaffee, S.  R., Kim-Cohen, J., Koenen, K. C., Odgers, C. L., … Viding, E. (2008). Research review:  DSM-V conduct disorder:  Research needs for an evidence base. Journal of Child Psychology and Psychiatry, 49, 3–33.

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Ryan, N. (2013). Severe irritability in youths: Disruptive mood dysregulation disorder and associated brain circuit changes. American Journal of Psychiatry, 170, 1093–1096. Skodol, A. E., Bender, D. S., Morey, L. C., Clark, L. A., Oldham, J.  M., Alarcon, R.  D., … Siever, L.  J. (2011). Personality disorder types proposed for DSM-5. Journal of Personality Disorders, 25, 136–169. Skodol, A. E., Krueger, R. F., Bender, D. S., Morey, L. C., Clark, L. A., Bell, C. C., … Oldham, J. M. (2013). Personality disorders in DSM-5 Section III. FOCUS: The Journal of Lifelong Learning in Psychiatry, 11, 187–188. Spitzer, R., Endicott, J., & Robins, E. (1978). Research Diagnostic Criteria:  Rationale and reliability. Archives of General Psychiatry, 35, 773–782. Spitzer, R.  L., Williams, J.  B., & Skodol, A.  E. (1980). DSM-III:  The major achievements and an overview. American Journal of Psychiatry, 137, 151–164. Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental Review, 28, 78–106. Strickland, C.  M., Drislane, L.  E., Lucy, M., Krueger, R.  F., & Patrick, C.  J. (2013). Characterizing psychopathy using DSM-5 personality traits. Assessment, 20, 327–338. Wakefield, J.  C. (2013). DSM-5:  An overview of changes and controversies. Clinical Social Work Journal, 41, 139–154. Widiger, T. A., Cadoret, R., Hare, R. D., Robins, L., Rutherford, M., Zanarini, M., … Frances, A. (1996). DSM-IV antisocial personality disorder field trial. Journal of Abnormal Psychology, 105, 3–16. 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 attention deficit/hyperactivity disorder symptom dimensions and subtypes. Journal of Abnormal Psychology, 121, 991–1010. World Health Organization. (WHO). (1981). Nomenclature and classification of drug- and alcohol-related problems:  WHO memorandum. Bulletin of the World Health Organization, 99, 225–242.

CH A PT E R

2

Attention-Deficit/Hyperactivity Disorder: Similarities to and Differences from Other Externalizing Disorders

Shaikh I. Ahmad and Stephen P. Hinshaw

Abstract Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed in over 10 percent of youth in the United States. It is a complex syndrome, conferring lasting and often severe impairment across the life span. When accompanied by high levels of impulsivity, ADHD is often a precursor to other forms of externalizing pathology, including oppositional defiant disorder and conduct disorder. Multiple models have suggested that ADHD represents deficits in inhibition/executive function and motivation/ delay aversion. Neurobiological findings suggest significant differences from controls in structure and function of numerous brain regions and pathways in people with ADHD. The authors present ADHD within a developmental psychopathology framework, wherein highly heritable vulnerabilities transact with environmental factors to produce multiple developmental pathways leading to both ADHD and other externalizing behaviors over the life course. Girls with ADHD appear to be at high risk for later self-harm, revealing outcomes moderated by sex. We conclude with discussion of future research directions. Key Words:  attention-deficit/hyperactivity disorder, developmental psychopathology, externalizing disorders, comorbidity, neurobiology, gene-environment interplay

Introduction

Attention-deficit/hyperactivity disorder (ADHD) is a complex, impairing, and sometimes controversial syndrome, which has a long-documented history of associations with other childhood externalizing disorders. Great strides have been made in the past few decades to move away from solely descriptive, symptom-based diagnoses in favor of more dynamic models based in developmental psychopathology (Hinshaw & Beauchaine, this volume). These newer models are grounded in genetics and neurobiology; they are also informed by interactions between intraindividual vulnerabilities and the ever-changing environments in which such vulnerabilities exist, thus affording the best opportunities for discovering the underlying

mechanisms of ADHD and its relations to externalizing behavior patterns (Beauchaine, Shader, & Hinshaw, this volume). We emphasize that understanding the etiology, development, and maintenance of ADHD, plus its linkages with other externalizing syndromes, is best accomplished by leveraging multiple levels of analysis (from genetics to neural pathways to behaviors and contextual forces), focusing on the complex transactions across levels that play out across development—and by re-examining prior assumptions with respect to comorbidities and categorical models of diagnosis. We begin with a brief history of ADHD, discuss key research results and prominent theoretical models, summarize current genetic and neurobiological findings (and their integration with psychosocial and 19

environmental risk and protective factors), and end with current issues surrounding ADHD, as well as brief suggestions for future research. ADHD is a syndrome1 that has had different names throughout history. Early forms of ADHD have been discussed since the early 1800s, given such labels as deficits in moral control, minimal brain dysfunction, and hyperkinetic syndrome (Barkley, 2014; Clements & Peters, 1962; Taylor, 2011). Some believed such children had behavioral issues due to a lack of “moral” regulation—that is, inability to modulate comportment despite normal intellectual functioning (Still, 1902). With its introduction in the Diagnostic and Statistical Manual of Mental Disorders (DSM-II) as “hyperkinetic reaction of childhood,” ADHD was characterized by symptoms ranging from restlessness and overactivity to lack of attention and distractibility, under the umbrella of a reaction or response to unspecified family forces or other stressors (American Psychiatric Association [APA], 1968). In DSM-III, the syndrome was renamed attention deficit disorder (ADD) and classified as a disorder of infancy/childhood/ adolescence. It was specified as existing in forms either with or without hyperactivity (APA, 1980). In the DSM-IV and DSM-IV-TR, the syndrome was renamed once again—as ADHD—sharing a new overarching classification with oppositional defiant disorder (ODD) and conduct disorder (CD) as “attention deficit and disruptive behavior disorders” (APA, 1994, 2000). This represented the first official attempt to group together all childhood disorders that represented an externalizing dimension of behavior (see Achenbach & Edelbrock, 1978, 1983). However, in DSM-5, partially in an attempt to reflect recent research findings in the neurosciences, ADHD was reclassified into the “neurodevelopmental disorders” grouping, whereas ODD and CD were inserted into the group of “disruptive, impulse-control, and conduct disorders” (APA, 2013). Whatever the validity of considering ADHD as a neurodevelopmental condition, we hope to make evident that it has clear links with externalizing syndromes and the development of impulse control and self-regulation, which we hope will not become lost with this current reclassification (see also Beauchaine & Hayden, in press). Starting with the DSM-III, the three core symptom categories of the syndrome (inattention, hyperactivity, and impulsivity) were treated as three separate, underlying dimensions of ADHD. However, there was a temporary change in DSM-III-R, when ADHD was viewed as a single, 20

unidimensional syndrome (APA, 1987). Since the DSM-IV, ADHD has retained its current two-factor model, with inattention/disorganization and hyperactivity/impulsivity as the two salient, underlying dimensions. Phenotypically, ADHD presents with three main subtypes (now termed “presentations” in DSM-5, given their lack of strong developmental stability):  the “predominantly inattentive” form (ADHD-I), the “predominantly hyperactive/ impulsive” form (ADHD-HI), and the “combined” form (ADHD-C)—which is diagnosed when both symptom criteria are met (APA, 1994, 2000, 2013). Most often, the ADHD-HI subtype/presentation is diagnosed only in young children and usually does not persist over time; these children tend to present with ADHD-C by late childhood, given attentional demands of schooling (Lahey, Pelham, Loney, Lee, & Willcutt, 2005; Wilens, Biederman, & Spencer, 2002). ADHD-I is often diagnosed later in childhood than the other presentations, largely because its lack of associated disruptive behavior does not trigger referral by teachers and because of increased pressure for organization and self-regulation by middle school. Still, underlying symptoms may well have been present earlier (Applegate et  al., 1997; Hinshaw & Scheffler, 2014; Loeber, Green, Lahey, Christ, & Frick, 1992). In fact, early manifestations of inattention are highly predictive of subsequent academic underachievement (see Hinshaw, 1992). Although there is considerable evidence for an association between ADHD-HI/ADHD-C and childhood externalizing syndromes (Angold, Costello, & Erkanli, 1999; Eiraldi, Power, & Nezu, 1997; Hinshaw, 1992, 2002a; Jensen et al., 2001), a key question is the extent to which ADHD-I (or, dimensionally speaking, inattention/disorganization) relates to other externalizing behaviors—and whether it may be better understood as an etiologically distinct entity (see also Barkley, 2001; Diamond, 2005; Hinshaw, 2001; Milich, Balentine, & Lynam, 2001). Although not without controversy (Barkley, 2003; Willcutt & Carlson, 2005), there is empirical support for this underlying two-factor model, namely, the distinction between children who present with primary inattention and those who present with primarily hyperactive/impulsive symptoms (e.g., Barkley, DuPaul, & McMurray, 1990; Lahey et al., 1994, 1998; Morgan, Hynd, Riccio,  & Hall, 1996; Pelham, Gnagy, Greenslade, & Milich, 1992; see also Barkley, 2014). Along with all other conditions presented in the DSM, ADHD is considered a diagnostic category,

At tention-Deficit/Hyperactivit y Disorder

but the relevant behavioral patterns are far better represented as spectra of inattention and hyperactivity/impulsivity (Angold et  al., 1999; Coghill & Sonuga-Barke, 2012; Hinshaw, 2002a; Rutter & Sroufe, 2000). Most individuals within a population display at least some of the relevant behaviors; the diagnostic question pertains to levels of severity and impairment linked to such symptoms, which may well involve “fit” within family and classroom socialization environments. The crucial questions for the current chapter are (1)  how best to utilize dimensionally based research to inform issues of linkages across externalizing conditions and (2) how to use a developmental framework to understand such associations (and differences between the externalizing dimensions; see also Hinshaw & Beauchaine, this volume). Rates of diagnosis for ADHD among children and adolescents have increased markedly over the past 20–30 years in the United States, with prevalence estimates in the 1990s of 3–5%, in the 2000s of 7–9%, and by 2012 as high as 11% among all youth ages 4–17 (APA, 1994; Visser et  al., 2010, 2014). Meta-analyses indicate that the worldwide prevalence of ADHD for youth ranges from 5% to 7%, with variations primarily related to assessment methods, diagnostic criteria, and comorbidity restrictions (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007; Willcutt, 2012). Controversy therefore exists with respect to the escalating rates of diagnostic prevalence in the United States. Explanations include loosening of DSM diagnostic criteria over time, increased research and public education/ awareness of ADHD, national educational and special educational policy changes and their effects on schools and clinic referrals, increased consumer awareness and demand for stimulant medication, and overreliance on cursory diagnostic appraisals by nonspecialists (see Hinshaw & Scheffler, 2014). In childhood, sex differences are estimated at 3:1 in the general population, with more boys receiving a diagnosis than girls. However, this ratio drops closer to 1.5:1 by adulthood (APA, 1987, 1994; Barkley, Murphy, & Fischer, 2010). This developmental shift is intriguing and not fully understood. It may in part reflect the tendency of girls and women to present more saliently with inattention than hyperactivity/impulsivity—with the former symptom domain showing more stability over time. Although ADHD was initially considered exclusively a childhood (and perhaps adolescent) disorder, considerable research reveals that 50% or more of children diagnosed with ADHD continue to

meet diagnostic criteria in adulthood—although the specific criteria and assessment methods for adult diagnosis are highly implicated in variable rates of persistence across key investigations. Persistence may be even higher if modifications are made to symptom levels required for adult diagnosis (Faraone, Biederman, & Mick, 2006). Yet, even when full diagnostic criteria are not met in adulthood, childhood diagnoses predict significant adult impairment across multiple domains of functioning, including academic achievement, employment, health and well-being, substance use, accidental injury, social functioning, and close relationships (Barkley et al., 2010; Hinshaw et al., 2012; Kessler et al., 2006). Thus, despite current debate over whether ADHD is overdiagnosed when evidence-based evaluation procedures are lacking, it is clearly a condition that yields major impairments in crucial life domains, with serious consequences for competence. In terms of adult diagnostic criteria, DSM-5 includes two changes from DSM-IV: it spells out adult-relevant manifestations of many of the core symptoms and reduces the number of symptoms needed for diagnosis from 6 to 5 per domain among individuals older than 16 (APA, 2013). Of all the childhood externalizing behavior patterns, ADHD has the highest heritability, with estimates ranging from .7 to even higher (e.g., Thapar, Holmes, Poulton, & Harrington, 1999; Willcutt et al., 2010; see Gizer, Otto, & Ellingson, this volume). Other biological triggers and insults, from the earliest years of life, are clearly associated with development of ADHD symptoms (Nigg, 2006). Along with ODD, ADHD has an earlier age of onset than CD, substance use disorders (SUD), and antisocial personality disorder (ASPD). In light of such clear evidence, and following a developmental psychopathology framework (e.g., Hinshaw, 2013; Rutter & Sroufe, 2000), we assume at the outset that ADHD is a highly heritable syndrome with significant neurobiological underpinnings that may take on specific developmental trajectories in the face of environmental stressors and challenges. Beauchaine and McNulty (2013) characterize the underlying vulnerability as “trait impulsivity,” a dopaminergically mediated tendency toward irritability and sensation seeking (see also Zisner & Beauchaine, this volume), which interacts and transacts with discordant family environments and with later behavioral consequences of the symptoms to yield escalating rates of externalizing features over time. Complex transactional models ensue, producing Ahmad, Hinshaw

21

multiple developmental pathways. Multifinal outcomes of this underlying vulnerability include serious conduct problems/delinquency, substance abuse, and, in some cases, antisocial behavior patterns in adulthood. Moreover, accumulating evidence suggests strongly that girls with ADHD (particularly when impulsivity is salient in childhood) are at markedly high risk for self-injurious behavior as they mature (Hinshaw et  al., 2012), signifying the importance of considering sex in elucidating developmental mechanisms and pathways (see Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009). Overall, despite historical changes in what ADHD was assumed to encompass (and even what it was called)—and despite current controversies over its fast-rising diagnostic prevalence—this syndrome comprises behavioral dimensions that result in significant impairment in large numbers of youth. Related symptoms are highly heritable; over time, their interactions with toxic contexts yield environmentally mediated intensification and expansion of the symptoms into other externalizing dimensions and conditions. Clearly, not all youth who experience ADHD (particularly those with the inattentive form) progress into more “advanced” forms of externalization/disruption; moreover, SUDs may emanate from alternate pathways that do not include early ADHD. Yet there is a clear subset of children—those who presumably display the most severe manifestations of disinhibition and trait impulsivity in their early years—who are particularly likely to become embroiled in family conflict and disharmony, academic disidentification, patterns of peer rejection, and discordant neighborhoods, thus setting the stage for progression into other, more clearly externalizing behavior patterns and diagnoses.

Underlying Models

As noted, early notions of ADHD suggested a lack of moral control or self-regulation (Still, 1902; see review in Taylor, 2011). Subsequently, inability to regulate motor activity rose to the fore (Laufer, Denhoff, & Solomons, 1957). Deficits in sustained attention and impulse control predominated research models of the 1970s (e.g., Douglas, 1972), prompting the switch in terminology to ADD in DSM-III. Investigations from the 1980s and 1990s examined neuropsychological deficits in ADHD, with the underlying idea that executive functions were often compromised (Barkley, Grodzinsky, & DuPaul, 22

1992; Pennington & Ozonoff, 1996; Stuss & Benson, 1986). Douglas (1988) believed that four key deficits typically underlie ADHD, all arising out of a more general impairment in self-regulation:  inability to create or maintain effort, lack of arousal, need for immediate reinforcement, and issues with impulse control. Building on prior reviews and analyses (Douglas, 1988; Quay, 1988a, 1988b; Sergeant, 1995a, 1995b), Barkley’s influential model attempted to create a “unifying theory” grounded within a neuropsychological framework that would account for both (1)  the cognitive and behavioral deficits associated with the syndrome and (2) deficits in both attention and hyperactivity/impulsivity (Barkley, 1997). He posited that the central deficit of ADHD is a lack of behavioral/response inhibition due to both structural and functional abnormalities in the prefrontal cortex and interconnected brain regions. As a result of this core deficit, downstream problems in four key executive functions emerge:  working memory, internalization of speech, self-regulation of affect/arousal/motivation, and reconstitution (for reviews of this theory, see Nigg, 2001; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005). Deficiencies in these areas ultimately lead to inabilities in executing goal-oriented behaviors, filtering irrelevant responses, and responding appropriately to one’s environment—all of which are vital for a child’s development, with clear implications for education, language, and social skills and relationships with peers. Another model of ADHD was initially guided by the effectiveness of stimulant medications given to children with ADHD, which began in earnest during the 1960s and were later supported by findings from structural and functional imaging studies of specific brain regions and tracts (Swanson et al., 2007; Volkow et al., 2009). This “dopamine (DA) hypothesis,” is predicated on the supposition that ADHD symptoms follow from reduced dopaminergic neurotransmission, potentially caused by some combination of reduced DA production and/ or transmission, reduced numbers of DA receptors, or overzealous reuptake of DA in the synapse. From this view, stimulant medications (which increase dopaminergic activity largely through blocking reuptake) may actually redress a neural deficit in individuals with ADHD (e.g., Swanson, McBurnett, Christian, & Wigal, 1995; Volkow, Wang, Fowler, & Ding, 2005). Two additional, somewhat complementary perspectives have described individuals with

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ADHD as exhibiting dysfunction in two different neurocognitive systems. First is a “top-down” system of executive functions and cognitive processes (such as planning, impulse control, set shifting, and working memory), which leads to more general failures in self-regulation and the pursuit of goal-oriented behaviors (Barkley, 1997; Castellanos, 1997; Douglas, 1983; Tannock & Schachar, 1996). Second is a “bottom-up” system of reward/motivation processing, in which individuals with ADHD have undersensitivity to reinforcing stimuli and are therefore relatively unable to delay gratification, thus favoring immediate rewards (Quay, 1988b; Sergeant, Geurts, Huijbregts, Scheres, & Oosterlaan, 2003; Solanto et al., 2001a; Volkow et al., 2010). Although each view explains some specific behavioral manifestations and whereas each has received support in the literature (Sonuga-Barke, Dalen, & Remington, 2003), a key challenge remained:  how do these two systems interact in a cohesive manner to explain the etiology and symptoms of ADHD? Sonuga-Barke proposed a “dual-pathway” model, in which two substrates of ADHD (the top-down executive dysfunction [EDF] and the bottom-up delay aversion [DEL]) have complementary neural and developmental pathways, supported by separate brain circuitry but both tied to branches of the DA system (Sonuga-Barke, 2002, 2003). In this model, the “executive circuit” is tied to both the mesocortical and nigrostriatal DA networks (linked with cognitive functions), whereas the “reward circuit” is more closely tied to the mesolimbic DA network and is associated with motivation (Castellanos, 1997; Castellanos, Sonuga-Barke, Milham, & Tannock, 2006; Sonuga-Barke, 2003). This perspective was recently updated with a proposed third pathway composed of temporal processing deficits, such as time discrimination and motor synchronization, based on relevant behavioral evidence for deficits in time-related tasks (Sonuga-Barke, Bitsakou, & Thompson, 2010). Initial findings and factor analysis, as well as evidence of family associations and co-segregations, support this third pathway, although it requires replication with larger samples. As discussed earlier, a recent model aims to elucidate two key challenges facing any theory regarding the etiology and development of early-onset externalizing disorders:  homotypic comorbidity (i.e., the presence of multiple externalizing disorders at the same time) and heterotypic continuity (i.e., the development of seemingly different

disorders over time). In this model, a highly heritable, neurobiologically based vulnerability—trait impulsivity—is present early in development and in some cases escalates into more advanced externalizing problems over time, contingent on interactions and transactions with environmental risk factors (Beauchaine & McNulty, 2013; Zisner & Beauchaine, this volume). Encompassing multiple potential transactional pathways, this single externalizing liability factor can precipitate a complex set of related behaviors, starting with early childhood ADHD, which may co-exist with ODD—then often progressing to ODD/CD and later SUD and/or ASPD in young adulthood. This model, however, is not intended to address ADHD-I, which nearly always lacks the disruptive behaviors and comorbidity with childhood externalizing disorders found for the presentations marked by impulsivity. Indeed, the heterogeneous nature of ADHD presents a major challenge to any model that purports to convey, in comprehensive fashion, its features, impairments, and comorbidities. It may well be the case that, once etiologic paths are better elucidated, what we currently term ADHD will be found to comprise several partially distinct entities. Questions have been raised regarding whether two-factor presentations (i.e., inattentive vs. HI symptom domains) are truly variants of the same underlying syndrome (see Lahey et  al., 2005). A heuristic approach to explaining this heterogeneity is a latent, “bifactor” model of ADHD, modeled through a form of confirmatory factor analysis. Here, a general (“g”) factor of ADHD is posited, along with two specific (“s”) factors of inattention and HI. If the model is valid, specific factors should account for unique variance, over-and-above that pertaining to the general ADHD factor. This approach has been validated in multiple clinical and community samples (Gomez, Vance, & Gomez, 2013; Martel, Roberts, Gremillion, von Eye, & Nigg, 2011; Toplak et al., 2009). Indeed, it has been extended to a bifactor model of childhood disruptive behaviors more generally, incorporating a general “disruptive behavior” factor and three specific factors of inattention, HI, and oppositionality (Burns, de Moura, Beauchaine, & McBurnett, 2014). Although evidence supports the presence of a general, underlying externalizing factor, questions still remain regarding both mechanistic accounts of progressions of externalizing behavior (cross-sectional factor analytic models cannot, in and of themselves, account for such mechanisms) Ahmad, Hinshaw

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and underlying processes linked to more purely inattentive aspects of ADHD.

Neurobiology of ADHD

Over the past 20  years, neuroscientific approaches have been informed by three primary lines of inquiry:  neuropsychological testing (reviewed elsewhere; see Nigg, 2005; Sergeant, Geurts, & Oosterlaan, 2002; Willcutt et al., 2005); structural neuroimaging, usually including magnetic resonance imaging (MRI) techniques that measure regional and whole brain volume, surface area, and surface contour, but also including diffusion tensor imaging (DTI), used to examine white matter structural connectivity (see Kieling, Goncalves, Tannock, & Castellanos, 2008; Valera, Faraone, Murray, & Seidman, 2007; van Ewijk, Heslenfeld, Zwiers, Buitelaar, & Oosterlaan, 2012); and functional neuroimaging. The latter include positron emission tomography (PET), single photon emission computed tomography (SPECT), and functional magnetic resonance imaging (fMRI). fMRI investigations, which assess brain metabolism and infer brain activation by measuring changes in cerebral blood flow, are usually connected to specific cognitive tasks but have expanded to include “resting state” investigations (also called resting state functional connectivity MRI [rs_fcMRI]) that measure brain activation when individuals are not engaged in cognitive or motor tasks. In addition to shedding light on psychological and behavioral aspects of ADHD, relevant findings have provided insight into differences in brain structure and function throughout development more generally (for reviews, see Bush, Valera, & Seidman, 2005; Giedd, Blumenthal, Molloy, & Castellanos, 2001; Konrad & Eickhoff, 2010; Seidman, Valera, & Makris, 2005; Valera et al., 2007).

Structural Imaging

In a landmark study, Castellanos and colleagues (2002) found children with ADHD to have significantly smaller overall brain volumes than comparisons, on the order of 3%. Importantly, brain volume correlates significantly (and negatively) with symptom severity among those with ADHD. Structural differences in specific brain regions, including cerebellum size, overall white matter volume, and caudate nucleus volume, were also found (Castellanos et al., 2002). These investigators also found significant differences in white-matter volumes between medicated and medication-naïve children with ADHD (Castellanos et  al., 2002), 24

suggesting possible neuroprotective effects of stimulant medication (for a recent review further suggesting such neuroprotective mechanisms via naturalistic medication investigations, see Spencer et al., 2013). Additional structural imaging studies replicate the core finding of reduced overall brain volume (e.g., Almeida et al., 2010; Batty et al., 2010, Seidman et al., 2005, 2006; Shaw et al., 2007) and differences in surface contours and surface area (Shaw et al., 2014; Sobel et al., 2010). Volumetric differences were found in neuropsychologically predicted regions of interest, including prefrontal cortex (PFC), anterior cingulate cortex (ACC), and basal ganglia (Giedd et  al., 2001; Seidman et  al., 2005; Valera et  al., 2007). In addition, mounting evidence reveals structural differences across wider brain regions, including parietal, temporal, and occipital regions, as well as subcortical structures including the thalamus, corpus callosum, and cerebellum (see Bush, 2011; Castellanos & Proal, 2012; Cortese et  al., 2012; Kieling et  al., 2008; Konrad & Eickhoff, 2010; Krain & Castellanos, 2006; Purper-Ouakil et al., 2011; Schneider, Retz, Coogan, Thorne, & Rösler, 2006). In normative development, the shape and thickness of the cerebral cortex undergoes developmental changes during childhood and adolescence (Shaw et  al., 2008), with maximal thickness achieved by around 6  years of age. Children with ADHD exhibit delays of approximately 3 years in such cortical thickening (Shaw et al., 2006, 2007; see also Almeida et al., 2010), particularly in frontal regions. These different trajectories continue through adolescence (Shaw et al., 2007). Intriguingly, patterns of cortical development correlate with symptom severity (Giedd & Rapoport, 2010). More recently, DTI has been leveraged to study the diffusion of water along white matter brain structures, which provides for inferences about organization/orientation and integrity of axons without the need for invasive procedures. This technique is used to assess increases and decreases in axonal branching, density, and myelination, which are potential indicators of microstructural anatomical abnormalities (Konrad & Eickhoff, 2010; Weyandt, Swentosky, & Gudmundsdottir, 2013). Initial findings with ADHD samples indicate widespread altered white matter in multiple brain regions, including prefrontal, temporal, parietal, and occipital lobes, as well as the basal ganglia and cerebellum (Konrad & Eickhoff, 2010; Nagel et al., 2011; Silk, Vance, Rinehart, Bradshaw, & Cunnington, 2009;

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van Ewijk et  al., 2012). The most common finding pertains to fronto-striatal-cerebellar circuitry (see Liston, Cohen, Teslovich, Levenson, & Casey, 2011; van Ewijk et al., 2012; Weyandt et al., 2013). Structural findings to date indicate that children and adolescents with ADHD exhibit neurodevelopmental differences from an early age. These differences are likely to result from genes, early environmental effects, or their interaction (Kieling et al., 2008; Swanson et al., 2007). Although fewer studies have focused on adults with ADHD, findings indicate that at least some structural differences persist into adulthood (Schneider et al., 2006; Seidman et al., 2006). The overall implication in some ways parallels the phenotypic heterogeneity of ADHD, as well as the diversity of underlying models for the condition: multiple brain regions and neurological pathways are likely to be involved in the development and manifestation of the syndrome.

Functional Imaging

Functional neuroimaging studies provide a wealth of information on potential brain regions and neural pathways involved in ADHD (for reviews, see Bush et al., 2005; Cortese et al., 2012; Dickstein, Bannon, Castellanos, & Milham, 2006; Hart, Radua, Mataix-Cols, & Rubia, 2012; Konrad & Eickhoff, 2010; Weyandt et al., 2013). Initial research focused on frontal and parietal brain regions and on multiple subcortical regions including the basal ganglia, limbic system, and cerebellum (Bush et al., 2005; Cortese et al., 2012; Dickstein et al., 2006; Hart et al., 2012; Liston et al., 2011). The most common findings in task-based studies of ADHD show hypoactivation (reduced blood flow) in the PFC, ACC, striatum, thalamus, parietal cortex, and cerebellum during cognitive performance and incentive responding, thus implicating deficits in cortico-striato-thalamo, fronto-parietal, and fronto-cerebellar brain circuits. Such patterns of hypoactivation are hypothesized to account for deficits in cognitive/impulse control and attention. However, paralleling structural findings, additional research has revealed differences in other regions (Bush, 2011; Castellanos & Proal, 2012; Cortese et al., 2012). Interestingly, some investigations have found increased activation in other brain regions during certain tasks, including ventral attention areas as well as somatomotor and visual systems, leading some to hypothesize that individuals with ADHD may compensate for lack of attention/cognitive control by leveraging a more diffuse network of brain regions during performance tasks (Bush et al., 2005; Cortese et al., 2012; Durston et al., 2003).

In other research, rs_fcMRI has been used to explore the so-called default mode network (DMN), a system of intrinsically interconnected neural networks that are thought to represent baseline brain patterns (Greicius, Krasnow, Reiss, & Menon, 2003; Raichle et  al., 2001). Given cognitive and attentional dysfunction in ADHD, research utilizing rs_ fcMRI has particular salience with this population because some have hypothesized that individuals with ADHD should exhibit distinct brain activation patterns while at rest (Castellanos & Proal, 2012; Fassbender et  al., 2009; Sonuga-Barke & Castellanos, 2007). Indeed, ADHD might represent difficulties in suppressing the DMN during task-oriented behavior, essentially creating interference between brain networks and leading to behavioral problems of inattention, hyperactivity, and impulsivity (Cortese et al., 2012; Fassbender et al., 2009; Sonuga-Barke & Castellanos, 2007). At the level of neurotransmitter systems, studies implicate norepinephrine (NE) in addition to DA. Both human and animal models demonstrate effectiveness of stimulant medications on attention (Biederman & Spencer, 1999; Czerniak et al., 2013; Pliszka, 2005; Pliszka, McCracken, & Maas, 1996; Solanto, Arnsten, & Castellanos, 2001b). Although other neuromodulators are likely involved—and although it is mistaken to think that deficits involve simple excesses or deficiencies in one or another neurotransmitter system—DA and NE pathways are almost certainly involved in the expression of ADHD. As discussed earlier, the DA hypothesis of ADHD contends that behavioral difficulties in attention, hyperactivity, and impulsivity are in part due to imbalances of DA, especially in frontal-striatal and in subcortical reward pathways. The three primary DA pathways believed to be involved in ADHD are the mesocortical (executive function), mesolimbic (reward/motivation), and nigrostriatal (motor control); (see Nigg, 2005). The primary action of stimulants (such as methylphenidate) is to block the reuptake of DA, thereby increasing DA levels, at least in the short run. Behaviorally, this action results in increased attention and reduced hyperactive/impulsive behaviors. Importantly, PET investigations reveal fewer DA receptors and transporters in never-medicated individuals with ADHD in key frontal and subcortical regions, with major implications for trait impulsivity and altered reward-related behavior (Swanson et al., 2013; Volkow et al., 2009, 2010). In sum, converging evidence indicates that among individuals with ADHD (1)  the brain Ahmad, Hinshaw

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manifests structural and functional differences from early in life, (2) brain development (particularly in frontal-cortical areas) during childhood and adolescence differs from typically developing youth, (3)  DA and NE neurotransmitter systems are implicated in key brain regions and pathways, and (4) levels of clinical impairment/symptoms are often associated with these structural and functional differences. Particularly related to dopaminergic dysfunction, such research has clear implications for genetic investigations, as highlighted later.

Other Externalizing Disorders

A key point regarding the majority of participants in neurobiological studies of externalizing and disruptive behavior disorders is that they are comorbid for ADHD, which is not surprising given the extremely high rates of such comorbidity across the board (e.g., Angold et  al., 1999; Beauchaine, Shader, & Hinshaw, this volume; Krueger, this volume). This comorbidity would appear to prevent identification of specific, independent contributions to neurobiological risk in ODD, CD, or ASPD beyond those contributed by ADHD. One might therefore hope for investigations of “purely” externalizing samples, with ADHD either eliminated during subject selection or via statistical adjustment. As noted by Beauchaine and McNulty (2013), however, these practices may provide false hope: if such comorbidity is as strong as it appears, attempting to obtain pure samples or to covary ADHD symptoms may be an exercise in futility—because these other externalizing conditions (ODD, CD, ASPD) may actually be developmental extensions of ADHD rather than distinct categories. Still, investigations that focus more specifically on other externalizing syndromes (for reviews, see Blair, 2010; Matthys, Vanderschuren, & Schutter, 2013; Rubia, 2011; Siever, 2008) suggest differences from comparison samples in frontal and subcortical regions, including the ventromedial PFC, ACC, amygdala, and hippocampus, among others (Arnsten & Rubia, 2012; Matthys et  al., 2013; Raine & Yang, 2006). Indeed, externalizing behavior may be more strongly associated with fronto-limbic than ADHD-linked fronto-striatal circuits. A  smaller body of evidence also suggests structural differences in the orbital, parietal, and temporal cortices between those with externalizing conditions and comparison participants (e.g., Tiihonen et al., 2008). Of particular note are differences in neural (and physiological) behavior based on emotional stimuli, which highlights the critical role of the amygdala and brain 26

regions involved in emotion regulation among individuals who exhibit psychopathic and antisocial behavior (Blair, 2010). However, such findings are often based on samples of young adults. Finally, similar to neurobiological theories suggested for ADHD, some have hypothesized a “cool” top-down (frontal circuitry) versus “hot” bottom-up (emotionality/limbic-driven) dysfunction in aggression and psychopathy (Rubia, 2011; Siever, 2008). Overall, there may be more dysregulation in emotion-related circuitry for individuals with other externalizing disorders than for those with purer forms of ADHD, but this statement is not supported by voluminous research and may ignore the developmental linkages from early ADHD-related symptomatology to later aggression/antisocial behavior patterns in a substantial subgroup of individuals (Beauchaine, Shader, & Hinshaw, this volume).

Heritability, Biological Risk, and  Psychosocial Risk Factors Heritable Risk

In research on heritable influences (which involve mainly those emanating from the genes with which one is born), the two most common approaches are behavioral genetics, which pertain to population-wide genetic heritability as well as both shared and nonshared environmental effects; and molecular genetics, which investigate behavioral differences related to specific genetic markers (Beauchaine & Gatzke-Kopp, 2013). Heritable factors play a significant role in the development of ADHD. Indeed, family studies show that first-degree relatives are 2–8 times more likely to have ADHD than comparable relatives of controls, and twin studies indicate that the heritability of ADHD is at least .75 (Faraone et al., 2005; Thapar et al., 1999; Willcutt et al., 2010). Not only does ADHD have the highest heritability of all externalizing syndromes, it comprises one of the highest among all mental illnesses (Burt, 2009; Sullivan, Daly, & O’Donovan, 2012). Intriguingly, one meta-analysis also found that the development of ADHD was not influenced by shared environmental factors, yet all other externalizing disorders did show such influence, suggesting a weaker etiologic role for parenting, for example, in ADHD than in other externalizing conditions (Burt, 2009). Also of interest, one meta-analysis found significant differences in dominant versus additive genetic effects between ADHD-I and ADHD-HI, potentially indicating different causal mechanisms between ADHD subtypes (Nikolas & Burt, 2010).

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Whereas behavioral genetics findings support the significant contribution of heritability to the development ADHD, molecular genetics findings have proved to be complex and often inconclusive despite their initial promise (Faraone & Mick, 2010; Khan & Faraone, 2006; Schachar, 2014; Sullivan et al., 2012; see also Gizer, Otto, & Ellingson, this volume). Multiple research designs have been used in molecular genetic studies of ADHD, including (1) candidate gene studies, which test the effects of theoretically selected genes on phenotypic expression (to determine if a particular allele of a gene is more highly associated with ADHD than other alleles); (2)  genome-wide linkage scans (GWLS), which explore genetic differences between family members to determine if certain regions of DNA are shared more frequently than expected; and (3)  genome-wide association studies (GWAS), which test for genetic mutation/variation between individuals with ADHD and comparison groups to determine if specific alleles are more frequently associated with ADHD (Faraone et  al., 2005; Li et  al., 2014; Schachar, 2014). Often, GWAS are driven by findings from candidate gene studies. In a recent review, an estimated 180 candidate genes were identified in studies of ADHD, with more than half being associated with the syndrome (Li, Chang, Zhang, Gao, & Wang, 2014). Still, none of the associations was of even medium effect size. The most commonly identified genes are involved in development of key neurotransmitter systems, particularly dopamine, serotonin, and norepinephrine, as well as a number of other genes involved in neural plasticity (Faraone & Mick, 2010; Li et al., 2014; Schachar, 2014). More recent collaborative efforts have focused on creating larger sample sizes to overcome some of the challenges associated with genome-wide studies, including lack of statistical power. As just noted, however, the estimated variance that each candidate gene confers to the risk of developing ADHD is extremely small. The more likely scenario for explaining phenotypic expression of ADHD and other externalizing behaviors lies in the interactions among multiple genes (polygenetic; epistatic) and as the interplay between genes and the environment (discussed later). Indeed, although findings are not yet convergent, current research suggests that a number of specific genes appear related to multiple externalizing syndromes (Gizer, Otto, & Ellingson, this volume). Intriguingly, several common genes appear to underlie risk for ADHD, for autism-spectrum disorders, and for schizophrenia

along with mood disorders (Cross-Disorder Group of the Psychiatric Genomics Consortium, 2013). The prior belief that specific genes create unique risk for psychiatric illness is probably misguided; complex gene-environment interplay is doubtless involved (see, e.g., Beauchaine & Gatzke-Kopp, 2013).

Other Early Biological Risk

A number of other early biological risk factors have been studied in ADHD and other externalizing syndromes, which can be grouped into preand perinatal influences, environmental toxins, and diet (Crocker, Fryer, & Mattson, 2013; Nigg, Lewis, Edinger, & Falk, 2012; Thapar, Cooper, Eyre, & Langley, 2013). The most commonly studied prenatal risk factors are related to substance use (especially maternal alcohol and smoking). It is well-established that alcohol consumption during pregnancy can lead to fetal alcohol spectrum disorder (FASD). Some of the cognitive and behavioral symptoms in FASD are very similar to those in ADHD, and children with FASD have noticeably higher rates of disruptive behaviors (the most significant of which is ADHD) than the general population (Crocker et  al., 2013; Fryer, McGee, Matt, Riley, & Mattson, 2007). Similarly, maternal smoking during pregnancy is associated with higher rates of externalizing behaviors, including ADHD, antisocial behavior, and delinquency. Other maternal substance usage during pregnancy has been less studied, and the findings are less conclusive (see Thapar et al., 2013). Another risk factor for ADHD is low birth weight, which is linked to other risk factors during pregnancy (such as diet, substance use, maternal stress, and the like; see Nigg, 2013). Environmental-level chemicals and toxins, such as mercury, lead, and pesticides, are increasingly implicated in ADHD and other neurodevelopmental disorders. Associations between both preand postnatal exposure to certain chemicals and externalizing behaviors, including ADHD, have been found, although such findings are not always consistent across studies (Crocker et  al., 2013; de Cock, Maas, & van de Bor, 2012; Kuehn, 2010; Thapar et al., 2013). It is possible that genetic predispositions interact with these factors; such complex interactions may trigger expression of ADHD (see Mill & Petronis, 2008; Nigg, Nikolas, & Burt, 2010). Diet has also received considerable attention in many facets of healthcare (including child and adolescent psychopathology), both as a preventive measure and a potential treatment. In ADHD, Ahmad, Hinshaw

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such studies often explore restriction diets (such as eliminating food colors, preservatives, and other additives) as well as nutrient deficiencies (such as iron, zinc, and fatty acids). Although findings are sometimes mixed and effects are relatively small, it does appear that at least a subset of children with ADHD experience symptom improvement from dietary changes (for reviews, see Millichap & Yee, 2012; Nigg et  al., 2012; Stevenson, 2010). More fundamentally, temperamental traits present in the earliest years of life, particularly those related to self-regulation, are linked to later ADHD (see Martel & Nigg, 2006; Nigg, Goldsmith, & Sachek, 2004; Posner & Rothbart, 2000; Rothbart, Ahadi, & Evans, 2000; see also Beauchaine & McNulty, 2013, and Zisner & Beauchaine, this volume, for information on early manifestations of trait impulsivity in relation to ADHD). Speech and language problems in early childhood, which might also result from cognitive/executive dysfunction, are also associated with increased rates of ADHD (Beitchman et  al., 1996; McGrath et al., 2008; Tannock & Schachar, 1996; Toppelberg & Shapiro, 2000).

Psychosocial Risk Factors

Although a number of genetic, early environmental, and neurobiological factors are implicated in the etiology of ADHD, a large body of research focuses on the role of psychosocial risk factors, specifically parenting and the family environment, as maintaining or exacerbating factors (for a review, see Johnston & Mash, 2001; Nigg, 2006). Investigations have revealed significant associations between ADHD symptoms and family factors such as maternal psychiatric history, excessive parental alcohol use, family conflict/dysfunction, parental criminality, and low socioeconomic status (Biederman et al., 1995; Johnston & Mash, 2001; Spencer, Biederan, & Mick, 2007). However, some caution is required here:  without well-designed, genetically informative studies, it is difficult to disentangle the potentially causal role of psychosocial risk factors from genetic confounds. For example, it takes real work to assess whether a negative parenting style played a causal role in the development of a child’s ADHD versus whether the child’s ADHD behaviors in fact exacerbated and brought on a change in parenting style (Johnston & Mash, 2001). Although early environmental theories posited that poor/ineffective parenting was a cause of ADHD, this claim is largely unsubstantiated. The more likely scenario is one of a transactional process model, in which a child displays inherited/ 28

biological vulnerabilities, with parenting/family factors then playing a role in both triggering and sustaining of phenotypic behaviors. In such a scenario, parent-child interactions might fall into maladaptive patterns:  a child’s trait impulsivity leads to behaviors that bring about harsh parenting, which in turn causes the child to “act out,” leading to a cyclical pattern of coercion (Patterson, 2002; see also Snyder, this volume). On the other hand, there is strong evidence (including findings from experimental treatment trials) that oppositional and aggressive behavior patterns are clearly, even causally linked to maladaptive parenting (Kimonis, Frick, & McMahon, 2014; Johnston & Mash, 2001; Loeber, Green, Lahey, Frick, & McBurnett, 2000; Patterson, 2002). Thus, it may well be the case that the disruptive, externalizing, antisocial behavior patterns so often emerging from early histories of ADHD are in part produced from such transactional patterns of discordant, harsh, inconsistent, and coercive parenting. Related patterns also emerge in early school and social settings: young children with ADHD often face challenges in the classroom and in peer groups. Given the significant impact of nonshared environmental influences on the development of antisocial behavior (Rhee & Waldman, 2002), such experiences outside the home present pathways that promote academic underachievement, delinquency, and substance use (Barkley, 2014; Beauchaine, Hinshaw, & Pang, 2010; Hinshaw, 1992; Loeber, Burke, & Pardini, 2009).

Gene-Environment Interplay

With the high heritability of ADHD paired with the small observed contribution of specific genetic “main effects,” investigations focusing on the interplay between heritable and environmental factors have furthered our knowledge of etiology and mechanisms (Moffitt, Caspi, & Rutter, 2005; Rutter & Silberg, 2002). In gene × environment interaction studies2 (G×E), only a small percentage of candidate genes (primarily related to neurotransmission) and environmental variables (such as maternal smoking, alcohol use, low birth weight) have been studied to date. Nevertheless, findings are promising and have generated new opportunities for research on moderation of genetic vulnerability by environmental risk and vice versa (Nigg et  al., 2010, Thapar, Langley, Asherson, & Gill, 2007; see also Moffitt, 2005, for a review on ASB). In short, it may be that environmental risk applies solely or largely to the subset of vulnerable youth with certain genotypes.

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However, there are methodological challenges with this approach as well (for an overview, see Nigg et al., 2010; Risch et al., 2009), and the differential effects of development and timing on phenotypic expression of combined genetic and environmental risk factors must be taken into account. Another important mechanism comprises gene-environment correlation (rGE; see Knafo & Jaffee, 2013; Rutter & Silberg, 2002). This construct refers to the confounded nature, in biological families, of genes and environments. That is, what might be assumed to be environmental effects may actually be partially heritable. For example, the child of inattentive and impulsive parents may need to contend with the parents’ less-than-organized parenting styles (passive rGE); the same child may sensation-seek, entering into risky environmental settings (active rGE), and he or she may elicit responses from teachers and peers that reinforce his or her genetically mediated tendencies (evocative rGE). Without genetically informative designs, misattribution of all three examples to environmental causal factors may occur. In a bold attempt show independent effects of socialization on ADHD-related behavior, Harold, Leve, Barrett et  al. (2013) and Harold, Leve, Elam et  al. (2013) presented intriguing findings from adoption designs. Among adoptive parents of children with ADHD, child hostility predicted maternal hostility, in a classic example of reciprocal effects—but without the possibility of passive rGE as a confound because parents and children were unrelated. Moreover, such maternal hostility predicted the longitudinal course of relevant ADHD-related symptoms (see also Lifford, Harold, & Thapar, 2008). Thus, despite the strongly heritable nature of ADHD symptoms, parenting effects can, in fact, be partially causal of eventual outcomes.

Developmental Progression and  Comorbidity

In childhood, ADHD is often a developmental precursor to (and risk factor for) other externalizing conditions and of internalizing problems (particularly in females). Although different developmental trajectories exist, some clear patterns do emerge. Specifically, increased severity of ADHD symptoms during childhood predicts broader and more severe impairment in other domains of functioning later in life (Angold et  al., 1999; Beauchaine & McNulty, 2013; Castellanos, 1997; Jensen, Martin, & Cantwell, 1997; Loeber et  al., 2009; Spencer et  al., 2007). Also, ADHD-C is

more significantly associated with other externalizing behaviors in childhood and adolescence than is ADHD-I (Beauchaine et  al., 2010; Diamond, 2005; Eiraldi et  al., 1997; Milich et  al., 2001). As discussed earlier, one hypothesis for this developmental association is that it represents a developmental outcome of heritable trait impulsivity (Beauchaine & McNulty, 2013; Zisner & Beauchaine, this volume). The eventual development of externalizing comorbidities is likely to be attributable to the transaction between such vulnerability and exposure to toxic psychosocial environments in the home, at school, and in the peer group. Therefore, one common developmental progression leads from ADHD-C to ODD/CD, with the child’s environment (especially family and social/school settings) playing a critical role in the manifestation and exacerbation of these additional externalizing phenotypes (Beauchaine et al., 2010). Alternatively, the ADHD-I presentation typically lacks the externalizing liability that leads to the same degree of oppositionality and aggression (Castellanos, 1997; Diamond, 2005; Milich et  al., 2001). In sum, childhood ADHD can lead to multiple outcomes based on symptom severity, psychosocial and environmental factors, and comorbidity profiles. In adolescence, most individuals with ADHD continue to experience serious clinical symptoms as well as family, academic, and social problems (see Hinshaw, 2002b; Lee et  al., 2008; Molina et  al., 2009). The influence of environmental factors during this sensitive stage, coupled with salient impulsivity during childhood, provides a developmental progression into other forms of externalizing behavior, delinquency, SUD, and criminal behavior. For youth with ADHD-I, academic problems, peer difficulties, and impairments in romantic, family, and peer relationships are common in adolescence (Hinshaw et  al., 2006; Milich et  al., 2001; Molina et al., 2009). The influence of deviant, substance-abusing peers may also provide a potential pathway into SUD for a number of youth with ADHD. Persistence rates of ADHD into adulthood vary dramatically as a function of definitional criteria and source of relevant information on symptoms (see Barkley et al., 2010). Current estimates are that 50% or more of children with ADHD retain a diagnosis into adulthood (Faraone et al., 2006; Kessler et al., 2006). The most severe developmental progression, at least in males, starts with severe childhood ADHD, leading to ODD/CD later in childhood, delinquency/SUD by adolescence, and Ahmad, Hinshaw

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then ASPD by young adulthood (Beauchaine et al., 2010; Loeber et al., 2009; Moffitt & Caspi, 2001). In females, however, for whom rates of CD and ASPD appear far lower than in males, a pathway to serious self-injury exists (Beauchaine et al., 2009; Hinshaw et al., 2012). This trajectory is predicted by high levels of childhood impulsivity and is mediated by both poor response inhibition and externalizing comorbidities during the teen years (Swanson, Owens, & Hinshaw, 2014). Additional data suggest that peer rejection and victimization during childhood and adolescence are key contributors as well (Meza, Owens, & Hinshaw, 2015). The field needs to prioritize further knowledge of sex-specific developmental progressions, which may well signal greater multifinality (i.e., a greater dispersion of impairing outcomes) among girls with early ADHD than among boys. Although the focus thus far has been on risk factors for increased psychopathology, a number of protective factors during childhood, adolescence, and young adulthood have also been investigated. Among these, authoritative/positive parenting (and parenting intervention programs that focus on improved parent–child communication and interaction), friendships and supportive peers, and evidence-based treatments (such as behavior therapy and medication) appear promising (Cardoos   & Hinshaw, 2011; Chronis, Chacko, Fabiano, Wymbs, & Pelham, 2004; MTA Cooperative Group, 1999; Webster-Stratton, Reid, & Beauchaine, 2013), although the absence of long-term, placebo-controlled trials greatly limits causal assertions. Although too many with childhood ADHD experience sustained impairments in multiple domains, a continued press to discover protective factors is essential.

Current Issues

A review of the current state of the field reveals several important themes, which we have space to present only in headline form. First, regarding etiology and classification, similar to other externalizing disorders, a challenge remains that current diagnostic criteria do not take into account the continuous nature of the underlying dimensions of impulsivity and attention, transdiagnostic approaches in which multiple levels of analysis are applied to understand core dimensions of underlying mechanisms (see Insel et al., 2010), and developmental continuities and comorbidities—which may help to clarify the fragmented, comorbid set of apparently different externalizing conditions that are actually linked across the life span 30

(Beauchaine & McNulty, 2013). With continued research focus on longitudinal investigations that measure genetic, neurobiological, and environmental factors and their mutual influence over development, we envision a better elucidation of causal mechanisms underlying the developmental course of ADHD and the externalizing (as well as internalizing) conditions that accompany it over time. Second, it is essential that clinicians take the thorough diagnosis of ADHD seriously. Without evidence-based evaluations, false-positive diagnoses are highly likely (Hinshaw & Scheffler, 2014), thus fueling increases in diagnosed prevalence rates and medication use, as well as high levels of diversion of medication to those without legitimate ADHD. Involved here are issues of professional training, enforcement of professionally endorsed standards for valid evaluations, and adequate reimbursement for the time and effort needed. Indeed, without developmental histories, informant rating scales, and supplemental cognitive and attentional assessments, issues such as maltreatment, other medical conditions, or chaotic home or school environments can and will be misattributed to ADHD. These are crucial mental health and public health issues that require further attention and discussion. Third, related to types (or “presentations”) of ADHD, no theoretical model fully accounts for differences between the more inattentive form and the combined form (with the latter marked by salient impulsivity/hyperactivity). Too little research has focused on ADHD-I; still unknown is whether this is a distinct syndrome from ADHD-C. As noted throughout this chapter, developmental progressions related to ADHD-I do not tend to include the externalizing dimensions—leading to serious antisocial behavior—found more saliently for those presentations marked by early impulsivity. Fourth, although ADHD is no longer considered exclusively a childhood/adolescent disorder, additional longitudinal research is sorely needed to characterize adults with ADHD, including relevant symptoms and the developmental progressions of underlying attentional, inhibitory, and motivational mechanisms. Such efforts also have major potential to illuminate the course of other externalizing behaviors. Included here is the need for additional work on evidence-based diagnostic procedures for adults. Fifth, the vast majority of research on ADHD has focused on males. Yet a growing body of research reveals potentially differing life-course pathways for

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females with salient ADHD symptoms (Hinshaw et al., 2012; Swanson et al., 2014). Further research is required to elucidate potential differences in genetic and neurobiological, as well as psychosocial factors that lead to different developmental pathways for boys and girls diagnosed with ADHD. Moderation of key outcomes by sex appears likely, as discussed earlier. Finally, although stimulant medication and behavioral interventions each has a significant evidentiary base as effective treatments for children and adolescents with ADHD, findings suggest that that a combination of these two modalities over the long term could be the optimal approach to prevent significant, cross-domain impairment by adulthood (see Hinshaw & Arnold, 2015). Yet long-term clinical trials with appropriate control groups present logistic, financial, and ethical challenges.

Conclusion and Future Directions

ADHD is both a set of behavioral dimensions linked to inattention and hyperactivity/ impulsivity and a diagnostic condition experiencing soaring rates of diagnosis and considerable controversy. There is no doubt that the underlying features are strongly heritable, yet the current climate of performance demands and educational pressures (along with economic incentives for diagnosis—linked to accommodations, plus far too many cursory evaluations) have fueled recent surges in diagnosed prevalence. Despite the highly neurobiological roots of ADHD-related symptoms, it is in the context of conflictual interaction patterns at home and a lack of “fit” in current, achievement-oriented classroom settings that ADHD (particularly the form marked by salient impulsivity and hyperactivity) often yields highly externalizing paths and trajectories, including diagnostic categories of ODD, CD, SUD, and ASPD. At the same time, females with ADHD are more likely to display self-injurious behavior patterns by adolescence and young adulthood and the inattentive form is more specifically linked to achievement problems and social isolation than to aggressive and antisocial behavior per se. ADHD is far from unidimensional: multiple pathways and developmental progressions are operative. Among a number of potential directions for ADHD-related investigations, we highlight that investigators must work to (1)  clarify dimensional and categorical conceptions, (2)  emphasize

diversity of developmental progressions emanating from early displays of cognitive and motivational challenges (e.g., low effortful control or high trait impulsivity in the preschool years), (3)  reconcile constructs of temperament and behavior disorder across development, and (4) integrate—in multilevel fashion—neurobiological risk with potentially toxic family and school environments in elucidating progressions toward increasingly externalizing behavior patterns across the life span. The stakes are high, given the major conceptual and basic science issues linked to inattention and disinhibition, as well as the psychological and economic costs to individuals, families, communities, and society at large associated with the consequences of ADHD-related and externalizing behavior.

Notes

1. Although the term disorder is commonly used, we characterize ADHD as a syndrome rather than a disorder to reflect that a syndrome is a collection of observable, behavioral symptoms for which the underlying cause is not yet known (indeed, there is undoubtedly no single underlying cause for such a complex syndrome). 2. A  related concept that is often discussed in developmental psychopathology literature is the diathesis-stress model, in which inherited/inborn vulnerabilities interact with environmental stressors. Reaching a certain theoretical threshold for an individual, such stressors then bring about the onset of mental illness.

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CH A PT E R

3

Substance Use Disorders as Externalizing Outcomes

Christopher J. Patrick, Jens Foell, Noah C. Venables, and Darrell A. Worthy

Abstract This chapter discusses substance use disorders (SUDs) as externalizing outcomes while also touching on psychopathy. It begins by reviewing available evidence regarding general dispositional vulnerability to SUDs and conditions involving impulsivity and antisocial behavior. It then considers brain systems implicated in inhibitory control and reward-seeking behavior, along with their relationship to substance use problems. It also describes an empirically based organizing framework, the externalizing spectrum model, for identifying similarities and differences among externalizing outcomes in terms of symptomatic features and causal origins. It explores the psychological and neurobiological mechanisms underlying general vulnerability to externalizing problems, as well as the factors that influence this vulnerability in the direction of SUDs compared to other outcomes. The chapter concludes with an assessment of some relevant unresolved questions and directions for future research. Key Words:  substance use disorders, externalizing outcomes, psychopathy, impulsivity, antisocial behavior, brain systems, inhibitory control, reward-seeking behavior, externalizing spectrum model

I went looking for trouble, and I found it. —Charles Ponzi (1934)

We wants it, we needs it. Must have the precious … —Gollum (2002)

As discussed elsewhere in this volume, considerable evidence suggests that impulse control (externalizing) problems of differing types co-occur frequently and that this comorbidity is attributable to common dispositional tendencies. However, despite this overlap, different externalizing conditions can and do present as quite distinct from one another, and individuals with matching diagnoses can exhibit their pathologies in markedly contrasting ways. Two externalizing conditions that may appear strikingly different—although they co-occur at levels well above chance—are psychopathy and substance abuse. This chapter focuses in particular 38

on substance use disorders (SUDs) as externalizing outcomes while also providing some perspective on psychopathy. We describe an empirically based organizing framework, the externalizing spectrum model, for discerning commonalities versus distinctions among externalizing outcomes—both in terms of symptomatic features and causal origins. We review what is known about brain systems relevant to inhibitory control (see also Corr & McNaughton, this volume) and reward-seeking behavior (see also Zisner & Beauchaine, this volume), and we discuss the interplay between these systems vis-à-vis substance use problems. We consider the question of what a general vulnerability to externalizing problems might entail, psychologically and neurobiologically, and what influences shape this vulnerability in the direction of SUDs compared to other outcomes. We conclude with a discussion of key unanswered questions and suggested avenues for future research.

Dispositional Liability for  Substance Problems

Problems with alcohol and illicit drugs run in families, and genes are known to play an important role in intergenerational transmission of SUDs. Current etiological models of SUDs provide compelling evidence for generalized heritable liability toward experimentation with substances of differing types and subsequent development of SUDs, as defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013). Regarding illicit drugs, results from twin studies indicate that a common heritable factor contributes to usage and problems with multiple classes of illicit substances (cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates) rather than specific genetic factors accounting for problems with each class of substance (Kendler, Jacobson, Prescott, & Neale, 2003a). Furthermore, considerable evidence exists that the heritable factor that accounts for problems with illicit drugs of differing types also contributes to problematic use of alcohol (Hicks, Krueger, Iacono, McGue, & Patrick, 2004; Krueger et  al., 2002) and nicotine (Han, McGue, & Iacono, 1999; Hicks et al., 2007)—although some evidence also exists for more specific genetic influences (i.e., apart from the general factor) on problems with alcohol versus on illicit drugs (Kendler, Prescott, Myers, & Neale, 2003b). Taken together, research on the etiological bases of SUDs points to a common heritable factor. Of course, twin studies tell us nothing about the pathophysiology of SUDs and other externalizing behaviors. Thus, questions emerge regarding precisely what is inherited that confers vulnerability to SUDs. As discussed in detail in other chapters of this volume (e.g., Corr & McNaughton, this volume; Zisner & Beauchaine, this volume), this broad liability appears to involve impairment in the capacity for inhibitory control (“disinhibition”) or perhaps proclivities that operate against normal development of inhibitory capacity (cf. Beauchaine & McNulty, 2013; Nigg & Casey, 2005), which contribute to other externalizing problems, including childhood disruptive behavior disorders (conduct disorder, oppositional defiant disorder, attention-deficit/hyperactivity disorder; e.g., Burt, Krueger, McGue, & Iacono, 2001; Young, Stallings, Corley, Krauter, & Hewitt, 2000; Young et  al., 2009) and adult antisocial behavior (Hicks et al., 2004; Krueger et al., 2002; Malone, Taylor, Marmorstein, McGue, & Iacono, 2004). Although this highly heritable (Krueger et  al.,

2002; Young et  al., 2000) vulnerability confers broad risk for externalizing problems, its specific behavioral expression (e.g., as dependence on one substance vs. another or persistent aggressive deviance) is determined substantially by environmental influences (Kendler et  al., 2003b; Krueger et  al., 2002; see also Beauchaine & McNulty, 2013; Beauchaine, McNulty, & Hinshaw, this volume). Notably, some evidence suggests that heritable disinhibitory liability also contributes modestly to the occurrence of certain internalizing problems as well (those involving anhedonia, dysphoria, and distress in particular; see e.g., Kendler et  al., 2003b; see also Nelson, Strickland, Krueger, Arbisi, & Patrick, 2015; Sauder, Derbidge, & Beauchaine, in press; Vaidyanathan, Patrick, & Iacono, 2011). Consistent with the idea of a general dispositional vulnerability to SUDs and conditions involving impulsivity and antisocial behavior, problems of these types also show common personality correlates. Investigators in this area (e.g., Sher & Trull, 1994) have identified two trait domains as particularly relevant:  disconstraint, which encompasses traits such as impulsivity, sensation seeking, and unconventionality; and negative affectivity, which encompasses traits such as anxiety, suspiciousness, and aggressiveness. In the three-factor model of personality embodied in Tellegen’s (Tellegen & Waller, 2008) Multidimensional Personality Questionnaire (MPQ), these two broad domains are represented by higher order factors of Constraint (reversed) and Negative Emotionality (NEM). Prior research has demonstrated relations between these MPQ factors and externalizing conditions of various types including SUDs, along with child and adult antisocial behavior (e.g., Krueger, Caspi, Moffitt, Silva, & McGee, 1996). Furthermore, Krueger (1999) reported that scores on the NEM and CON factors of the MPQ at age 18 predict subsequent diagnoses of antisocial personality disorder (APD) and substance dependence at age 21. Thus, available evidence supports an integrative perspective on externalizing problems and tendencies in which antisocial behaviors and substance-related disorders (or partial symptomatic expressions thereof ), along with the personality traits disconstraint/impulsivity and negative emotionality, are indicators of a largely heritable common liability factor. In the next section, we describe a comprehensive model of problems and traits in this domain (the externalizing spectrum model) that provides a useful point of reference for thinking about alternative behavioral expressions of Patrick, Foell, Venables, Worthy

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disinhibitory liability (e.g., in the form of SUDs vs. other problems).

The Externalizing Spectrum Model

Krueger et al. (2007) formulated a measurement model of externalizing conduct, the self-report externalizing spectrum inventory (ESI). Building upon earlier work by Krueger et al. (2002) and others (Kendler et al., 2003b; Young et al., 2000), these investigators undertook a fine-grained analysis of disinhibitory behaviors and traits in order to delineate more clearly the scope and structure of the externalizing spectrum. They began by identifying various constructs embodied in DSM definitions of externalizing disorders included in the Krueger et al. (2002) analysis, and they then developed questionnaire items to indicate these constructs. They also surveyed the literature to identify other behavioral and trait constructs linked conceptually or empirically to the externalizing dimension and developed items to index these constructs. Over three iterative rounds of data collection and analysis (using item response modeling and factor analytic techniques) with a total of 1,787 participants, the authors refined the overall item set to clarify the nature of constructs associated with the broad externalizing factor and arrived at a final array of constructs, each operationalized by a separate subscale.

The resultant inventory, the ESI, consists of 415 items organized into 23 unidimensional subscales reflecting content domains of impulsiveness/ sensation-seeking, irresponsibility/externalization of blame, aggression, deceitfulness, and substance use/problems of differing types. The subscales of the ESI exhibit a bifactor structure: all 23 scales load on a general factor labeled externalizing (Krueger et al., 2007) or disinhibition (Patrick, Kramer, Krueger, & Markon, 2013a), with certain scales also loading on one of two subsidiary factors. Scales that index recreational and problematic use of alcohol, marijuana, and other drugs load together on a subsidiary substance abuse (Patrick et  al., 2013a) or addiction proneness (Krueger et al., 2007) factor. Another set of scales—those indexing relational aggression and deficient empathy, along with destructiveness, excitement seeking, rebelliousness, and dishonesty—load together on a separate callous-aggression subfactor (Patrick et al., 2013a). A schematic depiction of the ESI measurement model is presented in Figure 3.1. A brief (160-item) form of the ESI was developed by Patrick et  al. (2013a) to provide for more efficient assessment of both the lower order facets of the model (through shorter length content scales) and the higher order factors (through item-based scales indexing the ESI’s general factor and two subfactors).

General Externalizing Proneness (Disinhibition)

S1

S2

S3

S4

Callous-Aggression

S5

S6

S7

S8

S9

S10

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Figure 3.1  Schematic depiction of best-fitting confirmatory bifactor model of the Externalizing Spectrum Inventory (ESI; Krueger et al., 2007; Patrick et al., 2013a). The model is represented schematically because the 23 subscales of the ESI included in the model are too numerous to depict in full. Subscripted “S's” denote differing subscales. Some of the ESI subscales (those labeled in darkest font, including irresponsibility, problematic impulsivity, theft, impatient urgency, planful control [−], dependability [−], and alienation) load exclusively on the general externalizing (disinhibition) factor. Other subscales, in addition to loading on the general externalizing factor, also load on either the callous-aggression subfactor (those labeled in lightest font [on left], including relational aggression, empathy [−], destructive aggression, excitement seeking, physical aggression, rebelliousness, and honesty [−]) or the substance abuse subfactor (those labeled in medium-dark font [on right], including marijuana use, drug use, marijuana problems, alcohol use, drug problems, and alcohol problems).

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Some aspects of this model warrant specific attention. First, the emergence of distinctive subfactors within the ESI model is attributable to the fact that many more indicator variables, which capture more nuanced expressions of externalizing proneness, were included in comparison with previous models focused on fewer (mostly disorder-related) indicators (Kendler et  al., 2003b; Krueger et  al., 2002; Young et  al., 2000). Krueger et  al. (2007) interpreted these subfactors as indicative of broad thematic trends in the expression of externalizing liability—i.e., toward aggressive-exploitative behavior on one hand, and hedonistic self-medication on the other—which may reflect shaping effects of other etiological influences on general externalizing liability. Possibilities along these lines are considered further later. A second and related point is that the general factor of the ESI model, although presumably quite similar to factors of earlier externalizing models, is probably not identical. One reason is that the ESI is entirely self-report based, whereas earlier models included symptom variables assessed through diagnostic interview (for a discussion of method variance effects in externalizing assessment, see Blonigen et al., 2010). Another reason is that the ESI general factor is parameterized to be independent of the model’s two subfactors by partitioning covariance among content subscales into that associated with the general factor versus (in the case of subscales containing variance separate from this and reflecting either callous-aggressive tendencies or substance abuse) one or the other subfactor. As a consequence, the general factor is defined most strongly by scale measures indexing broad behavioral proclivities toward irresponsibility and problematic impulsivity in particular (with loadings above .9). Scales tapping narrower dispositional or behavioral tendencies, including most of those associated with the ESI’s two subfactors, callous-aggression and substance use, load to lesser degrees (.45–.79). The notable exceptions are scales that assess tendencies toward theft, fraudulence, and drug problems, which load only slightly lower on the general factor (i.e., .87 in each case). One further point of note is that the subfactors of the ESI model, although parameterized to be independent of the general factor, are in fact defined by residual variances in scales that load as well on the general factor. Thus, although the factors are independent of each other within the ESI bifactor model, the scales that demarcate the factors are all correlated. Work directed at identifying indicators of one

or the other ESI subfactor that covary minimally with the ESI general factor would, if successful, support the presence of distinct influences contributing to contrasting expressions of disinhibitory liability and help to clarify the psychological nature of these influences. Work of this kind will likely need to consider variables from domains other than self- or interview-based report (e.g., behavioral, biological) and make use of longitudinal-developmental designs (cf. Patrick & Drislane, 2014).

Neurobiological Systems Relevant to Trait Disinhibition and Substance Abuse

This section considers brain systems implicated in control of behavioral tendencies and down-regulation of emotional responses, and systems theorized to mediate reactivity to pleasurable events and cues signaling the possibility of reward. Following this discussion, we proceed with another major section focusing on the externalizing spectrum model and its implications for differing outcomes associated with disinhibitory liability.

Brain Circuitry for Inhibitory Control

At the most basic level, priming of defensive or appetitive motivational behavior can arise through exposure to simple conditioned stimuli in the environment that automatically activate the amygdala or the midbrain (mesolimbic) dopamine (DA) system. For example, LeDoux (1995, 2000) described a “quick and dirty” processing pathway from the sensory thalamus to the lateral nucleus of the amygdala along which simple acoustic information can be transmitted; because of the existence of this pathway, fear activation can occur to a conditioned tone (CS) even following massive destruction of the neocortex. A similar fast processing pathway appears to exist for the visual system, involving the basolateral nucleus of the amygdala (Davis & Lee, 1998); this pathway has been the focus of human research on “unconscious” processing of visual fear cues including faces (Whalen et al., 1998) and phobic objects (Öhman, 1993). Berridge and Robinson (1998) likewise characterized the mesolimbic dopamine system as having a low-level, implicit processing capacity, whereby simple cues in the environment can instigate appetitive mobilization (“wanting”) in the absence of “conscious” awareness. Importantly, however, both the amygdala and midbrain DA systems exhibit extensive neural connectivity with various regions of the neocortex. These connections afford mechanisms through which higher brain processes (e.g., memories, Patrick, Foell, Venables, Worthy

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images, plans) can influence processing and reactivity to emotional events, and emotional reactions can in turn influence these higher brain processes. Especially important in the present context are connections between these subcortical motivation/ affect systems and the prefrontal cortex (PFC). The existence of these connections leads to the question:  What specific functional role does the PFC play in affective-motivational processing? In general, the PFC is thought be crucial for “top-down” processing; that is, the guidance of behavior by internal representations of goals or states (Miller & Cohen, 2001). The PFC is the region of neocortex that is most highly evolved in primates, and it is believed to account for the diversity and flexibility of behavioral strategies exhibited by humans. A number of investigators have proposed that the PFC is especially important for coping with novel or dynamic situations in which selection of appropriate behavioral responses needs to be made on the basis of internal representations of goals and strategies rather than immediate stimulus cues alone (e.g., Cohen & Servan-Schreiber, 1992; Miller, 1999; Wise, Murray, & Gerfen, 1996). Miller and Cohen (2001) proposed an elegant, integrative model in which the control functions of the PFC arise from its specialized capacity for online maintenance of goal representations: by maintaining patterns of activation corresponding to goals and the means needed to achieve them, the PFC provides biasing signals to other regions of the brain with which it connects. These signals serve to prime sensory-attentional, associative, and motoric processes that support the performance of a designated task by directing activity along relevant brain pathways. An appealing feature is that this model provides a mechanistic account of PFC function that avoids the circularity of mentalistic (i.e., PFC as “executive”) accounts. The major focus of Miller and Cohen’s (2001) model was on cognitive control functions (i.e., guidance of behavior on the basis of internal representations) associated with the dorsolateral PFC. This subdivision of the PFC plays a critical role in working memory processes, involving the maintenance of a discrete stimulus representation across a temporal delay (Goldman-Rakic, 1996). For example, in humans, performance of a working memory task that involves matching current stimuli to earlier stimuli in an ongoing stream (the “n-back” task; Cohen et  al., 1994) preferentially activates the dorsolateral PFC, with the degree of activation increasing as a function of memory load (Cohen, 1997). The dorsolateral PFC is also distinguished 42

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by its close connections with sensory association cortices (including occipital, temporal, and parietal); its prominent projections to premotor areas in the medial and lateral frontal lobes, as well other motor structures including the basal ganglia, the cerebellum, and the frontal eye fields; and its ability to encode relations between stimulus events and thus represent rules (mappings) required to perform complex tasks (Roberts, Robins, & Weiskrantz, 1998). As a function of these capacities, this region also plays a role in more active processes associated with inhibition and regulation of behavioral responses (cf. Petrides, 2000). For example, the control function of the dorsolateral PFC is important for performance on the Stroop color-naming task (MacDonald, Cohen, Stenger, & Carter, 2000) and for performance of the visual antisaccade task, which entails active inhibition and redirection of reflexive eye movements (Broerse, Crawford, & den Boer, 2001; Müri et al., 1998). A further, and intriguing, element in the Miller and Cohen (2001) cognitive control model of PFC function is the role ascribed to DA neuron activity. Recognizing that patterns of PFC activity contributing to attainment of a goal (i.e., by biasing other brain systems to respond in goal-relevant ways) must be reinforced in order to recur under appropriate circumstances in the future, the suggestion is that this reinforcing function may be served by dopaminergic projections to the PFC from the midbrain DA system, as well as by DA neurons within the PFC itself. Here, the reward prediction error or “incentive salience” function of DA (see later discussion) serves to strengthen connections between neurons that signal expectation of reward and representations in the PFC that guide the actions required to achieve the reward. In other words, the mesocorticolimbic DA activity supplies the incentive for appropriate PFC representations to recur in a task context in which those representations have previously facilitated goal attainment. With regard to pathologic function, Montague, Hyman, and Cohen (2004) proposed—in line with Robinson and Berridge (2000)—that the normal role of the DA system as a facilitator of complex, PFC-mediated behavior (such as that required to function in a complex work environment or obtain a college degree) can be “hijacked” by drugs of abuse that sensitize the system and direct its activity toward ritualized, maladaptive action patterns (see also Zisner & Beauchaine, this volume). From this perspective, it is reasonable to think that deficits in PFC function that arise from genetic and/

or experiential factors could render an individual especially vulnerable to this sort of hijacking (i.e., because of a lack of incentive to engage in activities that do not lead to immediate, tangible rewards). Lesser attention was devoted in Miller and Cohen’s (2001) model of the ventromedial and orbitofrontal regions of the PFC, which have collectively been termed the orbitomedial PFC (e.g., Blumer & Benson, 1975). These regions connect more directly and extensively than the dorsolateral PFC with medial temporal limbic structures including the amygdala, hippocampus and associated neocortex, and hypothalamus. As a function of these limbic connections, the orbitomedial PFC appears to play a more dominant role in the anticipation of affective consequences of behavior (Bechara, Damasio, Tranel, & Damasio, 1997; Wagar & Thagard, 2004) and in the unlearning of stimulus-reward associations (i.e., reversal learning; Dias, Robbins, & Roberts, 1996; Rolls, 2000). Both the dorsolateral and orbitomedial divisions of the PFC are themselves richly interconnected, so their functions need to be viewed as interdependent. Nevertheless, Bechara, Damasio, Tranel, and Anderson (1998) reported that patients with dorsolateral PFC lesions showed impairments on a working memory tasks but not on a gambling task involving affect-guided decision making, whereas the reverse was true of patients with ventromedial PFC lesions. Particular research attention has been devoted in recent years to another key function of the orbitomedial PFC—namely, its role in regulating emotional reactivity and expression. It has long been known that lesions of this brain region are associated with dramatic increases in impulsive, irresponsible, and aggressive behavior. The best known example of this is the railway worker Phineas Gage, who in 1848 suffered an accident in which an iron tamping rod was driven through his skull from the base to the top, causing extensive damage to the PFC—in particular, the orbitomedial region (Damasio, Grabowski, Frank, Galaburda, & Damasio, 1994). Prior to the accident, Gage was described as capable, dependable, and courteous, whereas after he was characterized as impulsive, stubborn, antagonistic, and reckless. This constellation of features arising from damage to the orbitomedial PFC has been labeled “acquired sociopathy” (Damasio, Tranel, & Damasio, 1990). Other more recent cases of this type have been reported on by Anderson, Bechara, Damasio, Tranel, and Damasio (1999) and Blair and Cipolotti (2000). Impulsive aggressive behavior was identified as a prominent feature in each.

Davidson, Putnam, and Larson (2000) proposed that the orbitomedial PFC functions to suppress emotional activation elicited automatically by cues for reward or punishment. These authors further suggested that deficits in the ability to regulate negative affect associated with orbitomedial PFC impairment may be an important factor underlying impulsive, angry aggression among some individuals. Miller and Cohen (2001) conceptualized this affect suppression function of the orbitomedial PFC in terms of the general biasing function: the orbitomedial PFC, with its direct connections to limbic structures, operates to bias task-relevant processes against competition from “hot” (motivationally charged) processes arising in social or emotional contexts. Consistent with this perspective, human neuroimaging studies provide evidence that the orbitomedial PFC is selectively activated during efforts to suppress affect evoked by positive or negative emotional stimuli (Beauregard, Levesque, & Bourgouin, 2001; Ochsner, Bunge, Gross, & Gabrieli, 2002; Ochsner et al., 2004). Human and animal studies also support a role for the orbitomedial PFC in the extinction of fear (e.g., Phelps, Delgado, Nearing, & LeDoux, 2004; Quirk, Russo, Barron, & Lebron, 2000), an active process of relearning rather than a passive process of forgetting (LeDoux, 1995, 2000). Two other important brain regions for regulating emotional behavior are the hippocampus and the anterior cingulate cortex (ACC). The hippocampus connects with the amygdala and midbrain DA system as well as the PFC and appears to be important for linking affective responses and goals to complex configural stimuli (contexts). Thus, lesions of the hippocampus block acquisition of contextual fear conditioning, but not simple cue conditioning (LeDoux, 1995). Regarding the PFC, Cohen and O’Reilly (1996) postulated that its connections with the hippocampus provide a mechanism through which goal representations can be activated dynamically by contextual cues in the environment to guide complex, delayed action sequences (e.g., stopping by the store at the end of the day to pick up groceries needed for dinner). Impairments in hippocampal function would be expected to contribute to a simpler, explicit, cue-driven style of affective processing. On the other hand, the ACC, which connects with premotor and supplementary motor regions as well as with limbic structures (including amygdala and hippocampus) and the PFC, has been conceptualized as a system that invokes control functions Patrick, Foell, Venables, Worthy

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of the PFC as required to perform a task successfully by detecting errors in performance as they occur (Scheffers, Coles, Bernstein, Gehring, & Donchin, 1996), by monitoring conflict arising from activation of competing response tendencies (Carter et  al., 1998), or by estimating the likelihood of committing an error at the time a response is called for (Brown & Braver, 2005). Impairments in ACC function would be expected to interfere with the ability to inhibit prepotent behavioral responses and to avoid repetition of errors.

Brain Circuits for Reward and Incentive Salience

It has long been known that the mesolimbic DA system, including DA neurons in the ventral tegmental area and their projections to structures including the nucleus accumbens, as well as the mesocortical DA system, including DA neurons in the ACC, PFC, and other regions of the forebrain, play crucial roles in reward processing and reward-related behavior (see Zisner & Beauchaine, this volume). The mesolimbic (midbrain) dopamine system has been emphasized in particular as playing a crucial role in addictive behaviors. The prevailing perspective for many years was that this system mediates the hedonic value (pleasurableness) of incentives (e.g., Olds, 1956; Olds & Milner, 1954; Phillips, 1984; Shizgal, 1999; Wise, 1985). However, this view was challenged in the 1990s by single-cell recording studies demonstrating that DA neurons of the ventral tegmental area and substantia nigra in monkeys respond primarily to events that predict reward rather than to rewards themselves (Schultz, 1998; Schultz, Apicella, & Ljunberg, 1993). For example, in appetitive conditioning paradigms entailing delivery of a food rewards following light cues, DA cells in these brain regions show increased firing upon occurrence of the reward itself—but only on initial learning trials when the reward is unexpected (i.e., not predicted). As animals learn contingencies between reward cues (light) and reward delivery (food), DA firing propagates backward from reward delivery to cue presentation. Additionally, once learning is established, (a)  DA neurons exhibit a decrease below their tonic rate of firing on occasions when the food reward is withheld following light cues, and (b) neuronal firing is observed during reward presentation itself if such presentation occurs at a time other than after the light cue (i.e., when the reward is unexpected). 44

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A key conclusion is that neurons in the midbrain (mesolimbic) DA system code for “prediction error”—that is, the degree to which a reward stimulus or a cue for reward is unexpected (Montague, Dayan, & Sejnowski, 1996; Schultz, 1998). In the appetitive conditioning paradigm just described, neuronal firing shifted from the reward to the light CS because the timing of the reward became predictable, whereas the occurrence of the CS remained unpredictable. By extension, the midbrain DA system may not be involved so much in coding the hedonic (pleasurable affective) value of reward, but in the process of learning to connect rewards to cues in the environment and thus in recognition of opportunities for reward and in the sequencing of goal-directed actions. An extension of this view of the role of the midbrain DA system in reward processing was put forth by Berridge and colleagues (e.g., Berridge & Robinson, 1998; Berridge, Venier, & Robinson, 1989). These investigators proposed that neurons in the mesolimbic DA system mediate the incentive salience of rewards, as opposed to their hedonic value (pleasurableness). A key concept in this model is the distinction between “wanting” and “liking.” “Wanting” entails attentional saliency accompanied by an active inclination to pursue, whereas “liking” refers to the pleasure derived from consuming a reward; both processes are posited to include a core implicit element, such that “wanting” or “liking” can be instigated in the absence of conscious awareness. Berridge and colleagues proposed that dopaminergic neurons in the mesolimbic system are critical for the “wanting” component of reward (i.e., the attribution of incentive salience to rewards and reward cues, such that they become objects of desire to be actively pursued), but not for the “liking” component (registering the hedonic impact of reward stimuli). A foundation for this viewpoint was work demonstrating that neurotoxic destruction of neurons in key regions of the midbrain DA system (nucleus accumbens, neostriatum) eliminates food-seeking behavior in rats without affecting facial indicators of hedonic responses to the food itself (cf. Berridge & Robinson, 1998). Thus, damage to the midbrain DA system diminishes “wanting” of rewards (i.e., they are no longer desired, attended to, and actively pursued) without affecting “liking” (i.e., rewards, when administered, are still “enjoyed”). Other work by these investigators suggests that the hedonic (“liking”) component of reward is mediated by other interconnected structures within the basal forebrain

and hindbrain—including the opioid receptor-rich shell of the nucleus accumbens, the ventral pallidum, and the brainstem parabrachial nucleus (cf. Berridge, 2003). A key point of divergence between the “prediction error” model of Schultz and colleagues and the incentive salience model set forth by Berridge and Robinson is that the former model implies an essential role for DA in reward learning. Berridge and Robinson (1998) cast doubt on this role by presenting evidence that rats with extensive neurotoxin-induced DA depletion still show attenuation and enhancement of hedonic reactivity to a rewarding stimulus, respectively, after a stimulus is paired with a nausea-inducing agent (lithium chloride) or a palatability-enhancing agent (diazepam). From this, Berridge and Robinson concluded that midbrain DA neurons are not essential for reward learning, defined as changes in the hedonic value of rewards arising through associative pairings with other pleasurable or aversive stimuli. However, McClure, Daw, and Montague (2003) subsequently proposed an alternative reward-learning model (the “actor-critic” model) that reconciles the prediction error position with the incentive salience model. Here, the reward-prediction error coded by DA neural activity serves the dual purpose of imbuing relevant stimuli with incentive value and biasing action selection so as to maximize reward outcomes (see also Gatzke-Kopp & Beauchaine, 2007). The incentive salience model set forth by Berridge and colleagues served, in turn, as the foundation for an influential model of processes underlying drug addiction: the incentive sensitization model (Berridge & Robinson, 1995; Robinson & Berridge, 1993, 2003). The central idea is that repeated ingestion of drugs causes the midbrain DA system to become sensitized to drug cues. Once established, this sensitization is extremely persistent. Evidence for enduring changes in this system as a function of drug taking includes animal data showing increased effects of stimulant drugs on psychomotor activation and accompanying morphologic changes in DA neurons with repeated use, and human neuroimaging findings showing that the midbrain DA system is activated strongly when individuals addicted to substances are exposed to drug-associated stimuli—and when they receive the drug (cf. Robinson & Berridge, 2000; Volkow, Fowler, & Wang, 2004; Volkow, Wang, Fowler, & Tomasi, 2012). According to the incentive sensitization model, the idea that “wanting,” mediated by striatal DA neurons, is sensitized by repeated

drug taking (and potentially by other forms of addictive behavior) helps to explain the inordinate salience that drug cues have for addicts and their compulsion to find their drug of choice (i.e., craving = “wanting”). The model also accounts for why addicts persist in seeking and ingesting drugs even after the pleasure achieved by taking the drug has waned and aversive consequences accrue. Individual differences are presumed to exist in susceptibility of the “wanting” system to sensitization as a function of variables such as genes, sex-related hormones, and experience (Robinson & Berridge, 2000). In sum, neuroscientific research indicates that the midbrain DA system is integral to reward processing. The system harnesses attention in the direction of cues for reward and simultaneously energizes goal-seeking behavior. It provides a mechanism through which neutral cues achieve “incentive salience” through primary and secondary association with rewarding events, and thereby instigate action sequences that promote attainment of reward. Destruction of this system does not appear to eliminate the capacity to “enjoy” rewards, but it does eliminate interest in reward-related cues and in active pursuit of reward. Sensitization of this system through repeated and intense stimulation (e.g., ingestion of drugs) can lead to intense feelings of “wanting” (i.e., craving), resulting in compulsive drug-seeking behavior. Although the majority of research on reward prediction error and incentive salience models of the midbrain DA system has been conducted using food and psychoactive drugs as reward stimuli, there is evidence that this system plays a similar role with respect to other basic appetitive drives (e.g., thirst, sex; Horvitz, Richardson, & Ettenberg, 1993; Fiorino, Coury, & Phillips, 1997). Thus, on the basis of available evidence, there is reason to believe that the midbrain DA system comprises a core neural substrate of appetitive motivation, defined as mobilization for approach behavior (see Zisner & Beauchaine, this volume). It bears repeating that distinct (albeit interconnected) neural structures appear to mediate the “liking” (hedonic) component of reward (i.e., the nucleus accumbens shell and ventral pallidum and the brainstem parabrachial nucleus to which these structures project). Berridge (2003) suggested that these structures, which are innervated by the mesolimbic DA system, may comprise a core “liking” circuit that participates in hedonic reactivity to a variety of reward stimuli. It should also be noted that other distinct brain structures contribute to mediation of overt consummatory behaviors tied to Patrick, Foell, Venables, Worthy

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specific drive states. For example, fiber tracts running through the lateral and medial divisions of the hypothalamus play a crucial role in eating behavior (hunger and satiety, respectively), whereas the medial preoptic area of the hypothalamus appears to be especially crucial for sexual behavior.

Interplay of Inhibitory Control and Reward/Incentive Circuitry in SUDs

Drawing on the foregoing conceptions of control and appetitive systems and relevant findings from neuroimaging, genetic, and developmental research, Karoly, Harlaar, and Hutchison (2013) advanced a compelling three-stage model of dysregulations in brain circuitry that give rise to and maintain substance-related addictions. The model considers interplay between inhibitory control circuitry and appetitive-motivational circuitry (termed “control network” and “incentive salience/reward network,” respectively) and their intersections with circuitry governing negative emotional states (e.g., irritability, distress, and dysphoria). In particular, the model describes how the relative influence exerted by control and incentive/reward networks shifts in the progression from recreational to urge-driven use, leading to withdrawal-related negative affect that contributes further to imbalance between the control and incentive reward networks. The model distinguishes three distinct stages in the addiction cycle. In the binge/intoxication stage (1), use of substances is driven mainly by impulsive proclivities and (expected) positive effects of the drug. With continuing regular use, processes associated with the incentive/reward system (i.e., sensitization) increase in strength, with concomitant diminishment in the strength of control functions, thus leading to less-regulated use. The result is a transition toward the withdrawal/negative affect stage (2), at which point withdrawal following use produces increased activation in negative motivational systems (i.e., the amygdala and affiliated circuitry), which feeds back to control and incentive reward networks. The result is even greater predominance of urge-driven use, leading to a preoccupation/anticipation stage (3) at which substance use is driven mainly by compulsion as opposed to ad hoc pleasure-seeking. Here, strength of the incentive reward network has increased to a level that renders influence of the control network ineffective. Importantly, Karoly et  al.’s (2013) three-stage model emphasizes the dynamic interplay between alterations in neural function that occur with sustained engagement in drug-taking behavior and 46

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dispositional factors that affect susceptibility to processes at particular stages. Although its focus is on processes occurring after initiation of substance use, some consideration is given to factors that predispose to initial and continuing use. Individual differences in impulsive risk-taking and sensitivity to the unconditioned pleasurable effects of particular drugs are likely to be particularly important for entry into Stage 1.  The authors draw attention in particular to research demonstrating reduced frontal brain activation during performance of tasks requiring inhibition of prepotent responses (e.g., antisaccade, go/no-go) among youth who are at risk for later development of SUDs as evidence for a role of weak inhibitory control capacity at this initial point. Yet the possibility exists that control network deficits evident at the time of adolescence could arise in some or perhaps even most cases from incentive/ reward network impairments present earlier in life (see section below titled “P3 Brain Response and the Nature of Disinhibitory Liability”; cf. Beauchaine & McNulty, 2013).

An Externalizing Spectrum Model Perspective on Disinhibitory Liability and Its Alternative Phenotypic Expressions

We now return to the question of what general vulnerability to externalizing problems entails, both neurobiologically and psychologically, and we consider which factors shape such vulnerability into SUDs compared to other outcomes. Clearly, impulsivity and emotion dysregulation, which typify disorders in the externalizing spectrum, point to prefrontal brain dysfunction as a key mechanism underlying general disinhibitory liability (see also Beauchaine & McNulty, 2013; Beauchaine, McNulty, & Hinshaw, this volume). As noted earlier, lesions to frontal brain regions result in impulsive, externalizing behaviors. In addition performance deficits on neuropsychological tests that assess frontal lobe function are evident across the externalizing spectrum: Both Morgan and Lilienfeld (2000) and Ogilvie, Stewart, Chan, and Shum (2011) reported meta-analytic evidence for robust deficits on frontal lobe tasks among individuals with conduct disorder and adult antisocial behavior and individuals at risk for alcoholism by virtue of a positive parental history show similar impairments (Peterson & Pihl, 1990; Tarter, Alterman, & Edwards, 1985). Furthermore, reduced activity in frontal brain regions during inhibitory task performance characterizes presymptomatic adolescents who later develop alcohol problems (Norman et al., 2011). Elsewhere, Barkley

(1997), based on an extensive review of neuropsychological studies, proposed that frontal brain dysfunction characterizes the hyperactive-impulsive and combined subtypes of attention-deficit/hyperactivity disorder (ADHD), with a primary role for deficits in response inhibition. Perhaps more crucially, a twin study by Young et al. (2009) demonstrated a robust negative association between scores on a general externalizing factor subsuming impulse-related problems of differing types (assessed via informant report and interview)—combined with a scale measure of novelty seeking—and scores on a common executive-function (EF) factor defined by performance on three inhibitory control tasks known to index EF (i.e., antisaccade, Stroop, stop-signal; Miyake & Friedman, 2012). Data for the inhibitory control tasks were collected at age 17; scores for the externalizing variables were based on data from age 17 together with data collected at earlier ages. The twin design of the study allowed for decomposition of scores on both the externalizing factor and the EF factor into variance attributable to heritable versus shared and nonshared environmental influences. The correlation between heritable variance from the disinhibitory factor and heritable variance from scores on the EF factor was −.61. Thus, a heritable propensity toward externalizing problems was associated with heritable deficits in EF. This work provides compelling evidence that general externalizing vulnerability reflects a heritable impairment in the capacity to inhibit prepotent responses, possibly reflecting a basic, constitutional weakness in the frontal-control network described by Karoly et  al. (2013). Another brain region playing a role in disinhibitory psychopathology is the ACC, a structure that operates in concert with the PFC to guide behavior. As described earlier, the ACC functions to monitor ongoing action sequences and to anticipate and detect errors. Notably, the error-related negativity (ERN), a brain potential response that occurs following performance errors in a speeded reaction time task and is mediated in part by the ACC (Miltner, Braun, & Coles, 1997; Holroyd, Dien, & Coles, 1998; Luu, Flaisch, & Tucker, 2000), shows reduced amplitude among high-externalizing individuals (Hall, Bernat, & Patrick, 2007; see also Dikman & Allen, 2000; Pailing & Segalowitz, 2004). Furthermore, neuroimaging studies indicate the ACC is not activated during extinction of previously rewarded behaviors among externalizing males (Gatzke-Kopp et  al., 2009), a finding that

also points toward deficiencies in error monitoring. The hippocampus, another structure that operates in conjunction with the PFC to guide behavior (cf. Miller & Cohen, 2001), may also be dysfunctional among externalizing individuals (e.g., Raine et al., 2004; Soderstrom, Tullberg, Wikkelsoe, Ekholm, & Forsman, 2000). An underlying weakness in the PFC and regions with which it interacts would be likely to confer a propensity to act on the basis of salient cues in the immediate environment rather than on the basis of internal representations of goals and methods for achieving them. In particular, dysfunction in the PFC and affiliated systems would compromise an individual’s ability to (a) ascribe incentive salience to representations for more complex, distal, but ultimately more fulfilling behavioral goals; (b)  anticipate obstacles and formulate strategies for overcoming them before they become overwhelming (e.g., deal proactively with frustrating or threatening circumstances); (c)  detect conflict between competing response tendencies (i.e., recognize, online, the probability of making an error); and (d) monitor and regulate affective responses in the service of remote goals. Such a weakness would contribute to a range of impulse-related problems because it would produce an active response style centered on immediate cues in the environment and short-term gratification. Individuals would lack the capacity to pursue complex goals and long-term strategies. However, although evidence for frontal control network deficits among externalizing individuals is extensive and compelling, it has been suggested that control network deficits might actually be the developmental consequence of a more basic neural dysfunction. Specifically, citing evidence from a variety of sources, Beauchaine and McNulty (2013) postulated that a weakness in the mesolimbic (midbrain) DA system, entailing reduced availability of DA in the ventral tegmental area (VTA) and pathways connecting it to the nucleus accumbens, along with impaired functional connectivity between this system and the mesocortical (VTA→frontal cortex) system, comprises a core neural substrate for disinhibitory liability (which they term “trait impulsivity”). Part of the basis for this argument is that the mesolimbic DA system is an earlier maturing network that provides foundational neural input via direct and indirect afferent pathways to the later maturing frontal control network. A constitutional weakness in this system, the authors argue, would establish a primal orientation toward immediate Patrick, Foell, Venables, Worthy

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over remote reward that, in turn, would compromise normal development of the mesocortical system essential to inhibitory control. As noted earlier, others (e.g., Miller & Cohen, 2001) have proposed that input to the PFC from the DA system provides the motivational impetus for biasing influences exerted by the PFC (i.e., in the service of goal attainment). As partial evidence for their hypothesis, Beauchaine and McNulty (2013) reference extensive and well-replicated findings from studies demonstrating reduced mesolimbic DA transporter and D2/D3 receptor binding and blunted reactivity of mesolimbic and mesocortical systems to incentives among individuals with externalizing problems including ADHD (cf. Bush, Valera, & Seidman, 2005; Dickstein, Brannon, Castellanos, & Milham, 2006), conduct disorder (e.g., Rubia et al., 2009), and SUDs (e.g., Martin-Soelch et al., 2001; Volkow et  al., 2004, 2012). Seemingly at odds with such findings, Buckholtz et al. (2010a) reported that individuals high on the impulsive-antisociality dimension of the Psychopathic Personality Inventory, a dispositional factor closely related to the concept of externalizing proneness or disinhibitory liability (Blonigen, Hicks, Krueger, Patrick, & Iacono, 2005; see also Patrick, Fowles, & Krueger, 2009; Patrick, Hicks, Krueger, & Lang, 2005), showed enhanced release of DA within the nucleus accumbens in response to amphetamine administration together with augmented reactivity of the nucleus accumbens during anticipation of reward in a monetary incentive delay task. However, the Buckholtz et  al. results can be reconciled with Beauchaine and McNulty if (a) one conceives of the monetary incentive delay task as an “immediate” as opposed to “remote” reward task (i.e., involving presentation of cue for impending, certain reward outcomes) and enhanced DA release to amphetamine as compensatory to weak DA availability under normal resting conditions, or (b)  impulsive antisocial individuals are exhibiting sensitization to amphetamines as described by Robinson and Berridge (see earlier discussion). Supporting possibility (a), another study by this same research group (Buckholtz et al., 2010b) reported enhanced release of DA in the striatum following amphetamine administration in conjunction with diminished midbrain D2/D3 receptor binding potential at baseline in participants higher as compared to lower in trait impulsivity as measured by the Barrett Impulsiveness Scale (Barratt, Stanford, Dowdy, Liebman, & Kent, 1999). This pattern of results fits with the interpretation of enhanced DA response to amphetamine 48

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as compensatory to low baseline DA availability in impulsive-externalizing individuals (see next section on normalizing effects of DA-enhancing stimulant drugs on impulsive behavior, and affiliated brain response deficits, in individuals with ADHD).

Reduced P300 Amplitude and Disinhibitory Liability

Prefrontal control system/EF and incentive system/DA perspectives as core bases of disinhibitory psychopathology are interesting to consider in relation to the best-established neural indicator of externalizing proneness among adults—namely, reduced amplitude of the P3 brain potential response. (The term “P3” is used here for a set of brain potential components including the P3 response to attended target stimuli in variants of the well-known oddball task [termed “P300,” or “P3b”] and the P3 response to unexpected novel events [termed “novelty P3,” or “P3a”]). Although initially investigated as a possible indicator of biological risk for alcoholism (Begleiter, Porjesz, Bihari, & Kissin, 1984), further research revealed reduced P3 responses to be associated with various disinhibitory conditions including adult APD, child conduct disorder and ADHD, and dependence on other drugs (cf. Iacono, Malone, & McGue, 2003). In turn, studies demonstrating a coherent heritable factor underlying such conditions suggest that P3 might represent a neural indicator of this general liability factor. As compelling support for this hypothesis, Patrick et al. (2006) reported that externalizing proneness, as indexed by scores on the factor in common among differing disinhibitory disorders, was associated negatively with amplitude of P3 responding to target stimuli in a visual oddball task, and this association accounted for relations of all individual disorders with P3. In a subsequent large-sample (N = 1,196) twin analysis, Hicks et al. (2007) demonstrated that the relation between general externalizing proneness and oddball P3 response was mediated almost entirely by heritable influences (see also Yancey, Venables, Hicks, & Patrick, 2013). Other follow-up work demonstrates reduced P3 responses among externalizing-prone college students and community adults to stimuli of other types across differing tasks—including novel nontargets within a three-stimulus oddball task, feedback stimuli within a choice-gambling task, stimulus arrays within a flanker discrimination task, and incidentally occurring noise probes within a picture-viewing task (Bernat, Nelson, Steele, Gehring, & Patrick, 2011; Nelson, Patrick, & Bernat, 2011; Patrick et al., 2013b).

Findings along these lines, in conjunction with work demonstrating that reduced P3 in presymptomatic at-risk youth predicts later development of diagnosable externalizing problems (e.g., Berman, Whipple, Fitch, & Noble, 1993; Hill, Steinhauer, Lowers, & Locke, 1995; Iacono, Carlson, Malone, & McGue, 2002), point to reduced P3 amplitude as a robust neural indicator of disinhibitory liability. However, the underlying neuropsychological processes are unclear. One challenge to interpretation is that the P3 is a broadly distributed cortical potential that reflects coordinated activity in multiple brain systems—with variants of P3 (e.g., novelty P3a, target P3b) differing in topography and presumed neural sources (Polich, 2007). Another challenge is that reductions in amplitude associated with externalizing proneness represent only a fraction of the overall systematic (e.g., temporally reliable, heritable) variance in P3 response (Yancey et al., 2013), rendering it unclear whether processing parameters known to affect P3 generally (i.e., across subjects) are the basis of externalizing-related variation. Certainly, one perspective on reduced P3 in relation to externalizing conditions is that it reflects, either directly or indirectly, impairments in functioning of inhibitory control systems in the brain (e.g., Begleiter & Porjesz, 1999; Iacono, Carlson, & Malone, 2000; see also Giancola & Tarter, 1999; Polich, 2007). Consistent with this perspective, there is evidence for executive dysfunction (including impaired inhibitory task performance) linked to reduced P3 amplitude in young individuals exhibiting externalizing problems (e.g., Kim, Kim, & Kwon, 2001; Roca et al., 2012). At the same time, other evidence points to a role of the midbrain DA system in P3 responding. Evidence for a direct role of input from the midbrain DA system is especially strong for the more fronto-centrally distributed novelty P3 (P3a) variant (cf. Polich, 2007). A  prominent role for noradrenergic activity, associated with the locus coeruleus in particular, has been posited for the more parietally distributed target-elicited (or, more broadly, task-relevant) P3b (Nieuwenhuis, Aston-Jones, & Cohen, 2005), but evidence exists as well for some role of the midbrain DA system in P3b. For example, diminished amplitude of both P3a and P3b response is observed among patients with Parkinson’s disease (Antal, Dibó, Kéri, Gábor, & Janka, 2000; Poceta, Houser, & Polich, 2006; Wang, Kuroiwa, & Kamitani, 1999), a degenerative condition resulting from depletion of DA-producing cells in the substantia nigra

region of the midbrain. It is also present among individuals with restless leg syndrome (Jung et al., 2011; Poceta et  al., 2006), a condition also associated with reduced DA function (Trenkwalder & Winkelmann, 2003). Regarding externalizing conditions, administration of methylphenidate—a DA agonist that increases synaptic DA availability (Cooper, Bloom, & Roth, 2003) and reduces symptoms of ADHD (Faraone, Spencer, Aleardi, Pagano, & Biederman, 2004; Van der Oord, Prins, Oosterlaan, & Emmelkamp, 2008)—produces normalizing effects on abnormalities in P3 response associated with ADHD (e.g., Hermens et al., 2005; Verbaten et al., 1994). Of further note, ADHD is associated both with restless leg syndrome (Cortese et  al., 2005) and with patterns of sleep disturbance that are in turn associated with abnormal P3 responding under normal waking conditions (Salmi et al., 2005; Sangal & Sangal, 1997). Sleep patterns themselves are known to be subject to DA system influence (e.g., Dzirasa et al., 2006). Although the foregoing lines of evidence point to a possible role for DA dysfunction in P3 response deficits associated with externalizing proneness, notable inconsistencies are evident across studies, and these need to be reconciled before stronger conclusions can be drawn. These include differences in task paradigms used to assess P3 response, inconsistencies in findings for P3a versus P3b in some studies, and inconsistencies in findings for P3 amplitude versus latency in other studies. There are other reasons, as well, to suppose that the association between externalizing proneness and P3 response may not be attributable exclusively to DA dysfunction. For one thing, as noted earlier, the locus coeruleus-norepinephrine system has been implicated more strongly in P3b response than any DA system. Relatedly, regarding the normalizing effects of methylphenidate on P3 response in ADHD, this medication exerts dose-dependent effects on noradrenergic neurotransmission as well (Kuczenski & Segal, 1997). More broadly, as discussed later, other perspectives on the basis of the broad liability toward disinhibitory problems and its relationship to deficient P3 response may be useful to consider in complement to the DA dysfunction hypothesis.

Alternative Addictive and Aggressive Expressions of General Disinhibitory Liability

The model of externalizing behavior set forth by Krueger et al. (2007) provides a useful point of reference for thinking about general disinhibitory Patrick, Foell, Venables, Worthy

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liability (trait disinhibition) and its role in SUDs. The model demonstrates that when externalizing problems are considered in terms of thematically distinct traits and problem tendencies, rather than as “disorders” encompassing multiple, loosely related symptoms, a multifactor structure emerges in which tendencies toward callous aggressiveness and problematic use of substances are each associated with a general externalizing factor and as well with a subsidiary factor separable from this general factor. As suggested by Krueger et al. (2007), and expanded upon in subsequent writings (e.g., Patrick, Durbin, & Moser, 2012; Patrick et  al., 2009; Venables & Patrick, 2012), this structure suggests that tendencies to act against others in callous-uncaring ways and to use alcohol and drugs in excess arise in part from general disinhibitory liability—but additionally, in each case, reflect the shaping impact of other coherent influences distinct from this broad liability. In the case of the callous-aggression subfactor of the externalizing spectrum model, an obvious connection exists to the literature on psychopathic personality. As discussed by Patrick et  al. (2009), most measures of psychopathy designed for adults include representation of callous-uncaring (e.g., coldhearted, antagonistic; Lilienfeld & Widows, 2005; Lynam & Derefinko, 2006) tendencies, and the scales that demarcate the ESI callous-aggression subfactor (in particular, empathy-reversed, relational and destructive forms of aggression, and excitement seeking, rebelliousness, and dishonesty; Krueger et  al., 2007; Patrick et  al., 2013b) mirror symptoms and behavioral correlates of the “callous-unemotional traits” construct described in the child psychopathy literature (Frick & Marsee, 2006; Frick, Ray, Thornton, & Khan, 2014). What distinct etiologic influence(s) might contribute to expression of general disinhibitory liability in this direction? Broadly speaking, influences that promote use of force or exploitation as a means to achieve gratification or relief from distress would operate to shape disinhibitory tendencies toward an active aggressive (i.e., callous/psychopathic) expression (cf. Verona & Patrick, 2015). Influences of this sort could include distinct dispositional characteristics (e.g., low fear temperament [Frick & Marsee, 2006]; weak affiliative capacity [Patrick et  al., 2009]), physical strength or size, and environmental factors (e.g., early physical abuse; modeling by others). This expression of disinhibitory liability could be termed a predation/antagonism pathway. 50

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From the viewpoint of the externalizing spectrum model, influences that contribute to expression of disinhibitory liability toward SUDs are presumed to differ from those contributing to callous-aggressive outcomes. This supposition follows from the observation that callous-aggressive and substance-abuse tendencies are unrelated within the model, apart from their joint association with general externalizing tendencies. Clearly, factors that promote initial and continuing use of alcohol and or/drugs as a means to achieve gratification or relief from immediate distress would operate to shape disinhibitory tendencies in the SUD direction (Karoly et al., 2013). The relative loadings of content-relevant scales on the substance abuse subfactor of the ESI model provide some clues as to the nature of such influences. Per table 5 of Krueger et al. (2007), scales indexing marijuana use and other drug use load most strongly on this substance abuse subfactor, followed by the marijuana problems scale (reflecting dependency and adverse consequences from use). The implication is that this subfactor reflects a proclivity to seek out illicit drugs of differing types (more so than alcohol) and to engage in regular use of those that are most readily available (marijuana, in particular) to the point of dependency and problems. Notably, findings from a 25-year longitudinal study by Fergusson, Boden, and Horwood (2008) indicate that use of cannabis during late adolescence through early adulthood predicts later use of other illicit drugs above and beyond other variables (including psychosocial variables, personality traits, and conduct/ attentional problems as assessed by parent/teacher report). Factors that may contribute to this proclivity include access to channels for obtaining illegal substances (e.g., relatives, deviant peers; Gillespie, Neale, & Kendler, 2009) and physical/ psychological enjoyment of the consciousness and mood-altering effects of such drugs. Accordingly, this directional expression of disinhibitory liability could be described as a pleasure/hedonism pathway. A key question in this regard is whether any heritable trait tendencies aside from general disinhibitory liability contribute to the proclivity to try and continue using drugs of differing types, in particular illegal ones (i.e., marijuana and other drugs; cf. Krueger et  al., 2007). For example, it is clear that stable variations exist in the tendency of the midbrain DA “wanting” pathway to become sensitized to discrete cues for reward that affect susceptibility to drugs. In work with rodents, Flagel, Watson, Akil, and Robinson

(2008) demonstrated that animals drawn more to cues predictive of food reward than to the location of reward delivery showed increased psychomotor sensitization to repeated administrations of cocaine. Other work by this group (Flagel et al., 2011) has shown that this bias toward tracking reward cues is associated with increased release of DA to such cues. However, it remains to be determined whether increased susceptibility to DA incentive sensitization and general disinhibitory liability are the same or different. Another possible contributor to the “appetite” for drugs, apart from trait disinhibition, may be variation in unconditioned pleasurable responses to psychoactive drugs of differing types, as determined by the basal forebrain/hindbrain “liking” system (Berridge, 2003). For example, deficits in the liking system associated with major depression and manifested in symptoms of anhedonia and impaired reward learning may serve as a distinct source of motivation for drug use (i.e., to attain pleasurable experiences not achievable in other ways; Baskin-Sommers & Foti, 2015). Behavior genetic studies conducted to date are equivocal on the question of whether a dispositional liability for SUDs (in particular, illicit drug use/problems) exists that is distinct from trait disinhibition. As noted earlier, Kendler et al. (2003a) found evidence for a single heritable factor contributing to covariance among abuse/dependence diagnoses for illegal substances of six types (cannabis, cocaine, hallucinogens, sedatives, stimulants, opiates). However, contrasting results were obtained in another twin study in which alcohol dependence and other drug abuse/dependence were examined in modeling analyses that also included conduct disorder, adult antisocial behavior, major depression, and anxiety disorders of differing types (Kendler et al., 2003b). Evidence was found for both a common heritable factor and more specific heritable factors that contribute separately to alcohol versus drug problems. Additional common factors contributing distinctively to disorders involving anxiety-misery (depression, generalized anxiety) and fear (phobias, panic) were also found. In other work, Kendler, Myers, and Prescott (2007) modeled symptom data for assorted illicit (cannabis, cocaine) and licit substances (alcohol, caffeine, nicotine) and found evidence for separate, albeit correlated heritable factors contributing to abuse/ dependence of drugs within each class. Cannabis and cocaine were associated, at similarly high degrees (~.8 each), with the illicit-drug heritable

factor, whereas alcohol was linked most strongly (~.7) to the licit-drug heritable factor, followed by nicotine (~.5), then caffeine (.15). Alcohol and nicotine also showed stronger bivariate associations with illicit drugs than with caffeine, indicating that interrelations among this group of drugs accounted mainly for the high correlation (~.8) between the two heritable factors. Evidence was also found for distinct additive heritable influences contributing to problems with drugs of each type—appreciably in the case of nicotine and caffeine and more modestly in the case of cannabis, cocaine, and alcohol. Findings from this latter study by Kendler et al. (2003b, 2007) point to a coherent heritable component to cannabis and other illegal drug abuse/ dependence, separate from that associated with alcohol and nicotine. However, without additional nonsubstance indicators of externalizing proneness in the model (e.g., child conduct problems, adult antisocial behavior, disinhibitory traits), it remains unclear how the two heritable factors relate to general disinhibitory liability and whether the proclivity to use illegal drugs reflects heritable tendencies separate from this general liability. In an effort to address this question, we undertook biometric analyses of ESI scale data from a sample of adult twins (N  =  476) who comprised a subset of participants from a study by Kramer et  al. (2012). Scores on the 23 ESI scales for this twin sample were combined with scores for the full participant sample (N = 1,787) from Krueger et al. (2007), and the resulting dataset was used to specify the bifactor model, as described in Patrick et  al. (2013a). Manifest scores on the three ESI factors (general disinhibition factor and subfactors corresponding to callous-aggression and substance abuse, parameterized as independent of one another) were computed for participants in the twin sample using maximum likelihood estimation. Contributions of heritable and environmental influences to scores for each factor were then evaluated through univariate biometric ACE models fit to the cross-twin, within-trait covariances. As anticipated, estimated scores for the general disinhibitory factor were appreciably heritable, with the coefficient for influences of this type in the biometric model exceeding .5. Moreover, a significant contribution of heritable influence was found for scores on the substance abuse subfactor—with a coefficient exceeding .3. These results provide preliminary evidence for a contribution of coherent heritable influences—apart from Patrick, Foell, Venables, Worthy

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general disinhibitory liability—to the proclivity to abuse substances of differing types, particularly cannabis and other illicit drugs.

Unresolved Questions and Directions for Research General Disinhibitory and SUD-Specific Liabilities

The central unresolved question that emerges out of the current review is whether a coherent heritable liability toward drug and alcohol problems exists apart from the general liability that contributes to disinhibitory problems as a whole. Available data indicate that there are likely to be specific heritable influences that affect proneness to use of particular substances (e.g., cocaine vs. marijuana vs. alcohol; Kendler et al., 2012); such influences may either be promotive (e.g., Saccone et al., 2007) or protective (e.g., Shen et al., 1997). Still, is there a genotypic proclivity that shapes general disinhibitory liability toward substance abuse outcomes broadly? Or, stated more prosaically, is there a distinct heritable “appetite” for the psychoactive effects of pharmacologic agents that facilitates rapid and powerful “bonding” with such agents? Cumulative research evidence to date indicates that questions regarding the etiology of SUDs and other externalizing conditions are likely to be addressed most effectively by considering conditions of these types in relation to one another, rather than in isolation. For example, the picture that emerges of the etiology of SUDs differs depending on whether modeling analyses focus on illegal drugs only (Kendler et  al., 2003a), licit as well as illicit drugs of differing types (Kendler et al., 2007), or SUDS in conjunction with other forms of psychopathology (Kendler et  al., 2003b). Regarding disinihibitory conditions as a whole, the externalizing spectrum model highlights the possibility that callous-psychopathic behavior and substance abuse are each determined in part by reckless-impulsive proclivities associated with general disinhibitory liability—but that other dispositional tendencies intersect with this general liability to shape its expression in one direction or the other. As with work focused on the etiologic basis of illegal drug abuse per se (e.g., Kendler et al., 2003a), behavior genetic research focusing on antisocial behavior indicates that the configuration of impulsive conduct problems together with callous-unemotional tendencies constitutes a highly heritable phenotype (Viding, Blair, Moffitt, & Plomin, 2005). However, it must be presumed that heritable influences 52

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contributing to this phenotype overlap substantially with those that contribute to abuse of illegal drugs of differing types. Given this, it will be necessary to examine externalizing conditions of various types together in a joint etiological model in order to clearly establish which heritable influences contribute in common to SUD and callous-aggressive outcomes, and whether and to what degree other heritable factors (vs. distinct environmental influences) shape the expression of shared disinhibitory proclivities in one direction or the other. Findings from our preliminary twin-sample analysis of scores on the general disinhibition factor and substance abuse subfactor from the externalizing spectrum model suggest separate sources of genetic influence contributing to each. However, interpretations are constrained by several key limitations, including reliance on a modest sample size, purely questionnaire-based assessment of externalizing tendencies, and the cross-sectional nature of the data (i.e., all scale measures were collected at a single point, in adulthood). A more compelling answer to this overall question will require a systematic multidomain, longitudinal developmental approach. A crucial foundation for systematic developmental research on the etiology of SUDs exists in work undertaken by Vanyukov et al. (2003, 2009) to operationalize general disinhibitory liability early in life through a transmissible liability index (TLI). Consisting of a composite of self- and other-report (i.e., parent, teacher) indicators of non–substance-themed behavioral tendencies that differentiate between offspring of parents with and without SUDs, scores on the TLI measure are substantially heritable (Vanyukov et al., 2009) and prospectively predict emergence of substance-related problems from earlier to later life (Kirisci et  al., 2009; Vanyukov et  al., 2009). This work demonstrates that proneness toward development of substance problems can be quantified well before the emergence of such problems—as early as age 10 (Kirisci et al., 2009) and perhaps even earlier if based entirely on data from informant raters. Notably, recent longitudinal-twin research by Hicks, Iacono, and McGue (2012) shows that a close variant of the TLI exhibits a level of heritability more comparable to an aggregate measure of non-SUD externalizing tendencies (i.e., estimated A > .7) than to a composite of SUD symptoms (estimated A  ~.5) and prospectively predicts non-SUD externalizing behaviors even more strongly than SUD outcomes. The implication is that the TLI may tap general disinhibitory liability rather than SUD liability per se. Either way,

availability of a measure of this type for quantifying liability at early ages creates avenues for many interesting and potentially profitable lines of research. One valuable focus will be to examine relations across time of early liability as indexed by TLI scores with biological and behavioral variables including (a) DA availability in the mesolimbic system at baseline and reactivity of mesolimbic structures (e.g., VTA, nucleus accumbens) to DA agonists and cues for reward; (b)  differing variants of P3 response; (c) task-behavioral measures of EF, including performance on inhibitory control paradigms (cf. Young et al., 2009); and (d) attention-allocation and brain reactivity to reward signals versus goal locations in a human analog version of the “sign-tracking” task used by Flagel and colleagues (2008, 2011). Also of central importance will be work on interrelations from earlier to later ages between indices of mesolimbic DA system function (e.g., baseline receptor binding and phasic cue reactivity) and measures of executive-inhibitory function, along with the mediating role these functional domains play across time in general disinhibitory tendencies (assessed via non-SUD/nonaggressive indicators), substance abuse, and callous-aggressive behavior. In conjunction with ongoing behavioral and molecular genetics studies, such work can help to establish whether the root source of disinhibitory liability lies in limbic system dysfunction or frontal-control system deficits (or perhaps both)—and whether separate dispositional factors operate over time to shape this general liability toward SUD outcomes.

P3 Brain Response and the Nature of Disinhibitory Liability

Another crucial question is what the general liability to impulse-control problems—variously referred to as externalizing (Krueger et  al., 2002), externalizing proneness (Nelson et  al., 2011), disinhibition (Iacono, Carlson, Taylor, Elkins, & McGue 1999), trait impulsivity (Beauchaine & McNulty, 2013), or deficient self-control (Gottfredson & Hirschi, 1990)—entails in psychological and biobehavioral terms. The nature of this general liability has been characterized in differing ways depending on the clinical phenomena of main interest and the range and types of evidence considered. For example, Vanyukov et  al. (2012) characterized the “common liability to addiction” as entailing “an identifiable circumscribed group of mechanisms underlying behavioral regulation and socialization” (p. S6). These authors highlight a strong role for self-selection of experience (i.e.,

gene–environment correlation) in the phenotypic expression of this core liability. From this perspective, the externalizing-prone individual is, in the words of Ponzi (1934), born “looking for trouble.” Drawing on evolutionary theory, Vanyukov et  al. postulate that the basis of disinhibitory liability is a nervous system overadapted for survival in a highly volatile natural environment. Individuals with this biological orientation seek out change on an ongoing basis to compensate for a diminished affective response capacity:  “[T]‌heir status of the nervous system is underarousal, resulting, e.g., in high novelty and sensation seeking (including that from substance use), risky and antisocial behavior, etc. This becomes emphasized particularly at transition to the reproductive period (which defines fitness) and relative independence, i.e., at adolescence.” (p. S11) Other investigators have theorized about the nature of this general liability in the context of work on ADHD. As noted earlier, Beauchaine and McNulty (2013) posited in this context that disinhibitory liability (termed “trait impulsivity”) is rooted early in life in dysfunction of the mesolimbic DA system, which results in a bias toward immediate reward seeking. This tendency in turn compromises normal development of frontal control functions, which contributes to continued impulsivity across the life span. A  related perspective was advanced by Nigg and Casey (2005). Focusing in particular on the variant of ADHD that involves impulsive-hyperactive tendencies combined with inattentiveness, these investigators proposed that the core liability entails deficits in “the ability to predict temporal and contextual structure in the environment” (p.  788) mediated by frontostriatal and frontocerebellar circuits of the brain. Particular emphasis is placed in this model on the delicate interplay between affective-subcortical and cognitive-prefrontal systems across sequential stages of development:  Dysfunction from infancy in core circuitry required to recognize unexpected events and contingency shifts in the environment is thus detrimental to later-occurring development of prefrontal networks required for inhibitory control and planning. This conception is broader than Beauchaine and McNulty’s in that it posits a dysfunction in basic event detection/prediction systems, arising from alternative possible sources (“altered catecholinergic modulation of the circuitry, altered prefrontal projections, or poor prediction-related functions presumably related to altered development of striatal and/or cerebellar neuronal regions or connections” [p. 791]), that has implications for punishment as well as reward learning. Patrick, Foell, Venables, Worthy

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From the perspective of Nigg and Casey (2005) and the general developmental principle of multifinality (Cicchetti & Rogosh, 1996), it may be most useful to conceive of general externalizing proneness (Krueger et  al., 2002; Nelson et  al., 2011; Young et  al., 2009) as an emergent condition of executive-control dysfunction arising from alternative root sources that operate across time to compromise the formation of frontal regulatory networks. It is conceivable that the variance in P3 amplitude that intersects with externalizing proneness (Nelson et al., 2011), known to be attributable to common heritable influence (Hicks et al., 2007; Yancey et  al., 2013), reflects this emergent condition of executive-control dysfunction. Patrick and Bernat (2009) hypothesized that reduced P3 amplitude reflects a failure to link and integrate ongoing stimulus events with cognitive/affective representations stored in long-term memory (cf. Ericsson & Kinsch, 1995), a normally automatic process that is crucial to anticipation, reflection, and self-regulation of emotion and behavior. Evidence for this comes from notable instances in which externalizing individuals show normal enhancement of early-latency brain response to affective versus neutral visual stimuli, indicating intact processing of motivational salience, followed by diminished amplitude of subsequent P3 response for stimuli of all types, indicating reduced postperceptual elaborative processing (Bernat et al., 2011; Olson, 2014; Patrick & Bernat, 2009). The implication is that part of P3 responsivity entails a “reaching out” between pre-existing representations of experience and perceptual-motivational processes instigated by ongoing stimulus events—and it is this natural interplay between stored representations and immediate processing that is reduced among externalizing individuals. Systematic longitudinal research that employs measures from multiple domains (including selfand informant-report, behavioral performance, and neurophysiology) will be needed to clarify the nature and origins of executive-control dysfunction among externalizing-prone individuals and the psychological significance of reduced P3 amplitude vis-à-vis this dysfunction. Key questions include: (1) Can early functional deficits in core circuitry as described by Nigg and Casey (2005) be indexed in a manner that predicts later emergence of executive system impairments? (2) For individuals who exhibit early circuitry dysfunction associated with later executive impairment, does reduced P3 amplitude precede or emerge concurrently with 54

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executive impairment? (3) Would interventions that prevent the cascade from early circuitry dysfunction to executive system impairment leave P3 intact?

“Functional Addicts” and “Successful Psychopaths”

A further key issue that comes out of the externalizing spectrum framework, as illustrated in Figure 3.1, is whether differing degrees of disinhibitory liability in conjunction with varying levels of SUD or callous-aggressive shaping influences give rise to distinct symptom patterns. Earlier, we distinguished between pleasure/hedonism and predation/antagonism pathways toward which disinhibitory liability might progress, depending on shaping influences of one kind or another. Operating from this viewpoint, it is conceivable that high levels of SUD-specific (hedonism-promoting) or aggression-specific (antagonism-promoting) influences, in the absence of high disinhibitory liability, could give rise to substance-addicted or callous-psychopathic individuals capable of functioning effectively in many areas of life. For example, there are clearly examples of people who manage to achieve success in work and relationships despite finding drugs of one sort or another irresistably “precious” (in the words of Gollum, 2002). Likewise, there exist people who operate in ruthless, exploitative ways without concern for others, who achieve wealth or fame as opposed to ostracization or imprisonment. It can be hypothesized that individuals of these types, despite having excessive appetites for pleasure or power, have well-established extended working memory structures (cf. Ericsson & Kinsch, 1995) for goals and consequences that dysfunctional high-disinhibited persons lack. Systematic investigation of higher functioning cases of these types, along with individuals high in biological risk for externalizing psychopathology who manage to lead functional lives, can help to provide unique insight into factors that shape the expression of disinhibitory liability in alternative maladaptive directions or exert healthy compensatory effects.

Acknowledgments

Preparation of this chapter was supported by grants MH089727 from the National Institute of Mental Health and W911NF-14-1-0027 from the US Army. The views, opinions, and/or findings contained in this chapter are those of the authors and should not be construed as an official position, policy, or decision of the Department of the Army.

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CH A PT E R

4

Self-Injury, Borderline Personality Development, and the Externalizing Spectrum

Erin A. Kaufman, Sheila E. Crowell, and Stephanie D. Stepp

Abstract This chapter examines self-inflicted injury (SII) and borderline personality disorder (BPD) as overlapping—but not identical—constructs showing similar patterns of comorbidity with externalizing disorders. More specifically, it discusses similarities in biological vulnerabilities and contextual risks for SII, BPD, and externalizing disorders. Before assessing links between SII, BPD, and externalizing psychopathology, the authors provide historical context on the evolving conceptualizations of BPD and SII over time. They then describe the current state of research, citing behavioral genetics and family studies as well as studies that focus on the role of environmental risk factors. The chapter also describes prevalence rates, typical age of onset, and developmental course with respect to externalizing disorders; controversies surrounding the diagnosis of BPD early in the developmental trajectory; and how understanding connections among SII, BPD, and externalizing disorders may help elucidate etiological mechanisms. The chapter concludes by outlining future directions for research. Key Words:  self-inflicted injury, borderline personality disorder, comorbidity, externalizing disorders, biological vulnerabilities, psychopathology, behavioral genetics, family studies, environmental risk factors

Introduction

Self-inflicted injury (SII) and borderline personality disorder (BPD) are complex clinical conditions that share many defining features, such as emotion dysregulation, impulsivity, and interpersonal conflict (Crowell, Kaufman, & Lenzenweger, 2012). Not only do these conditions co-occur at an unexpectedly high rate, but they also show similar patterns of comorbidity with other clinical diagnoses. Interestingly, SII and BPD are often observed/diagnosed with both internalizing (e.g., depression) and externalizing (e.g., attention-deficit/hyperactivity disorder [ADHD]) disorders (American Psychiatric Association [APA], 2006; Becker, Grilo, Edell, & McGlashan, 2000; Hinshaw et  al., 2012; Pepper et al., 1995; Trull, Sher, Minks-Brown, Durbin, & Burr, 2000; Zanarini et  al., 1998a, 1998b; Zimmerman & Mattia, 1999). Furthermore,

factor analyses of comorbidity patterns place SII and BPD at the intersection of the internalizing and externalizing spectra (Eaton et al., 2011; Shin et al., 2009). A growing body of research has also examined developmental trajectories leading to SII and BPD (e.g., Biskin, Paris, Renaud, Raz, & Zelkowitz, 2011; Brezo et al., 2010; Lenzenweger, 1999; Zanarini, Frankenburg, Hennen, & Silk, 2003). This work reveals considerable overlap with internalizing and externalizing etiologies, yielding a characteristic constellation of mood and behavior problems. Although traditionally SII and BPD have been viewed as internalizing spectrum behaviors/disorders, examining links with externalizing disorders may help elucidate etiological mechanisms (see, e.g., Beauchaine, Klein, Crowell, Derbidge, & Gatzke-Kopp, 2009), as detailed herein. Furthermore, understanding SII and BPD 61

trajectories holds great promise for developing preventive interventions that target vulnerabilities and risk factors for both internalizing and externalizing clinical features. Before proceeding further, it is important to emphasize that SII and BPD are overlapping but not identical constructs. There are people who self-injure yet do not meet diagnostic criteria for BPD, and there are people with BPD who do not self-injure (Selby, Bender, Gordon, Nock, & Joiner, 2012). There is also considerable heterogeneity within both BPD and SII. The borderline diagnosis, for example, is defined via a polythetic criterion set in which five of nine symptoms are required for a diagnosis (APA, 2013). Similarly, there are many distinct manifestations and functions of SII. Broadly defined, SII includes all acts of deliberate SII, ranging from nonsuicidal behaviors that typically serve an emotion regulation function (see, e.g., Nock & Prinstein, 2004) to completed suicide (Brown, Comtois, & Linehan, 2002; Linehan et al., 2006). Heterogeneity within conditions and phenotypic differences between conditions mask important similarities between BPD and SII. We hypothesize that associations among BPD, SII, and externalizing psychopathology are explained by a common set of neurobiological vulnerabilities, shaped over time into different but related behavioral expressions (see Beauchaine et  al., 2009; Crowell, Beauchaine, & Linehan, 2009). Specifically, heritable trait impulsivity and negative affectivity emerge early in the ontogenesis of SII, BPD, and externalizing disorders, which may explain commonalities and comorbidities across these seemingly distinct clinical problems (Stepp, Whalen, & Pedersen, 2014a). These biological vulnerabilities also interact with environmental risk factors to produce multifinal developmental outcomes (Beauchaine et  al., 2009; Lynam et al., 2000). A primary objective of this chapter is to highlight similarities in biological vulnerabilities and contextual risks for BPD, SII, and externalizing disorders.

Historical Context

Factor analytic models indicate that vulnerability to most psychiatric disorders is conferred through a limited number of core underlying traits (e.g., Achenbach & Edelbrock, 1978, 1995; Caspi et al., 2014; Krueger et al., 1998; Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011). However, BPD, SII, and traditional externalizing disorders (i.e., those best characterized by outwardly displayed behaviors that affect others negatively) have historically 62

been conceptualized and studied separately because of their phenotypic differences (Crowell et  al., 2012). Here, we briefly describe the historical context through which each of these conditions has been defined. We give particular attention to how conceptualizations of BPD and SII have evolved over time.

Borderline Personality Disorder

The label borderline came into use during the early 20th century. The term originated because it was unclear whether patients who were on “the borderline of psychosis and neurosis” (Stern, 1938, p. 467) would later develop symptoms of psychotic disorders such as schizophrenia or neurotic disorders such as anxiety and depression (Knight, 1953). This initial description of borderline pathology spurred researchers to delineate concrete diagnostic criteria. Kernberg (1967) argued that “borderline personality” was a unique and persistent personality organization, independent from both psychotic and neurotic conditions. Following Kernberg’s influence, two important reviews (Gunderson & Singer, 1975; Spitzer, Endicott, & Gibbon, 1979) established criteria that would eventually be used to characterize BPD in the DSM-III (APA, 1980). BPD is now understood as one of the most costly and debilitating mental health conditions (Trull, Distel, & Carpenter, 2011). It is characterized by marked disturbances across a broad range of functions, including identity, interpersonal, emotional, cognitive, and behavioral domains (APA, 2013). BPD is also a common personality disorder (PD), affecting at least 2% of adults in the community, 10% of outpatients, and approximately 20% of inpatients (Grant et al., 2008; Lenzenweger, Lane, Loranger, & Kessler, 2007; Trull, Jahng, Tomoko, Wood, & Sher, 2010; Widiger & Trull, 1993). Importantly, more than half of those with BPD engage in repetitive SII, and as many as 10% eventually commit suicide (APA, 2006; Selby et al., 2012). Prevalence estimates among adolescents range from 2% to 3%. However, diagnosis among this age group is controversial, and further research is needed (Leung & Leung, 2009; Zanarini, Laudate, Frankenburg, Reich, & Fitzmaurice, 2011). Most recently, theory-driven experimental studies have begun to uncover developmental precursors to BPD, including dysfunctional psychosocial and biological underpinnings (e.g., Koenigsberg et al., 2009; Paris et al., 2004). Some of those with BPD may present with predominately internalizing features (e.g., dissociation, identity disturbance,

Self-Injury and Borderline Personalit y Development

affective instability, chronic feelings of emptiness), others with predominately externalizing features (e.g., impulsivity, unstable interpersonal relationships, anger outbursts), and still others with a combination. There is a push to reduce heterogeneity within the BPD diagnosis and identify clearer, more homogeneous subgroups (Lenzenweger, Clarkin, Yeomans, Kernberg, & Levy, 2008). Additionally, dimensional conceptualizations of BPD have been proposed and are currently under investigation (APA, 2013; Trull et al., 2011).

SII

As noted earlier, SII includes all intentional acts of self-harm, ranging from nonsuicidal self-injury (NSSI; e.g., repetitive self-mutilation such as cutting, burning) to attempted and completed suicide. This broad description has benefits and limitations. By conceptualizing SII along a continuum, important distinctions between various SII behaviors may be overlooked (see Linehan, 1997; Muehlenkamp & Gurierrez, 2004). For decades, both suicidal self-injury and NSSI were mistakenly assumed to serve identical functions (Simpson, 1950; Zilboorg, 1936a, 1936b). This view was challenged in the mid-20th century when samples of hospitalized youth were identified who harmed themselves in the absence of suicidal intent (Offer & Barglow, 1960). Following this discovery, researchers sought to divide SII into meaningful subtypes based on its functions, level of suicidal intent, lethality of method, and physical outcomes associated with self-harm (e.g., Beautrais, Joyce, & Mulder, 1996; Linehan et  al., 2006; Zlotnick, Mattia, & Zimmerman, 1999). These factors are useful for understanding diverse forms of SII. For example, Offer and Barglow (1960) note that NSSI is a learned behavior that can serve both instrumental and emotional functions, whereas suicide is more often conceptualized as a solution for overwhelming psychological pain (Joiner, Brown, & Wingate, 2005; Shneidman, 1985). Unfortunately, elements affecting SII (function, lethality, intent, physical outcomes, etc.) are often poorly assessed, which has led to faulty assumptions in the literature. For example, many researchers infer level of suicidal intent from the lethality of the method chosen, even though these are not necessarily related (Linehan et  al., 2006). An individual may engage in a low-lethality behavior yet have a high intent to die or vice versa. Similarly, researchers often also assume that physical outcomes of SII, such as death or severe injury, indicate high intent and that

minor injuries indicate low intent. Although sometimes accurate, these are problematic suppositions. Nevertheless, such assumptions have shaped how research on SII is conducted. For many decades, studies of NSSI were conducted almost exclusively with individuals who engaged in low-lethality behaviors such as repetitive cutting, burning, or bruising (e.g., Simpson, 1975), whereas research on suicidal self-injury addressed behaviors such as hanging, drowning, and gun injuries (e.g., Seiden, 1978). These sampling strategies undoubtedly missed considerable variability in SII (and level of intent) that occurs in the real world. Additionally, most research has been conducted within rather than across diagnostic groups and age ranges, further limiting our understanding of the development of SII (Crowell, Derbidge, & Beauchaine, 2014b; Derbidge & Beauchaine, 2014). Researchers who study personality, mood, and psychotic disorders often examine risk for suicide and SII only within their respective samples and not across diagnostic groups (e.g., Dworkin, 1994; Heisel, Conwell, Pisani, & Duberstein, 2011; Large, Nielssen, & Babidge, 2010; Reutfors et al., 2010), a strategy that may obscure overlap between SII and traditional externalizing disorders.

Links to Traditional Externalizing  Disorders

Traditionally, SII and BPD have been conceptualized and treated as internalizing problems. This branding related, in part, to the high rates of subjective distress associated with these conditions. Both have high comorbidity with depression and anxiety disorders (Eaton et  al., 2011), and some scholars have even conceptualized BPD as an extreme variant of depression, bipolar, or posttraumatic stress disorder (Akiskal, Chen, & Davis, 1985; Gunderson & Sabo, 1993; Perugi, Toni, Travierson, & Akiskal, 2003). However, empirical findings indicate that BPD does not reflect the same underlying organization as these forms of psychopathology (see Gunderson & Phillips, 1991; Koenigsberg et  al., 1999; for reviews, see Golier et  al., 2003; Henry et al., 2001). Similarly, most intervention studies for SII are designed primarily to target major depressive disorder (MDD; e.g., the Treatment for Adolescents with Depression Study, TADS; Brent et al., 2013). Yet researchers often identify self-harm and suicidal ideation as obstacles to effective care among depressed samples (Kennard, Ginsburg, Feeny, Sweeney, & Zagurski, 2005). In the Adolescent Depression Antidepressants and Psychotherapy Trial Kaufman, Crowell, Stepp

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(ADAPT), participants who endorsed NSSI were less responsive to treatment than were those who did not (Wilkinson, Kelvin, Roberts, Dubicka, & Goodyer, 2011). Such diminished treatment response indicates that traditional approaches to treating internalizing disorders are not particularly successful at combating SII. In short, conceptualizing and treating BPD and SII as (exclusively) internalizing problems may be both inaccurate and ineffective. In contrast, a substantial and growing body of research demonstrates links among SII, BPD, and externalizing disorders, particularly antisocial personality disorder (ASPD) and substance use disorders among adults (Beauchaine et  al., 2009; Duberstein & Conwell 1997; Haw, Hawton, Casey, Bale, & Shepherd, 2005; Kessler, Borges, & Walters, 1999; Verona, Patrick, & Joiner, 2001; Verona, Sachs-Ericsson, & Joiner, 2004; Wilcox, Conner, & Caine, 2004), as well as ADHD, oppositional defiant disorder (ODD), and conduct disorder (CD) among youth (e.g., Hinshaw et al., 2012; Stepp et  al., 2014b). Trull and colleagues (2000) found that roughly 50% of their sample with BPD had comorbid substance use disorders, and an additional 40% met criteria for a comorbid drug use disorder. Epidemiological studies indicate that ASPD and substance use disorder symptoms are associated with both suicide attempts and completions (Verona et  al., 2004; Hills, Cox, McWilliams, & Sareen., 2005), and approximately 25% of those with BPD also meet criteria for ASPD (Zanarini et al., 1998b). Importantly, suicide risk may be even greater among those with BPD who also present with antisocial traits (Black, Blum, Pfohl, & Hale, 2004; James & Taylor, 2008). Some have suggested that BPD and ASPD represent sex-linked manifestations of the same underlying pathophysiology (BPD occurring more frequently among females and ASPD among males; Beauchaine et al., 2009; Paris, 1997). Contemporary developmental theories of BPD highlight interactive effects of biological vulnerabilities and environmental risk factors. Particular attention has been given to stressors that promote emotion dysregulation and subsequent manifestations of other externalizing features (e.g., maladaptive behavioral and interpersonal patterns; Linehan, 1993). Linehan’s biosocial theory (1993) is one of the most widely studied etiological models of BPD. Within this framework, BPD is hypothesized to emerge when temperamentally vulnerable youth are raised in an emotionally invalidating environment 64

(i.e., one in which the child’s emotional experiences are dismissed or rejected, emotional needs are repeatedly not met, and negative emotional expressions are intermittently reinforced). Since the biosocial theory was originally published, researchers have continued to refine our understanding of the specific developmental trajectories, antecedents, environmental contexts, and individual characteristics that promote risk for BPD. Recent models of BPD development focus on the role of externalizing traits and behaviors. Trait impulsivity has been established as a central characteristic of externalizing spectrum disorders for decades (Cloninger 1987; Gorenstein & Newman, 1980; McGue, Slutske, & Iacono, 1999; Moeller & Dougherty, 2002; Sher & Trull, 1994; Verona & Patrick, 2000). However, recently, researchers have claimed that trait impulsivity actually predates and predisposes individuals to BPD features such as emotion dysregulation and self-harm—particularly when youth are raised in the context of high-risk family environments (described later; see Beauchaine et  al., 2009; Beauchaine & Zalewski, in press; Crowell et al., 2009). Stepp and colleagues (2014a) propose that a subset of impulsive youth who engage in impulsive behaviors while experiencing strong affect may be at particular risk for developing BPD. Therefore, although BPD is characterized by both internalizing and externalizing features, individuals may arrive at this diagnosis via primarily externalizing routes. Importantly, impulsive youth are also highly vulnerable to other externalizing spectrum disorders, including ADHD, ODD, CD, and substance use disorders (see Beauchaine & McNulty, 2013; McNulty, Beauchaine, & Hinshaw, this volume).

Current State of the Science

Recently, investigators have focused on disentangling specific contributors to BPD, SII, and externalizing disorders, with particular emphasis on understanding biological vulnerability × environmental risk interactions and how these change and accrue across development. Shared biologically based vulnerabilities are likely to account for a substantial portion of the heightened comorbidity across these conditions. For example, biometric models of twin data indicate that many externalizing symptoms can be traced to a single, highly heritable latent trait. However, there is also significant variance attributable to environmental effects for each individual disorder (Krueger et al., 2002; Krueger, Markon, Patrick, Benning, & Kramer,

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2007). Contextual factors are likely to contribute to the diagnostic heterogeneity observed both in family studies and across diagnostic categories. A wide range of outcomes is possible even when traits that increase vulnerability are particularly heritable and/ or common among the general population. Those with shared biological and contextual risks are most likely to experience similar symptoms. However, reciprocal transactions between individuals and their environments result in an array of complex behavior patterns. Furthermore, an individual’s biological vulnerabilities and maladaptive behaviors may be shaped and expressed differentially across the life span. This set of processes results in multifinality and heterotypic continuity, whereby the same underlying traits manifest differently at various stages of development (see Beauchaine & McNulty, 2013; Hinshaw et  al., 2012; McNulty et  al., this volume). In the following sections, we review current evidence for individual vulnerabilities to these conditions, as well as contextual and familial influences and their interactions. Two developmental precursors, impulsivity and emotional instability, in the context of a risky family context, undoubtedly amplify traits and symptoms that eventually manifest as self-harm and/or BPD by adolescence or young adulthood (Crowell et al., 2012; Stepp et al., 2014a,b). Externalizing forms of psychopathology arise from similar biologically based origins and appear to be shaped via similar processes (see, e.g., Beauchaine & Zalewski, in press).

Biological Vulnerabilities

Researchers who study biological substrates of BPD, SII, and traditional externalizing disorders have explored structural/brain, neurochemical, and genetic vulnerabilities. A  comprehensive review of this literature is beyond the scope of this chapter. We focus our discussion on behavioral genetics and familial studies and the two most relevant monamine neurotransmitter systems: serotonin (5-HT) and dopamine (DA). Although our review is somewhat restricted, other biological systems have also received attention in the literature.1

Behavioral Genetics and Family Studies

Behavioral genetics research on the heritability of BPD, SII, and traditional externalizing disorders indicates strong evidence for transgenerational transmission of these specific conditions and the more general, higher order latent vulnerabilities that account for their frequent co-occurrence

(Caspi et  al., 2014; Krueger et  al., 2002; White, Gunderson, Zanarini, & Hudson, 2003). Hicks and colleagues (2004) used data from the Minnesota Twin Family study to examine familial transmission of CD, ASPD, alcohol dependence, and drug dependence. They found a highly heritable (h² = 0.80) general externalizing vulnerability, which accounted for most familial resemblance. Disorder-specific vulnerabilities were also detected for CD, alcohol dependence, and drug dependence. Krueger and colleagues (2002) examined the covariance among symptoms of CD, ASPD, alcohol and drug dependence, and unconstrained personality style and found similar heritability (h2  =  0.81) in their sample of 17-year-old twins. Studies of BPD among adults indicate that up to 69% of the variance in symptoms is attributable to additive genetic effects (Torgersen et  al., 2000), and first-degree relatives of those with BPD show a three- to fourfold increase in risk for the disorder (Gunderson, Zanarini, Choi-Kain, Mitchell, Jang, & Hudson, 2011). SII and vulnerabilities that promote risk for self-injurious behaviors also aggregate within families (Brent et al., 2002; Brent, Bridge, Johnson, & Connolly, 1996; Brent & Mann, 2005; Runeson & Åsberg, 2003; Hicks et  al., 2004). Relatives of self-injuring individuals are at threefold higher risk of any SII and at fivefold higher risk for attempted and completed suicide (Baldessarini & Hennen, 2004; Spirito & Esposito-Smythers, 2006). There is considerable evidence that trait impulsivity and affective instability predispose individuals to a number of psychiatric conditions and are likely to account for much of the vulnerability to BPD, SII, and externalizing disorders (Beauchaine et  al., 2009; Beauchaine, Gatzke-Kopp, & Mead, 2007; Beauchaine & Neuhaus, 2008; Bornovalova, Lejuez, Daughters, Rosenthal, & Lynch, 2005; Crowell et  al., 2005; Trull et  al., 2000). There is also growing evidence that the externalizing trajectory to BPD and SII begins with this early, highly heritable vulnerability to trait impulsivity. This vulnerability is shaped across development via potentiating and transactional processes and gives rise to more severe emotional and behavioral dysregulation over time (see Crowell et al., 2012, for a recent review). Both impulsivity and affective instability are heritable, with coefficients of around 80% and 50%, respectively (Livesley & Jang, 2008; Livesley, Jang, & Vernon, 1998; Price, Simonoff, Waldman, Asherson, & Plomin, 2001; Sherman, Iacono, & McGue, 1997; Widiger & Simonson, 2005), and their overlap appears to account for many distinct Kaufman, Crowell, Stepp

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outcomes (Beauchaine et  al., 2009). Additionally, each appears to arise from individual differences in dopaminergic and serotonergic function (Beauchaine et al., 2009; Beauchaine, Hinshaw, & Pang, 2010; Glenn & Klonsky, 2009; Gratz, 2003; Gratz et al., 2009; Verona & Patrick, 2000).

DA

The mesolimbic dopaminergic (DA) network matures very early in development, and it is a primary neural substrate underlying disinhibition throughout the life span (see Beauchaine, Katkin, Strassberg, & Snarr, 2001; Castellanos, 1999; Gatzke-Kopp & Beauchaine, 2007; Kalivas, & Nakamura, 1999; Sagvolden, Johansen, Aase, & Russell, 2005a). Dysfunction in this system has been linked to both internalizing and externalizing symptoms—specifically, low positive affectivity common among depressed individuals, trait irritability, and trait impulsivity, all of which arise from hypodopaminergic states (see Beacuhaine et al., 2009; Crowell et al., 2009; Gatzke -Kopp & Beauchaine, 2007; Laakso et  al., 2003; Sagvolden et  al., 2005b). Findings from numerous research groups indicate that a polymorphism of the DAT1 gene is linked to ADHD (Kim, Kim, & Cho, 2006), CD (Young et  al., 2002), and alcohol use among individuals high in novelty seeking (Bau et al., 2001). There is also emerging consensus that DA dysfunction may contribute to the affective, cognitive, and behavioral traits seen in BPD and SII (Friedel, 2004; Skodol et al., 2002b). It appears that high central DA activity results in positive affectivity (Ashby, Isen, & Turken, 1999), whereas low central DA activity predicts negative affectivity and trait irritability (Laakso et al., 2003).

5-HT

Although it appears that a hypoactive mesolimbic DA system confers considerable vulnerability to externalizing behavior, central DA dysfunction is not the only route to impulsivity (see, e.g., Beauchaine, 2001; Beauchaine et  al., 2009; Beauchaine & Neuhaus, 2008). Central serotonergic function mediates trait anxiety, which, when deficient, can result in phenotypically similar impulsive behavior as that produced through the DA pathway (see Beauchaine et  al., 2001). In contrast to DA-mediated impulsivity, disinhibition among individuals low in trait anxiety derives from a failure to monitor punishment cues and inhibit ongoing behaviors (Beauchaine & Neuhaus, 2008). There is also substantial evidence that 5-HT 66

dysfunction is associated with borderline pathology, self-injury, suicide, and impulsive aggression, and antisocial behavior (e.g., Crowell et  al., 2008; Gollan, Lee, & Coccaro, 2005; Joiner et al., 2005; Kamali, Oquendo, & Mann, 2002; Lee & Coccaro, 2007; Lis, Greenfield, Henry, Guile, & Dougherty, 2007; van Goozen, Fairchild, Snoek, & Harold, 2007). Importantly, compromises to the 5-HT system (present early in the developmental trajectory) predict later maladaptive outcomes. Longitudinal studies of at-risk children with disruptive behavior disorders implicate reduced 5-HT in later aggressive and antisocial behavior (Kruesi et al., 1992). Flory and colleagues (2007) found reduced 5-HT reactivity to fenfluramine (which ordinarily causes synaptic 5-HT release) among children predicted for antisocial personality traits 9 years later.

Environmental Risk and Biology × Environment Interactions

There is a long tradition of research investigating environmental risk factors for oppositional and antisocial behaviors, BPD, and SII. The family or home context appears to be one of the most salient sources for protection and risk (Stepp et al., 2014b). Whereas warm and supportive parenting can buffer vulnerable youth from maladaptive outcomes, negative or hostile family environments promote psychopathology (Eley et al., 2004; Fruzzetti, Shenk, & Hoffman 2005; Linehan, 1993). Longitudinal research indicates that parent–child relationships can be compromised as early as infancy, and those characterized by early disrupted attachment, poor quality of care, and significant trauma confer risk for borderline and antisocial symptoms in childhood (Winsper, Zanarini, & Wolke, 2012) and adulthood (Lyons-Ruth, 2008). Furthermore, parents who have externalizing pathology or BPD themselves are more likely to inadvertently replicate adverse rearing environments for their offspring—such as those characterized by socioeconomic disadvantage, disrupted parenting, and relationship violence (Feldman, Zelkowitz, Weiss, & Vogel, 1995; Golomb et al., 1994; Grinker, Werble, & Drye, 1968; Jaffee, Belsky, Harrington, Caspi, & Moffitt, 2006). These parents may pass down heritable vulnerabilities and simultaneously be more likely to create risky developmental contexts for their children. For example, children of mothers with ASPD are more likely to inherit trait impulsivity and be exposed to drugs in-utero compared to children of mothers without antisocial features (Beauchaine et  al., 2009). Parents with poor

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impulse control and emotion regulation skills may also inadvertently socialize similar difficulty in their children (Stepp, Burke, Hipwell, & Loeber, 2012); such children may go on to seek out high-novelty or risky environments. Such gene–environment correlations influence the development of ASPD, BPD, and SII (see Beauchaine & Zalewski, in press; Crowell et al., 2012; Moffitt, 2005). Stepp and colleagues (2014b) examined bidirectional effects of parenting and BPD symptoms among girls aged 14–17  years based on annual longitudinal data from the Pittsburgh Girls Study. They found that adolescents who manifested higher levels of BPD symptoms across ages 14 through 17 were more likely to experience harsh punishment and low warmth from parents during this period. Furthermore, BPD symptoms predicted increases in harsh parental punishment during the subsequent year. Thus, parents may react with more behavioral control strategies in response to girls’ BPD symptoms. Importantly, these interactions are reciprocal in nature. Much of the literature on development of externalizing psychopathology has focused on coercive family processes that begin in early childhood. Coercive family processes are exchanges between parent and child characterized by conflict escalation and intermittent negative reinforcement of extreme emotional expressions. Such coercive patterns exacerbate externalizing symptoms such as aggression and extreme emotional outbursts (Patterson, DeBaryshe, & Ramsey, 1989, 2000; Snyder, Edwards, McGraw, Kilgore, & Holton, 1994; Snyder, Schrepferman, & St. Peter, 1997). In a reciprocal pattern of negative reinforcement, parents escalate controlling discipline to high levels; similarly, children raise their own levels of aversive behavior in the attempt to get parents to relent. Over the course of development, heightened autonomic arousal, emotional lability, and aggression are reinforced and maintained (see Beauchaine et al., 2007). Moreover, illustrating the bidirectional nature of such processes, impulsive and emotionally labile children are also more likely to elicit reactions from their caregivers that intensify behaviors reflecting their genetic vulnerabilities (O’Connor, Deater-Deckard, Fulker, Rutter, & Plomin, 1998). Linehan (1993) proposed a similar socialization mechanism to explain the development of features such as emotional lability and behavioral impulsivity. According to recent iterations of the biosocial model (Crowell et  al., 2009), biological vulnerabilities such as trait impulsivity lead to affective

instability and are expressed within invalidating family contexts. Such contexts include conditions in which parents struggle to tolerate their child’s outward expression of private emotional experiences, dismiss or reject their child’s experiences, or repeatedly fail to meet the child’s emotional needs. Within such an environment, impulsive children are unable to learn the appropriate strategies for understanding, labeling, and coping with their emotions. Similar to the coercive family process just outlined, caregivers also intermittently reinforce extreme emotional expressions, communicating to their child that his or her needs are more likely to be met following intense emotional/behavioral displays. Crowell and colleagues (2008, 2013, 2014a) have tested core components of the invalidating environment theory by examining both biological and relational indices of vulnerability and risk for SII and BPD. In one study (2013), they compared self-injuring adolescents with typical controls during a mother–child conflict discussion task. Results revealed an association between maternal invalidation and adolescent anger across both self-injuring and control groups. Furthermore, maternal invalidation and coerciveness were each related to adolescent oppositional and defiant behavior. Importantly, mothers in the SII group were more likely to escalate conflict with their children, whereas control mothers were most likely to de-escalate discussions. This finding replicates earlier work with aggressive youth, suggesting that similar contextual mechanisms may underlie risk for both populations (see, e.g., Snyder et al., 1994). Finally, mother and teen aversiveness interacted to predict a biological index of emotion dysregulation (resting respiratory sinus arrhythmia [RSA]). Specifically, aversive adolescents in with highly aversive mothers had the lowest RSA (commonly observed in samples characterized by emotion dysregulation; see Beauchaine, 2001; Crowell et al., 2005). Overall, results support developmental theories that identify emotion invalidation and conflict escalation as contextual risk factors for SII (e.g., Beauchaine et al., 2009; Crowell et al., 2009). Joint effects of biological and contextual risk have led to development of biological vulnerability × environment risk models for antisocial behavior and BPD (see Beauchaine et  al., 2009; Crowell et  al., 2009, 2012; Hiatt & Dishion, 2008; Putnam & Silk, 2005; Tremblay, 2005). Recent studies suggest that interactive effects of biology and environment often account for more variance in behavioral outcomes than do biological vulnerabilities or Kaufman, Crowell, Stepp

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environmental risk factors alone (Crowell et  al., 2008; Raine, 2002). For example, Caspi and colleagues (2002) found that child maltreatment combined with a polymorphism in the monoamine oxidase-A gene (MAO-A) predicts juvenile and adult antisocial behavior. Similarly, children with high trait impulsivity are at greater risk for delinquency when they live in risky neighborhoods (Lynam et  al., 2000; Meier, Slutske, Arndt, & Cadoret, 2008). Joyce and colleagues (2003) found that experiences of neglect and abuse interact with temperamental novelty seeking and harm avoidance to account for significant variance in BPD development. Importantly, meaningful biology × environment interactions can be observed in the absence of main effects (Beauchaine et al., 2008; Caspi et al., 2002). Therefore, it is essential that the combined effects of theoretically supported vulnerabilities and risk factors be examined, even when each is only modestly or weakly associated with adverse outcomes in isolation.

Developmental Considerations

Prevalence rates, typical age of onset, and developmental course are relatively well established for many of the disorders on the externalizing spectrum. Yet epidemiological surveys with adolescents at risk for BPD are only beginning to appear, and few life span developmental studies of SII risk are available. Current estimates suggest that approximately 3% of children and adolescents meet criteria for BPD (Bernstein et al., 1993; Zanarini et al., 2011). SII is far more prevalent across development. Approximately 15–29% of 14- to 17-year-olds report suicidal ideation, 12–19% form a suicide plan, and 7–10% attempt suicide (Nock et al., 2008). A staggering 30,000-plus adolescents and young adults commit suicide each year (Kochanek, Xu, Murphy, Miniño, & Kung, 2011). Rates of NSSI are even higher, affecting between 15% and 20% of community adolescents (see Heath, Baxter, Toste, & McLouth, 2010). Some researchers report rates as high as 56% (Hilt, Cha, & Nolen-Hoeksema, 2008); levels of severity may influence such widely diverging prevalence rates. There is great interest in delineating possible developmental trajectories to BPD (Beauchaine et al., 2009; Derbidge & Beauchaine, 2014; Gratz et al., 2009; Lenzenweger & Cicchetti, 2005; Stepp, 2012). Although research on childhood borderline pathology developed in tandem with the adult literature, existing research with youth is relatively limited in scope (Crowell et  al., 2012). Recent 68

empirical findings offer support for a heterotypically continuous externalizing trajectory. Specifically, there is emerging evidence that a subset of adults with BPD may have followed a developmental course beginning with a biological vulnerability for trait impulsivity that manifests behaviorally as ADHD by the early school years, progresses to ODD by childhood, and results in SII during late childhood or adolescence. By late adolescence, features specific to BPD often emerge and manifest in a pattern that is consistent with the criteria for adult diagnosis (Bornovalova, Hicks, Iacono, & McGue, 2009; Lenzenweger, 1999; Winograd, Cohen, & Chen, 2008). No single study or research group has mapped out this developmental chain in its entirety. Rather, multiple research groups have conducted studies to establish support for the individual links. As stated earlier, the associations among trait impulsivity and ADHD, ODD, SII, and BPD symptoms are well established (e.g., Apter, Gothelf, Orbach, Weizman, & Ratzoni, 1995; Burns, de Moura, Beauchaine, & McBurnett, 2014; Crowell et al., 2005; Kingsbury, Hawton, Steinhardt, & James, 1999; Mann, Waternaux, Haas, & Malone, 1999; Maser et  al., 2002). So, too, is the link between BPD and SII (see Crowell et  al., 2012, for a review). Most recently, important longitudinal research indicates that externalizing disorders in elementary or middle school predict self-injurious behavior and borderline pathology in young adulthood. Hinshaw et al. (2012) conducted a 10-year prospective followup study and found that girls with the combined subtype of ADHD showed higher rates of young-adult SII than did controls or girls with the inattentive subtype. These effects remained when controlling for several covariates including age, IQ, demographics, and comorbidities. Moreover, adolescent externalizing (but not internalizing) behaviors, as well as a measure of poor response inhibition, mediated the predictive association between childhood ADHD and young-adult NSSI—whereas young-adult suicide attempts were mediated by adolescent internalizing symptoms (Swanson, Owens, & Hinshaw, 2014). Burke and Stepp (2012) analyzed prospective data from a large sample of boys and found that childhood ODD, ADHD, and marijuana use predicted BPD symptoms during early adulthood. They reported that oppositional symptoms uniquely predicted BPD features, whereas internalizing symptoms did not. Finally, Stepp and colleagues (2012) demonstrated that ADHD and ODD symptoms at age 8 predicted BPD symptoms

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at age 14, whereas depressive symptoms and CD did not. Furthermore, rates of increase in ADHD symptoms from ages 8–10 years and ODD symptoms from ages 10–13 years predicted BPD features at age 14. Although a heterotypically continuous externalizing pathway is unlikely to be the sole trajectory by which BPD develops, it may explain a significant portion of the comorbidity among SII, BPD, and other externalizing disorders. Future research should continue to explore whether SII and other externalizing behaviors are an early marker of a borderline trajectory, whether they potentiate risk for BPD, or whether both processes are at work. Further longitudinal research is needed to map these symptoms across development more effectively (Stepp, 2012).

Controversies

Although many disorders on the externalizing spectrum are diagnosed commonly in childhood and adolescence (e.g., ADHD, ODD, CD; APA, 2013), and youth frequently engage in SII (e.g., Hilt et  al., 2008), diagnosing BPD early in the developmental trajectory is controversial. The APA discourages researchers and clinicians from applying the BPD label to youth outside of the most extreme cases, particularly among those younger than age 16  years (APA, 2013). Several factors influence this recommendation, which undoubtedly emanated from unsystematic use of the term “borderline” personality to label a large number of youth in previous eras. First, there is a general misunderstanding that personality pathology is intransigent and unremitting. Because children and adolescents are in a developmental period of rapid change, assigning a “permanent” diagnosis seems inappropriate. However, it appears that DSM-defined BPD remits within approximately 3  years for most adults (Mattanah, Becker, Levy, Eell, & McGlashan, 1995; Meijer, Goedhart, & Treffers, 1998; Shea et  al., 2002; Zanarini et  al., 2003). Note, however, that this approach may miss some lingering problems. Research on the permanence of borderline features would benefit from a dimensional approach toward classification, which results in greater stability and reliability (Clark, 2009). Second, BPD is highly stigmatizing. Third, although many BPD features appear prior to age 18  years (Crowell et  al., 2012; Kernberg, 1990; Shiner, 2009), children with such symptoms often go on to develop multifinal outcomes (Lofgren, Bemporad, King, Lindem, & O’Driscoll, 1991; Biskin et al., 2011).

Despite these concerns, there are several compelling reasons to study borderline pathology and how it relates etiologically to SII and externalizing conditions among youth. Several current theories of borderline personality development do not require or assume that youth will meet adult criteria (Tackett, Balsis, Oltmanns, & Krueger, 2009). It is also understood that as many as two-thirds of those who meet criteria in adolescence will not maintain the diagnosis by adulthood (Biskin et  al., 2011). This seeming discontinuity may occur for many reasons (e.g., effective intervention, positive peer influences, heterotypic continuity) but does not imply that an earlier BPD diagnosis was inappropriate. Rather, we need to reconsider the assumption that BPD is characterological and immutable. Although some adolescents will desist naturally from a borderline trajectory (Johnson et  al., 2000; Lenzenweger, Johnson, & Willett, 2004), there appears to be a high rank-order stability of behavioral criteria across time (Bornovalova et al., 2009; Lenzenweger, 1999). Early identification, prevention, and intervention hold the most promise for reducing the burden of BPD. A second major area of controversy concerns the underlying structure of BPD and the heterogeneous population captured by this diagnostic label (Lenzenweger et al., 2008). As with many DSM disorders, BPD is assigned using a polythetic criterion set (APA, 2013). However, unlike some diagnoses, the symptoms listed for BPD span the externalizing and internalizing spectra. Any five out of nine criteria are sufficient for diagnosis, producing 151 different potential combinations of symptoms (Skodol, Gunderson, Pfohl, Widiger, Livesley, & Siever, 2002a). Fortunately, all combinations are not equally likely, and there appear to be some core features among those with the diagnosis. For example, more than 90% of those with BPD endorse the affect dysregulation criterion (Zanarini, Frankenburg, Hennen, Reich, & Silk, 2004). Others assert that identity disturbance is an essential characteristic of the disorder (Meares, Gerull, Stevenson, & Korner, 2011; Wilkinson-Ryan & Westen, 2000). Given the heterogeneity among those with a BPD diagnosis, researchers have sought to determine whether BPD criteria cohere as a single dimension or are better represented across multiple dimensions. Results from confirmatory factor analyses have generally supported a one-factor solution, suggesting that the criteria are best conceptualized as a unified, distinct construct (Clifton & Pilkonis, 2007; Fossati, Maffei, Bagnato, Donati, Namia, & Novella, Kaufman, Crowell, Stepp

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1999; Johansen, Karterud, Pedersen, Gude, & Falkum, 2004; Sanislow, Grilo, & McGlashan, 2000; 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 (rs ≥ 0.90). Researchers have also applied latent class analysis to explore potential subtypes of individuals. In further support of a unidimensional conceptualization of the BPD criteria, these studies have identified two or more latent classes that differentiate individuals based on symptom severity rather than a profile type (i.e., based on a specific constellation of symptoms; Bornovalova, Levy, Gratz, & Lejuez, 2010; Clifton & Pilkonis, 2007; Fossati et al., 1999; Shevlin, Dorahy, Adamson, & Murphy, 2007; Thatcher, Cornelius, & Clark, 2005). Thus, some evidence supports conceptualizing BPD criteria as a unidimensional construct. These data suggest that DSM criteria are useful in describing what is similar about patients with BPD but may not capture the heterogeneity observed within clinical samples. In attempts to further investigate heterogeneity among patients with BPD, research has examined additional clinically relevant features beyond the DSM criteria. Two investigations have applied a model-based taxonomy for studying heterogeneity within the BPD diagnosis based on antisocial, aggressive, poor identity formation, and paranoid features (Hallquist & Pilkonis, 2012; Lenzenweger et  al., 2008). Lenzenweger and colleagues (2008) found three phenotypically distinct groups: the first presented with low levels of antisocial, aggressive, and paranoid features; the second showed elevated paranoid features and low levels of antisocial and aggression; and the third group demonstrated high antisocial and aggressive features but low paranoid features. Although Hallquist and Pilkonis (2012) conducted analyses with a different sample and used different measures of BPD features, subtypes that emerged were similar to that of the previous investigation. Specifically, these investigators found evidence supporting four subtypes characterized by (1) high anger and aggression, (2) high anger and high mistrustfulness, (3)  poor identity and low anger, and (4) low aggression and low mistrustfulness. Individuals who belonged to each of these subtypes are likely to have arrived at their diagnosis via different etiological pathways. Those in the third group, with elevated externalizing symptoms, may have followed the hypothesized externalizing trajectory outlined earlier. Understanding etiological differences will be essential for intervention efforts 70

(see also Paris, 2007). For example, those who arrive at a BPD diagnosis via an externalizing pathway may be most responsive to treatments targeting poor behavioral inhibition. Further research is needed to replicate these findings and to assess whether particular intervention strategies differentially affect those representing BPD subtypes.

Research Agenda

Researchers have published valuable findings in the past decade regarding the relation among BPD, SII, and externalizing pathology—yet much remains unknown. A better understanding of the neurodevelopmental processes that drive these conditions is critical for early identification and prevention (Beauchaine, Neuhaus, Brenner, & Gatzke-Kopp, 2008). Given the complexity of longitudinal pathways to these clinical disorders and phenotypically diverse expressions of common vulnerabilities, researchers should identify developmental antecedents associated probabilistically with later BPD and SII. Identifying those individuals who are likely to have biological vulnerabilities (e.g., children of parents with externalizing pathology) and those who exhibit early behavioral impulsivity and emotional lability may be an excellent place to start. Although longitudinal biosocial research has begun to surface in recent years, there are limitations to this important work. Unfortunately, most existing research on the development of BPD has relied heavily on retrospective reports. Additionally, previous work has used models that fail to account for bidirectional and transactional effects that shape the maladaptive behaviors and emotions associated with BPD and SII. As stated earlier, these clinical problems appear to develop via interactions between somewhat stable child-level vulnerabilities (e.g., trait impulsivity) and characteristics of the environment (e.g., caregiver responses), in which both the state and trait components of these processes reciprocally influence each other. Although no single vulnerability or risk factor is unique to BPD development, we believe that multivariate longitudinal research holds great promise for elucidating which factors are most likely to produce BPD, SII, and other externalizing disorders. Prospective longitudinal research is greatly needed that uses multimethod designs to examine relevant biologically based traits, important environmental factors such as parenting strategies, and parent–child interactions. Ideally, future studies would map markers of genetic risk temperamental characteristics in infancy, neurological

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functioning, parental psychopathology and health behaviors across development, contextual risk factors at multiple levels of analysis, and internalizing and externalizing problems at every developmental stage. By early adolescence, adult BPD criteria could be assessed along with developmentally normed measures. Genotyping would allow for tests of gene × environment interactions (G×E) and gene-environment correlations (rGE). Such studies would continue into young adulthood, at which time most cases of BPD could be identified. Current longitudinal studies have either neglected to assess BPD-relevant problems across development, have inadequate sample sizes, or have not followed participants into young adulthood. Stepp and colleagues (2014a) have also expressed the need for research that uses “multiple levels of resolution,” including real-time observational assessment, physiological activity monitoring, experimental manipulations, and longitudinal charting. Furthermore, these authors argue for studying transdiagnostic features such as impulsivity and emotional lability to inform the relation between internalizing and externalizing problems and how they affect real-world developmental processes such as parent–child interactions.

Future Directions

Although attention to diagnostic labels has benefited researchers and clinicians in many ways, separate diagnostic categories also obscure important areas of overlap between etiologically similar conditions. We believe it is important for researchers and clinicians to view BPD as a cluster of emotions and behaviors rather than as a reified diagnostic entity. Considering etiology—and particularly the function of individuals’ behavior within the context of their environments—should reduce confusion about what to “target” in the face of co-occurring diagnoses. When transdiagnostic features common to externalizing disorders, SII, and BPD are considered and treated, available theory would predict improvement across these conditions (see, e.g., NIMH, 2011). Aiding families with vulnerable children should be a chief clinical priority. If youth with vulnerabilities for impulsivity and emotion dysregulation are identified and treated early in the developmental trajectory, it may prevent more severe symptoms at later developmental time points. Furthermore, given the heritability for many of these transdiagnostic traits, parents may require additional support to effectively manage

challenging child behaviors. Promoting parenting skills and parental emotion regulation may be critical intervention targets to prevent intergenerational transmission of psychopathology. As others have demonstrated empirically through parent training intervention research (e.g., Conduct Problems Prevention Research Group, 2002; Patterson, Chamberlain, & Reid, 1982; Webster-Stratton & Hammond, 1997), parenting behaviors can be learned, changed, and can causally influence children’s outcomes. Self-injury, BPD, and traditional externalizing disorders are likely to be linked by common causal factors. However, it is currently unclear at which point(s) in development these trajectories may cross and how some individuals go on to develop co-occurring disorders whereas others do not. We cannot yet be certain whether SII and externalizing pathology play a causal role in the development of BPD or whether each of these conditions derives from a common etiology and results from similar vulnerabilities and risk factors. Although the latter seems probable, further multimethod, prospective longitudinal research is imperative for answering these important questions. We hope that future research efforts will disentangle these possible associations in order to determine the optimal timing and content of intervention strategies.

Note

1. Functional magnetic resonance imaging (fMRI) studies have identified dysfunctional activation patterns and reduced functional connectivity within fronto-limbic regions of the brain (Hart et  al., 2013; see Hughes, Crowell, Uyeji, & Coan, 2012, for a review). Measures of peripheral psychophysiology have demonstrated differences among those with BPD, SII, and externalizing disorders verses controls across the biological systems that govern behavioral inhibition and emotion regulation abilities (see Beauchaine et al., 2001 for a review; Crowell, Beauchaine et al., 2014; Crowell, Beauchaine, McCauley, Smith, Stevens, & Sylvers, 2005; Kuo & Linehan, 2009; Thorell, 2009). Theoretical and empirical work highlights possible dysregulation of acetylcholine, norepinephrine, endogenous opioids, and the limbic-hypothalamic-pituitary-adrenal axis (LHPA; Bandelow, Schmahl, Falkai, & Wedekind, 2010; Coryell & Schlesser, 2001). Readers are referred to Depue and Lenzenweger (2001, 2005) for integrative reviews of neurotransmitter dysfunction (see also Beauchaine et al., 2009; Crowell et al., 2009; Depue, 2009; Gurvits, Koenigsberg, & Siever, 2000).

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CH A PT E R

5

The Externalizing Spectrum of Personality and Psychopathology:  An Empirical and Quantitative Alternative to Discrete Disorder Approaches

Robert F. Krueger and Jennifer L. Tackett

Abstract Mental disorders involving antisocial behavior, disinhibition, and antagonism tend to occur in the same persons, along with related personality traits. These observations can be modeled by positing a coherent externalizing spectrum of personality and psychopathology. In this essay, the authors describe evidence for the externalizing spectrum, encompassing phenomena such as the continuity of externalizing tendencies, genetic coherence of the spectrum, and connections between the spectrum and the broader hierarchical structure of personality and psychopathology. They emphasize the importance of interweaving neuroscience and developmental perspectives with research on clinical externalizing phenomena. A developmentally informed perspective on the externalizing spectrum and its connections with neuroscience and clinical phenomena represents a promising approach for ongoing research, particularly research connected with the National Institute of Mental Health Research Domain Criteria initiative. Key Words:  dimensions, RDoC, DSM, classification, nosology development, neuroscience, externalizing

Introduction

The focus of this essay is on movement away from discrete disorder approaches historically characteristic of authoritative nosologies (e.g., the Diagnostic and Statistical Manual of Mental Disorders [American Psychiatric Association, 2013], the International Classification of Diseases [World Health Organization, 2010]), toward more empirically based models of externalizing personality and psychopathology. Traditionally, psychiatric diagnostic manuals are developed through a process of clinical authority. Experts are assembled to discuss specific sorts of patients (e.g., children with disinhibitory problems), and experts’ opinions are assembled into diagnostic constructs for use in the clinic, under the a priori constraint that all diagnostic constructs must be both polythetic and

categorical. By polythetic, we mean that a number of specific criteria are delineated for a specific disorder (e.g., “talks too much”), and some proportion (e.g., around 50%) of the criteria must be present in order to consider the diagnosis applicable to the patient. That is, all patients showing combinations of criteria that exceed the diagnostic threshold (e.g., at least 5 of 9 criteria) are considered equivalent diagnostically. By categorical, we mean that diagnoses are considered either applicable or not, with no index of the degree of applicability. Diagnoses are dichotomous, and information about criteria are converted into a binary (0,1) representation for clinical purposes. For example, having a “1” value on the diagnosis is represented by writing the category label in the patient’s chart. 79

The clinical authority approach to psychodiagnosis that results in polythetic dichotomies is also leavened with a series of political processes. For example, a workgroup assigned to a specific diagnostic area typically lacks the authority to move their deliberations directly into the official nosology. Instead, efforts of a specific workgroup wend their way through a series of additional committees. These additional committees may have concerns motivating their decision-making that differ from those of the workgroup (e.g., whether a specific diagnostic construct will be generally acceptable to the public, as opposed to regarded as unfairly stigmatizing). Details of this process are described in the DSM-5 itself, in the Introduction (APA, 2013, pp. 5–10). Limitations of this type of political process for producing a scientifically meaningful nosology have been noted by many persons dedicated to studying and ameliorating psychopathology. A striking example occurred just before the DSM-5 was released in mid-2013. The director of the U.S. National Institute of Mental Health (NIMH), Dr. Thomas Insel, repudiated the political approach taken in constructing the DSM-5, stating on his blog that “Patients with mental disorders deserve better [than the DSM-5]” (http://www.nimh.nih.gov/ about/director/index.shtml; April 29, 2013, blog entry). After this initial blog post, Dr. Insel collaborated with Dr. Jeffrey Lieberman (president elect of the APA) to note that the DSM was probably the best we could do currently, given limitations on scientific understanding of psychopathology (http:// www.nimh.nih.gov/news/science-news/2013/ d s m - 5 - a n d - rd o c - s h a r e d - i n t e r e s t s . s h t m l ) . Nevertheless, Insel’s point was made, and NIMH has parted ways with the APA and is pursuing its own path. Specifically, NIMH’s Research Domain Criteria (RDoC) project sets DSM categories aside, seeking to start anew by encouraging research focused on constructs from a cognitive neuroscience perspective, in which individual differences in those constructs are assumed to vary continuously in the population. Thus, the RDoC approach is explicitly dimensional, as opposed to categorical. This current situation in psychopathology research, underlined by the relatively conservative nature of the DSM-5, contrasted with NIMH’s stated goal to go in an entirely new direction, creates a real quandary for both research and practice. Although we can easily question the political processes that create authoritative nosologies such as the DSM-5, must we abandon the kinds of clinical phenotypes described in the DSM-5 entirely? That 80

is, the RDoC approach, at least as currently portrayed in the RDoC matrix on the web, has little to say about clinical presentations of psychopathology, and is focused instead on constructs such as e.g., “frustrative nonreward,” “habit,” and “attention,” as opposed to the sorts of concerns that might lead a patient or a concerned family member to consult a mental health professional. That is, people do not typically seek mental health services complaining about their problems with frustrative nonreward per se. Even if frustrative nonreward is a useful construct for understanding specific clinical psychopathological signs and symptoms, the link between frustrative nonreward and clinical signs and symptoms is where the rubber meets the road in terms of research that might have the potential to elucidate and ameliorate clinical psychopathology. Our view is that it is possible to move from the DSM approach, toward constructs that are clinically applicable, empirically based, and connected with neuroscience—and incorporate clinical description in the process. In this essay, we focus on one such construct, the externalizing spectrum. The externalizing spectrum refers to the observation that specific DSM disorders and related phenotypes (e.g., personality traits) that involve problems such as disinhibition and antagonism tend to cluster together in the same people. As we describe below, evidence that these types of problems form a coherent spectrum of human variation extends beyond co-occurrence patterns, also encompassing evidence of etiologic commonalities (e.g., evidence that genetic contributions to distinguishable externalizing syndromes are partly shared, as opposed to being entirely distinct). Nevertheless, the externalizing spectrum is also a hierarchical construct. By this, we mean that elements within the spectrum (e.g., relational as opposed to physical aggression) have both shared and distinguishable aspects. Moreover, the hierarchy of individual differences that includes externalizing behavior extends beyond those phenomena to encompass other clinically relevant phenomena (e.g., internalizing problems, such as those involving depression and anxiety).

Historical Context

In many ways, the externalizing spectrum conceptualization is not new. A  key reason is that there are essentially two historical streams that ran in parallel in psychopathology research for most of the late 20th century:  the neo-Kraepelinian and quantitative perspectives (Blashfield, 1984). The neo-Kraepelinian perspective is embodied in

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DSM-III to IV-TR. Its basic idea is to delineate categorical mental disorders based on clinical expertise, as described earlier. The assumption is that research on these clinically delineated constructs would eventually converge on an etiologic and pathophysiologic understanding of specific discrete mental disorders. Circa 1970 (when this paradigm became ascendant in American psychiatry and grew to influence the DSMs), this was not an unreasonable notion. Scientific understanding of psychopathology was in its infancy, and the zeitgeist was one of questioning the very foundations of psychiatry as a branch of medicine. Thus, delineating mental disorders as discrete clinical syndromes that (a) could be diagnosed reliably and that (b) were presumed to have a straightforward genetic etiology (e.g., caused by a major polymorphism) was not an unreasonable response to the antipsychiatry movements of that era (Klerman, 1978). Nevertheless, we now know that this model of mental illness is incompatible with diverse data. For example, contemporary molecular genetics research clearly converges on a polygenic model of etiology, in which numerous specific polymorphisms contribute to risk for mental disorder, with no specific polymorphism being necessary or sufficient (e.g., Ripke et  al., 2013). Moreover, genetic expression is moderated by exogenous environmental factors (e.g., Slavich & Cole, 2013). Owing to this complex etiology, putatively discrete mental disorder categories are neither empirically discrete nor mutually exclusive. Comorbidity (whereby most patients do not fit neatly into a single DSM category) and heterogeneity (in which patients meeting criteria for a single DSM category are different in numerous ways) are rules, not exceptions (e.g., Krueger & Markon, 2006). Fortunately, there is another way to delineate mental illness that is more compatible with what we now know about the multifaceted nature of etiologic contributions to psychopathology. This approach can be termed the quantitative approach. It begins with basic observations and questions about individual differences (e.g., does a child have the capacity to focus attention? does a child tend to follow rules? And so on.) and then assembles those observations into syndromes inductively, by seeing how they tend to “go together” based on data, as opposed to clinical experiences and political processes. A major reason the quantitative approach has been influential in conceptualizations of externalizing psychopathology is because of the pioneering

work of Thomas Achenbach. Working in parallel with the DSM endeavor, Achenbach developed the Child Behavior Checklist (CBCL), using quantitative approaches. Data from the CBCL and related instruments in the Achenbach System for Empirically Based Assessment (ASEBA) converge on an externalizing spectrum of specific syndromes, united by disinhibitory characteristics (Achenbach & Rescorla, 2001). We turn now to selectively review research from this tradition, and how it interweaves with research from the DSM tradition.

DSM-Defined Externalizing Disorders and Syndromes from the ASEBA Tradition

In youth, the externalizing spectrum has historically been defined by the primary dimensions of Achenbach’s Child Behavior Checklist:  (physical) aggression (often termed “aggressive behavior”) and rule-breaking behaviors (often termed “delinquent behavior) (see Achenbach & Rescorla, 2001). These dimensions were derived from data on the way children’s behaviors tend to cluster empirically. Nevertheless, they overlap meaningfully with DSM-defined disorders, such as oppositional defiant disorder (ODD) and conduct disorder (CD). Whereas symptoms of ODD primarily overlap with the CBCL aggressive behavior subscale, symptoms of CD show differential associations with both CBCL scales, leading to the suggestion that DSM-defined CD is composed of both aggressive and rule-breaking behavioral subtypes (Burt, 2012; Tackett, Krueger, Sawyer, & Graetz, 2003). Although some connections can be seen between quantitatively derived syndromes and DSM disorders, other relevant forms of disinhibitory psychopathology are not well captured by either DSM disorders or the CBCL (e.g., relational aggression). Moreover, some key disinhibitory problems may vary in manifestation based on stage of development. For example, some highly consequential phenomena are captured by primarily adult DSM disorders that are not developmentally sensitive to early manifestations of the same problems (e.g., early experimentation with substances that may antedate serious problems with substance use). Further limitations to direct DSM-CBCL correspondences is evidenced by the DSM distinction between attention-deficit/hyperactivity disorder (ADHD) presentations (i.e., inattentive vs, hyperactive/impulsive) versus a single attention problems scale in the CBCL [note—DSM-5 downgraded types to “presentations”]). Indeed, the CBCL attention problems scale demonstrates content overlap Krueger, Tacket t

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with both DSM inattentive symptoms (as evidenced by shared connections with low conscientiousness) and hyperactive symptoms (as evidenced by shared connections with high surgency/activity level). Furthermore, the CBCL attention problems scale shares predominant variance with the personality trait neuroticism/negative affectivity, which is completely absent from the DSM-defined ADHD construct (Herzhoff, Tackett, & Martel, 2013). In addition to these distinctions between CBCL and DSM conceptualizations, both approaches are further limited in assessing externalizing content that is highly relevant for identifying youth who may benefit from early intervention (e.g., relational aggression and risky sex).

Current State of the Science

As this brief review makes clear, although some connections between the CBCL and DSM literatures on externalizing phenomena can be discerned, neither approach provides an entirely comprehensive model that is also sensitive to developmental considerations (although the CBCL certainly does a better job with age-based norms). In working forward from both the neo-Kraepelinian (DSM) and quantitative traditions, we emphasize three specific lines of research that could provide the broad outlines of a more comprehensive, empirically based account of the externalizing spectrum.

Direct Comparison of Discrete and Dimensional Models of Externalizing Phenomena

Authoritative classifications such as the DSM proceed from the assumption that mental disorders are discrete. This assumption can now be rendered as a hypothesis and evaluated directly, because it is now amenable to empirical evaluation. At least three broad types of models can be fit to data:  (a)  dimensional models (e.g., latent trait models), in which individual differences in externalizing tendencies vary continuously, with no breakpoints; (b) categorical models (e.g., latent class models), in which individual differences in externalizing tendencies delineate discrete subgroups of persons; and (c)  hybrid models, which combine features of dimensional and categorical models (Hallquist & Wright, 2013). For example, a hybrid model might posit two discrete groups of persons, such as “very high externalizers” vs. “typical externalizing,” with both continuous variation within these groups but also a categorical distinction between them. 82

With regard to externalizing syndromes in particular, these models have been compared in a number of studies. Some studies have focused on comorbidity among DSM-defined externalizing disorders (Krueger et  al., 2005; Markon & Krueger, 2005). These studies indicate that comorbidity patterns are best fit by dimensional models, that is, variation in tendencies to meet criteria for multiple externalizing disorders is continuous as opposed to discrete. Other studies have focused on specific syndromes within the externalizing spectrum. For example, Wright et al. (2013) examined diverse psychopathological syndromes common in the general population, including alcohol and drug problems, and concluded that variation in these syndromes was best accounted for by a dimensional model. Studies have also examined CBCL constructs. Specifically, Walton, Ormel, and Krueger (2011) fit the aforementioned series of models to the delinquent and aggressive behavior syndromes of the CBCL and concluded that dimensional models fit best. Finally, some studies have used other quantitative techniques such as Paul Meehl’s taxometric methods (see Beauchaine, 2007, for an excellent primer), which examine the possibility that a set of indicator variables delineates a “taxon” (a discrete group) in nature. A  recent review of the taxometrics literature concluded “the domains of normal personality, mood disorders, anxiety disorders, eating disorders, externalizing disorders, and personality disorders (PDs) other than schizotypal yielded little persuasive evidence of taxa” (p.  903). Still, this review also concluded that discrete variation is sometimes observed in the area of substance use disorders (Haslam, Holland, & Kuppens, 2012). In sum, the preponderance of the evidence points to the continuity of externalizing phenomena.

Etiologic Coherence of the Externalizing Spectrum and Development of the Externalizing Spectrum Inventory

In addition to evidence that externalizing syndromes are distributed continuously in the population, there is also evidence that they are correlated because of shared etiology. Specifically, a number of studies have examined comorbidity patterns among DSM-defined externalizing syndromes in twins, thereby making it possible to parse connections among these syndromes into genetic and environmental contributions. These types of studies (Kendler & Myers, 2013; Krueger et al., 2002; Young et  al., 2000) have consistently identified a major genetic factor connecting diverse externalizing

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syndromes, thereby indicating that one source of coherence for the externalizing spectrum is shared genetic etiology. Of course, this state of affairs does not imply a single gene that is responsible but rather overlap in overall genetic risk or vulnerability. Working from these observations of continuity and shared etiology, we developed a novel model of externalizing phenomena and an associated assessment instrument for adults, the Externalizing Spectrum Inventory (ESI; Krueger et  al., 2007). The ESI was designed to provide additional detail about externalizing propensities, compared with what one can glean from existing approaches such as the DSM or the CBCL. It delineates 23 distinct “facets” or fine-grained individual differences in externalizing tendencies, and is now available in a shorter form (Patrick, Kramer, Krueger, & Markon, 2013). Because of this additional content, the ESI appears capable of distinguishing among broad tendencies toward disinhibition, antagonism, and substance use problems (Krueger et al., 2007). Because the DSM tends to collapse this content in various ways (e.g., the CD criterion set contains both antagonistic and disinhibitory criteria, and all the information in those diverse criteria are collapsed into a 0,1 variable when making a CD diagnosis), studies focused on comorbidity among dichotomous DSM disorders tend to identify primarily one broad externalizing propensity. However, some studies of DSM constructs also distinguish subelements of externalizing behavior patterns, akin to what is observed with the ESI. For example, Witkiewitz et  al., (2012) identified separable disinhibitory (ODD, ADHD, and CD) and substance use factors. In general, it would be useful to bring the DSM more closely in line with the empirical structure of personality and psychopathology, a topic to which we now turn.

Hierarchical Structure of Personality and Psychopathology

The externalizing spectrum is one aspect of an even broader hierarchal model of personalitypsychopathology. The distinction between externalizing and internalizing (e.g., problems such as depression and anxiety) phenomena appears to form the first “broad band” subdivision in research on the structure of individual differences in psychopathology (at the second level of the hierarchy; see DSM-5; APA, pp.  12–13). However, there are levels of distinction below this basic broad-band subdivision that help connect a variety of models articulated in the literature (Krueger & Markon,

2013). At the third level of the hierarchy, for example, internalizing subdivides into more introverted and more neurotic aspects, delineating a model resembling “big three” models of temperament (e.g., Clark, 2005). At the fourth level of the hierarchy, externalizing further subdivides into more disinhibitory and more antagonistic aspects, delineating the “big four” model of abnormal personality variants (cf. Kushner, Quilty, Tackett, & Bagby, 2011). Finally, at the fifth level, a separable domain related to cognitive dysregulation (often termed psychoticism) emerges (cf. Harkness et al., 2012). In general, subdivisions beyond this “five level” are difficult to detect, although some personality models extend into these regions (e.g., the HEXACO model adds a sixth domain by making a distinction between agreeableness vs. antagonism, and honesty-humility vs. arrogance; Ashton & Lee, 2007). This hierarchical structure can be delineated by a variety of specific instruments that are complementary in their ability to assess specific variants within the broad hierarchy. For example, this literature on the empirical structure of personality-psychopathology now connects directly with the DSM, via the Personality Inventory for DSM-5 (PID-5; Krueger et al., 2012). The PID-5 is freely available at http://www.psychiatry.org/ practice/dsm/dsm5/online-assessment-measure s#Personality, for use in research or by clinicians for use with their patients. The PID-5 focuses specifically on maladaptive personality variants within this hierarchy, consistent with the focus of the DSM on delineating forms of mental disorder (as opposed to more adaptive aspects of personality; Wright et  al., 2012). Other instruments supplement those focused on more maladaptive personality variants by also folding into the hierarchy in meaningful ways, complementing and extending the variants assessed by the PID-5. For example, the PID-5, the NEO-PI-R, and the static form of the CAT-PD (Computerized Adaptive Test for Personality Disorder; Simms et al., 2011) jointly delineate the aforementioned hierarchy of personality-psychopathology (Wright & Simms, in press). However, the NEO-PI-R fleshes out the more adaptive variants within the structure, and the CAT-PD can efficiently assess a very wide range of maladaptive tendencies. An exciting topic for continued research is how these models and instruments, developed primarily for application in adult populations, may be useful in understanding individual differences in other age groups. Krueger, Tacket t

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Developmental Considerations

There are several relevant issues to consider in thinking about how to make the externalizing spectrum applicable to a wide range of ages. We begin by considering children and adolescents. First, primary issues of classification and taxonomy have not yet been resolved for a broad youth externalizing spectrum. Second, measurement and assessment of externalizing psychopathology in children and adolescents brings some unique challenges (see later). Third, developmental trajectories of externalizing problems are clearly grounded in normative developmental theory and knowledge, yet are largely missing from clinical and diagnostic approaches to early externalizing behavior patterns. In this section, we review these issues and end with some suggestions that may be useful in working toward a developmentally informed externalizing spectrum conceptualization. Efforts to draw explicit and empirically based connections between personality and psychopathology in children and adolescents, from a spectrum-based perspective, have lagged behind similar research in adults. Despite the early emergence of an externalizing spectrum in the CBCL (Achenbach & Edelbrock, 1978), which originated in the child/ adolescent literature, only recently has there been movement toward broadening, updating, and refining the content domain of early externalizing problems (e.g., Baker & Heller, 1996; Bezdjian et  al., 2011; Lahey et al., 2004; Witkiewitz et al., 2012). Relational aggression, which reflects social or interpersonal forms of aggression such as social exclusion and malicious gossip, is clearly linked to other forms of childhood externalizing (Card, Stuck, Sawlani, & Little, 2008; Crick, 1996), with emerging evidence that couches it firmly within an externalizing spectrum in youth (Tackett, Daoud, De Bolle, & Burt, 2013). Risky and addictive behaviors frequently co-occur with “core” aspects of youth externalizing psychopathology, and are frequently grouped with externalizing conceptualizations in adults, but are not as integral to classification of youth externalizing disorders (e.g., Slutske et  al., 2001; Tackett, Neighbors, Cooper, & Derevensky, 2013; Timmermans, van Lier, & Koot, 2008; Vitaro, Brendgen, Ladouceur, & Tremblay, 2001; Walther, Morgenstern, & Hanewinkel, 2012). Nevertheless, similar to findings in older age groups, there is emerging evidence that child externalizing symptoms (those associated with ADHD, ODD, and CD) cohere genetically (Tuvblad, Zheng, Raine, & Baker, 2009). 84

A second highly relevant issue when considering externalizing problems in youth involves practical concerns around assessment and measurement. Concerns regarding informant discrepancies in clinical assessment have typically received much more attention by those working with child, rather than adult, populations (e.g., Achenbach, McConaughy, & Howell, 1987). Specifically, assessment of child psychopathology most often emphasizes the need to collect data from multiple informants, although decisions about how best to integrate such data, which are often discrepant, continues to be debated (Achenbach, 2011; De Los Reyes, 2011). Moreover, these concerns are not limited to assessment of children (e.g., Oltmanns & Turkheimer, 2009). In general, informants tend to show higher agreement on externalizing psychopathology than internalizing psychopathology, in all likelihood due to higher observability of externalizing problems (Grills & Ollendick, 2002). However, more sophisticated approaches to integrating information across informants (e.g., through use of standardized difference scores or polynomial regression analyses) have emphasized domain-specific complex relations between informant disagreement, as well as the potential predictive validity of informant discrepancies in and of themselves (Laird & De Los Reyes, 2013; Tackett, Herzhoff, Reardon, Smack, & Kushner, 2013). Externalizing disorders are highly prevalent in youth, second only to anxiety disorders (Merikangas et  al., 2010). Major changes also emerge in externalizing psychopathology across childhood and adolescent development. Childhood externalizing problems are typically marked by manifestations such as physical aggression and oppositionality, with more salient forms of externalizing behavior shifting toward symptoms of rule-breaking/ delinquency, relational aggression, disinhibitory personality pathology, addictive disorders such as gambling and substance use, and risky sexual behavior, which often onset and/or peak during adolescence and emerging adulthood (e.g., Burt, 2012; Stanger, Achenbach, & Verhulst, 1997; Tackett, Herzhoff, Reardon, De Clercq, & Sharp, 2013; Tremblay et  al., 2004). Theories of adolescent risk-taking behavior emphasize adolescence as an important developmental period during which rates of sensation-seeking behavior and reward sensitivity are especially high, but adult levels of self-control and inhibition are not yet fully developed (Steinberg, 2008). Current dichotomous diagnosis-based approaches to classification do not

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allow developmental sensitivity to these normative changes, although dimensional classification systems offer much greater flexibility in tracking underlying domains across time. Dimensional systems offer the possibility of better tracking of the level and extent of specific behaviors over time, facilitating the ability to understand potential patterns of heterotypic continuity. For example, developmentally appropriate indices of sensation seeking allow tracking those behaviors over the course of development, as opposed to embedding those behaviors into dichotomous diagnostic schemes that are associated with arbitrary thresholds and relatively arbitrary changes in diagnostic schemes as a function of age (e.g., the differences in the way specific externalizing behaviors are distributed into dichotomous diagnoses in children vs. adults in the DSM). In this section, we have highlighted a few examples of developmental considerations that should be incorporated into an empirically informed externalizing spectrum approach with generalizability across the lifespan. Although these issues have become salient through research with children and adolescents, they certainly continue to apply throughout adulthood and later life. Specifically, an empirically informed externalizing spectrum must have flexibility to capture distinctions across different age groups (a reflection of the deeper need for such a spectrum to accommodate heterotypic continuity) and must include awareness of complications and nuances inherent in the use of different informants and assessment tools. Furthermore, it should reflect developmental sensitivity regarding aspects of the externalizing spectrum that wax and wane across the course of development (and the underlying developmental processes driving these changes).

Controversies in the Contemporary Post-DSM-5/RDoC Landscape

Controversies are often in the eye of the beholder. That said we now reemphasize the sorts of controversies we discussed at the outset of this essay. At this point in the history of the field, the clinical-authoritative approach to creating classification systems is insufficient. The DSM-5 endeavor began with a push to create a new, more dimensionally oriented approach (Kupfer, Kuhl, & Regier, 2013), but ended with a system that is essentially “DSM-IV-TR-Revised,” largely because conservative political forces created awkward compromises along the way. For example, the DSM leadership worked to develop dimensional cross-cutting assessment instruments (Narrow et al., 2013), and in the

end these were placed in Section III of DSM-5 as a set of “emerging measures and models”. Externalizing syndromes in the DSM-5 are largely the same as they were in DSM-IV. Nevertheless, some efforts to bring the DSM in line with the research literature were realized. For example, the chapter organization in DSM-5 was revised to recognize the externalizing spectrum. That is, chapters on “disruptive, impulse-control, and conduct disorders” and “substance-related and addictive disorders” are placed next to each other, to recognize how these syndromes are connected to the externalizing spectrum, as explained in the introduction to DSM-5 (APA, 2013, pp.  12–13). Nevertheless, the serial structure of the DSM’s chapter organization also limits its ability to recognize the multivariate nature of psychopathology variation. That is, the DSM is organized into a series of individual and separate chapters, in a serial format (from the first to the last chapter in sequence), making it challenging to recognize how specific psychopathology constructs are mutually interconnected to broader constructs like the externalizing spectrum. For example, ADHD is in the chapter on neurodevelopmental disorders, even though it clearly connects meaningfully with the externalizing spectrum (Beauchaine & McNulty, 2013; Carragher et al., 2013). Some minor aspects of specific externalizing disorders have also changed. For example, CD is augmented with a specifier of “limited prosocial emotions” (a phrase that was adopted to refer to callous and unemotional personality traits). Nevertheless, CD is still composed of diverse disinhibitory (e.g., breaking rules) and antagonistic (e.g., aggression) criteria, with a relatively low threshold of 3 of 15 criteria required for a diagnosis. Hence, children can meet criteria for DSM-5 CD showing somewhat heterogeneous patterns of behavior (some more disinhibited, others more antagonistic), and some of these children can be labeled “even more antagonistic” (i.e., a clinician could apply the limited prosocial emotions specifier, callousness being a specific aspect of the broader personality domain of antagonism; Wright & Simms, in press). Moreover traits described in the “limited prosocial emotions” specifier (much less traits described by the CD criteria themselves) are not explicitly interwoven with the DSM-5 model of personality traits. Although this connection was pursued throughout construction of the DSM-5 (e.g., Krueger was a liaison between the ADHD and Disruptive Behavior Disorders Workgroup and the Personality Disorders Krueger, Tacket t

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Workgroup), it was never formalized fully before publication of the DSM-5 in May 2013. The leadership of the NIMH clearly understands the need to escape from the political miasma of authoritative nosologies such as the DSM, but, taken at face value (i.e., simply reading the RDoC matrix of constructs posted at http://www.nimh.nih.gov/research-priorities/rdoc/ nimh-research-domain-criteria-rdoc.shtml#toc_ matrix), RDoC seems more focused on cognitive neuroscience per se than on clinical psychopathology, or on the connections between neuroscience constructs and psychopathology constructs (however, see Cuthbert & Kozak, 2013, for some useful clarifications about the RDoC initiative, that suggest a broader and more integrative perspective than might be gleaned by only consulting the RDoC matrix as displayed on the web). To be clear, neuroscience offers many helpful tools that can be interwoven with phenotypes to create a deeper understanding of human variation. To pick a specific example relevant to the externalizing spectrum, variation in the P3 event related potential is correlated with the broader externalizing spectrum, as opposed to being correlated only with specific externalizing syndromes (Patrick et al., 2006), pointing toward a potential neural basis for the coherence and breadth of the spectrum. Delineating connections between variables from neuroscience and clinical psychopathology (as opposed to pursuing neuroscience disconnected with clinical concerns) will help the RDoC endeavor to have lasting traction and impact, to move “from bench” (cognitive neuroscience) “to bedside” (clinical psychopathology; Cuthbert & Kozak, 2013).

Research Agenda and Future Directions

The externalizing spectrum research agenda going forward seems clear, but also somewhat daunting. It would be useful to work toward an understanding of externalizing phenomena that is both neurobiologically informed and applicable across the course of development. The categorical DSM approach to delineating mental disorders does not accord with the evidence, but the DSM-5 process and outcome illustrates that breaking out of the DSM’s categorical-polythetic paradigm within the DSM process itself is likely to be challenging. There are some innovative dimensional features of DSM-5 that are attracting research attention (e.g., the DSM-5 personality trait model has generated a body of literature reviewed by Krueger & Markon, 2013), but these features need to be reconciled with 86

other considerations that encumber the DSM. For example, one understandable objection to dimensional approaches is entirely practical, i.e., how can we use dimensional constructs to fill out third party reimbursement forms? This is an important issue, but also a tractable one. For example, most areas of medicine do not find it strange to set clinically meaningful thresholds on continuous variables such as blood pressure or cholesterol level. But addressing this issue head on requires admitting that DSM criterion sets and their relatively arbitrary thresholds do not delineate categories in nature, and many who have grown attached to specific DSM categories find this difficult to accept, leading to reification of DSM rubrics (Hyman, 2010). The NIMH has grown impatient with this situation and aims to catalyze novel dimensional research under the RDoC rubric, but how exactly how RDoC evolves remains to be seen (cf. MacDonald & Krueger, 2013). We would again emphasize that cognitive neuroscience research disconnected from clinical phenomena is unlikely to result in traction in the psychopathology research community. However, approaches that interweave indicators of neural functioning with assessments of phenotypic clinical phenomena have great potential to result in diagnostic constructs that are both clinically applicable, and informed by neurobiological evidence (Beauchaine & Gatzke-Kopp, 2012). The externalizing spectrum is likely to provide useful guidance as we move forward into the post-dsm-5/RDoC world. For example, cognitive (effortful) control is a major aspect of the RDoC matrix, and this construct is clearly relevant to understanding clinical externalizing phenomena (Gusdorf, Karreman, van Aken, Dekovic, & van Tuikl, 2011). It would indeed be useful to be able to delineate individual differences in neural circuits relevant to cognitive control, and then to observe how those individual differences manifest in behavioral differences. We would hypothesize, for example, that the domain of behavioral differences aligned with individual differences in neural circuits relevant to cognitive control would resemble the phenotypically delineated externalizing spectrum. Although it is scientifically useful to be freed from DSM constraints, it is also important to be able to work backwards to explain phenomena delineated in the DSM. To pick a specific example, ODD is likely an early developmental manifestation of the broader personality domain of antagonism vs. agreeableness (in the absence of interventions that may deflect the development of these tendencies).

The Externalizing Spectrum of Personalit y and Psychopathology

Pathological syndromes in children are closely linked to personality (Tackett, Herzhoff, Reardon, De Clercq, & Sharp, 2013), but personality development is also a dynamic process (i.e., traits are not fixed and unchanging; Roberts & Mroczek, 2008). The transition to a more empirically based approach can be facilitated by translating back to traditional DSM rubrics. The aim is to explain how those rubrics arose in the first place (e.g., children with oppositional tendencies are not hard to find in clinical settings, so a more empirically based system should endeavor to explain how presentations that resemble DSM-defined ODD fit within that system). Finally, as we described above, perhaps the most daunting challenge is delineating the externalizing spectrum in a way that is truly developmental in scope. The necessary psychometric tools exist to develop empirically based assessment systems for specific age groups, but accommodating developmental changes in phenotypic manifestations of psychopathology can be challenging. For example, individual differences in antagonism can be observed in diverse age groups, but an antagonistic two-year-old behaves differently from an antagonistic 80-year-old. Nevertheless, a comprehensive account of the externalizing spectrum would be able to capture not just individual differences within specific age groups, but also, the way those individual differences are shaped by developmental processes.

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CH A PT E R

6

The Developmental Psychopathology Perspective on Externalizing Behavior Dimensions and Externalizing Disorders

Stephen P. Hinshaw and Theodore P. Beauchaine

Abstract This chapter highlights key principles of developmental psychopathology (DP), the discipline that eschews static models, universal and unbending laws of development, and inflexible categorical diagnoses in favor of transactional pathways and integration of multiple levels of analysis to achieve maximal understanding of psychopathology. The chapter emphasizes the mutually informative nature of investigating normal and atypical development; importance of considering developmental continuities and discontinuities; inclusion of analysis levels spanning the spectrum from genes to community-level influences on atypical development; transactional models of influence, which subsume important constructs of equfinality and multifinality; and risk and protective factors and processes. The key conclusion is that unless DP principles, concepts, and models are incorporated explicitly into investigations of externalizing behavior, the field is likely to misunderstand the highly dynamic and developmental nature of externalizing disorders and thereby miss key opportunities for deep understanding, evidence-based prevention and treatment efforts, and stigma reduction. Key Words:  developmental psychopathology, development, externalizing spectrum disorders, externalizing behavior, stigma

Introduction

The discipline of developmental psychopathology (DP) became a reality more than 40 years ago. Achenbach’s (1974) textbook set the stage, and a compendium of articles in the prestigious journal Child Development subsequently served as a key launching point in DP’s developmental trajectory (e.g., Cicchetti, 1984; Sroufe & Rutter, 1984). Cicchetti’s founding of the journal Development and Psychopathology in 1989 galvanized investigators from multiple disciplines to undertake the time-costly yet essential tasks of (a)  integrating prior conceptual and empirical models from different fields (e.g., embryology, systems theory, genetics, neuroscience, psychology, and psychiatry, to name several) and (b)  performing theoretically informed longitudinal investigations to understand 90

developmental trajectories and pathways relevant to serious mental health problems. DP is an inherently multidisciplinary endeavor that is closely tied to clinical child and adolescent psychology and psychiatry but transcends the usual diagnosis-based emphasis of these fields (Cicchetti & Toth, 2009). Its focus on the dynamic interplay of biology and context, genes and environments, and intraindividual and environmental influences on the development of healthy and atypical functioning has come to dominate current thinking and research on psychopathology. The tenets of DP are no longer revolutionary or controversial:  indeed, they have come to represent the dominant paradigm in the study of the origins and maintenance of psychopathology. “Main effects” models have been largely replaced with interactive, transactional

perspectives that emphasize the probabilistic nature of risk factors for predicting later mental health conditions and the importance of considering individual patterns of growth in relation to a host of intertwined biological and contextual influences. How can DP be characterized succinctly? In their groundbreaking article, Sroufe and Rutter (1984) put forth the proposition that “links between earlier adaptation and later pathology generally will not be simple or direct. It will be necessary to understand both individual patterns of adaptation with respect to salient issues of a given developmental period and the transaction between prior adaptation, maturational change, and subsequent environmental challenges” (p. 17). From the outset, then, dynamic models were featured. Next, Cicchetti (1990) highlighted DP’s multi- and interdisciplinarity, stating that the emerging field should “bridge fields of study … [and] contribute greatly to reducing the dualisms that exist between … the behavioral and biological sciences, and … between basic and applied research” (p. 20). Thus, if DP is to thrive, traditional silos must come down in favor of collaborative research. More recently, Beauchaine and McNulty (2013) contended that, in this field, investigators must “specify biological vulnerabilities and environmental risk factors that span levels of analysis from genes to culture, and acknowledge that causal influences operate across these levels of analysis, sometimes changing in direction through both internal and external mechanisms” (p. 1507). In other words, through the lens of DP, patterns of development are not likely to be linear or straightforward but rather to operate via complex, multilevel pathways. Finally, in addressing DP’s insistence on spanning multiple levels of analysis, Hinshaw (2013) posited that “Without consideration of transactional processes, multi-level models, computational frameworks, gene-environment interplay, and a host of technological and conceptual advances related to the overall field of developmental neuroscience, we will not be able to solve the problem of gaining deep understanding of relevant mechanisms” (p. 9). What stands out in these quotes is the increasing recognition that isolated risk factors rarely have major explanatory power; that single theoretical perspectives are inadequate to the task at hand; that developmental processes unfold, across biological and contextual processes, in cascading and even “symphonic” fashion (Boyce, 2006); and that static, categorical “boxes” of mental disorders fail to capture the dynamic unfolding of relevant mechanisms

that eventuate in mental disturbance—or, in some cases, resilient functioning. Indeed, traditional models may be misleading, in that individuals with substantially different risk factor configurations or developmental trajectories can and will be lumped together in the “same” brand of mental disorder. Significant progress is not likely to emerge with such a state of affairs; the complexities of DP’s transactional pathways are proving to be far more accurate and predictive. In this chapter, we aim to bring to life a number of core DP percepts, concepts, and principles. This is a daunting task for a handbook chapter operating under the current page limitations. Indeed, multivolume sets exist that elaborate on the underlying principles of DP (see Cicchetti & Cohen, 2006, 2015. For more general overviews of this discipline and its underlying beliefs and models, with applications to broader domains of psychopathology, see (among many others) Cicchetti (2006), Cicchetti and Toth (2009), Hinshaw (2013), Mash and Dozois (2003), Rutter and Sroufe (2000), and Sroufe and Rutter (1984). Note that this chapter follows the outline of key principles as elucidated by Hinshaw (2013) but, in keeping with the focus of the current handbook, does so in the context of externalizing dimensions and behavior patterns—and the transactional patterns and pathways leading to their manifestations. We note at the outset that the stakes are high in this entire endeavor. First, suffering, pain, and impairment are legion with respect to externalizing problems. For example, costs linked to attention-deficit/hyperactivity disorder (ADHD) at the levels of both personal impairment and downstream economic consequences are vast (Barkley, 2015; Hinshaw & Scheffler, 2014; Robb et  al., 2011). Oppositional defiant disorder (ODD) and conduct disorder (CD)—particularly physical aggression and violence—wreak havoc on families and communities, too often leading to tragic consequences and astronomical economic burden (e.g., Moffitt, 2006). Substance abuse is a leading cause of morbidity and mortality, with adolescence serving as the key developmental window for its initiation and intensification (Brown, Tomlinson, & Winward, 2013). Persistent antisocial behavior—classified diagnostically as antisocial personality disorder (ASPD; see Dishion & Racer, 2013)—is an especially pernicious outcome of earlier externalizing tendencies. Indeed, it is well known that a relatively small number of antisocial individuals are responsible for a substantial amount of criminal and violent behavior (Farrington, 1995). Certainly, betterthan Hinshaw, Beauchaine

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expected outcomes may transpire for those with externalizing conditions and problems, as well as other risk factor combinations (e.g., Sapienza & Masten, 2011). Yet, in far too many cases, impairments span multiple years—if not decades—and reach deeply into the lives of both perpetrators and victims. Thus, the clinical imperative for enhanced comprehension of the entire domain is compelling. Second, understanding interrelations across supposedly separable forms of externalizing behavior—and their progressions over development— is essential for full conceptual understanding and emergence of truly evidence-based intervention strategies (Beauchaine & McNulty, 2013). We cannot afford to continue to act as though ADHD, ODD, CD, substance use disorders (SUD), and APSD will be well understood (or even partially understood) by referring to separate headings in diagnostic nomenclatures or distinct textbook chapters. In fact, we address the high rates of “homotypic comorbidity” across these externalizing conditions later in this chapter. Furthermore, the lack of thorough knowledge of these conditions, fueled in part by the piecemeal science afforded by categorical thinking, is one of many contributors to the lasting stigma that mental disorders continue to receive (for additional issues related to stigma and mental illness, see Hinshaw, 2007; Martinez & Hinshaw, 2015). Indeed, stigma and shame remain a major barrier to adequate funding levels, access to care, and opportunities for personal growth and advancement (Hinshaw & Stier, 2008). It is our contention that education, narrative, and humanization must dovetail with advances in DP to promote needed intervention. In short, DP can provide the needed tools to gain the kinds of mechanism-based, process-oriented knowledge required for full comprehension of the underlying dimensions and varied manifestations of impulsive, poorly self-regulated, and often violent and self-destructive behavior. We turn now to a review of some of the basic tenets of DP to provide grounding for this entire handbook.

DP Concepts and Principles

We begin with several basic ideas, which might be termed axioms rather than principles per se. First, pathways toward maladaptive versus adaptive outcomes are neither linear nor determined completely by initial values. They are inherently interactive and, over time, transactional. As a result, two core constructs follow. First, alternative causal routes or pathways may lead to a common syndrome, thus 92

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exemplifying the concept of equifinality. For example, aggressive and antisocial behavior patterns are predictable from a host of variegated risk factors, including physical abuse and other forms of maltreatment, a heritable tendency toward disinhibition, injury to the frontal lobes, coercive parenting interchanges, cognitive deficits (including verbal, nonverbal, and “executive” dysfunction), and/or prenatal and perinatal risk factors acting in concert with early experiences of insecure attachment or parental rejection as well as peer conflict. Indeed, combinations of these vulnerabilities and risk factors are highly likely for the most troubled cases (e.g., Crocker, Fryer, & Mattson, 2013; Jaffee & Maikovitch-Fong, 2013; Jaffee, Strait, & Odgers, 2012; Moffitt, 1993; Tremblay, 2010). In short, differentiable etiologic and maintaining influences can yield clinical end states that appear phenotypically quite similar. A key implication is that what might appear to be the “same” condition of ADHD, aggressive conduct problems, or substance abuse in different youth may actually originate from highly divergent causal mechanisms. Indeed, just because two different youth display equivalent levels of conduct-disordered symptoms or use the same amounts of a given illicit substance does not mean that they arrived at such destinations along similar roads. This axiom has profound implications for current categorical models of psychopathology classification, which may in fact produce misleading findings at levels of risk factors, mediator pathways, and clinicaltrials research. Second, the construct of multifinality applies when a given risk factor, vulnerability, or initial behavioral configuration predicts disparate outcomes for different subsets of youth. The implication is that risk status is probabilistic, not fully determinative of later outcome. We know, for instance, that maltreatment may or may not lead to serious externalizing tendencies depending on a host of intervening factors (Cicchetti & Valentino, 2006; Jaffee & Maikovich-Fong, 2013). Similarly, gene × environment interactions dictate that a given risk genetic vulnerability may be moderated by contextual factors to produce quite disparate outcomes. An equivalent interpretation is that environmental risks moderate genotypes (see Rutter, Moffitt, & Caspi, 2006). Both equifinality and multifinality indicate that single-risk-factor models and linear associations are inadequate to the task of facilitating full understanding of child and adolescent psychopathology (Cicchetti & Rogosch, 2006).

Second, DP models prioritize, when possible, person-centered research designs in which the 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, other key risk factors—or by differentiated outcomes predicted from initial risk (see Bergman, von Eye, & Magnusson, 2006). Put in slightly different language, unique developmental pathways may well apply to relatively homogeneous subgroups of participants. Even in variable-centered research, key moderator variables (which characterize the sample as subdivided by baseline characteristics) and key mediator processes (signaling relevant mechanisms that occur temporally between initial measurement and outcomes of interest) must be taken into account (e.g., Fairchild & MacKinnon, 2009; Hinshaw, 2002; Howe, Reiss, & Yuh, 2002; Kraemer, Stice, Kazdin, Offord, & Kupfer, 2001). Without consideration of moderators, an entire sample may be inappropriately considered as uniform with respect to underlying processes. Unless mediating variables are accounted for, underlying mechanisms of change may be missed. Taking one example among many candidates, Jaffee and Maikovich-Fong (2013) reviewed the extensive literature on maltreatment as a risk factor for psychopathology. This incisive review addressed, for instance, that although maltreatment does serve to elevate risk, its effects on later antisocial behavior are not likely to result from shared genes between parents and children—making it largely an environmental risk mechanism (see Jaffee, Caspi, Moffitt, & Taylor, 2004). Still, it may do so via psychobiological (e.g., epigenetic mechanisms, elevation of “stress” axes, direct structural or functional brain effects) or psychosocial (e.g., hostile attribution bias; self-blaming) mechanisms. In addition, gene × environment interactions may be operative (i.e., only certain children, defined by genotype, display particularly negative outcomes related to maltreatment), and maltreatment’s specific effects are compounded or qualified by moderator variables and processes (e.g., child sex, inner resources of the victim, general family support, and social support). Hence, the study of maltreatment requires large, representative samples—affording investigation of relevant subgroups—longitudinally followed and subject to rigorous, multilevel assessments with the potential to examine heterotypically continuous outcomes.

Third, DP investigators must attend to the role of genes and neural pathways—and to neuroscientific principles in general—in providing more comprehensive explanations of extant externalizing pathologies and their devastating effects (see Cicchetti & Curtis, 2006). Strictly environmentalist views, which dominated clinical psychology and psychiatry for large swaths of the 20th century, are both antiquated and inadequate to the task of comprehending the many manifestations and impairments related to externalizing psychopathology. At the same time, it is equally mistaken to revert to biogenetic reductionism. Gene-environment interplay and environmental “programming” of the brain and its downstream behavioral sequelae are crucial for understanding the interactive influences on complex externalizing behaviors (see Beauchaine & McNulty, 2013; see also Zisner & Beauchaine, this volume). Grand, overarching theoretical models— “theories of everything” that attempt to explain the myriad ways in which externalizing behavior patterns originate and self-propagate in a single scheme—can no longer support themselves. They are consistently outperformed by the far more complex and interactive models incorporated into DP approaches. We now turn to five core DP principles. Depending on the particular theorist and conceptual perspective one consults, there may well be others. Our intention herein is to be heuristic rather than exhaustive; our discussion here incorporates many important ancillary points, some of which could be elevated to the status of separate principles and concepts.

Normal and Atypical Development: Mutually Informative

DP posits that investigation of normative patterns of development is a prerequisite for gaining understanding of atypical patterns—and, in parallel, studying psychopathology sheds light on normal development in unprecedented ways. We discuss, in turn, each part of this dual principle. (i) Phenomena defined as abnormal, from the DP perspective, represent aberrations in normative developmental pathways and processes rather than qualitatively distinct categories that are entirely separate entities from the norm (“natural kinds”). Without a full understanding of typical development, therefore, the study of psychopathology will remain barren, incomplete, and decontextualized, operating under the assumption that aberrant outcomes occur outside the bounds of typical Hinshaw, Beauchaine

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ontogenetic processes. The crucial point is that psychopathology is inextricably tied in with mechanisms of normal development. A  key issue relates to determining when and how these processes become derailed or canalized into less than optimal functioning. Examples from work on externalizing conditions are salient (see Hinshaw, 2013). For instance, with respect to ADHD, there is no viable evidence that those who meet diagnostic criteria form a separate group or taxon; the underlying dimensions of inattention/disorganization and hyperactivity/ impulsivity are continuously distributed across the population (e.g., Nigg, 2013; see also Ahmad & Hinshaw, this volume). Thus, anyone purporting to understand this common, puzzling, and highly impairing set of symptoms must thoroughly understand normative development of attention, impulse control, motivational systems, and self-regulation. As a case in point, infant temperament features of activity level and intensity are less predictive of later ADHD symptoms than is effortful control exhibited during the second year of life (see Nigg, 2006). This fact reveals that ADHD is not simply a matter of “excessive motor activity” or “excessive emotionality” but instead follows from deficiencies in more generalized self-control, the precursors of which appear quite early in development. At the same time, developmental processes that lead to high levels of aggression and violence are not inherently separate from pathways and mechanisms that lead to normative inhibition of such tendencies, as are seen (thankfully) in most children. Even though youth with early-onset aggression are highly impaired and show signs of attachment-related, neuropsychological, and inhibitory dysregulation (e.g., Moffitt, 1993), it is precisely these patterns of insecure or disorganized attachment, poor executive function, and deficient impulse control (often interwoven with less well developed verbal skills and family conflict) that coalesce to produce long-lasting antisocial behavior. Indeed, roots can even be traced prenatally (Tremblay, 2010). Thus, it is not that aggressive behavior suddenly emerges in the most high-risk children but rather that, in such cases, typical socialization processes leading to the normative decline of aggressive actions during the preschool years do not materialize (Tremblay, 2000). In short, in 21st-century DP models, few would doubt the importance of understanding developmental sequences and processes associated with healthy outcomes as extremely relevant to the elucidation and explication of pathology. 94

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(ii) Yet this process works in the opposite direction as well. That is, investigation of pathological conditions—sometimes referred to as “adaptational failures” (Sroufe, 1997)—could and should inform normal developmental mechanisms. In short, the study of disrupted developmental progressions should illuminate our understanding of what is normative. Neurology contains key examples. For example, there is a long tradition of studying disrupted neural systems to enhance understanding of healthy brain function and vice versa. Famous case studies such as those of HM, in whom key brain structures/regions were surgically removed (in his case, the bilateral hippocampus), greatly facilitated knowledge of how memory systems operate (see Gazzaniga, Ivry, & Mangun, 2014; for details on neuroscience-related approaches more generally, see Cicchetti & Curtis, 2006). More relevant to externalizing behavior, investigation of the single recessive gene condition of phenylketonuria (PKU) has clear implications for elaborating normative development of dopaminergic systems, executive functions, and ADHD-related symptoms (Diamond, Prevor, Callender, & Druin, 1997). Beyond neurological conditions or single-gene diseases, however—which might involve relatively straightforward disruptions in downstream pathways of development—it is an open question as to whether investigation of more diffuse and complex externalizing pathology can reciprocally inform normative developmental paths. That is, there are few behavioral and emotional equivalents to the surgical procedures of creating lesions in certain brain regions or tracts or single-gene forms of pathology such as PKU. Perhaps, then, the two-way street is not as clear-cut in DP as it is in neurology. Yet, as we have emphasized, almost no forms of mental disorder constitute clearly demarcated, qualitatively distinct categories or taxa. Processes that apply to individuals at the tails of the normal distribution may well be applicable to those nearer the peak of the curve. As an example, studies of the reward sensitivity of individuals with ADHD (e.g., Sagvolden, Johansen, Aase, & Russell, 2005; see also Sonuga-Barke, Bitsakou, & Thompson, 2010, for a more complex and multifaceted formulation) reveal that performance decrements occur when rewards are tapered or stopped, presumably related to dopaminergically mediated difficulties with responding during extinction. In fact, the fascinating research of Volkow et al. (2009) reveals that never-medicated adults with ADHD have markedly

deficient numbers of dopamine receptors and transporters in core reward and motivational brain pathways than do non-ADHD comparison subjects. This finding has served to revive motivational theories regarding the origins of ADHD, revealing a biologically driven undersensitivity to reward—along with a seemingly paradoxical oversensitivity when immediate reinforcement is, in fact, delivered. Such reward undersensitivity, linked to trait impulsivity, may well underlie the entire spectrum of externalizing psychopathology (Beauchaine & McNulty, 2013; Zisner & Beauchaine, this volume). Importantly, these insights have direct implications for basic developmental processes and mechanisms that underlie normative motivation, affect regulation, and impulse control (Ahmad & Hinshaw, this volume). In short, ADHD-related reward processes may well elucidate normative patterns of motivation, persistence, and effort. Another instance relates to tragic experiments of nature that occurred when infants and toddlers were subjected to brutal deprivation and lack of human contact in Romanian and other Eastern European orphanages during the 1980s (see O’Connor, 2006). Intriguingly, the most salient behavioral sequelae of such deprivation were inattention and overactivity, as opposed to aggression or internalizing features (Kreppner, O’Connor, Rutter, & the English and Romanian Adoptees Study Team, 2001). Thus, it may well be that social contact and responsive caregiving in the earliest years of life, in tandem with the coming online of emerging neural substrates, subserve development of well-regulated attention and behavioral control, which become evident by middle childhood and beyond. Here is another potential “reverse direction” example, whereby the study of extreme environmental inputs and resultant psychopathology can inform typical development. It is also a prime example of equifinality:  inattention and overactivity may emanate from manifestations of heritable functions of monoaminergic neurotransmitter systems as well as from environmental deprivation. We note, as well, that the kind of inattention and overactivity displayed by formerly institutionalized youngsters is usually accompanied by clear attachment-related deficits, such as indiscriminate friendliness. Thus, examining hyperactive behavior or inattentive behavior in isolation—without investigating additional social, cognitive, and motivational features of the developing child—is inadequate to the task of understanding larger pictures of disrupted development. In short, DP investigations

rarely provide optimal input when symptoms are viewed in isolation. Finally, such work has been followed up by intriguing experimental investigations in which in-home foster placements mitigated, in part, effects of early deprivation in terms of cognitive growth (Nelson et al., 2007). Although not the topic of this chapter, we emphasize that intervention studies may not only improve the lives of children and adolescents but also place a clear spotlight on underlying developmental processes and mechanisms (e.g., Hinshaw, 2002). Implications of this mutual interplay principle are profound. Indeed, a DP investigator may be as comfortable in a genomics laboratory (see Rende & Waldman, 2006) or as part of a team that studies neighborhood influences on typical development as in a traditional clinical psychology or child/adolescent psychiatry setting. DP is an inherently translational discipline in which investigations of basic neural, cognitive, social, family, and community processes inform work on pathology and vice versa. Given the wide range of its subject matter and its interest in linking normative and atypical development, DP stretches disciplinary bounds.

Developmental Continuities and  Discontinuities

It is commonly asserted that DP models emphasize both continuous and discontinuous processes at work in the development of pathology. Yet what does this ambitious statement actually imply? Taking the example of externalizing behavior, it is well known that antisocial behaviors show stability in the sense that correlations between early and later measures of such behavior patterns are significant and at least medium in effect size. In short, rank order remains relatively preserved:  the most antisocial individuals at early points in development tend to retain their place in such behavior patterns across development. Yet this fact does not imply that the precise forms of externalizing behavior remain constant. Rates of specific behaviors (e.g., early tantrums, defiance) decrease with time whereas more “advanced” forms of externalizing behavior come online with development. As explicated by Moffitt (1993, 2006), the subgroup of youth with persistent antisocial behavior has a high likelihood of displaying physical aggression in grade school, covert antisocial behaviors in preadolescence, various forms of delinquency by their teen years (e.g., sexual assault, property crime, violence), followed by adult manifestations of antisocial behavior after adolescence (e.g., partner violence). In short, the stability of Hinshaw, Beauchaine

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specific acts is not high, but there is clear predictability over time, thus exemplifying what is termed heterotypic continuity, wherein the actual forms of the underlying antisocial trait change with development. Thus, whether continuity or discontinuity is present depends on the bandwidth of the behavioral construct under consideration. As noted earlier in this chapter, patterns of continuity may differ considerably across separable subgroups who display different developmental trajectories. Not all highly aggressive or antisocial children remain so:  some are prone to desist with the transition to adolescence. Others, however—members of the so-called early starter or life-course-persistent subgroup—maintain high rates of antisociality through at least early adulthood, yet specific forms of antisocial behavior may well change with development. In addition, a large subset of youth do not display major externalizing problems in childhood but instead shows a sharp increase in adolescence. Indeed, by the teen years, this adolescent-onset group may well display rates of antisocial behavior comparable to those of the early-onset group, although their propensity for violence is clearly lower (for a review, see Moffitt, 2006). Plotting such continuities and discontinuities within homogeneous subgroups is more informative than calculating overall curves of “growth” across the population. Increasingly used are sophisticated statistical strategies (e.g., growth mixture modeling, group-based trajectory modeling) to facilitate the search for separable trajectories or classes defined via patterns of change of the relevant dependent variable. Such differentiable groups may well yield insights into underlying causal mechanisms (Muthén et  al., 2002; Nagin & Odgers, 2010a, 2010b). We note that the subgroup with early developing antisocial behavior patterns is highly likely to display, at various times throughout development, more than one psychiatric condition within the externalizing spectrum. Yet such homotypic comorbidity does not necessarily imply that the individual in question truly has two or more separate, independent disorders. Indeed, the “progression” of impulsive, defiant, aggressive, and substance-abusing patterns—which may well be diagnosable as ADHD, ODD, CD, and SUD, as well as ASPD by adulthood—could well reflect heterotypic continuity. In other words, the sequential display of apparently different behavior patterns across the life span may well be part of an unfolding elaboration of early vulnerability shaped by transactional influences with high-risk 96

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contexts over the course of development (for discussion, see Beauchaine & McNulty, 2013). DP models help to uncover causal mechanisms better than does cross-sectional examination of supposedly independent behavioral disorders because the latter cannot model longitudinal patterns of interacting biological and environmental risk.

Multiple Levels of Analysis

DP investigators show maximum progress when they traverse back and forth between within-individual and wider system levels to understand mechanisms underlying the development of maladjustment and adjustment. The essential task for new generations of DP investigators is to link events at the level of genes (e.g., polymorphisms; epistatic effects [i.e., gene-gene interactions]; transcription and translation; epigenetic influences) to neurotransmission and neuroanatomical development and, subsequently, into individual differences in temperament, social cognition, emotional response patterns, and the like (for detailed discussion, see Cicchetti, 2008). Simultaneously, such bottom-up conceptions must be supplemented by a top-down understanding of the 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, thus invoking the concept of epigenesis. Required are multidisciplinary efforts in which investigators ranging from geneticists and biochemists, clinicians who focus on individual pathology, experts on family and neighborhood processes, school-level researchers, investigators of clinical service systems, to public health officials must work collaboratively and in increasingly diversified ways. As stated by Hinshaw (2013), “The phenomena under consideration are too complex, too dynamic, and too multi-faceted to be understood by focusing exclusively on psychobiological processes, family factors, peer processes, or cultural factors in isolation” (p. 9). Key exemplars of multiple levels of analysis perspectives involve instances of gene-environment interplay. Gene-environment correlation, discussed by Gizer, Otto, and Ellingson (this volume), occurs when environmental exposure (i.e., family, school, and wider community socialization influences) is linked to a person’s genotype. These contextual forces conspire to accentuate early genetically mediated behavioral tendencies. Moreover, gene-environment interaction (see Dodge & Rutter, 2011) remains a hot topic (for

debate about the strengths of effect of such interactive processes, see, for example, Risch et al., 2009, with a rejoinder by Caspi et al., 2010). Here, the essential idea is that certain genetic configurations are “vulnerability” factors for psychopathology: only in the context of unfolding environmental strain do pathological outcomes occur. Moreover, such genetic vulnerability may in fact yield better than expected outcomes in optimal contexts by serving as “plasticity” factors as opposed to risk factors per se, given their differential susceptibility to both positive and negative environmental settings (Belsky & Pluess, 2009). The essential point for the present discussion is that molecular and molar processes need to be explicitly interlinked to yield progress in understanding psychopathology across development.

Reciprocal, Transactional Models

Linear models of causation imply that static psychological variables respond in invariant ways to influencing factors, whether biological or contextual. Yet such models are not remotely adequate to the task of explaining psychopathology and its development (see Richters, 1997, who highlights that quite different explanatory systems are needed to deal with open systems, including human beings, than more static chemical or physical processes). As highlighted decades ago by Bell (1968), relevant pathways are marked by reciprocal patterns or chains in which children influence parents, teachers, and peers, who in turn shape further individual development of the child. Such mutually interactive processes can themselves escalate over time, leading to transactional models, which may cascade across development (Masten & Cicchetti, 2010). Given the strong potential for nonlinear change in such processes, dynamic systems models help to explicate core developmental phenomena (see Granic & Hollenstein, 2006). Gene-environment correlations and interactions are highly relevant here. At the same time, early-maturing brain regions that give rise to expression of key emotional and behavioral characteristics may influence the developmental maturation of other, later maturing regions; through epigenetic processes, environmental events and factors may actually aid in the expression of genes that further reinforce similar neural and behavioral actions. Prospective, longitudinal research designs utilizing sensitive data analytic strategies are needed to gain full understanding of such multilevel, transactional, often nonlinear phenomena.

Despite the increasing emphasis on psychobiological models and approaches (e.g., Beauchaine & Hinshaw, 2013), 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 behaviors at one point in human evolutionary history may be maladaptive in current times, given the major environmental and cultural changes that render certain genetically mediated traits far less advantageous than previously. Certainly, some behavior patterns (e.g., poor cognitive functioning in the context of extreme emotion dysregulation) may yield serious impairment almost regardless of context but, overall, few absolutes exist in terms of either behavior patterns that are inherently maladaptive or risk factors that inevitably yield dysfunction. Cultural settings and context are all-important for shaping and even defining healthy versus unhealthy adaptation. Indeed, certain parenting styles are not always uniformly positive or uniformly negative in terms of the developmental outcomes they yield. Deater-Deckard and Dodge (1997) have shown, for example, that harsh, authoritarian parenting predicts antisocial behavior in white, middle-class children but not necessarily in African-American families. Many forms of mental disorder are present at roughly equivalent rates across multiple cultures, revealing key evidence for universality, but the effects of risk or protective factors may differ markedly depending on their developmental timing, the family and social context in which they are experienced by the developing child, and the niche or “space” that exists in a given culture for their expression and resolution (Serafica & Vargas, 2006). An exemplary means of understanding environment and context is to use genetically informative designs (i.e., twin or adoption studies). In groundbreaking research, investigators discovered that, among adoptive parents of children with ADHD, maternal hostility is (a) elicited by child impulsivity, in a classic example of reciprocal effects (but without the possibility of passive gene-environment correlation as a confound) and (b)  such hostility predicts the longitudinal course of relevant ADHD-related symptoms (see Harold, Leve, Barrett et al., 2013; Harold, Leve, Elam et al., 2013; see also Lifford, Harold, & Thapar, 2009). Thus, despite the strongly heritable nature of ADHD symptoms, parenting effects are in fact partially causal of eventual outcome. For other examples of means of exploiting various research designs to Hinshaw, Beauchaine

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understand environmental/contextual factors, see Jaffee et al. (2012) and Rutter, Pickles, Murray, and Eaves (2001). In such enterprises, distinguishing between the (plentiful) environmental correlates of maladaptive behavior and those rarer, more specific, and more causal environmental pathogens is a key task (see Caspi et al., 2010). Processes of equifinality and multifinality are clearly relevant here:  transactional models clearly point to differentiable pathways that converge on a phenotypically similar outcome (equifinality) and divergent outcomes from a similar, initial risk configuration (multifinality). In all, the DP model emphasizes malleability, flexibility, and plasticity in development, with transactional pathways the norm. In what they termed “probabilistic epigenesis,” Gottlieb and Willoughby (2006) posited that genes do not provide a one-way causal influence on neural structures and behavior because of highly interactive, reciprocal, and bidirectional influences with epigenetic factors (e.g., other brain structures and products, behavioral patterns, and environmental influences). Once again, only detailed, prospective, longitudinal investigations that prioritize multiple levels of analysis to uncover transactional pathways are up to the task.

Risk and Protective Factors

The 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 dimensions, syndromes, and disorders. As we have emphasized herein, genetic vulnerabilities, temperamental and other psychobiological risk factors, environmental potentiators, and cultural-level norms all play major roles in defining and understanding behavioral manifestations that are considered abnormal and/or pathological in a particular social context. The ultimate goal is to locate those vulnerabilities and risk factors that are both malleable and potentially causal of the disorder in question (Kraemer et al., 1997). We reiterate that risk factors are not inevitable predictors. Being female is a protective factor against most forms of psychopathology in the first decade of life but serves as a risk factor for internalizing conditions during adolescence (Hinshaw, 2009). As noted earlier, maltreatment is a risk factor for later pathology, via both biological and psychological mediation, but it is not inevitably so (Cicchetti & Valentino, 2006). Furthermore, for most individuals with diagnosable forms of mental disorder, symptoms and impairments tend to wax 98

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and wane over time, making it difficult to know when dysfunction precisely begins. The myth that mental disturbance is uniformly debilitating, handicapping, and permanent is a key reason for the continuing stigmatization of mental illness (Martinez & Hinshaw, 2015). “Resilience” is the term often used to define unexpectedly good outcomes, or competence, despite the presence of adversity or risk (Luthar, 2006; Luthar & Brown, 2007; Sapienza & Masten, 2011). Indeed, the concept of multifinality, discussed earlier, implies directly that variegated outcomes emanate from risk factors—contingent upon later developmental influences—with the distinct possibility for positive adaptation in some cases. DP is thus involved in the search for protective factors, which include variables and processes that mitigate risk and promote successful outcomes. Yet controversy surrounds the construct of resilience, the nature of protective factors, and the definition of competent functioning (see Luthar & Brown, 2007; Masten, Burt, & Coatsworth, 2006). For example, many so-called protective factors are merely the opposite pole of what are considered risk factors, failing to inspire confidence that unique protective influences can be identified. Even so, 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 provide key conceptual leads toward the understanding of both pathology and competence. Indeed, advances in the study of resilience show that some aspects of resilient functioning have psychobiological and even genetic underpinnings and that a systemic, transactional, and even epigenetic model is needed to understand the multipronged nature of resilience-enhancing processes (see review in Sapienza & Masten, 2011). Furthermore, Luthar and Brown (2007) contend that interpersonal relationships are central to any conception of resilience, despite current work in psychobiological undercurrents. In the realm of externalizing behavior, the downstream consequences of early impulsivity and related ADHD symptoms—which are strongly heritable—include antisocial and substance abuse behaviors that are less heritable and, presumably, more amenable to well-timed psychosocial input (Zisner & Beauchaine, this volume). In short, investigating positive developmental outcomes is a necessary supplement to focusing on pathology per se. Understanding competence may well shed light on the pathways that deflect away

from impairment and, in so doing, reveal insights into the developmental components of adjustment versus maladjustment.

Conclusion

Clearly, the development of psychopathological functioning is multidetermined and multilevel, as well as complex, transactional, and nonlinear. Indeed, how could it be otherwise, given the staggering computational capacities of the brain; the wide-ranging developmental influences across different families, communities, and cultures; and the potential permutations and combinations of risk and protective influences under discussion? Still, a finite number of relevant pathways appear to exist, meaning that lawful relations can and should be definable with the right tools and approach. Externalizing behavior patterns, in particular, should benefit from concerted efforts to utilize DP conceptual models—combined with optimal multisource/ multimethod assessments and a keen awareness of the developmental timing of relevant processes—to help unlock the secrets of these highly impairing, even devastating conditions. Finally, early in this chapter, we noted in passing the continuing stigmatization of mental disorder. Indeed, among the general public over the past half-century, despite clear gains in knowledge about psychopathology, corresponding changes in attitude and social distance have not followed suit (e.g., Pescosolido et al., 2010). One plausible reason is that acts of mass gun violence, which attract virulent media attention, are tainting public attitudes. Thus, externalizing behavior, at its extremes, may well be linked to the ongoing propensity to blame and castigate individuals with psychopathology (see Martinez & Hinshaw, 2015)—meaning that efforts to stop aggression and violence have yet another motivation. An additional explanation, however, relates closely to DP principles. Although it was formerly believed, on the basis of attribution theory, that ascribing deviant behavior to noncontrollable causes (such as genetic liability) would reduce stigmatization, accumulating data suggest otherwise. The meta-analysis of Kvaale, Haslam, and Gottdiener (2013) reveals that whereas biogenetic attributions to mental disorder do lower perceptions of blameworthiness, at the same time they increase pessimism and enhance the desire for social distance between perceivers and those who exhibit abnormal behavior. In such cases, genetic risk for psychopathology (and particularly, in the externalizing spectrum,

ADHD behavior patterns) is undoubted but an exclusive, reductionistic attempt to brand individuals with mental illness as products of genetic vulnerability may actually increase dehumanization and distance (see Haslam, 2006; Martinez, Piff, Mendoza-Denton, & Hinshaw, 2011). DP models, of course, let us know that undoubted biological vulnerability always coexists and transacts with contextual, psychological, and cultural factors in “producing” psychopathology. Although communicating the complexities of DP principles, which transcend headline coverage, may be a formidable task, it may well be the case that fostering understanding of the person-in-context is relevant not only for modern diseases that are the most lethal (e.g., cancer, diabetes, coronary disease) but also for psychopathology. In sum, we hold out the hope that the principles of DP covered in this chapter can be translated into messages of humanization and hope not only for scientists and clinicians but also for the public at large. The study of DP is foundational for extending basic knowledge, promoting clinical application, and enhancing humans’ capacity for empathy and inclusion.

References

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Biological Vulnerabilities to Externalizing Spectrum Disorders

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Behavioral Genetics of the Externalizing Spectrum

Devika Dhamija, Catherine Tuvblad, and Laura A. Baker

Abstract This chapter presents key findings from behavioral genetic studies of the externalizing spectrum, with particular emphasis on quantitative genetic research on genetic and environmental factors using twin and adoption studies. It first provides a historical overview of behavioral genetic studies of externalizing problem behaviors before turning to a discussion of the primary research designs—twin and adoption studies—and their methodological designs and drawbacks. It then reviews evidence for genetic and environmental influences on individual disorders within the externalizing spectrum, namely, attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, antisocial personality disorder, and substance use disorders, along with their comorbidities. It also considers twin and adoption studies of antisocial behavior such as aggression, criminality, and delinquency, as well as traits that characterize psychopathy. The chapter concludes with a discussion of the current state of the science and suggests directions for future research on the behavioral genetics of the externalizing spectrum. Key Words:  behavioral genetic studies, externalizing spectrum, twin and adoption studies, attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, antisocial personality disorder, substance use disorders, antisocial behavior, behavioral genetics

Introduction

Behavioral genetic studies of externalizing problems originated much earlier than the term “externalizing spectrum” itself became a popular characterization of dysregulated behaviors and disorders. As reviewed by Drabick, Steinberg, and Hampton (this volume), the umbrella term of externalizing spectrum encompasses both Diagnostic and Statistical Manual (DSM)-based diagnoses—conduct disorder (CD), oppositional defiant disorder (ODD), and attention-deficit/ hyperactivity disorder (ADHD) in childhood, and antisocial personality disorder (ASPD) and substance abuse disorders (SUDs) in adulthood—and personality traits and other behaviors that are often the symptoms of these DSM disorders, including aggression, impulsivity, criminal behavior, delinquency, and callous/unemotional traits. Indeed,

adoption studies in the 1970s and 1980s provided compelling evidence for the importance of heritable influences on criminal outcomes (Cloninger, Sigvardsson, Bohman,  & von Knorring, 1982; Crowe, 1972; Martin, Cloninger, & Guze, 1982a, 1982b; Mednick, Gabrielli,  & Hutchings, 1984). Since then, numerous twin, family, and adoption studies have revealed heritable contributions to various components of what eventually became known as the externalizing spectrum (for reviews, see Agrawal  & Lynskey, 2008; Biederman, 2005; Faraone  & Doyle, 2001; Franke et  al., 2012; Goldman, Oroszi,  & Ducci, 2005; Nikolas  & Burt, 2010; Rhee & Waldman, 2002; Vanyukov & Tarter, 2000). Genetic studies—encompassing both classical twin design, adoption, and family studies of behavior genetics plus molecular genetic approaches—have 105

shed much light on the genetic and environmental etiology, underlying mechanisms, developmental processes, and risk factors associated with the symptoms, behaviors, and disorders of the externalizing spectrum. The behavioral genetic approach involves the study of resemblance among relatives and informs us not only about familial transmission of traits and the extent to which they are influenced by genetic and environmental factors throughout development, but also contributes to our understanding of common and specific genetic factors across various aspects of the externalizing spectrum. The behavioral genetic approach also examines the extent to which environmental factors may act in isolation and/or interact with genetic factors in producing a higher or lower risk for these problem behaviors. Molecular genetic approaches attempt to identify specific genes and biological pathways involved in the development of externalizing disorders, as well as how environmental factors might moderate gene expression or effects (Gizer, Otto, & Ellingson, this volume). Collectively, these designs hold potential to inform better intervention strategies and uncover key developmental time points at which these strategies might be applied. The present chapter reviews key findings from behavioral genetic studies of the externalizing spectrum. The focus is on quantitative genetic studies, which investigate genetic and environmental influences broadly using twin and adoption studies. Molecular genetic studies are reviewed elsewhere (see Gizer, Otto,  & Ellingson, this volume). We begin with historical context, followed by a brief overview of the primary research designs—twin and adoption studies—and their methodological designs and limitations. Evidence for genetic and environmental influences on individual disorders within the externalizing spectrum are summarized—specifically, ADHD, ODD, CD, ASPD, and SUDs—as well as their comorbidities. We also review twin and adoption studies of the wider construct of antisocial behavior (ASB), which typically includes aggression, criminality, and delinquency, and traits that characterize psychopathy, including callous-unemotional traits and impulsivity. We close with an appraisal of the current state of the science, as well as future directions in behavioral genetic studies of the externalizing spectrum. Although there is considerable support for genetic influences that appear common to many aspects of the externalizing spectrum, we emphasize throughout this chapter the importance of heterogeneity and the need to understand the 106

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mechanisms that lead to the various manifestations of externalizing problem behaviors.

Historical Context

The earliest behavioral genetic studies of externalizing problems can be traced to several adoption studies in both Scandinavia and the United States investigating heritable factors for alcohol and drug use as well as antisocial and criminal behavior (Bohman, Cloninger, Sigvardsson, & von Knorring, 1982; Cloninger, 1978; Cloninger et  al., 1982; Crowe, 1972; Earls, Reich, Jung,  & Cloninger, 1988; Mednick et al., 1984). Across the board, these studies found that adoptees with criminally offending biological parents had a much higher risk of offending compared to adoptees from nonoffending biological backgrounds, even if the former had been raised by nonoffending adoptive parents. Moreover, recidivistic criminal offending among the adoptees was associated with criminality in the birth but not adoptive parents (Martin, Cloninger, & Guze, 1978). These studies established the importance of inherited factors in vulnerability to antisocial and criminal behavior. These early studies focused on not only assessing genetic contributions to criminal and ASB, but also on the potential for gene × environment (G×E) interactions through identifying important environmental factors that might exacerbate genetically mediated risk. Genetic (heritable) risk for property offending appeared to be exacerbated for adoptees raised by criminal adopted fathers (Mednick et al., 1984). A series of papers based on a Swedish adoption study (Bohman et al., 1982; Cloninger et al., 1982) demonstrated that genetic effects depended on the nature of the criminal offense and the presence or absence of alcohol abuse. Specifically, although property offending showed strong genetic liability, violent criminal offending did not. Although criminality tended to be more severe, repetitive, and of violent nature among individuals with alcohol abuse (Bohman et al., 1982), there appeared to be little or no genetic influences on criminality among them. There were also sex differences in the way postnatal environmental factors (i.e., adverse adoptive environments) interacted with existing genetic risks for criminality and alcoholism (i.e., antisocial biological parent) (Sigvardsson, Cloninger, Bohman,  & von Knorring, 1982). Specifically, adverse postnatal factors in the adoptive environment—including length of institutional care, urban rearing, temporary placements, and low socioeconomic status (SES)—were more predictive of criminality than

alcoholism among women but not men. These early studies not only demonstrated a biological basis for criminality but also highlighted the importance of gene–environment interplay in the etiology of deviant behaviors, as well as the importance of distinguishing between various forms of criminal offending. It is noteworthy that early studies of criminal offending were based primarily on qualitative aspects of what is now considered part of the externalizing spectrum. Classifications of individuals as either criminal or noncriminal defined the predominant outcome, with some exceptions such as measuring recidivism or rates of offending. This practice is in marked contrast to contemporary studies with a more quantitative focus, in which ASB is apt to be measured along a continuum, with a focus on symptom counts of disorders and models that presume (or estimate) a liability toward criminal offending or other manifestations of externalizing behavior. The progression toward studying a quantitatively based externalizing spectrum rather than qualitatively defined individual disorders/behaviors can be traced through a progressive increase in the number of studies investigating common genetic risk factors of various disorders now classified under the externalizing spectrum. The use of latent constructs to classify psychopathologies into two broad categories of internalizing and externalizing behaviors was advocated initially in child psychopathology research (Achenbach & Edelbrock, 1978, 1984) and is important from a standpoint of studying the shared etiology of the behavior problems comprising these constructs. A  similar two-factor solution was also found in adult samples (Kendler, Prescott, Myers, & Neale, 2003a; Krueger, 1999; Krueger, Caspi, Moffitt, & Silva, 1998; Vollebergh et al., 2001).

Classical Research Designs in Behavioral  Genetics

Current understanding of genetic and environmental underpinnings of symptoms, behaviors, and disorders in the externalizing spectrum has come through a multitude of behavioral genetic studies over the past half century. Although recent advances in molecular genetics have spawned new approaches aimed at identifying specific genetic markers that contribute to vulnerability for disorders (e.g., ADHD) and normal trait variation (e.g., aggression and delinquency) (Niv, Tuvblad, Raine,  & Baker, 2013), by and large, the bulk of genetically informative studies to date are based on what are considered

to be “classical” behavioral genetic designs, based on quantitative models (Falconer, Mackay,  & Frankham, 1996). The primary approach involves the study of resemblance among family members of varying degrees of genetic and environmental relatedness. These involve three basic research designs—twin studies, family studies, and adoption studies—that aim to parse the effects of genetic factors from environmental ones in familial resemblance. Genetic effects most often involve additive effects (A), which contribute to similarities among parents and offspring, siblings, and twins, although nonadditive genetic effects (D) due to the dominant or recessive nature of alleles at a particular locus and interactions among genes across different loci (known as epistasis) are sometimes distinguished. Dominance and epistasis increase resemblance among twins and other siblings but not between parents and offspring. Environmental effects are generally separated into those that are common to family members raised together (C) and those that are not shared but instead are unique to each individual (E). A graphical depiction of a path model showing the effects of A, C, D, and E in two relatives can be seen in Figure 7.1. As shown, each effect is correlated to different degrees in various types of relatives (e.g., monozygotic [MZ] and dizygotic [DZ] twins, parents, and offspring). Genetic and environmental influences in classical designs are typically expressed as proportions of variance explained by each effect (i.e., a2, c2, d2, and e2). In reporting heritability (h2), a distinction is often made between narrow-sense heritability, which reflects only additive genetic effects (h2N = a2) and broad-sense heritability, which encompasses both additive and nonadditive genetic effects ( h 2 B = a 2 + d 2 ). Environmental effects are generally reported as being shared (c2) or nonshared (e2), with the caveat that the latter also contains measurement error unless modeled separately. Ways in which these effects are separated in each of the three major behavioral genetic designs are described briefly in the sections that follow, followed by a summary of key methodological issues in these designs.

Family Studies

Studying the transmission of behavior and psychological traits across generations is often considered a first step toward understanding the role of genetic and environmental influences. Family studies could involve various combinations of parents, siblings, and other relatives to quantitatively assess the risk of inherited pathologies. As shown Dhamija, Tuvbl ad, Baker

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γ

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a

es

MZ: γ = 1 DZ: γ = 1 PO: γ = 1 Separated at birth: γ = 0

P2

ens

E2

ens

D2

d

Figure 7.1  Path model of resemblance between two relatives (P1 and P2). A, additive genetic; C, shared environment; E, unique environment; D, genetic effects due to dominance or epistasis; MZ, monozygotic twins; DZ, dizygotic twins; PO, parent–offspring.

in Figure 7.1, parent–offspring resemblance can arise from additive genetic influences (A), as well as their shared or common environmental effects (C):  rPO = .5a 2 + c 2 . Sibling resemblance can result from these same effects, although the nonadditive genetic effects due to dominance or epistasis can further increase their similarity compared to parents and offspring:  rSIBS = .5a 2 + .25d 2 + c 2 . Family studies are also often useful in evaluating the effects of assortative mating or the extent to which spouses resemble one another for the trait under study. Positive correlations on a trait or behavior between mothers and fathers may lead to increased resemblance between other family members, including parents and offspring, as well as siblings and DZ twins, with the potential for underestimating heritability if not taken into account. It is important to note that, in family studies, the effects of shared environmental influences common to family members living together cannot be parsed from genetic factors. Nonetheless, observed sibling or parent–offspring resemblance suggests shared family effects, or “familiality” (i.e., the combination of heritable and shared environmental influences). If discovered, these effects can justify further investigations to separate these effects, such as twin or adoption studies.

Twin Studies

Twin studies are based on the assumption that “identical” or MZ twins share 100% of their genes, whereas “fraternal” or DZ twins share only 50%, on average. By comparing observed twin similarity for MZ and DZ pairs, the relative 108

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contributions of genetic and environmental influences on behavior and psychological traits can be parsed. Greater resemblance in MZ compared to DZ twins is taken as evidence for genetic influence, whereas equivalent correlations for both twin types suggests that common environmental influences are of primary importance. Within-pair differences in MZ twins (or deviations of rMZ from 1.0) indicate the importance of nonshared environmental factors unique to each individual co-twin. The effects of additive (A) and nonadditive (D) genetic influences, environmental factors common to co-twins (C), and nonshared environment (E)  on a given phenotype (P)  in two twins are depicted in Figure 7.1, from which one can derive expected correlations between co-twins for MZ pairs ( rMZ = a 2 + d 2 + c 2 ) and DZ pairs ( rDZ = .5a 2 + .25d 2 + c 2 ). In the absence of dominance or epistasis (D), doubling the difference between the two correlations provides an estimate of narrow-sense heritability:  a 2 = 2 (rMZ − rDZ ) . Evidence for D is suggested when rMZ far exceeds twice the value of rDZ, although it is impossible in twin studies alone to distinguish such effects from violations of the equal environments assumption, as discussed later. Assortative mating (e.g., spouse resemblance) for the trait (also discussed later) will tend to increase DZ but not MZ twin resemblance, thus leading to underestimated genetic and overestimated environmental effects in twin studies if not taken into account.

Adoption Studies

Adoption studies involve children who were separated from their biological parents, often close to birth, and raised by (genetically unrelated) adoptive parents. Such designs provide an important advantage in parsing genetic and environmental influences because their effects can be directly estimated and often with greater statistical power than in twin studies. Similarities between biological parents and their adopted-away children (or between twins reared apart) provide strong evidence for genetic influence (A)  in behaviors or traits. Conversely, similarities between adoptive parents and their biologically unrelated offspring provide strong tests of common environment (C). Differences in MZ twins reared apart are, as in the case of twins raised together, indicative of individual-specific (nonshared) environmental influences (E)  on a given trait. Adoption studies rest on the critical assumption that children were not selectively placed into homes based on characteristics under investigation (e.g., externalizing behavior problems) or their correlates. Among the major adoption studies to date, little or no evidence of selective placement has been found (Plomin, DeFries, Knopik & Neiderhiser, 2013).

Methodological Issues in Behavioral Genetic  Studies Assortative Mating

A general concern in behavior genetic research—including in twin studies in which only twins themselves are studied—is whether random mating has occurred between parents. Estimated genetic and environmental effects in twin studies rest on the assumption of random mating, and, if not met, A, C, and E estimates may be biased. One form of nonrandom mating is assortative mating, which is the aggregation of certain traits in mating pairs that result in observed correlations between mates, including parents of twins. Assortative mating-based phenotypic traits can lead to genetic similarity between mates and hence increases genetic relatedness and thereby phenotypic similarity between parents and offspring and between siblings (including DZ twins). Importantly, MZ twin similarity is not affected by assortative mating because such twins are already genetically identical (i.e., share 100% of their genes even under random mating). As noted earlier, increased correlations between relatives due to assortative mating, if not taken into account, can lead to biased estimation of parameters in behavioral genetic analyses. As

described earlier, unless taken into account, assortative mating will spuriously increase estimates of C and decrease estimates of A in twin studies. Assortative mating has been shown for aspects of the externalizing spectrum, including ASB and correlated personality traits. For example, Krueger and colleagues investigated assortative mating for antisocial tendencies among 360 couples in New Zealand. Spouse correlations were only moderate (r = 0.15) for personality traits such as negative emotionality and constraint, which are robustly correlated with ASB. However, the spouse correlations were quite high (r  =  0.54) for self-reported ASBs and associations with antisocial peers (Krueger, Moffitt, Caspi, Bleske, & Silva, 1998). Similar results have been obtained by other studies, suggesting that mate selection for externalizing behavior problems is likely to be nonrandom and may lead to bias in behavior genetic studies of externalizing behaviors including substance use (Agrawal et  al., 2006), violent crime (Frisell, Pawitan, Langstrom,  & Lichtenstein, 2012), and the wider realm of ASB (Maes, Silberg, Neale, & Eaves, 2007).

Equal Environments Assumption (EEA)

Another key assumption in twin studies is that of equal environments between the two types of twins. It is assumed that MZ and DZ twin pairs reared together share environments to the same extent. It is known, however, that MZ twins tend to be treated more similarly by their parents (and others) compared to DZ twins, which can lead to spuriously high estimates of heritability (Plomin et al., 2013). It is important to realize that although MZ twins may experience more similar environments in many ways (e.g., dressing alike), these specific shared experiences may not be relevant to the traits and behaviors that are examined in twin studies. In other words, the ways that twins are dressed and confused with one another early in life may not influence their similarities in aggressive behavior, conduct problems, or attention deficits. Nonetheless, it is important to examine this assumption and determine (a)  its validity with respect to externalizing behavior problems and (b) the extent to which violation of the EEA may affect estimates of genetic and environmental influences. As summarized in a recent review, several studies have examined the EEA and found that it does indeed hold for most behavioral outcomes, including personality, health, and behavior (Felson, 2014). Among 32 outcomes studied, only neuroticism showed a significant decrease in heritability after Dhamija, Tuvbl ad, Baker

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controlling for different environments in MZ and DZ twins. Thus, even if the EEA is not always met in twin samples, for the most part, the bias is small.

Generalizability

Another important methodological concern, in both twin and adoption studies, is generalizability of results to the wider population. Twins are often born prematurely, many through Cesarean section, and have lower average birth weight and shorter gestational age than singletons. Numerous studies have investigated potential differences in mean levels of various traits or differences in rates of psychopathology for twin and non-twin individuals. Few, if any, differences exist. For example, a recent study of ASB found no systematic differences between twins and singletons on either the report of involvement in ASB or in the correlates most commonly predicting ASB (Barnes & Boutwell, 2013). These findings suggest that results from twin studies can be generalized to the wider population. Results from adoption studies may also limit generalizability to the population, to the extent that adoptive homes may not be representative of the population. Typically, only families with higher SES and more favorable environments would be permitted to adopt (Miles & Carey, 1997; Rutter, 2006). Ironically, adoptees are known to be at a higher risk for ASB than nonadopted individuals (Deater-Deckard & Plomin, 1999; van den Oord, Boomsma, & Verhulst, 1994), which suggests that adoptees themselves may not be representative of the general population. It is thus important to consider findings from adoption studies in combination with results from other designs to examine comparability and determine generalizability.

Links to Traditional Externalizing Disorders

Numerous twin, family, and adoption studies have investigated both environmental and genetic bases of the externalizing spectrum, including studies of DSM-based disorders in both childhood and adulthood. Behavioral genetic studies have moved away from the earlier categorical approach toward a greater focus on quantitative, dimensional aspects of externalizing behavior problems (Krueger, Caspi et  al., 1998). These studies have been at the forefront of research investigating overlap among various disorders in the spectrum and have contributed to our understanding of shared genetic and environmental etiologies that exist among these disorders. We provide a brief overview of twin, family, and 110

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adoption studies of individual disorders, followed by discussion of multiple disorders and their comorbidities. Excellent reviews can be found elsewhere for behavioral genetic studies of individual disorders, including ADHD (Faraone, 2004; Faraone & Doyle, 2001; Franke et  al., 2012; Levy, Hay,  & Bennett, 2006), CD (Simonoff, 2001), and SUDs (Agrawal & Lynskey, 2008; Goldman et al., 2005; Vanyukov & Tarter, 2000).

ADHD

ADHD is a behavior disorder characterized by inattention, impulsivity, and, in some cases, hyperactivity (Ahmad  & Hinshaw, this volume). ADHD is usually first diagnosed in childhood (American Psychiatric Association, 2013). The genetics of ADHD have been well studied across various changes in its nosology, with early studies focusing on inattention, hyperactivity, and DSM-III diagnoses of ADD and more recent studies examining DSM-IV-based subtypes of ADHD, their constituent symptoms, and their frequent covariation. Family studies indicate much higher prevalence of ADHD in biological relatives of probands, suggesting genetic influences (Biederman, Faraone, Keenan, Knee, & Tsuang, 1990; Faraone, Biederman,  & Friedman, 2000). Twin studies reflect a similar trend (see reviews by Biederman, 2005; Burt, 2009b; Faraone, 2004; Faraone  & Doyle, 2001; Franke et al., 2012; Levy et al., 2006; Willcutt, in press) and suggest strongly that individual differences in ADHD symptoms in both children and adults are largely attributable to genetic factors. Despite differences in design, raters, and samples used across studies, consistent findings of high heritability are reported, with estimates ranging from 0.70 to 0.75. The remaining variance in ADHD symptoms is in most studies explained by nonshared environmental factors (E) with little or no effect of shared environment (C). Although most of the literature focuses on the high heritability of ADHD symptoms, it is also important to recognize the heterogeneity of these genetic influences across symptoms. For example, in a meta-analysis of 23 twin and adoption studies, the relative contribution of additive (A)  and nonadditive (D) genetic factors was different across the two major dimensions of symptoms, hyperactivity and inattention (Nikolas & Burt, 2010). Although genetic factors contributed largely to variance in both dimensions (h2  =  0.71 for inattention and 0.73 for hyperactivity), the type of genetic factors contributing to these dimensions varied, with D

effects larger for inattention (d2  =  0.15) than for hyperactivity (d2  =  0.02). In contrast, A  effects were larger for hyperactivity (a2  =  0.71) than for inattention (a2  =  0.56). Another recent study also estimated high heritability for both dimensions of ADHD (a2 = 0.70) and found a significant genetic correlation (rG  =  0.55) between hyperactivity and inattention, indicating that some of the same genetic factors are important for these two ADHD dimensions (Greven, Rijsdijk,  & Plomin, 2011). These findings are consistent with earlier investigations (McLoughlin, Ronald, Kuntsi, Asherson,  & Plomin, 2007; Sherman, Iacono, & McGue, 1997). Although ADHD dimensions may be largely influenced by the similar genetic factors, there appear to be additional genetic factors specific or unique to hyperactivity and inattention (see Gizer, Otto,  & Ellingson, this volume). In the Virginia Twin Study of Adolescent Behavioral Development (VTSABD), symptoms of ADHD were assessed using multiple measures and informants. Both rater- and method-specific factors appeared in multivariate genetic analyses (Nadder, Silberg, Rutter, Maes,  & Eaves, 2001). Moreover, some reports of lower heritability of adult ADHD may be a function of measurement error because adult studies tend to rely on self-reports rather than more reliable ratings by clinicians (Franke et al., 2012). Although there is considerable evidence for heritability of various symptoms of ADHD, explaining upward of 70% of their total variance, it is important to recognize heterogeneity among these results. Specific genetic factors are apparent across informants, methods of assessment, and type of symptoms. Although there is some genetic overlap between inattention and hyperactivity, there is also a significant share of genetic variance unique to each dimension, thus highlighting the utility in separating the different components of ADHD.

ODD

Similar to ADHD, ODD is also a fairly common disorder. Typically, ODD occurs in childhood and is characterized by a pattern of angry/irritable behavior, including vindictiveness. However, children diagnosed with ODD usually lack symptoms of CD (although ODD is often a developmental precursor to CD; see Beauchaine, Shader,  & Hinshaw, this volume; Tuvblad, Zheng, Raine,  & Baker, 2009), in that they are not aggressive toward people or animals, do not destroy property, and do not show a pattern of theft or deceit (American

Psychiatric Association, 2013). The Twin Study of Risk Factors for Antisocial Behavior (RFAB) at the University of Southern California (USC) reported a heritability of 61% for ODD, with the remaining variance being explained by the nonshared environment (Tuvblad et al., 2009).

CD

CD is diagnosed in childhood or adolescence and is characterized by a repetitive and persistent pattern of behavior in which the basic rights of others or major age-appropriate norms are violated (American Psychiatric Association, 2013). CD is often comorbid with other externalizing disorders such as ADHD and ODD (see Beauchaine, Shader,  & Hinshaw, this volume). Thus, few behavioral genetic studies have investigated only CD, which is more often studied in combination with other disorders (comorbidity is reviewed in a later section of this chapter). Nonetheless, in a meta-analysis of behavior genetic studies of the wider domain of ASB, the average contribution of additive genetic effects on CD was a2  =  0.50, with significant effects of both shared environment (c2  =  0.11) and unique (nonshared) environment (e2 = 0.39) (Rhee & Waldman, 2002). Some studies have also reported differential heritability of conduct problems across informants (Scourfield, Van den Bree, Martin,  & McGuffin, 2004; Simonoff, Pickles, Meyer, Silberg,  & Maes, 1998) and age cohorts (Jacobson, Neale, Prescott, & Kendler, 2000). Furthermore, heritability estimates differ based on varying symptoms of conduct disorder and specifically in ways which these are measured (Gelhorn et al., 2005, 2006; Simonoff et al., 1998), thus suggesting heterogeneity in the etiology of CD. For example, Simonoff and colleagues measured conduct problems using the Child and Adolescent Psychiatric Assessment in addition to a dimensional measure and discerned greater heritability for property violations and aggressive behaviors than rule-breaking and oppositional behavior. However, when CD was measured through structured clinical interviews such as the Diagnostic Interview Schedule for Children (DISC; Robins, Marcus, Reich, Cunningham, & Gallagher, 1996), nonaggressive CD showed greater heritability than did aggressive CD (Gelhorn et al., 2005). Because CD is considered to fall under the umbrella of ASB, it is important to note that studies of ASB measured by the Child Behavior Checklist (CBCL; Eley, Lichtenstein, & Moffitt, 2003; Eley, Lichtenstein, & Stevenson, 1999) suggest a greater heritability Dhamija, Tuvbl ad, Baker

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of aggressive than nonaggressive (rule-breaking/ delinquency) behaviors. Furthermore, the covariation between the two forms of conduct problems was explained by genetic factors in the Gelhorn et al. study, although there were significant unique genetic and environmental factors at play, emphasizing again the utility of distinguishing different types of symptoms in this disorder. In general, CD symptoms tend to have a moderate heritability of about 50%, although this figure may vary across type of symptoms, measuring instruments, informants, and age. Similar to what has been found for ADHD, there appear to be genetic influences common to the various symptoms involved, but, at the same time, there is also genetic specificity, in this case for aggressive and nonaggressive symptoms.

Comorbidity of ADHD, ODD, and CD

Given the high co-occurrence of ADHD, ODD, and CD, these disorders are commonly grouped together under the umbrella term of disruptive behavior disorders (see Drabick, Steinberg, & Hampton, this volume). Because of their comorbidity, considerable research has investigated their shared genetic and environmental liabilities (Dick, Viken, Kaprio, Pulkkinen,  & Rose, 2005; Ehringer, Rhee, Young, Corley,  & Hewitt, 2006; Knopik et  al., 2014; Nadder, Rutter, Silberg, Maes, & Eaves, 2002; Silberg et al., 1996; Thapar, Harrington,  & McGuffin, 2001; Tuvblad et  al., 2009). Estimates of heritability and shared and nonshared environmental influences differ across these studies—perhaps due to differences in assessment and methods—although genetic influences are consistently found (h2 estimates:  ADHD [0.30–0.60], ODD [0.30–0.61], CD [0.39–0.56]). Moreover, although there is large genetic overlap among ADHD, ODD, and CD, there are also significant unique genetic effects for each individual disorder. Genetic overlap varies somewhat across disorders and is perhaps greatest between ODD and CD, leading some researchers to suggest that they may be combined into one, rather than two separate constructs (Eaves et al., 2000; Knopik et al., 2014). In contrast, Burt and colleagues have argued that covariation among these disorders is best explained by shared environmental effects rather than genetic overlap (Burt, Krueger, McGue,  & Iacono, 2001, 2003; Burt, McGue, Krueger,  & Iacono, 2005). Parent–child conflict may act as a vulnerability that increases risk for all three disorders, given that shared environmental effects on ADHD, ODD, 112

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and CD appeared common with the shared environmental factors influencing parent–child conflict (Burt et al., 2003). Larger genetic covariation found in other studies may be spurious, related to informant effects (Burt et al., 2005), thus suggesting caution in interpreting findings on their genetic overlap. Different presentations of ADHD (hyperactive/ impulsive, inattentive, and combined) also show differential overlap with ODD and CD (Martin, Levy, Pieka, & Hay, 2006). Whereas genetic variance of CD shows the highest overlap with hyperactivity/ impulsivity, genetic variance of ODD shows greatest overlap with the inattentive subtype. Specific genetic and environmental factors also contribute to variance in both CD and ODD, indicating important specificity in their etiologies that appears to be independent of ADHD. In brief, findings are mixed regarding genetic and environmental overlap among ADHD, ODD, and CD. Whereas some find covariation to be largely attributed to genetic factors, others report shared environment to be more important. Although there is utility in studying shared genetic liabilities to uncover risk factors associated with a broad spectrum of these disorders, it is still useful to consider each as a separate disorder given the unique genetic and environmental factors contributing to their respective etiologies.

ASPD and Psychopathy

ASPD is characterized by a pervasive pattern of disregard for and violation of the rights of others (American Psychiatric Association, 2013). Individuals diagnosed with ASPD fail to conform to social norms. They are impulsive, deceitful, aggressive, irritable, and show reckless behavior and lack of regard toward others. The few studies that have examined heritability of ASPD directly show significant genetic influences (h2 = 0.36 and e2 = 0 64%; see review by Rhee & Waldman 2002), with no significant effect of shared environment. A  similar pattern of results for ASPD was found more recently (h2  =  41% and e2  =  59%; Kendler et  al., 2008). The factor structure of ASPD symptoms appears to reflect two separate factors, both phenotypically and genetically: aggressive-disregard and disinhibition (Kendler, Aggen, & Patrick, 2012). Although not a DSM disorder, psychopathy (Hare, 2003) overlaps with symptoms of ASPD. In contrast to the relative paucity of behavioral genetic studies of ASPD per se, there are numerous twin

studies of psychopathic personality traits, and these consistently show genetic influences (see the later section on ASB for a review of studies specifically examining genetic and environmental influences on psychopathic personality traits). Psychopathy is also important to the externalizing spectrum, given its relationship to ASB. The results from twin studies of psychopathy are quite consistent, suggesting a moderate to high genetic influence and moderate nonshared environmental influences (Gunter, Vaughn, & Philibert, 2010; Waldman  & Rhee, 2006), with genetic influences in psychopathic personality traits being evident as early as 9–10 years of age (Bezdjian, Raine, Baker, & Lynam, 2011). The association between psychopathy and ASB is also mediated largely by genetic factors (Larsson et al., 2007), as is the relation between psychopathic personality traits and aggression in children (Bezdjian, Tuvblad, Raine, & Baker, 2011).

SUDs

Substance use and dependence have also received considerable attention in behavioral genetic research. Numerous studies have investigated heritability of alcoholism, as well as use of and dependence on other substances (for reviews, see Agrawal & Lynskey, 2008; Bevilacqua & Goldman, 2009; Duaux, Krebs, Loo,  & Poirier, 2000; Enoch  & Goldman, 2001; Goldman et  al., 2005; Kendler et  al., 2012; Li  & Burmeister, 2009; Lynskey, Agrawal,  & Heath, 2010; McGue, 1999; Vanyukov  & Tarter, 2000). Earlier studies examining heritability of substance dependence focused mainly on alcohol use and dependence, whereas more recent literature addresses shared liabilities for multiple classes of substances. Family studies (e.g., Bierut et al., 1998; Merikangas et al., 1998) reveal familial aggregation of both alcoholism and drug dependence, with as much as an eightfold increase in risk of substance use-related disorders in the relatives of those with substance-related disorders. In addition to increased overall risk of developing any substance-related disorder, there is also increased risk for developing dependence to an array of drugs. As noted earlier, a key limitation of family studies is that they cannot parse genetic and shared environmental effects from each other. Twin studies are able to better inform us about these specific effects and how they influence substance use symptoms and disorders. Twin studies confirm both shared and specific genetic liabilities for drug use, abuse, and dependence (Kendler, Jacobson, Prescott,  & Neale, 2003b; Tsuang et  al., 1996, 1998; van den Bree,

Johnson, Neale,  & Pickens, 1998). For example, Tsuang and colleagues investigated shared and specific vulnerabilities for abuse of a variety of drugs including marijuana, stimulants, sedatives, heroine, and psychedelics among 3,372 male twins from the Vietnam Era Twin (VET) Registry (Tsuang et  al., 1998). Genetic factors contributed between 26% and 54% of the variance in substance abuse, although for each drug type except heroin less than half of the genetic variance came from drug-specific factors. Similarly, a study of 1,196 male twins from the Virginia Twin Registry (VTR) (Kendler, 2003b) found both common and unique factors contributed to lifetime use of cannabis, cocaine, hallucinogens, sedatives, stimulants, and opiates. Once again, less than one-third of this genetic variance emerged from specific or unique factors, thus highlighting the importance of a shared genetic liability for use across a variety of substances. SUDs hold a special place in the study of externalizing behaviors because of their co-occurrence with other disorders on the spectrum. Numerous studies have examined comorbidity of alcohol and substance use with externalizing behaviors, mostly ADHD and CD (Button et al., 2006; Edwards & Kendler, 2012; Haber, Jacob, & Heath, 2005; Rose, Dick, Viken, Pulkkinen,  & Kaprio, 2004; Slutske et  al., 1998). Some investigations suggest that shared genetic liability explains this comorbidity, but others find that comorbidity is due largely to environmental factors (Knopik, Heath, Bucholz, Madden,  & Waldron, 2009; Rose et  al., 2004). High comorbidity and shared genetic liabilities have been driving forces behind including SUDs as part of the externalizing spectrum (Edwards & Kendler, 2012; Krueger et al., 2002). In summary, a common genetic liability explains the underlying vulnerability to use and dependence for a variety of drugs. There is also a small to moderate percentage of variance in liability for substance-specific use and dependence explained by genetic factors. Because SUDs are highly comorbid with all other externalizing behaviors, they have come to be viewed as a part of the spectrum of externalizing behaviors.

ASB

ASB is a general, umbrella term used to describe a host of symptoms that comprise a wider construct including violation of social norms and bringing harm to others and/or society, whether directly or indirectly. Although not itself a psychiatric disorder, ASB encompasses a host of externalizing behaviors Dhamija, Tuvbl ad, Baker

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such as violence, aggression, crime, delinquency, and rule-breaking behavior and is clearly related to several DSM-based disorders including ADHD, ODD, and CD in children and adolescents and ASPD in adults. Given its central role in various disorders within the externalizing spectrum and the tendency for studies to measure ASB quantitatively rather than qualitatively, it is important to consider behavior genetic studies of this wider construct. Twin and adoption studies of ASB reveal important information about heritability, development, and risk factors contributing to the etiology of deviant behaviors. A  multitude of studies have investigated gene–environment interplay in ASB and show systematically moderate heritability across development for various definitions of ASB (Burt, 2009a; Moffitt, 2005a; Rhee  & Waldman, 2002). The meta-analysis conducted by Rhee and Waldman (2002) included 51 studies (10 independent adoption samples and 42 independent twin samples) to investigate various methodological factors that could moderate the heritability of ASB, including operationalization of the construct, assessment method (i.e., self, parent, teacher, official records), age and sex of participants, method of determining zygosity, and study design (adoption versus twin). The best-fitting model summarizing all 51 studies was one in which additive genetic (A) influences explained 32% of the variance, nonadditive genetic factors (D)  explained 9%, shared environment (C)  explained 16%, and nonshared environment (E) explained 43%. Narrow-sense heritability was thus h 2 N = a 2 : 32% , and broad-sense heritability, which encompasses both A  and D, was h 2 B = a 2 + d 2 : 32% + 9% = 41% . Among methodological factors examined, all but sex were significant moderators of the magnitude of genetic and environmental influences on ASB. Specifically, both A and D were important for criminal behavior, whereas only A was operative for other definitions such as aggression and psychiatric disorder symptoms. Although genetic influences appear significant across the life span, the nature of environmental influences may shift across development, whereby shared family environment appears more important for children and adolescents but less important among adults displaying ASB—a pattern of results that has been found for cognitive abilities, personality traits, and other psychiatric disorders (Bergen, Gardner  & Kendler, 2007; Plomin et  al., 2013). However, methods of assessment of ASB also shift across development, in that studies of younger children tend to rely on parent and teacher 114

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ratings, in which rater bias can inflate estimated effects of C (i.e., due to the same person rating both twins). Thus, the larger estimate of C in ASB during childhood may simply reflect a methodological artifact due to method of assessment. Nonetheless, the meta-analysis by Rhee and Waldman (2002) emphasized the importance of recognizing heterogeneity of ASB, as well as various methodological issues in its measurement. Comprehensive, longitudinal, and genetically informative studies that use multiple raters and measures of ASB are required to resolve some of these issues. Indeed, in a multirater twin study of ASB during childhood (aged 9–10 years), genetic influences were found across the board for parent ratings, teacher ratings, and children’s self-reports. However, shared environmental effects were significant only for parent and teacher ratings, not self-reports. Rater effects were significant for teacher reports (i.e., by comparing twins who were rated by two different teachers to those rated by a single teacher), which explained some but not all of the shared environmental effects (Baker, Jacobson, Raine, Lozano  & Bezdjian, 2007). Differences in genetic and environmental influences across raters within the same study further highlight the importance of considering how ASB is measured. Parents, teachers, and children themselves have different information about the child’s behavior across different settings, and no single rater is ideal (see Baker et al., 2007). Nonetheless, when combining all three raters into a common factor model, the “shared view” of childhood ASB is explained almost entirely by genetic factors (h2 ≥ 90%). In another review, Moffitt (2005a) concluded that around 50% of the variance in ASB can be explained by genetic factors, 20% by shared environment, and 20–30% by nonshared environment. Moffitt noted several key findings, including previously discussed heterogeneity in results across different operational definitions of ASB. Another important finding is that even though 50% of the variance was explained by genetic factors, a large share was also explained by environmental factors. It is thus important to recognize these environmental risk factors as well as G×E interactions (discussed later), which might have greater influences on these behaviors than genes or environment alone. The nature and importance of G×E interactions is exemplified through investigations revealing that adverse environmental factors along with genetic risk for ASB can exacerbate behavioral outcomes in some individuals (Moffitt, 2005b).

On average, about 50% of the variance in ASB, broadly defined, is explained by genetic effects, with the remainder being accounted for by shared and nonshared environmental effects. However, these effects vary across definitions and measures of ASB, as well as across type of informants, study design, method of zygosity determination, and age. Psychopathic personality traits, which are related to ASB, are also influenced by additive genetic influences, with little or no evidence for shared environmental effects. It is also important to recognize that unmeasured G×E interactions (discussed in more detail in the next section) would typically lead to greater MZ than DZ twin resemblance, which could lead to inflated heritability estimates in twin studies However, even when significant, G×E interaction typically accounts for a small amount of the variance and is therefore likely to only modestly bias the estimates (Heath et al., 2002). In spite of the strong heritability for a general ASB factor, it noteworthy that there are meaningful subforms of ASB that show different patterns of gene–environment influences. Researchers often distinguish between aggressive and nonaggressive (rule-breaking, delinquent) forms of ASB—as has been done in studies of CD discussed previously—that are proposed to have different genetic and environmental etiologies, as well as important sex differences in genetic and environmental etiology (Burt, 2012; Eley et  al., 1999). In a meta-analysis of 34 twin and adoption studies of ASB in children and adolescents, Burt (2009a) reported heterogeneity in patterns of gene–environment influence between aggressive and nonaggressive ASB across development. Specifically, aggressive ASB can be largely explained by heritable (65%) and nonshared environmental factors (30%), with little effect of common environment (5%). Whereas nonaggressive ASB is also significantly heritable (48%), common environment was of greater importance (18%) in comparison to aggressive forms. These patterns of gene–environment influence also varied slightly but significantly across development (e.g., shared environmental effects increasing for nonaggressive ASB but decreasing for aggressive ASB from early childhood to late adolescence), thus further emphasizing the importance of distinguishing between different forms of ASB, at least in children and adolescents. In a follow-up study of 11 twin and adoption studies (n = 12,923), Burt (2012) examined the extent of the genetic and environmental etiological overlap between aggressive behavior and delinquent/rule-breaking

behavior. Genetic influences on aggressive behavior were largely, but not entirely, distinct from those on delinquent/rule-breaking behavior: only 38.4% of the genetic influences on aggressive behavior overlapped with those on delinquent/rule-breaking behavior. Similarly, only 10.2% of the nonshared environmental influences on aggressive behavior overlapped with those on delinquent/rule-breaking behavior. Importantly, age and informant were found to moderate the extent of the overlap (Burt, 2012), thus highlighting the need to consider the way in which ASB is defined and measured, as well as the age at which it is studied.

Current State of the Science

Early behavior genetic studies relied primarily on simplified statistical methods to establish heritability of a trait by comparing the phenotypic resemblance (e.g., intraclass correlations) between MZ and DZ twins. Typically, only a handful of participants were included in these early studies. One example of this is the Swedish twin studies that examined how smoking affects our health. Little was known then about the dangers of smoking; one of the hypotheses was that lung cancer is primarily caused by heritable factors. These studies formed the base for the Swedish twin registry, which was established in the 1960s. Today, the Swedish twin registry is the largest such registry in the world, with more than 85,000 twin pairs. In parallel with a global establishment of twin registries (e.g., the California twin registry, the Minnesota twin registry, the Dutch twin registry), there was a shift from strict environmental explanations to also recognizing the importance of heritable factors in the development of traits and disorders. In the past two decades, behavioral genetic research has been relying on more advanced statistical methods such as structural equation modeling to estimate regression coefficients (parameter estimates) between latent (unobserved) and observed variables (Boomsma, Busjahn,  & Peltonen, 2002). Modeling allows for actual testing of different hypotheses; for example, whether it is possible to set any parameters to zero and to formally compare models. These advances have made twin samples not only a powerful resource to understand how much of the total variance in a trait is explained by genetic and environmental influences, but also an excellent resource for examining G×E interactions. G×E, by definition, is a statistical term indicating that genetic effects on a given trait or behavior depend on environmental effects, or vice versa. Environmental factors interact with pre-existing genetic vulnerabilities Dhamija, Tuvbl ad, Baker

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to externalizing behavior. These interactions may increase or decrease the intensity of expression of such behaviors. By way of exploring G×E, behavior geneticists are able to identify important environmental risk factors for externalizing behaviors (Tuvblad & Baker, 2011). Recent studies aimed at identifying such risk factors have found important environmental variables that can alter expression of heritable externalizing problems, including exposure to different teachers and classrooms (Lamb, Middeldorp, Van Beijsterveldt, & Boomsma, 2012), parental conflict (Harold et  al., 2013; Nederhof, Belsky, Ormel,  & Oldehinkel, 2012), family dysfunction (Button, Scourfield, Martin, Purcell,  & McGuffin, 2005), parental criticism (Narusyte et al., 2011), parental overreaction (Lipscomb et al., 2014), early care and education (Lipscomb et  al., 2014), preschool attendance (Tucker-Drob  & Harden, 2013), SES (Tuvblad, Grann,  & Lichtenstein, 2006), and peer deviance and low prosocial behaviors (Kendler, Gardner, & Dick, 2011). These studies have a special importance in guiding intervention and prevention of externalizing behaviors through identification of risk factors and possible strategies for minimizing exposure to environments that may facilitate expression of deviant behavior. Behavior genetic approaches to studying externalizing behavior today use latent-trait models, wherein liability for externalizing psychopathologies is considered to be normally distributed in the population, and individuals who exceed a certain threshold are categorized as falling within diagnostic categories. Hierarchical structural equation modeling is used to estimate the heritability of a general externalizing factor plus disorder-specific genetic effects for CD, ASB, SUDs, and disinhibitory personality traits (Krueger et  al., 2002). This genetic factor structure—with both a common factor and additional disorder-specific effects—characterizes a variety of externalizing symptoms including antisocial personality and behavior, as well as alcohol and drug dependence in both adults (Kendler et  al., 2003b) and youth (Hicks, Krueger, Iacono, McGue, & Patrick, 2004). Most familial transmission of these symptoms appears to occur through a general vulnerability factor that is highly heritable (h2  =  0.80). After a decade of such an approach, there are still continued efforts made to tailor these analyses to best capture the genetic and environmental influences that explain the covariance of these disorders (Kendler  & Myers, 2014; Kendler et  al., 2011; Korhonen et  al., 2012; Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011). 116

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Another important area of behavior genetic research has focused on longitudinal studies to examine how genetic and environmental factors influence stability and change in externalizing behavior problems across development. Behavioral genetic research has also contributed to our understanding of genetic and environmental correlations of externalizing problems with comorbid disorders and behaviors, including internalizing disorders (Cosgrove et  al., 2011; Kendler  & Myers, 2014; Kendler, Aggen et  al., 2011; Lahey et  al., 2011; Mikolajewski, Allan, Hart, Lonigan,  & Taylor, 2013), personality traits (Hink et al., 2013; Kendler, Aggen et  al., 2011; Kendler  & Myers, 2014), and criminal behavior (Kendler, Patrick, Larsson, Gardner,  & Lichtenstein, 2013). The genetic correlation between internalizing and externalizing factors has ranged from 0.16 to 0.72, depending on the method of assessment, disorders, or measures being studied, and the age and sex of participants. The parallel correlation of the nonshared environment is estimated to be between 0.38 and 0.74 (Cosgrove et al., 2011; Kendler, Aggen et al., 2011; Kendler & Myers, 2014; Lahey et  al., 2011; Mikolajewski et al., 2013). With respect to personality traits, neuroticism, novelty-seeking, sensation-seeking, and extraversion index genetic vulnerability to externalizing behavior (Hink et al., 2013; Kendler, Aggen et al., 2011; Kendler & Myers, 2014).

Developmental Considerations

Several longitudinal studies have examined the influence of genetic and environmental effects on externalizing behavior problems across development. Statistical approaches include latent growth curve, cross-lag, and simplex modeling to answer questions about (1)  developmental stability of genetic and environmental influences (e.g., how well these effects may explain age-to-age correlations) and (2)  how genes and environment may contribute to intraindividual differences in externalizing problems over time (e.g., how much variation in initial level and change across development may be explained by genes and environment). Longitudinal studies have also identified various trajectories of externalizing psychopathology that individuals may follow across development. Several distinct developmental trajectories have been identified, including stable high (i.e., childhood onset/ life-course persistent), decreasing (i.e., childhood limited), increasing (i.e., adolescent onset/transitory), and stable low (i.e., low prevalent across development) (Broidy et  al., 2003; Dhamija, Tuvblad,

Raine, & Baker, under review; Mata & van Dulmen, 2012; Odgers et al., 2008; van Lier, Vitaro, Barker, Koot,  & Tremblay, 2009; Xie, Drabick,  & Chen, 2011). An adult-onset group has also been identified (Fontaine, Carbonneau, Vitaro, Barker, & Tremblay, 2009). Theories of developmental trajectories of externalizing problems have been put forward by both DiLalla and Gottesman (1989) and Moffitt (1993) (see also Beauchaine, Shader,  & Hinshaw, this volume). Behavioral genetic studies show that genetic influences contribute more strongly to the stable high/childhood-onset trajectory than to the increasing/transitory trajectory (Lyons et al., 1995; Silberg, Rutter, Tracy, Maes, & Eaves, 2007; Taylor, Iacono,  & McGue, 2000; Tuvblad, Narusyte, Grann, Sarnecki,  & Lichtenstein, 2011). An early study reported that shared environmental factors were more important for the increasing/transitory trajectory, whereas genetic influences were stronger for ASB beginning in adulthood (i.e., “late bloomers”) (Lyons et al., 1995). Early-onset delinquent behavior at age 11 years is also more strongly influenced by genetic effects than later onset delinquent behavior (Taylor et al., 2000). More recently (Fontaine, Rijsdijk, McCrory,  & Viding, 2010) examined to what extent genetic and environmental factors contributed to four developmental trajectories of callous-unemotional (CU) traits across ages 7, 9, and 12  years. For stable high and increasing trajectories, heritable factors were important but in boys only, whereas for girls shared environmental factors were important. For decreasing and stable low trajectories, heritable factors were primarily of importance. Several longitudinal behavior genetic studies have examined changes in genetic and environmental contributions to externalizing psychopathology across the life span, as well as the extent to which stability of externalizing problems (i.e., cross-age correlations) may be due to genes versus environment. For example, stability in ASB across four occasions from ages 8 to 20 years (as captured through a single latent factor) appeared to be explained largely by genetic influences, which accounted for 67% of the variance in this latent factor. Shared environment explained another 26%, and nonshared environment explained the remainder (7%) of latent factor variance. Significant age-specific shared environmental factors were also found at ages 13–14 years, suggesting that experiences shared by twins (e.g., peers) are important for ASB at this age (Tuvblad et  al., 2011). In a large Dutch twin study spanning ages 3–12 years, a similar pattern was found,

in that both genetic and shared environmental factors contributed to the stability of aggression as measured by the CBCL in both boys and girls. Genetic factors explained 67% of the age-to-age correlations of externalizing behavior problems in boys and 60% in girls, suggesting that genetic influences that appear early in childhood continue to exert their effects throughout development. Shared environmental factors also appear to exert continuous effects from childhood to early adolescence, explaining 25% and 32% of the stability for boys and girls, respectively, in these problem behaviors across time (van Beijsterveldt, Bartels, Hudziak, & Boomsma, 2003). Intraindividual differences in externalizing behavior changes across development have also been investigated in behavior genetic studies using latent growth curve modeling. This approach examines genetic and environmental contributions to both initial level and change in externalizing psychopathology. Initial level is often explained by genetic factors, and change is largely due to nonshared environmental factors (i.e., experiences that are unique to each twin in a pair; e.g., Haberstick, Schmitz, Young, & Hewitt, 2006; Tuvblad, Wang, Bezdijian, Baker,  & Raine, under review; van Beijsterveldt et  al., 2003). The Minnesota Twin and Family Study has also shown genetic influences to be largely responsible for initial levels of ASPD symptoms from late adolescence to mid-adulthood, whereas nonshared environmental influences were primarily responsible for individual differences in change (Burt, McGue, Carter, & Iacono, 2007). Both genetic and environmental factors contribute to stability of externalizing problems across development, although “new” or innovative environmental factors appear to come into play during adolescence, which may explain the increase in these behaviors during that developmental period. In spite of numerous longitudinal twin studies of externalizing problems across childhood and adolescence, there are fewer studies that span childhood to adulthood (Vrieze, Hicks, Iacono, & McGue, 2012; Wichers et al., 2013). Investigating how genes and environment may be stable or dynamic across the life span will be important to our understanding of trajectories that involve life-course-persistent externalizing psychopathology.

Controversies

Externalizing behavior problems such as criminality, violence, aggression, and other forms of ASB exact a high toll on society, including costs of Dhamija, Tuvbl ad, Baker

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the criminal justice system, compensation for victims and their families, and effects on communities. Behavioral genetic research has the potential to develop early intervention strategies by identifying and understanding genetic risk and, most importantly, ways in which environmental factors and exposure may amplify or mollify such risks for externalizing psychopathology. There are nonetheless several controversial issues, arising due to ethical concerns and implications of this research for individuals and society (Berryessa  & Cho, 2013; Berryessa, Martinez-Martin, & Allyse, 2013). The intertwined history of eugenics and behavioral genetic research is one of the biggest controversies surrounding genetic research on the externalizing spectrum. Past efforts to selectively breed out undesirable behaviors and traits through sterilization, especially when linked specifically with certain racial or other subgroups of society, have contributed to the sensitization of society to this research. Genetic research on externalizing problems has been viewed by some—perhaps unfairly—to have a eugenic undertone because of possible social implications. It has been misunderstood by some as a science to connect behavior to genes for the sole purpose of finding a heritable component to behavior and lend air to the fire of pre-existing prejudices in society. As described in a recent review (Berryessa et al., 2013), this line of research may come across as supporting determinism and thus advocating for social stratification based on heritable “superiority.” Another controversy relates to findings that heritable factors partly explain the variance in externalizing behaviors such as violence, crime, and aggression, most of which tend to occur at a higher rate in males and certain ethnic groups. It should be kept in mind, however, that in spite of the strong evidence for genetic underpinnings, behavioral genetic research also stresses the importance of environmental factors in development of these behaviors. Public policies for intervention should clearly take into account both genetic and environmental risks in order to effectively reduce externalizing problems. A third controversial implication of behavior genetic research relates to moral responsibility and punishment of unlawful individuals. Deeming a certain behavior or trait as heritable and proposing that genetic factors contribute to their etiology does not and should not be seen as a way to escape moral and personal responsibility for one’s actions. Unlawful behavior should not be justified and excused on the grounds of a person’s “biological 118

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makeup” or misused to escape legal consequences. The underlying goal of behavioral and molecular genetic research is not to find ways to justify behavior as a direct outcome of one’s genetic makeup, but to identify vulnerabilities and risk factors and possible intervention strategies to promote good health and prevent extreme behavioral outcomes in individuals. In summary, behavior genetic research has been scrutinized for ethical and legal reasons. Although some of these concerns are valid, the purpose of this line of research is often misunderstood. Understanding the scientific motivation behind behavior genetic research of externalizing behaviors and educating the media could help prevent misuse and avoid creating and intensifying prejudices in society.

Research Agenda and Future Directions

Genetic factors have been established unequivocally for externalizing spectrum symptoms and traits, with considerable evidence for a highly heritable general liability factor. Genetic influences appear early in childhood and continue to exert their effects across development. There has been substantial attention paid to childhood and adolescence in behavioral genetic research on externalizing disorders, and less is known about the specific genetic and environmental bases of life-course-persistent developmental trajectories. Life span longitudinal studies are required to fully understand genetic and environmental stability from childhood to adulthood, including the age period at which risk for onset of criminal offending and substance use disorders remains high (through age 25). Although much is known about the genetic basis of comorbidity among various externalizing disorders, there is still much to be learned about the comorbidity of externalizing problems with other disorders, including the internalizing spectrum. G×E has also proved to be of critical importance in explain the externalizing spectrum (see also Beauchaine, Shader,  & Hinshaw, this volume), as first demonstrated by early adoption studies of criminal behavior, although research identifying specific environmental moderators of gene expression is still in its infancy. Molecular genetic research will surely help advance the study of G×E, providing for identification of both specific genes and measured environmental moderators. Other areas where behavior genetic research continues to enlighten us regarding externalizing

psychopathology include direction of phenotypic causality (i.e., cross-lag modeling) and gene identification. Bidirectionality exists between parenting characteristics and children’s externalizing behavior, in which both child and parent effects operate in a reciprocal manner. Twin studies using cross-lag modeling have just started to provide valuable information regarding the genetic and environmental etiology of these bidirectional effects between parenting style and child externalizing behavior. In molecular genetic studies, which often involve population-based studies of nonrelated individuals, twin samples remain particularly valuable. MZ twin pairs who are either discordant for environmental exposure or phenotypic traits provide a powerful natural experiment that yields a matched case-control study design. MZ twin samples provide an excellent opportunity to examine epigenetic processes. Finally, the Research Domain Criteria (RDoC) approach set forth by the National Institutes of Mental Health (NIMH) provides an exciting new approach for understanding the development, etiology, and nosology of externalizing psychopathology. The Negative Valence (e.g., fear, anxiety, stress) and Arousal/Regulation constructs specified in RDoC are associated in both children and adults with various forms of externalizing psychopathology. Reduced reactivity to threat, as measured through skin conductance responses during fear conditioning tasks (Gao, Raine, Venables, Dawson,  & Mednick, 2010), and diminished startle-blink reflex (Patrick, Bradley,  & Lang, 1993) suggest a deficit in the brain’s defensive system. Specifically, fearlessness and novelty- and sensation-seeking may stem from deficits in amygdala and other neural systems associated with fear (LeDoux, 2003). Aggression has also been linked to underarousal (e.g., low resting heart rate and reduced skin conductance levels), reduced cortisol reactivity to stress (Susman, 2006), and regulatory deficits (e.g., decreased heart rate variability and lack of impulse control and other executive function deficits), suggesting the importance of prefrontal dysfunction in producing dysregulated behavioral outcomes (Raine, Venables, & Mednick, 1997; Raine, Venables,  & Williams, 1990). Importantly, these patterns of Negative Valence and Arousal/Regulation abnormalities appear to differ across the externalizing spectrum (e.g., startle modulation abnormalities appear only in psychopathic but not nonpsychopathic antisocial individuals; Patrick et al., 1993), thus highlighting the heterogeneous nature of externalizing psychopathology and its underlying etiologies.

Future behavioral genetic studies can make significant contributions to our understanding of the development and gene–environment underpinnings of fundamental dimensions of psychological functioning that cut across traditional diagnostic boundaries within the DSM and provide new insights into mechanisms underlying vulnerability to and risk for psychopathology. A  more profound understanding of the underlying etiology of externalizing psychopathologies will in turn enable more accurate prediction of dysregulated behavioral outcomes in adulthood from early childhood and adolescence, which will enhance treatment and prevention strategies.

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early childhood to adolescence: A longitudinal genetic analysis in Dutch twins. Behavior Genetics, 33, 591–605. van den Bree, M. B., Johnson, E. O., Neale, M. C., & Pickens, R. W. (1998). Genetic and environmental influences on drug use and abuse/dependence in male and female twins. Drug and Alcohol Dependence, 52, 231–241. van den Oord, E. J., Boomsma, D. I., & Verhulst, F. C. (1994). A study of problem behaviors in 10- to 15-year-old biologically related and unrelated international adoptees. Behavior Genetics, 24, 193–205. van Lier, P. A., Vitaro, F., Barker, E. D., Koot, H. M., & Tremblay, R.  E. (2009). Developmental links between trajectories of physical violence, vandalism, theft, and alcohol-drug use from childhood to adolescence. Journal of Abnormal Child Psychology, 37, 481–492. Vanyukov, M.  M.,  & Tarter, R.  E. (2000). Genetic studies of substance abuse. Drug and Alcohol Dependence, 59, 101–123. Vollebergh, W.  A., Iedema, J., Bijl, R.  V., de Graaf, R., Smit, F., & Ormel, J. (2001). The structure and stability of common mental disorders:  The NEMESIS study. Archives of General Psychiatry, 58, 597–603. Vrieze, S. I., Hicks, B. M., Iacono, W. G., & McGue, M. (2012). Decline in genetic influence on the co-occurrence of alcohol, marijuana, and nicotine dependence symptoms from age 14 to 29. American Journal of Psychiatry, 169, 1073–1081. Waldman, I. D. and Rhee, S. (2006). Genetic and environmental influences on psychopathy and antisocial behavior. In C. J.  Patrick (Ed.), Handbook of psychopathy (pp.  205–228). New York, Guilford. Wichers, M., Gardner, C., Maes, H.  H., Lichtenstein, P., Larsson, H.,  & Kendler, K.  S. (2013). Genetic innovation and stability in externalizing problem behavior across development:  A  multi-informant twin study. Behavior Genetics, 43, 191–201. Willcutt, E. (in press). The etiology of ADHD:  Behavioral and molecular genetic approaches. In D. Barch (Ed.), Cognitive and affective neuroscience of psychopathology. New York: Oxford University Press. Xie, H., Drabick, D.  A.,  & Chen, D. (2011). Developmental trajectories of aggression from late childhood through adolescence: Similarities and differences across gender. Aggressive Behavior, 37, 387–404.

CH A PT E R

8

Molecular Genetic Approaches to Studying the Externalizing Spectrum

Ian R. Gizer, Jacqueline M. Otto, and Jarrod M. Ellingson

Abstract Molecular genetic studies of psychiatric traits, including externalizing behaviors, aim to further our understanding of etiology by identifying specific genetic vulnerabilities that give rise to psychopathology. Recently, molecular geneticists have moved from candidate gene studies focused on small sets of genetic variants in single genes and coarse genome-wide linkage scans to genome-wide association studies and genome sequencing studies that examine millions of such variants in a single study. This has led to significant progress in our understanding of the genetic architecture of vulnerability for externalizing behavior. This chapter reviews methods that are used to study the molecular genetics of externalizing spectrum disorders. It begins with a review of the basic research designs used to study genetic variation, then describes how transdiagnostic approaches to phenotype measurement, combined with advances in analytic approaches and genotyping and sequencing technologies, will allow for more powerful molecular genetic investigations of the externalizing spectrum. Key Words:  molecular genetics, externalizing spectrum disorders, psychopathology, genome sequencing, genetic variation

Introduction

Molecular genetic studies of personality and psychopathology have long been the focus of interest and debate in the literature. Although justified by quantitative behavioral genetic studies demonstrating significant heritability of all psychiatric phenotypes, the search for genetic variants that confer vulnerability has been slow (see Gizer et al., Chapter 9). This is exemplified best in the large gap between heritabilities for externalizing spectrum disorders, which approach and exceed .80 in quantitative behavioral genetic research (e.g., Krueger et  al., 2002; Tuvblad, Zheng, Raine,  & Baker, 2009), and variance in externalizing behavior accounted for by all molecular gene candidates combined, which at best sum to only a few percent (e.g., Beauchaine  & Gatzke-Kopp, 2012; Plomin, 2013). Thus, goals that were once thought to be within reach—namely, providing

a clear understanding of genetic variation that contributes to psychiatric disorders—remain elusive. Despite this slow progress, significant gains have been made by focusing attention on phenotype measurement, formulating data-sharing arrangements for large-scale collaboration, advancing genotyping and sequencing technologies, and developing more refined analytic methods to study phenotype-genotype relations. In this chapter, we provide an overview of methods used currently to study molecular genetic vulnerabilities to externalizing spectrum disorders. To accomplish this, we begin with a brief history of molecular genetic research, including an introduction to primary methods used to study phenotype-genotype relations. We then review some current challenges facing the field and recent developments that hold potential to address many of these challenges. 125

Background

The human genome comprises more than 3 billion nucleotide base pairs (i.e., adenine-thymine [A-T] and cytosine-guanine [C-G] “rungs” of the double-helix DNA “ladder”). Population geneticists estimate that any two unrelated individuals share, on average, 99.7% of their genome. Despite this high rate of concordance, the 0.3% that varies across individuals is quite substantial given the size of the human genome, and researchers have sought to characterize sources of this genetic variation. The most widely studied type of genetic variation is the single nucleotide variant (SNV), in which a single base pair at a defined location in the genome varies among individuals in the population. Nucleotides observed at that locus are then used to define the alleles of that variant (e.g., A vs. C). When the less common allele (i.e., minor allele) of any genetic variant remains below a frequency of 1% in the population, the variant is typically referred to as a mutation or rare variant, although it should be noted that this cutoff is somewhat arbitrary. When the minor allele frequency (MAF) surpasses 1%, the variant is typically referred to as a polymorphism, and SNVs that meet this criterion are referred to as single nucleotide polymorphisms (SNPs). Repeat polymorphisms, formally referred to as variable number tandem repeat polymorphisms (VNTRs), represent another type of genetic variation in which segments of DNA vary in how many times they are repeated across individuals. These segments can range in length from two to more than 100 base pairs, and alleles of a VNTR are defined by how many times that segment is repeated (e.g., four repeats vs. seven repeats). Structural variation refers to duplication or deletion of larger genome segments. Microscopically visible rearrangements are greater than 3 million base pairs in length and can affect an entire chromosome. These structural variants typically result in well-characterized genetic syndromes (e.g., aneuploidies such as trisomy 21/ Down syndrome). Copy number variants (CNVs) represent a subcategory of structural variants that are submicroscopic and range from 1,000 to 3,000,000 base pairs in length (Feuk, Carson,  & Scherer, 2006). CNVs are causal factors in specific genetic syndromes (e.g., Williams syndrome) and are associated with a range of psychiatric traits (Malhotra & Sebat, 2012). Epigenetic modifications to chromatin structure, which are transmitted through DNA methylation, represent another type of variation that is important in regulation of gene expression. Such changes alter the shape of DNA to make nearby 126

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genes more or less accessible for transcription, thus regulating their expression. Notably, epigenetic changes—including methylation changes—can be relatively dynamic and allow environmental stimuli to influence gene expression—and subsequently the phenotype—without changing DNA sequence. Interested readers will find more information about epigenetic processes in externalizing spectrum disorders elsewhere in this volume (Nikolas; Stringaris & Krieger). Each source of genetic variation has been studied in relation to externalizing disorders and is discussed in this chapter. Nonetheless, SNVs and repeat polymorphisms have been the primary focus of these efforts, and thus methods designed for their study are featured most prominently in this chapter. The focus on repeat polymorphisms and SNVs in the study of complex traits is due to both the abundance of such variants and technological advances that have made it possible to interrogate large numbers of variants in a cost-effective manner. SNPs are currently estimated to occur in approximately 1 in every 300–1,000 base pairs, depending on the population under study (Kruglyak  & Nickerson, 2001). Currently, more than 20,000,000 SNPs have been catalogued in the National Center for Biotechnology Information database of genetic variation (dbSNP; Sherry et al., 2001). In addition, a much larger number of rare SNVs—those defined as exhibiting a MAF of less than 1%—have been catalogued, with more than 50,000,000 identified to date. Much of this variation (i.e., both common and rare SNVs) is unlikely to have a functional or regulatory effect and thus is not believed to influence phenotypic variation. As a result, the task facing molecular geneticists is to identify which of these variants are involved in the etiology of the phenotype under study. The difficulty of this task is due in part to the fact that psychiatric phenotypes are genetically complex (i.e., polygenically influenced; multifactorially inherited). Geneticists define a “complex trait” as a phenotype that does not exhibit classic Mendelian inheritance attributable to a single gene locus (Lander  & Schork, 1994). As a result, there are a number of challenges inherent to the study of complex heritable traits (e.g., Chakravarti, 1999; Lander  & Schork, 1994; Risch, 2000; Risch  & Merikangas, 1996). First, each of the multiple genes and variants that contribute to complex disorders confers only a relatively small risk for the disorder. Second, genetic heterogeneity is characteristic of complex traits, so different collections of

genotypes can result in the same or quite similar phenotypes (see Beauchaine & Gatzke-Kopp, 2012; Hinshaw & Beauchaine, this volume). Third, environmental risk factors moderate complex, multifactorially inherited disorders much more than they affect Mendelian diseases (see, e.g., Beauchaine  & McNulty, 2013). Thus, multiple environmental influences and multiple gene × gene and Gene × Environment (G×E) interactions likely contribute to the etiology of a complex trait. In short, complex traits and disorders arise from a multitude of susceptibility genes, each contributing only a small magnitude to overall vulnerability for the disorder. This conclusion has been supported by the growing number of large-scale genome-wide association (GWA) studies that indicate that individual variants typically explain less than 0.5% of variation in a complex trait, a finding that has been demonstrated for biometric phenotypes such as height and weight, as well as for psychiatric phenotypes such as schizophrenia and bipolar disorder (Sullivan, Daly, & O’Donovan, 2012). As described in this chapter and others of this volume, both twin and family studies indicate the presence of substantial heritable influences on development of attention-deficit/hyperactivity disorder (ADHD; e.g., Rhee  & Waldman, 2002), oppositional defiant disorder (ODD) and conduct disorder (CD; e.g., Slutske et  al., 1997; Tuvblad et al., 2009), antisocial personality disorder (ASPD; e.g., Gunter, Vaughn,  & Philibert, 2010), nicotine dependence (e.g., True et al., 1999), cannabis dependence (e.g., Agrawal & Lynskey, 2006), alcohol dependence (e.g., Heath et al., 1997), and other illicit drug dependence (e.g., Tsuang et  al., 1996; van den Bree, Johnson, Neale,  & Pickens, 1998). Although multivariate analyses demonstrate significant shared genetic etiology among these disorders (e.g., Krueger et  al., 2002; Tuvblad et  al., 2009), molecular genetics studies of externalizing phenotypes have typically focused on individual disorders rather than on transdiagnostic phenotypes that measure this shared etiology. Studies that have focused on transdiagnostic phenotypes such as impulsivity are fewer in number and have typically relied on smaller sample sizes, resulting in fewer replicable results. Therefore, we begin our review with an overview of the basic molecular genetic methods that have been applied to the study of externalizing spectrum disorders before turning to a discussion of how studies that use transdiagnostic approaches can begin to identify genetic influences that contribute to externalizing disorders more generally. Of

note, transdiagnostic approaches are consistent with several objectives of the Research Domain Criteria (RDoC) being developed by the National Institutes of Mental Health, which seek to identify genetic and neurobiological vulnerabilities that cut across traditional diagnostic boundaries (see Insel et  al., 2010; Sanislow et  al., 2010). Specific examples of how this is currently being accomplished are provided, as are suggestions for how future studies might build on these efforts. We conclude with a discussion of how rapid development of technologies for interrogating genetic variation will continue to further our understanding of genetic vulnerabilities that contribute to the development of externalizing spectrum disorders.

Approaches to the Study of Individual Loci Related to Externalizing Disorders

Molecular genetic studies of complex traits, including externalizing phenotypes, have taken two general approaches to identifying individual variants that confer vulnerability to the trait under study. The first is a genome scan, in which linkage or association is examined between a phenotype and genetic variants distributed across the entire genome. This approach therefore represents an exploratory, hypothesis-free search for putative genes that contribute to a trait. In contrast, candidate gene studies represent a targeted test of the role of specific genes selected based on a priori knowledge regarding the gene product and its involvement in the etiology of the trait under study. Due to technological limitations, early molecular genetics studies of psychiatric traits were conducted either as genome-wide scans using linkage analysis or as candidate gene studies using the association methods as described in the following sections.

Genome-wide Linkage Studies

Briefly, linkage analysis examines whether alleles of a polymorphism co-segregate with the presence or absence of a trait within family pedigrees (Craddock & Owen, 1996). Excess sharing of alleles at a DNA marker among affected family members indicates that the typed marker is sufficiently close to a functional variant involved in the etiology of that trait such that co-inheritance of the two loci is not disrupted by recombination that occurs during meiosis. Thus, linkage analysis focuses on shared inheritance of alleles from a common ancestor among family members separated by only one or two generations. Given that recombination events can be expected to occur Gizer, Ot to, Ellingson

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just once in every approximately 50,000,000 base pairs across a single generation, a single variant can be used to interrogate broad regions of the genome extending as far as 20,000,000 base pairs. As a result, a relatively small number of multiallelic repeat polymorphisms (~800) is sufficient to conduct a genome-wide linkage scan. Studies relating variation in the γ-aminobutyric acid (GABA) α2 receptor subunit (GABRA2) gene to externalizing behavior provide a useful illustration of how linkage analysis has been applied to the study of these phenotypes. Initial linkage studies from the Collaborative Study on the Genetics of Alcoholism study (COGA) reported a linkage signal on chromosome 4 near the alcohol dehydrogenase (ADH) gene cluster for alcohol dependence (T. Reich et  al., 1998). Subsequent investigations using electroencephalogram (EEG) waveform data suggested that this linkage peak could be further resolved into two distinct peaks, one localized to the ADH gene cluster and the second localized to a cluster of four GABA receptor genes located on the short arm of chromosome 4 (Porjesz et al., 2002). As the primary inhibitory neurotransmitter in the central nervous system that acts as an important mechanism for alcohol’s intoxicating effects, GABA system genes have received significant attention in the study of externalizing disorders, lending further support to this initial linkage finding. Additional studies refined this result by suggesting that the causal locus is likely to reside in the GABRA2 gene (Edenberg et  al., 2004). Furthermore, these studies indicated that participants who reported alcohol and co-occurring illicit substance use were largely responsible for the observed relation, suggesting a link to a broader externalizing phenotype (Agrawal et al., 2006). Additional support for this conclusion was provided by a longitudinal study of child participants in the COGA sample, which showed that GABRA2 was related to childhood conduct disorder symptoms and adult alcohol and other drug dependence (Dick et al., 2006), a finding that was later replicated in an independent sample (Dick et  al., 2009). Together, these studies provide a useful example of how linkage analysis has been applied successfully to the study of externalizing disorders.

Candidate Gene Studies

In contrast to linkage studies, association studies are based on the classic case–control design and are used to test whether a specific allele is overrepresented among affected “cases” relative to unaffected “controls” at the population level. If the associated 128

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allele is not the causal allele, our ability to detect association depends on the strength of the allele’s correlation with nearby variants at the population level, which typically extends to variants within 2,000–4,000 base pairs of its location. Hundreds of thousands of variants are thus required to conduct a GWA scan that comprehensively interrogates the genome. Because of limitations in available genotyping technologies and their associated costs, early association studies were therefore limited to candidate genes. A useful example of the candidate gene approach is provided by studies that examine relations between the dopamine D2 receptor gene (DRD2) and externalizing disorders. The DRD2 gene represents a particularly strong candidate given the critical role of dopamine in mesolimbic and mesocortical reward pathways (see Stringaris & Krieger, this volume; Zisner  & Beauchaine, this volume) and the high expression levels of DRD2 in the component regions of the former pathway, including the striatum, ventral tegmental area, and nucleus accumbens (Balleine, Delgado, & Hikosaka, 2007; Cools, Gibbs, Miyakawa, Jagust,  & D’Esposito, 2008; Grillner, Hellgren, Ménard, Saitoh,  & Wikström, 2005; Lammel et al., 2012). Candidate gene studies of DRD2 have largely focused on a TaqI restriction site (rs1800497), located more than 10 kilobases downstream from DRD2 (Blum et al., 1996; Noble, Blum, Ritchie, Montgomery, & Sheridan, 1991). Despite the fact that this polymorphism lies in an exon of a neighboring gene, ANKK1 (Neville, Johnstone, & Walton, 2004), it is related to both DRD2 expression (Laakso et al., 2005; Thompson et  al., 1997) and urinary levels of the dopamine metabolite homovanillic acid (Ponce et  al., 2003), suggesting a regulatory role. Candidate gene studies show that the minor allele (A1) of the TaqI polymorphism is associated with alcohol dependence (Gorwood et al., 2012; Le Foll, Gallo, Le Strat, Lu, & Gorwood, 2009; Munafò & Flint, 2009; Wang, Simen, Arias, Lu,  & Zhang, 2013), tobacco use, nicotine dependence, and opiate dependence (Gorwood et al., 2012). Additional studies suggest that these associations are strengthened further in the presence of co-occurring ASPD (e.g., Dick et  al., 2007a; Hill, Zezza, Wipprecht, Xu,  & Neiswanger, 1999; Kraschewski et  al., 2009), which suggests a potential vulnerability for externalizing behavior more broadly. However, there has been mixed support for associations between DRD2 and ADHD (e.g., Faraone et  al., 2005; Gizer, Ficks, & Waldman, 2009; Neale et al.,

2010a; Nyman et al., 2007; Qian et al., 2007; Wu, Xiao, Sun, Zou, & Zhu, 2012), indicating that this vulnerability may not generalize to all externalizing disorders. As these illustrations suggest, linkage and candidate gene studies have provided important insights into the genetic vulnerabilities that contribute to externalizing behavior; however, these successes have been relatively limited (see Gizer et al., Chapter 9). this volume, for a more detailed review). More problematically, poor replication rates have led researchers to justifiably question published findings produced by such studies. Thus, it is important to note that initial molecular genetics studies of externalizing disorders—and multifactorial traits in general—were based in part on the hypothesis that underlying genetic influences were oligogenic in nature, with a relatively restricted subset of genes involved in etiology. As a result, a number of linkage and candidate gene studies were conducted under the assumption that variants of moderate effect size could be found. Consequently, they were underpowered to detect the smaller effect sizes that are now known to be typical of common variants involved in the etiology of complex traits. Unfortunately, underpowered studies not only fail to detect real effects, but they also yield many false positives, which likely also contributed to poor replication rates across studies.

Genome-wide Association Studies

Poor replication rates forced investigators to consider that genetic etiologies of many complex traits are more polygenic than once anticipated, with greater numbers of genes, each with smaller effect sizes than originally anticipated. This led to refinements of the common-disease/common-variant hypothesis, which argued that large numbers of polymorphisms of relatively small effect could persist in the population given that they led to disorder only when present in combination within an individual (Lander, 1996; D. E.  Reich  & Lander, 2001). These refinements to our understanding of the genetic architecture of complex traits, combined with rapid advances in genotyping technology that enabled researchers to genotype hundreds of thousands of SNPs quickly and economically, led to widespread acceptance of GWA studies. These studies take an atheoretical approach to gene hunting by interrogating hundreds of thousands to more than 2  million SNPs across the entire genome, thereby allowing for fairly comprehensive (although typically underpowered) measurement of common genetic variation.

Despite much criticism, GWA studies have provided several important contributions to our understanding of psychiatric traits, including identification of novel vulnerability genes and gene pathways involved in the etiology of externalizing disorders. For example, cell adhesion proteins, as implied by their name, enable cells to bind to one another to form organized tissues, and they play important roles in neural development and synapse formation. Despite their likely relevance to psychiatric disorders, variation in genes that encode for cell adhesion proteins were not considered widely as candidate genes until structural variation in neurexin 1 (NRXN1), a cell adhesion protein involved in GABA and glutamate synapse formation, was linked to both schizophrenia (Kirov et al., 2008) and autism (Kim et al., 2008). Since these early findings were reported, NRXN1 variants have been top signals in GWA studies of both ADHD (Lesch et  al., 2008; Neale et  al., 2010b; Yang et al., 2013) and nicotine dependence (Bierut et al., 2007; Nussbaum et al., 2008). A number of additional cell adhesion genes are also related to externalizing disorders. For example, variants in the cadherin 13 gene (CDH13) were initially identified as top hits in two independent GWA studies of ADHD (Lasky-Su et al., 2008; Lesch et al., 2008) and were further identified as top hits in a subsequent meta-analysis (Neale et al., 2010a). CDH13 has also been related to various forms of substance dependence (Drgon et  al., 2011; Lind et  al., 2010). Similarly, variants in the latrophilin 3 gene (LPHN3), another cell adhesion protein, are related to both ADHD (Arcos-Burgos  & Muenke, 2010) and substance use (Lind et  al., 2010). Variants in CTNNA2 are also related to ADHD (Lesch et al., 2008; Neale et  al., 2010b) and CD (Dick et  al., 2008; Kendler et al., 2006) and to the personality trait of sensation-seeking, as described in further detail later (Terracciano et  al., 2011). Although the precise mechanisms through which variation in these genes might influence vulnerability to externalizing behaviors is currently unknown, early studies of schizophrenia that used induced pluripotent stem cells derived from patients as a disease model demonstrate that disruptions to cell adhesion genes can result in reduced synaptic connectivity. This provides compelling evidence of their importance in neural development and potential relevance to externalizing disorders (Brennand et  al., 2011). Although such results are intriguing, it should be noted that almost all of the reviewed findings for externalizing disorders failed to reach genome-wide Gizer, Ot to, Ellingson

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significance.1 Nonetheless, results from gene ontology and pathway analyses described more fully later provide further reason to take note of these preliminary findings. Linkage, candidate gene, and GWA studies have contributed substantially to our understanding of how specific genomic regions and variants confer vulnerability to externalizing disorders. Nonetheless, the proportion of variation in externalizing behavior that is explained by individual variants identified by these studies represents a very small fraction of heritability estimated from twin and adoption studies (see, e.g., Beauchaine  & McNulty, 2013). As reviewed elsewhere (see Baker, this volume; Krueger et al., 2002; Tuvblad et al., 2009), heritability estimates for externalizing disorders range from .30 to .80, and increase across development (see Bergen, Gardner, & Kendler, 2007), whereas genetic variation explained by identified variants accounts for no more than about 3% in psychiatric phenotypes (e.g., Vrieze, Iacono,  & McGue, 2012). This discrepancy has been referred to as the “missing heritability problem” (see Beauchaine & Gatzke-Kopp, 2012; Manolio et al., 2009; Plomin, 2013) and has led many to question the field of molecular genetics and GWA studies more specifically and to question whether the scientific community should continue to devote substantial resources toward them.

Approaches to the Study of Aggregate Genetic Vulnerability for Externalizing Disorders

Despite these criticisms, there are several approaches to investigating missing heritability that measure genetic vulnerability in aggregate and provide reasons for optimism regarding continued progress in the field. We focus on two such approaches here. The first involves creation of polygenic risk scores in which vulnerability alleles of SNPs exhibiting p-values that meet a prespecified cutpoint (e.g., p < .05) in a GWA study are used to create a count variable indicating the number of vulnerability alleles across SNPs possessed by each participant. Regression analyses are then conducted to determine the amount of variation in the phenotype that is explained by this risk score. The logic behind this approach is that many SNPs influence the phenotype of interest, but the combination of small effect sizes and insufficient sample size prevents these SNPs from achieving genome-wide significance. Thus, a composite score comprised from counts of risk alleles of variants with nominally significant p-values may capture at least some of these 130

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variants while at the same time reducing the effect of spurious associations. This approach first demonstrated that a significantly greater portion of genetic risk for schizophrenia can be identified than when using additive effects of genome-wide significant SNPs alone. Variance explained was tripled from less than 1% to about 3% (Purcell et  al., 2009), then to 6% in a more recent study using a larger sample (Schizophrenia Psychiatric Genome-Wide Association Study [GWAS] Consortium, 2011). Studies using polygenic risk scores have since been conducted for externalizing disorders and related phenotypes, but conclusions have been limited given their smaller samples. For example, Hamshere et al. (2013) used a combined dataset of about 3,000 ADHD cases and a similar number of controls to calculate a polygenic risk score for ADHD, which explained a small but significant portion (G SNP (rs25531) located in the l allele of the 5HTTLPR, which causes the l allele to exhibit expression levels similar to those of the s allele (Nakamura, Ueno, Sano, & Tanabe, 2000). Many studies have not taken this triallelic classification of the 5HTTLPR into account, which could also give rise to discrepant reports. Finally, allelic heterogeneity, defined as the presence of multiple variants within a single gene or genomic region that influence a studied trait, could provide another explanation for inconsistent results, if in fact multiple variants of SLC6A4 are associated with externalizing disorders. Allelic heterogeneity can make it more difficult to identify association signals, given that functional variants may differ in allele frequencies across studies and may show differing patterns of linkage disequilibrium with nearby markers. To highlight this possibility, a functional VNTR in intron 2 (5HTTVNTR) has shown evidence of association with externalizing disorders (Aluja

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et al., 2009; Garcia, Aluja, Fibla, Cuevas, & García, 2010; Yang, Kavi, Wang, Wu,  & Hao, 2012). Given the positive findings from meta-analyses of the 5HTTLPR, further studies that examine variation in SLC6A4 and explore these possibilities are needed to better understand relations of this gene to externalizing behavior. In addition to SLC6A4, 5-HT receptor genes have been studied widely in relation to externalizing disorders, with most of the candidate gene literature focused on HTR1B and HTR2A. HTR1B is expressed in brain regions relevant to externalizing disorders, including the frontal cortex (Dalley  & Roiser, 2012; Fletcher, Chambers, Rizos, & Chintoh, 2009), the basal ganglia (Hart, Radua, Nakao, Mataix-Cols,  & Rubia, 2013), the striatum (Dalley, Mar, Economidou,  & Robbins, 2008), and the hippocampus (Plessen et al., 2006). Moreover, mouse knockout models of the 5HT1B receptor show phenotypic associations with disinhibition and hyperactivity (Brunner & Hen, 1997). Candidate gene studies of HTR1B have focused primarily on two functional SNPs in the promoter region (rs130058 and rs11568817) and on a synonymous SNP in the single exon of the gene (rs6296). Both promoter polymorphisms show replicable evidence of association with alcohol dependence (Cao, LaRocque, & Li, 2013; Contini et al., 2012), and rs6296 was associated with ADHD in a meta-analysis (Gizer et  al., 2009). The latter polymorphism has also been studied in relation to antisocial behavior, although results are mixed (Kranzler, Hernandez-Avila,  & Gelernter, 2001; Moul, Dobson-Stone, Brennan, Hawes, & Dadds, 2013; Soyka, Preuss, Koller, Zill, & Bondy, 2004). HTR2A has also been studied in relation to externalizing disorders, with most reports focused on a functional promoter polymorphism (rs6311), a synonymous SNP in exon 1 (rs6313) that is in almost perfect linkage disequilibrium with rs6311, and a nonsynonymous SNP in exon 3 (rs6314). These polymorphisms have been related to both alcohol (Jakubczyk et al., 2012) and smoking phenotypes (Polina, Contini, Hutz,  & Bau, 2009; White, Young, Morris, & Lawford, 2011), as well as to antisocial traits (Burt & Mikolajewski, 2008; Moul et al., 2013). However, there is little evidence to support a relation with ADHD (Gizer et  al., 2009). Finally, recent studies have focused on the HTR3A and HTR3B genes, yielding significant associations with alcohol dependence (Enoch et al., 2011; Seneviratne et al., 2013) and comorbid alcohol dependence and ASPD (Ducci et  al., 2009).

Together, these studies support a role for serotonergic system genes in at least some forms of externalizing spectrum etiology.

Monoamine Degradation

The MAOA gene (MAOA) is located on the X chromosome and encodes for the monoamine oxidase A  (MAO) enzyme, which is responsible for degradation of all monoamine neurotransmitters, including DA and 5-HT. Given the roles that these neurotransmitter systems play in motivation, self-regulation, and mood regulation—each of which is compromised in externalizing spectrum disorders (Beauchaine et  al., 2011; see earlier discussion)—there is substantial interest in MAOA in the psychiatric genetics literature. A  rare deletion in the MAOA gene observed in a Dutch kindred leaves affected males without a functioning version of this gene, given that males possess a single copy of the X chromosome (males possess an X and Y sex chromosome; females possess two X chromosomes; see Eme, this volume). This results in an aggressive, antisocial phenotype among male carriers, supporting study of this gene in relation to externalizing behavior (Brunner, Nelen, Breakefield, Ropers,  & van Oost, 1993). Association studies have typically focused on a functional 30 bp VNTR in the promoter region of MAOA, with alleles that consist of two to five copies of the repeat segment. Functional analyses suggest that the shorter alleles (2–3 repeats) result in a low-activity version of the MAO enzyme, whereas the longer alleles (4–5 repeats) result in a high-activity version of the enzyme. As discussed in the previous chapter, this VNTR is a powerful moderator of relations between adverse environmental conditions and subsequent aggressive and antisocial behavior (Cicchetti, Rogosch, & Thibodeau, 2012; Ducci et  al., 2008; Fergusson, Boden, Horwood, Miller,  & Kennedy, 2011, 2012). Additionally, epigenetic studies suggest a relation between DNA methylation near MAOA and both alcohol and nicotine dependence (Philibert, Gunter, Beach, Brody, & Madan, 2008). Nonetheless, evidence for a main effect of genotype at this polymorphism on antisocial behavior (Buckholtz  & Meyer-Lindenberg, 2008) and ADHD (Gizer et al., 2009) has been inconsistent. The catechol-O-methyltransferase (COMT) gene is located on chromosome 22 and, like MAOA, is involved in monoamine degradation. The functional Val158Met polymorphism, rs4680, accounts for a three- to fourfold difference in COMT activity (Lotta et al., 1995) and has been the primary focus Gizer, Ot to, Ellingson

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of empirical research on this gene. Despite a number of studies investigating relations of this polymorphism with alcohol and other drug use outcomes, review studies have yielded mixed results (Bousman, Glatt, Everall,  & Tsuang, 2009; Mutschler et  al., 2013; Tammimäki & Männistö, 2010), making it difficult to determine its involvement in alcohol and drug use. Studies of ODD/CD and ASPD have also evaluated COMT polymorphisms, given evidence that they are associated with violent behavior among individuals with schizophrenia (Bhakta, Zhang, & Malhotra, 2012; Singh, Volavka, Czobor,  & Van Dorn, 2012). Much of the research on COMT and ODD/CD has relied on samples selected for ADHD, and several studies suggest an association with the rs4680 Val allele (Caspi et al., 2008; DeYoung et al., 2010; Langley, Heron, O’Donovan, Owen,  & Thapar, 2010; Qian et al., 2009; Salatino-Oliveira et al., 2012). One study extended this association by reporting stronger relations between the Val allele and aggressive/overt symptoms of CD relative to nonaggressive/covert symptoms of CD (Monuteaux et  al., 2009). This result suggests a more specific role for COMT in the development of ODD/CD. Findings related to antisocial behavior have been mixed, however, with limited evidence suggesting an association between the high-activity Val allele and antisocial behavior (Vassos, Collier,  & Fazel, 2014). Nonetheless, a recent investigation reported that a second functional polymorphism, also located in exon 4, Ala146Val (rs4986871), was more frequent among violent than nonviolent offenders with ASPD (Vevera et  al., 2009). Thus, multiple COMT polymorphisms may influence vulnerability to ASPD.

Additional Neurotransmitter Systems γ-Aminobutyric Acid

γ-Aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the central nervous system. Thus, genes that encode for GABA receptor subunits have received significant attention in the study of externalizing disorders. As described in the previous chapter, several studies have related variation in the GABRA2 gene, which encodes the GABAA α2 subunit, with broad measures of externalizing behavior that include indices of antisocial behavior and substance use (e.g., Dick et  al., 2006). This work was informed by earlier linkage and association studies suggesting a relation between GABRA2 and alcohol dependence (Cui, Seneviratne, Gu,  & Li, 2012; Edenberg et  al., 154

2004; Enoch, 2008; Long et  al., 1998; Olfson  & Bierut, 2012). Because additional studies reported relations with outcomes such as subjective response to alcohol (Uhart et al., 2013), illicit drug dependence (Agrawal et  al., 2006; Dick et  al., 2006), drug relapse (Bauer, Covault, & Gelernter, 2012), and CD/ASPD (Ehlers, Gilder, Slutske, Lind,  & Wilhelmsen, 2008), investigators posited that GABRA2 polymorphisms might represent a general vulnerability to externalizing behavior, which has been supported in subsequent studies using transdiagnostic phenotypes to study the effects of this gene (e.g., Dick et al., 2009). Studies of other GABA receptor genes have yielded mixed findings (Ittiwut et  al., 2012). Nonetheless, a recent meta-analysis of variants in multiple GABA receptor genes (GABRB2, GABRA6, GABRA1, and GABRG2 on chromosome 5q and GABRA2 on chromosome 4p12) replicated an association between GABRA2 (through rs567926 and rs279858) and alcohol dependence and also reported associations between both (a)  GABRG2 and alcohol and heroin dependence and (b)  GABRA6 and alcohol dependence (Li et al., 2013).

Glutamate

In contrast to GABA, L-glutamate (glutamic acid) is the major excitatory neurotransmitter in the central nervous system. Variation in the GRIN genes that encode for the N-methyl-D-aspartate (NMDA) ionotropic receptor subtype is associated with alcohol dependence (Domart et  al., 2012), nicotine dependence (Ma, Payne, Nussbaum,  & Li, 2010), and ADHD (Dorval et al., 2007; Turic et  al., 2004). More recently, a significant association was reported between ADHD and variation in a candidate metabotropic glutamate receptor gene, GRM7 (Park et al., 2013). Although not statistically significant, two recent GWA studies of ADHD reported variants in GRM7 (Yang et al., 2013) and another metabotropic glutamate receptor gene, GRM5 (Hinney et  al., 2011) among the top signals. These results are particularly intriguing given a study of rare copy number variants (CNVs)— segments of DNA between 1,000 and 3,000,000 bp that are either deleted or duplicated in the genome—in which deletions associated with both of these genes were observed in severe ADHD (Elia et al., 2010). Finally, variation in GAD1, one of two genes that encode for enzymes that catalyze production of GABA from glutamate, is associated with both heroin (Wu, Zhu,  & Li, 2012) and alcohol

Molecul ar Genetics of the Externalizing Spectrum

dependence (Terranova et  al., 2010). Together, these studies provide a strong case for involvement of glutamate genes in the etiology of externalizing spectrum disorders.

Acetylcholine

Nicotinic and muscarinic acetylcholine receptors, which differ in their pharmacological responses to specific agonists and antagonists, comprise two subtypes of cholinergic receptors. There are 12 different nicotinic cholinergic receptor subunits (α2–α10, β2–β4) expressed in the human brain, and these subunits combine with one another to form receptor subtypes. A number of substances bind to neuronal nicotinic acetylcholine receptors (nAChRs), including the endogenous neurotransmitter acetylcholine and exogenous nicotine. Nicotine most commonly binds to nAChRs consisting of α4 and β2 subunits, which are encoded by the CHRNA4 and CHRNB2 genes, respectively, and evidence suggests an association between nicotine dependence and variation in these genes (Breitling et al., 2009; Han, Gelernter, Luo,  & Yang, 2010; Han et  al., 2011; Hutchison et al., 2007; Li et al., 2005; Li, Lou, Chen, Ma, & Elston, 2008). Nonetheless, the majority of replicable findings on substance use and dependence relate to the CHRNA5-CHRNA3-CHRNB4 subunit gene cluster on chromosome 15. Numerous candidate gene studies have implicated the CHRNA5-CHRNA3-CHRNB4 gene cluster in nicotine dependence and related phenotypes. Many of these associations are attributable to rs16969968, a nonsynonymous SNP in CHRNA5, and rs578776 in CHRNA3 (Bierut et al., 2008; Saccone et  al., 2009; Saccone et  al., 2007; for reviews, see Berrettini  & Doyle, 2012; Bierut, 2009; Chen, Liao, Lai, & Chen, 2013; Ware, van den Bree, & Munafò, 2012). Notably, such results are also supported by GWA studies of smoking phenotypes. Data from the Collaborative Genetic Study of Nicotine Dependence (COGEND) and the Nicotine Addiction Genetics study (Saccone et al., 2007) were some of the first to provide evidence for GWA within this region, and several large consortium studies have confirmed these findings (Liu et al., 2010; Thorgeirsson et al., 2008; Tobacco and Genetics Consortium, 2010). This area of work provides one of the few examples of a confirmed association for an externalizing phenotype. Recent studies have extended this work by reporting associations between variants in the chromosome 15 CHRN gene cluster and alcohol use/ dependence (Chen et al., 2009; Joslyn et al., 2008;

Schlaepfer, Hoft,  & Ehringer, 2008; Sherva et  al., 2010; Wang et al., 2009), stimulant craving/cocaine dependence (Ehlers, Gizer, Gilder, & Wilhelmsen, 2011; Grucza et  al., 2008; Sherva et  al., 2010), opioid dependence (Erlich et  al., 2010), general substance use initiation (Lubke, Stephens, Lessem, Hewitt,  & Ehringer, 2012), and early externalizing behaviors that correlate with substance use (Stephens et al., 2012). The literature on muscarinic acetylcholine receptors (mAChRs) and the five genes that encode for each subtype (M1–M5) is much less extensive, but CHRM2 on chromosome 7 has been the focus of numerous studies following results from early linkage analyses on alcohol dependence and variation in electrophysiology measures (Dick & Bierut, 2006; Foroud et al., 2000; Reich et al., 1998). Initial association studies supported a relation between variants in CHRM2 and alcohol dependence (Luo et al., 2005a; J. C.  Wang et  al., 2004), although subsequent investigations suggest that this relation may be better explained by associations with externalizing behaviors more generally (Dick et al., 2008). The latter conclusion is supported by a more recent study that found associations between CHRM2 and substance use and disinhibition (Hendershot, Bryan, Ewing, Claus, & Hutchison, 2011).

Neurohypophysial Hormones

In addition to traditional neurotransmitter systems, studies of externalizing disorders have demonstrated significant associations with genes located in hormonal and cell signaling pathway systems. For example, oxytocin’s relations to social bonding are well-documented, and thus the oxytocin receptor gene (OXTR) has been studied in relation to antisocial behavior, with SNPs showing evidence of association with aggression (Malik, Zai, Abu, Nowrouzi, & Beitchman, 2012), callous-unemotional traits (Beitchman et al., 2012), and psychopathy (Dadds, Moul, Cauchi, Hawes, & Brennan, 2013). However, negative results have also been reported (Sakai et al., 2012). Notably, SNPs in OXTR were among the top signals reported in the GWA study of alcohol dependence conducted in the Collaborative Study on the Genetics of Alcoholism (COGA) dataset (Edenberg et  al., 2010), which suggests that variation in this gene may influence multiple externalizing phenotypes. A  similar pattern of findings has been reported for variants in the arginine vasopressin 1a receptor gene (AVPR1A; Maher et al., 2011; van West, Del-Favero, Deboutte, Van Broeckhoven,  & Claes, 2009), but, given the Gizer, Ot to, Ellingson

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relatively small numbers of studies reporting these associations, their relations to externalizing disorders should be considered preliminary.

Drug Metabolism Genes

Some of the earliest and most robust evidence for a genetic basis of substance use behaviors originated from studies of genes that encode for alcohol dehydrogenase (ADH) and aldehyde dehydrogenase (ALDH), enzymes involved in alcohol metabolism. This evidence derived from both candidate gene (Higuchi et al., 1994; Luczak, Glatt, & Wall, 2006) and genome-wide linkage studies (Ehlers, Spence, Wall, Gilder,  & Carr, 2004; Prescott et  al., 2006; Reich et  al., 1998). Variants in these genes that are associated with alcohol dependence (e.g., ADH1B—rs1229984, rs2066702; ADH1C—rs1614972; ALDH2—rs671) tend to produce isozymes that, through inefficient conversion of alcohol to acetate, result in rapid accumulation of acetaldehyde, a highly toxic intermediate metabolite of ethanol. Consequently, these variants are associated with lower drinking levels and confer protection against alcohol dependence, as indicated by numerous subsequent candidate gene and GWA studies (Biernacka et al., 2013; Bierut et al., 2010; Edenberg, 2007; Gizer, Edenberg, Gilder, Wilhelmsen, & Ehlers, 2011; Treutlein et al., 2009). Association analyses from research focused on multiple substances also indicate relations between genetic variation in the superfamily of genes that code for cytochrome P450 enzymes and a number of dependence phenotypes. Cytochrome P450 monooxygenases are a group of highly diverse enzymes encoded by 57 genes and involved in virtually all drug metabolism. For example, the CYP2A6 enzyme, which is expressed predominantly in the liver, is responsible for oxidation of nicotine to cotinine. Variants in CYP2A6 that result in less efficient nicotine metabolism (e.g., rs1801272 and rs28399433) are thus protective; they are associated with lower rates of cigarettes smoked per day, higher cessation rates, and positive response to replacement therapy (cf MacKillop, Obasi, Amlung, McGeary, & Knopik, 2010). It may seem that such genes would show associations with specific substances, but a considerable body of research now suggests that polymorphisms in these genes, as well as genes in other systems traditionally associated with specific endogenous and exogenous substances such as the opioid and endocannabinoid systems, show cross-substance effects. For example, ADH4 variants influence rates of 156

dependence of other drugs independent of alcohol dependence (Luo et  al., 2005b). Within the opioid system, a nonsynonymous variant, rs1799971 (118A>G, Asp40Asn), in the OPRM1 gene encoding for the G-protein-coupled μ-opioid receptor is associated not only with heroin and cocaine dependence (for reviews, see Kreek et al., 2012; Nielsen & Kreek, 2012), but also with alcohol dependence, alcohol treatment response (for a review, see Ray et al., 2012), and nicotine dependence (for a review, see Verhagen, Kleinjan, & Engels, 2012).The endocannabinoid CNR1 gene was identified as a vulnerability locus in an early linkage study of cannabis use and dependence (Agrawal et al., 2008). Subsequent candidate genes studies of CNR1 have supported this result, reporting associations between specific polymorphisms, including the (AAT)n triplet repeat, rs1049353 and rs806368, and marijuana problems and trait impulsivity (Bidwell et  al., 2013; Ehlers, Slutske, Lind, & Wilhelmsen, 2007), as well as with numerous substance use phenotypes (Benyamina, Kebir, Blecha, Reynaud,  & Krebs, 2011; Chen et al., 2008; Marcos et al., 2012). Drug metabolism genes also exhibit associations with externalizing behavior more broadly. For example, a linkage study conducted in the COGA sample identified a relation between the ADH gene cluster and externalizing behavior (Ghosh et al., 2008), and Luo et al. (2008) found associations between polymorphisms in ADH7 and personality traits related to ASPD. Additionally, the Asp40Asn polymorphism in OPRM1 is associated with both antisocial behavior (Corley et al., 2008) and ADHD (Carpentier et al., 2013), and polymorphisms in CNR1 are related to ADHD in linkage studies (Zhou et al., 2008) and an early GWA study of ADHD (Neale et  al., 2008). Whether findings such as these reflect direct, independent risk for each disorder or indirect associations mediated by the presence of dependence on the substance that is the primary target of the gene product requires further investigation. Nonetheless, these studies underscore the relevance of drug metabolism genes for vulnerability to the broader externalizing phenotype.

Genes and Gene Systems Identified by GWA  Studies

In recent years, the number of GWA studies has increased steadily. A complete discussion of findings is therefore not possible in a single chapter. Given that relevant GWA study results for specific candidate gene systems have already been discussed, such findings are not repeated here. Rather, in the

Molecul ar Genetics of the Externalizing Spectrum

following section, we review findings that were/ are relatively novel to the study of externalizing disorders.

Cell Adhesion Proteins

Cell adhesion proteins, as implied by their name, enable cells to bind to one another to form organized tissues, and they play important roles in neural development and synapse formation. Initial interest in cell adhesion genes as vulnerability loci for psychiatric disorders resulted from genome-wide studies of CNVs linking neurexin 1 (NRXN1), a cell adhesion protein involved in GABA and glutamate synapse formation, to both schizophrenia (Kirov et al., 2008) and autism (Kim et  al., 2008). Subsequent GWA studies have reported suggestive associations between NRXN1 SNPs and ADHD (Lesch et al., 2008; Neale et al., 2010b; L. Yang et al., 2013), as well as nicotine dependence (Bierut et  al., 2007; Nussbaum et al., 2008). A number of additional cell adhesion genes have also been related to multiple externalizing disorders. For example, a meta-analysis of ADHD linkage studies reported a genome-wide significant linkage signal near the cadherin 13 gene (CDH13; Zhou et al., 2008), and GWA studies of ADHD have further supported this relation (Lasky-Su et al., 2008; Lesch et al., 2008; Neale et al., 2010a). CDH13 has also been related to diagnoses of substance dependence in both linkage (Hill et al., 2004) and association studies (Drgon et al., 2011; Lind et al., 2010). A similar pattern of results has been reported for the latrophilin 3 gene (LPHN3), which includes evidence of linkage and association with both ADHD (Arcos-Burgos et al., 2004; Arcos-Burgos & Muenke, 2010) and substance use (Lind et al., 2010). Finally, linkage studies identified a region on the short arm of chromosome 2 as containing a susceptibility locus for conduct disorder and ASPD (Dick et al., 2004; Ehlers et al., 2008; Kendler et al., 2006), as well as alcohol dependence (Bierut et  al., 2004; Foroud et  al., 2000). Subsequent association studies have identified CTNNA2 (catenin [cadherin-associated protein], α2) as a strong candidate in this region, which contains variants that show association with ADHD (Lesch et  al., 2008; Neale et  al., 2010b), CD (Dick et  al., 2008; Kendler et  al., 2006), and the personality trait of sensation seeking, as described elsewhere (Gizer, Otto,  & Ellingson, this volume; Terracciano et al., 2011).

Voltage-gated Potassium Channel Genes

An important role of voltage-gated potassium channels is to allow potassium ions to exit a cell

following an action potential, thus helping to return the cell to its resting state. Action potentials enable neurons to communicate, and, as a result, associated genes may have relevance to psychiatric phenotypes. Although not a focus of candidate gene studies, several potassium channel genes show initial evidence of association with externalizing disorders. For example, KCNMA1 (potassium large conductance calcium-activated channel, subfamily M, α member 1) is a top GWA signal in studies of alcohol-related phenotypes (Edenberg et  al., 2010), nicotine dependence (Lind et al., 2010), polysubstance use (Drgon et al., 2011), and callous-unemotional traits in children (Viding et al., 2010). Once again, however, none of these results reached genome-wide significance. Even so, a recent GWA study of opioid dependence reported multiple significant associations with variants in two genes that encode proteins involved in potassium channel pathways, KCNC1 and KCNG2 (Gelernter, Kranzler, et al., 2014).

Additional Findings from GWA Studies

Despite ongoing difficulties in identifying significant genome-wide associations, GWA studies of externalizing disorders continue to be conducted, resulting in more adequately powered studies, some of which are yielding genome-wide significant findings. Meta-analyses are particularly helpful in this regard because the pooling of many studies improves statistical power. For example, variants in the ADH gene cluster on chromosome 4 have yielded genome-wide significant associations in meta-analytic reviews of alcohol dependence (Gelernter et al., 2013; Rietschel & Treutlein, 2013). Gelernter et  al. (2013) also identified two novel genome-wide significant associations, including a chromosome 2 locus near MTIF2 and PRORSD1P, which was previously associated with risk for schizophrenia. A GWA study of alcohol consumption reported a genome-wide significant association with a variant in AUTS2, a vulnerability locus identified previously for autism (Schumann et  al., 2011). Notably, the genomic region containing AUTS2 has also been implicated in previous linkage scans of alcohol (Foroud et al., 2000) and cannabis misuse phenotypes (Ehlers, Gilder, Gizer, & Wilhelmsen, 2009). Nonetheless, the metabotropic glutamate receptor 3 gene (GRM3) is also located in this region, making it difficult to determine to what extent reported linkage findings support a specific relation between AUTS2 and externalizing disorders. GWA studies of cocaine (Gelernter, Sherva, et  al., 2014) and other stimulant dependence Gizer, Ot to, Ellingson

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(Uhl et  al., 2008) have implicated the FAM53B gene (rs2629540) among multiple ancestral populations. Finally, a study of opioid dependence identified a genome-wide significant association between NCK2 in African-origin men (Liu, Guo, Jiang, & Zhang, 2013), and a GWA study of opioid sensitivity and vulnerability to drug dependence reported genome-wide significant associations with CREB1 (rs2952768), a gene widely studied in relation to neuroplasticity (Nishizawa et  al., 2012). Although these findings are relatively few in number and are specific to individual disorders focusing on substance use, they suggest that when statistical power increases, we are more likely to identify genes implicated in etiology.

Additional Genomic Regions Identified in Linkage Studies

Several chromosomal regions have also been identified through linkage analysis as containing potential vulnerability loci for externalizing spectrum disorders. We focus here on regions that show repeated evidence of linkage (although often not significant at the genome-wide level) across multiple externalizing spectrum disorders and related phenotypes. It is important to repeat that linkage signals implicate broad chromosomal regions that can contain more than 100 genes. As a result, we provide a brief overview of implicated regions and possible candidate genes in these regions, but these linkage signals cannot be conclusively tied to mentioned candidates. Suggestive evidence of linkage for alcohol dependence (Hill et  al., 2004) and level of response to alcohol (Schuckit et  al., 2001) has been reported for 2q34–q35, a region that also showed suggestive linkage with a comorbid alcohol dependence and depression phenotype (Nurnberger et al., 2004) and cannabis and other substance use phenotypes (Ehlers et al., 2009; Gelernter et al., 2005; Gelernter et al., 2006). This region contains the candidate genes CYP27A1 and FEV, the latter of which is exclusively expressed in neurons of the central 5-HT system. Notably, the peroxisomal trans–2-enoyl-CoA reductase gene (PECR), which was identified as the top signal in one of the first published GWA studies of alcohol dependence (Treutlein et al., 2009), and CREB1, which showed a genome-wide significant association with opioid sensitivity as described earlier (Nishizawa et al., 2012), are also located in this region. A  region on the long arm of chromosome 1 (1q23.3–q24.2) has shown evidence of linkage to a number of externalizing phenotypes including 158

alcohol dependence (Hill et al., 2004), tobacco use (Ehlers & Wilhelmsen, 2006), CD (Kendler et al., 2006), and harm avoidance (Zohar et  al., 2003). This region includes a number of interesting candidates including CHRNB2, which, as described earlier, has a high affinity for nicotine, and KIAA0040, which was implicated in a meta-analysis of GWA studies of alcohol dependence (Zuo et al., 2012; see also Gizer, Otto, & Ellingson, this volume). In addition to these results, a region on the short arm of chromosome 3 (3p13–p22.3) shows evidence of linkage to a range of externalizing behaviors, including co-occurring cocaine use and CD (Gelernter et  al., 2005), alcohol dependence (Foroud et  al., 2000), neuroticism (Neale, Sullivan,  & Kendler, 2005), and cannabis use (Ehlers et al., 2009). A second region on chromosome 3, located on the long arm (3q26.2–q29), has also been identified in multiple linkage scans of externalizing behaviors, including cannabis use (Ehlers et  al., 2009), CD/ASPD (Ehlers et  al., 2008), illicit substance use (Gelernter et  al., 2006), and co-occuring CD and drug dependence (Stallings et al., 2003, 2005). Both regions contain potential candidate genes, including the metabotropic glutamate 2 receptor gene (GRM2) and cholecystokinin gene (CCK) in the 3p13–p22.3 region, and the neuroligin 1 gene (NLGN1) and a cluster of 5-HT receptor genes (HTR3C, HTR3D, HTR3E) in the 3q26.2–q29 region. Finally, regions on the short and long arms of chromosome 17, including 17p13.2–p11 (Arcos-Burgos et  al., 2004; Hill et al., 2004; Wang, Kapoor, & Goate, 2012), and 17q25.2–q25.3 (Gelernter et  al., 2006; Ehlers et al., 2008; Straub et al., 1999), have been linked to a similar range of externalizing phenotypes, with several candidate genes located in each region (17p13.2–p11—ARRB2, GABARAP, CHRNB1, UBB; 17q25.2–q25.3—GRIN2C, GALR2). As noted previously, the regions implicated by linkage analysis are broad, meaning that further studies are required to evaluate whether individual variants in the proposed candidate genes can account for the observed linkage signals.

Conclusion

The present review of linkage, candidate gene, and GWA studies of Diagnostic and Statistical Manual of Mental Disorders-defined externalizing syndromes indicates progress made in studying genetic contributions to the etiology of these phenotypes. Several genes are related to multiple externalizing disorders, including those that affect

Molecul ar Genetics of the Externalizing Spectrum

monoamine, acetylcholine, GABA, and glutamate neurotransmitter systems; those that encode for cell adhesion proteins; and those involved in potassium ion channel function. Such findings demonstrate the potential for molecular genetic studies to further inform our understanding of externalizing psychopathology. The lack of overlap in findings across these different study designs, however, suggests that caution is required when interpreting findings from molecular genetic studies of these phenotypes. Although we have identified regions that show some overlap across studies when considering externalizing disorders more broadly, a general lack of overlap in genomic regions identified across linkage studies for given phenotypes has been noted in several reviews (e.g., Li, Hewitt, & Grant, 2007; Zhou & Pearson, 2013). A lack of overlap between linkage and association studies (both candidate gene and GWA) is also apparent. As stated at the outset of this chapter, linkage studies are ideally suited for identifying lower frequency risk variants of modest effect size. Thus, one potential explanation for lack of replication is that a number of rare variants influence externalizing spectrum disorders and that these variants are sufficiently diverse that they are private to specific pedigrees within each study. In this scenario, we might not expect linkage studies sampling different populations and using different ascertainment procedures to replicate. Importantly, this state of affairs could also explain the lack of overlap with association studies, which require impractically large samples to study rare variants, given their low frequencies in the population. Simulation studies suggest that both rare and common variants are involved in the development of psychiatric traits (Schork, Murray, Frazer, & Topol, 2009; Visscher, Goddard, Derks,  & Wray, 2012). As a result, it is possible that linkage and association studies may be identifying different frequency variants, given their respective strengths. If so, results from linkage studies should not be dismissed out of hand as false positives. With the advent of exome and whole-genome sequencing studies, it will soon be possible to return to those early linkage studies to evaluate whether there are rare variants related to the studied traits in regions that previously showed evidence of linkage. In fact, given the high degree of polygenicity suggested for psychiatric traits by GWA studies, the range of genomic regions implicated by linkage studies now could be seen as an expected result, even though they were more readily dismissed as false positives when an oligogenic

model of inheritance was considered likely. Given the wide regions implicated by linkage analysis, it may be difficult to demonstrate this point conclusively, but in cases where the original linkage sample is later sequenced, such studies will be well positioned to address this question. Perhaps more concerning is the lack of overlap between candidate gene and GWA studies. As evident from the present review, candidate gene studies have been conducted for a number of genes and phenotypes, some of which have been at least partially replicated in subsequent studies. Nonetheless, these candidate gene findings have not been replicated in GWA studies, with only a few notable exceptions (e.g., CHRNA5/CHRNA3 and smoking phenotypes). Discrepant findings are of particular concern because GWA studies conducted for a given disorder often include more participants than the candidate gene studies for that disorder, even when combined in meta-analysis. For example, Tielbeek and colleagues (2012) conducted the first GWA study of adult ASPD using a sample of 4,816 individuals. The study failed to identify any SNPs that reached genome-wide significance, and a secondary analysis failed to replicate any previously reported candidate gene associations using a p-value of .05. Despite null results such as these, other studies suggest that discrepant results between candidate gene and GWA studies may be the result of a lack of statistical power in both types of studies. In their meta-analysis of ADHD GWA studies, Neale et al. (2010a) conducted a combined analysis of SNPs in candidate genes and demonstrated that, collectively, p-values from the SNPs in these genes were lower than expected by chance, even though many of them failed to reach even nominal significance. This suggests that as sample sizes continue to increase, findings from candidate gene and GWA studies are likely to converge, yielding a subset of genes that show evidence for association, and the remaining findings will be demonstrated to be false positives. It is also possible that some of the lack in overlap of findings between candidate gene and GWA studies is the focus on VNTRs in the former studies, along with the inability to include such variants in the latter studies. Many VNTRs have been demonstrated to have functional consequences (e.g., MAOA promoter VNTR) or to influence gene expression (e.g., DAT1/SLC6A3 3′ UTR VNTR). VNTRs also tend to show relatively low correlations with nearby SNPs because of the highly polymorphic nature of VNTRs (Breen, Collier, Craig, & Quinn, 2008). As a result, they tend to be poorly tagged by Gizer, Ot to, Ellingson

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SNP microarrays. Additionally, current sequencing technologies that rely on the parallel sequencing of short DNA fragments of approximately 200 base pairs also cannot determine genotypes of VNTRs. Although this fact can only explain, at best, a small proportion of the inconsistent results across candidate gene and GWA studies, it does warrant mention, given that some relatively robust associations between VNTRs and externalizing spectrum disorders have been demonstrated (e.g., DRD4 exon 3 VNTR and ADHD). As described elsewhere in this volume (Gizer, Otto, & Ellingson), a number of strategies are currently being pursued that have the potential to yield more robust relations between measured genes and externalizing behavior, including an increasing focus on transdiagnostic approaches to phenotype measurement. Although many of the molecular genetic studies of externalizing behavior have focused on individual disorders, we hope that the present review provides an overview of the progress that has been made thus far. The present volume provides evidence from multiple domains of study demonstrating the shared etiology among these disorders, and molecular genetic studies provide another avenue for studying their overlap. As the field continues to develop, we believe molecular genetic studies have the potential to make important contributions to our understanding of the underlying vulnerability and risk factors that contribute to the externalizing spectrum.

Note

1. Readers who are not familiar with molecular genetics research are encouraged to read the previous chapter (Gizer, Otto, & Ellingson, this volume, Chapter  8), which provides definitions and examples of molecular genetics terms and study designs.

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CH A PT E R

10

Temperament and Vulnerability to  Externalizing Behavior

Fernanda Valle Krieger and Argyris Stringaris

Abstract Temperamental traits are defined as early developing, biologically rooted behavioral and emotional tendencies that are stable across situations and time and play an important role in the etiology, manifestation, and maintenance of most common emotional and behavioral disorders. This chapter examines the role of individual differences in temperament as vulnerabilities to externalizing behavior problems in children, adolescents, and adults. First, different models of temperament and general pathways to psychopathology are described. Then, specific temperamental traits that are associated with externalizing problems and their likely underlying mechanisms are detailed. This chapter provides a critical view of the complexity of interactions and correlations among temperament, genetics, and environmental factors that may increase the risk for externalizing behaviors. Key Words:  temperament, negative affect, emotionality, effortful control, fearfulness, irritability, externalizing behavior

Children differ markedly from one another in their behavioral responses to external events. One child may be fearful and cry at even moderately stimulating play, whereas another may enjoy vigorous play and seek out exciting events. One child may be reserved, whereas another may be exuberant. Some children are described as aggressive, yet others are gentle. From early infancy, children show considerable variability in their reactions to the environment. Similar stimuli may evoke distinct reactions across individuals, whereas diverse stimuli may evoke similar response within the same individual (Shiner  & Caspi, 2003). Temperamental traits are defined as early developing, biologically rooted behavioral and emotional tendencies that manifest particularly (but not exclusively) in social interactions and that are relatively stable over time and across situations (e.g., Goldsmith et al., 1987). Temperamental characteristics also follow developmental differentiation. For example, newborns show primarily distress and avoidance movements. 170

By age 3  months, however, anger and frustration are evidenced concomitantly with approach reactions such as smiling, laughter, and body movement (Bridges, 1932). Physical approach preferences are observed between ages 4–6  months as motor systems develop (Rothbart, 2007). Fear (behavioral inhibition) differentiates from general stress proneness by age 7–10  months (e.g., Bridges, 1932; Kagan, 2013). Temperamental constructs capture observed behavior regardless underlying motivations or content. Therefore, temperament represents the how of behavior; that is, the style or form of behavior, distinct from the why, which addresses the motivational component, and the what, which addresses the content or ability (Goldsmith et al., 1987; Rutter, 1987). Temperamental elements include the amount of energy to kick, persistence in trying to reach toys, and intensity of reactions when objectives are not accomplished (level of fussing or crying). All of these behavioral and emotional

tendencies vary along key dimensions (Goldsmith et al., 1987), with implications for later development and vulnerability to psychiatric morbidity (Kagan, 2013; Rutter, 1987). Children vary in their propensity to develop psychopathology, and temperament plays an important role in the etiology, manifestation, and maintenance of most common emotional and behavioral disorders (Muris & Ollendick, 2005; Rettew, 2013). In this chapter, we examine the role of individual differences in temperament as vulnerabilities to externalizing behavior problems in children, adolescents, and adults. In the first section, we describe broad aspects of temperament and summarize different models of temperament and general pathways to psychopathology. In the next section, we detail specific temperamental traits that are associated with externalizing problems, and we examine likely mechanisms underlying these associations.

General Concepts of Temperament Models of Child Temperament

Among diverse concepts of temperament, three key components are common to most widely accepted definitions. First, temperament manifests very early in life, from infancy onward. Second, temperament is influenced considerably by genetic and other heritable factors. Third, temperament shows at least moderate stability across time (Cloninger, 2004; Lemery, Goldsmith, Klinnert,  & Mrazek, 1999; Zentner & Shiner, 2012). Nevertheless, there is still controversy among specialists on the precise structure of temperament. Here, we briefly summarize the most widely used models and discuss similarities among them (Zentner & Bates, 2008). In 1966, Chess and Thomas described the structure of temperament using nine dimensions (Chess & Thomas, 1966), including (1)  activity level, which refers to the motor component of a child’s functioning; (2)  rhythmicity, which is the predictability of any function in time; (3)  approach or withdrawal, which comprise the initial response to a novel stimulus; (4)  adaptability, or characteristic responding to changes in the environment; (5) threshold of responding, or the intensity of stimulation required to evoke a response; (6) intensity of reaction, which refers to the energy level of a response; (7) quality of mood, or the amount of pleasant and joyful behavior in contrast to unpleasant, unfriendly behavior; (8) distractibility, which refers to how extraneous stimuli alter ongoing behavior, and (9) attention span and persistence, or the length of time an activity is pursued in the face of obstacles.

Combinations of these categories lead to three fundamental temperament types. The first, “easy temperament,” is characterized by regularity, positive responses to new stimuli, high adaptability, and a preponderantly positive mood. The second, “difficult temperament,” is marked by signs of irregularity, negative withdrawal responses to new stimuli, slow adaptability to changes, and predominantly negative mood expressions. The third, “slow-towarm-up,” refers to children who exhibit negative responses to new stimuli but adapt progressively after repeated contact (Thomas & Chess, 1977). In contrast to Chess and Thomas (1966), Buss and Plomin proposed the EmotionalityActivity-Sociability (EAS) model (Buss & Plomin, 1975, 1984). According to this conceptualization, emotionality is characterized by psychological instability and proneness to fear, anger, and sadness; activity refers to characteristics such as tempo and vigor; and sociability refers to tendencies to affiliate with and be responsive to others (Buss  & Plomin, 1984). Rothbart and colleagues’ model defines temperament as a set of individual differences in reactivity and self-regulation (Rothbart, 1981; Rothbart  & Ahadi, 1994). Reactivity refers to physiological excitability of neural systems, whereas self-regulation refers to processes that promote both volitional and automatic control over behavior. The combination of these constructs results in three broad dimensions, including negative affectivity, which reflects a tendency to experience negative emotions; surgency, which refers to social orientation, motor activity, and a tendency to experience positive emotions; and effortful control, which includes inhibitory control and attentional focusing (Rothbart & Ahadi, 1994). According to Kagan (e.g., Kagan, Reznick,  & Snidman, 1987) individual differences in behavioral reactions to unfamiliarity, threat, and challenge are due to tonic differences in neurobiological substrates of reactivity (amygdala, hypothalamus, and pituitary-adrenal axis). For example, children with a low threshold to initiate a neurophysiological response in the face of novel stimuli present as behaviorally inhibited—or with tendencies to display fear, distress, and avoidance. An integrative perspective encompassing basic temperamental components in childhood is suggested by Zentner and Bates (2008). It includes (1)  behavioral inhibition/fear, or inhibition of behavior in response to novel or unfamiliar people and situations; (2) irritability/frustration, defined as aggressive or irritated behavior in response to painful Krieger, Stringaris

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and/or frustrating events; (3)  positive emotionality, the propensity to experience positive emotions typically associated with approach behaviors; (4) activity level, or the frequency, speed, and vigor of gross motor movements and locomotion, as well as intolerance of enforced idleness; (5) attention/persistence, defined as the capacity for attentional focusing and control as a basis for voluntary behavior including persistence; and (6) sensory sensitivity, or proneness to sensory (e.g., visual, auditory) discomfort. Although there are several differences across these models, there is increasing support for a tripartite theory of temperament among children (Chorpita, Plummer,  & Moffitt, 2000; Rettew, 2013). Activity level is represented clearly in each of the temperament models. Emotionality is correlated highly with both adaptability/mood and negative affectivity and correlated moderately with social fearfulness (Zentner & Shiner, 2012). Factor analysis of the Infant Behavior Record (IBR), an observational measure of infant temperament, yields three dimensions found in most systems:  activity, task orientation (attention span and persistence), and affect-extraversion (positive emotionality and sociability; Matheny, 1980). In addition, a theoretical distinction between reactive temperaments (emotionality, activity, sociability, and positive/negative affect) and regulatory temperaments (effortful control, inhibitory control, behavioral inhibition, and attentional focusing) has been proposed (Rothbart, 2007). However, the degree to which reactivity can be separated from regulation on the basis of behavioral measures is open to debate. A potential consensus framework suggests a hierarchical structure of temperament, wherein high-order traits reflect underlying, basic systems of reactivity, from which emerge “lower order” traits (see, e.g., Nigg, 2006). At the top of the hierarchy are two incentive factors:  approach and withdrawal. Approach is defined as willingness to approach potential rewarding contexts and is associated with reinforcement learning. Lower order traits associated with approach include positive emotionality, sociability, and activity level (Shiner  & Caspi, 2003). In contrast, withdrawal is defined as promptness of avoidance of possible unrewarding or uncertain situations and is associated with affective reactivity, fear, and sadness (Nigg, 2006). A second-order factor is comprised of behavioral responses including reactivity and effortful control (Eisenberg  & Spinrad, 2004). Effortful control is the ability to inhibit a dominant response and activate a subdominant response. It includes elements 172

Temperament and Vulnerabilit y

of attentional control, attentional shifting, and focusing behavior (Rothbart, 2007). Effortful control is related to executive functions and involves top-down modulation of motivational response systems (approach, withdrawal). Reactive control is defined as the relatively automatic suppression of behavioral and emotional expression. Reactive control is less associated with executive functions and is related conceptually to low approach and high withdrawal (Eisenberg & Spinrad, 2004). Data indicate that effortful control is closely related to conscientiousness, whereas reactive control represents broad regulatory processes (Nigg, 2006). Nevertheless, the hierarchical framework suggests that the interplay between approach/withdrawal systems and reactive/ effortful controls leads to distinct temperamental traits. For example, high levels of approach can be associated with either high levels of reactive control (prosocial behaviors) or low levels of reactivity control (anger) (Eisenberg & Spinrad, 2004; Carver & Harmon-Jones, 2009). Figure 10.1 displays a schematic hierarchical model.

Models of Adult Temperament

Consistent with the hierarchical perspective, adult personality researchers emphasize the importance of a few higher order factors rather than a large number of more specific traits in conceptualizations of adult temperament. Among various models for adult personality is the five-factor model (Big Five), which includes five broad dimensions: (1) neuroticism, (2) extraversion, (3) openness, (4) agreeableness and, (5) conscientiousness (Digman, 1990). Although associations between adult personality and temperament have long been a source of debate, given the increasing role of socialization on behavior across development, Cloninger (1987) proposed a three-factor model of adult personality that is rooted in neurobiological substrates of approach, withdrawal, and social affiliation. Cloninger’s model comprises novelty seeking, harm avoidance, reward dependence, and persistence. Later, Cloninger complemented his model of temperament by including self-directedness, cooperativeness, and self-transcendence as additional domains required to fully capture adult personality (Cloninger, 2004). Although Cloninger’s (1987) conceptualization provides a particularly good example of an adult temperament model, others also specify at least three high-order dimensions, including (1) approach, as manifested generally in expressions of positive emotion; (2)  avoidance, as manifested

Schematic Hierarchical Model Incentive systems

Approach

Withdrawal

Effortful control Executive functions

Reactive control Regulatory processes

First order HIGH ORDER TRAITS Second order

LOW ORDER TRAITS

Activity

Emotionality Fear

Behavioral responses

Sociability Anger

Figure 10.1 A model in which temperament comprises of both higher order (approach, withdrawal) and lower order (activity, emotionality) behavioral tendencies. Adapted from Nigg, 2006.

generally in expressions of negative emotion; and (3) constraint, as manifested in tendencies to control expressions of emotion and behavioral impulses (Shiner & Caspi, 2003).

Genetic Influences on Temperament

Behavioral genetic studies estimate the extent to which heritable and environmental influences contribute to behavioral variability (Saudino, 2005; Jones & Smith, this volume). This methodology involves decomposing phenotypic variation into heritable and environmental components. Heritability is the proportion of phenotypic variance that can be attributed to heritable factors.1 The remaining variance is attributed to environmental factors, which comprise shared environmental effects (familial resemblances that are not explained by genetic factors) and nonshared environmental effects (environmental influences that are unique to each individual, along with measurement error (see Beauchaine & Gatzke-Kopp, 2013; Plomin, DeFries,  & McClearn, 2001; Chapter 8). Twin and adoptive/nonadoptive sibling designs are the methodological approaches most frequently used to disentangle heritable and environmental influences on behavior (Plomin et al., 2001). However, influences of environment on temperament are complex—and become more so over time—because they involve dynamic interplay of genes and environment (Plomin,

DeFries, & Loehlin, 1977). Evidence for gene × environment (G×E) interactions in the expression of temperament and psychopathological trajectories is detailed later (gene-environment correlations [rGE] may be relevant as well). Most behavioral genetics studies of temperament yield moderate to high heritability coefficients (Goldsmith, Lemery, Buss,  & Campos, 1999; Saudino, 2005), although rates of heritability vary across ages (Goldsmith et  al., 1999). There is evidence of very low heritability in the neonatal period (Riese, 1990). Heritability increases from early to middle childhood across a variety of temperamental traits (Cyphers, Phillips, Fulker,  & Mrazek, 1990; Goldsmith, Buss,  & Lemery, 1997). By toddlerhood, heritability is substantial, with h2 estimates indicating that heritability accounts for 20–60% of the variance in temperamental traits (Hicks, Krueger, Iacono, McGue, & Patrick, 2004; Plomin et al., 1993). Among toddlers, heritability of effortful control is estimated to be 58%, with estimates of 42% for negative affect and 41% for extraversion/surgency (Goldsmith et al., 1997). Such broad ranges in heritability result from a number of factors, including methodological issues, such as random sampling error, influences of assessment instruments and observers (Saudino, 2005), and the consistent finding that heritabilities of nearly all human behavioral traits (including Krieger, Stringaris

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psychopathology) rise across development (see Beauchaine  & Gatzke-Kopp, 2013; Bergen, Gardner, & Kendler, 2007). For our purposes, it is particularly important to note differences in heritability estimates based on parent ratings versus other observer ratings. Studies based on parent ratings of temperament commonly show greater gaps between monozygotic (MZ) and dizygotic (DZ) twin correlations (Stevenson  & Fielding, 1985) signifying higher estimates of heritability. Parent-rating MZ correlations are typically moderate, whereas DZ correlations are much lower than expected (< 50% of MZ correlations). In contrast, when temperament is assessed objectively via observer ratings, high heritability is still evident, but low DZ correlations do not emerge (Plomin et al., 1993). In fact, very low DZ resemblance and higher heritability estimates occur only when parent-rating measures are used, and these are likely to emanate from contrast effects—rater biases that exaggerate the differences between DZ twins. In other words, parents may be especially likely to perceive DZ twins as having differing temperaments (Braungart, Plomin, DeFries, & Fulker, 1992; Saudino, McGuire, Reiss, Hetherington,  & Plomin, 1995). This artifact can result in overestimates of heritability in twin studies. Nevertheless, moderate heritability based on parent ratings is supported by similar findings with more objective measures. Therefore, there is little space for doubt that temperament is heritable (Saudino, 2005). Because child temperament is only partly heritable, environmental factors must account for the remaining variance. Indeed, research shows consistent associations between family environment and children’s temperament (Lemery-Chalfant, Kao, Swann,  & Goldsmith, 2013; Leventhal  & Brooks-Gunn, 2000). For example, a chaotic, unstructured home typically does not support development of foundational skills such as self-regulation (Baumrind, 1993; Dumas et  al., 2005; Wachs, 1988). Importantly, however, influences of environment on temperament are complex, involving interactions with genes. We discuss such gene-environment interplay later, in the section on moderators of temperament.

Sex Differences

In a meta-analysis, Else-Quest and colleagues (2006) estimated magnitudes of sex differences in dimensions of temperament among children between ages 3 months and 13 years. Three broad dimensions, including effortful control, negative 174

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affectivity, and surgency were examined. A large sex difference was found for effortful control favoring girls (weighted effect size (d)  =  −1.01, 95% confidence interval [CI]  =  –1.37 to −0.64, p < 0.05). Thus, girls have greater ability to manage and regulate their attention and inhibit their impulses than do boys. In contrast, boys exhibit greater surgency (d = 0.55, 95% CI = 0.22–0.89, p < 0.05) and are therefore more active, less shy, and derive more pleasure from high-intensity stimuli than do girls. There was no sex difference in negative affectivity, indicating that boys and girls do not differ significantly in the extent to which they are difficult, emotionally reactive, or soothable. Analyses of potential moderators such as age, socioeconomic status, and source of assessment showed no significant interactions with sex.

Temperament and Vulnerability to Psychopathology

Development is affected continuously by causal processes that occur across various levels of analysis (see Cicchetti, 2008). Cultural, socioeconomic, and community-level influences are considered distal factors, whereas family relationships and parenting represent proximal factors. Risk factors present within these contexts may increase risk for psychopathology throughout development. However, individual differences also affect one’s risk for psychiatric disorders and may moderate (i.e., interact with) effects of environmental risk. Among such individual differences, temperament affects children’s vulnerability or resilience to psychopathology (e.g., Lengua & Wachs, 2012). Rather than simply describe mere correlations, it is essential to examine how temperament and psychopathology are linked causally, including the specification of intervening variables (see Frick, 2004; Hoyle, 2000). Shiner and Caspi (2003) suggest distinct processes through which temperament might be associated with concurrent or future psychopathology. One such process is “spectrum association,” whereby psychopathology represents the severe end of continuously distributed temperamental traits. For example, some have argued that attention-deficit/hyperactivity disorder (ADHD) lies at the extreme end of temperamental impulsivity and is therefore not a discrete condition (Jensen et al., 1997). Others have suggested “vulnerability association” (Lahey, 2004; Lengua, West, & Sandler, 1998), whereby certain temperamental traits increase risk for psychopathology through interactions with independent processes such as learning

(e.g., reinforcement, punishment; Kochanska, 1997), environmental risk exposure (e.g., maltreatment, neighborhood violence/criminality; Lynam et  al., 2000; Owens, Shaw,  & Vondra, 1998), or environmental selection (i.e., active rGE; Scarr  & McCartney, 1983). Such processes are dynamic and multifaceted, involving interactions between individual-level vulnerabilities (in this case temperament) and exogenous influences such as family risk, culture, and socioeconomic status. Complex interactions between individual vulnerabilities and environmental risk factors—the mechanisms by which temperament shapes environment and vice versa—may end up contributing to development of psychopathology.

Temperament and Pathways to Externalizing Behavior

A vast literature provides evidence for associations between certain temperamental styles and externalizing behaviors (Rettew, 2013; Rettew, Copeland, Stanger,  & Hudziak, 2004). Associations between temperament and externalizing problems have been found among preschool children (Hirshfeld-Becker et  al., 2002), school-aged children (Blackson, Tarter, Martin,  & Moss, 1994), and adolescents (Giancola, Mezzich,  & Tarter, 1998). Prospective studies indicate that dimensions of temperament measured early in life predict externalizing problems later—in preschool (Keenan, Shaw, Delliquadri, Giovannelli, & Walsh, 1998), in childhood (Raine, 2002), into adolescence (Caspi & Silva, 1995), and beyond. In this section we examine three mechanisms through which temperament is associated with externalizing behaviors (Frick  & Morris, 2004; Kochanska, Barry, Jimenez, Hollatz,  & Woodard, 2009; Nigg, 2006). The first pathway involves negative affectivity, in which irritability plays a critical role. The second pathway involves deficient development of empathy and may be mediated by low trait anxiety/fearfulness. The third pathway involves low effortful control and deficient inhibition. Here, we describe each pathway and relevant mechanisms related to externalizing behaviors.

Negative Emotionality

Negative emotionality is a heritable individual difference (see earlier discussion). As children develop, their tendencies to react disproportionately to environmental events with strong emotions of negative valence may indicate and predict a failure

to acquire adequate emotional regulatory abilities and therefore confer risk for psychopathology (Eisenberg et  al., 2001; Hoeksma, Oosterlaan,  & Schipper, 2004). Vulnerability to externalizing behaviors through negative affectivity involves low reactive control (Carver  & Harmon-Jones, 2009). Thus, anger, irritability, defiance, and reactive aggression become progressively problematic, as manifested in oppositional behaviors and conduct problems (Hubbard et al., 2002). Diverse temperamental traits characterized by high negative reactivity show associations with externalizing behaviors (Eisenberg et  al., 2001). Chess and Thomas’s “difficult temperament” (irregularity, negative withdrawal responses to new stimuli, slow adaptability to change, and negative emotionality) can be interpreted as similar to negative reactivity. Children who score high on indices of a difficult temperament are at high risk for conduct problems (Keenan et  al., 1998; Kingston  & Prior, 1995). Similarly, Buss and Plomin’s “emotionality” predicts oppositional defiant disorder (ODD; Burke, Waldman,  & Lahey, 2010), particularly ODD and comorbid internalizing behaviors (Stringaris, Maughan, & Goodman, 2010). In fact, temperamental traits characterized by high negative emotional reactivity are associated strongly not only with externalizing behaviors but also with childhood mental disorders in general. They may be central to understanding the overlap between externalizing and internalizing disorders (see Chapter  26). This hypothesis suggests that associations between two dimensions (or, categorically, two disorders) reflect differing manifestations of common underlying vulnerability (Angold, Costello, & Erkanli, 1999). Indeed, heritable contributions to comorbidity between major depressive disorder and conduct disorder (CD) are shared with genetic influences on negative emotionality (Tackett, Waldman, Van Hulle,  & Lahey, 2011). Crucially, after accounting for heritable influences on negative emotionality, heritable influences on this comorbidity are not significant, suggesting that the overlap between the two disorders may derive from heritable influences on temperament. Additional support for the centrality of negative emotionality to psychopathology comes from studies addressing irritability. Defined as a low threshold to experience anger in response to frustration, irritability is akin conceptually to negative emotionality or negative reactivity (Leibenluft, Blair, Charney, & Pine, 2003). Irritability is present in a wide range of disorders in childhood such as ODD, CD, major Krieger, Stringaris

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depression disorder, ADHD, depression, and anxiety (Krieger, Leibenluft, Stringaris,  & Polanczyk, 2013a). Evidence suggests that irritability is associated with a family history of depression and that shared genetic factors underlie both irritability and depression (Krieger et al., 2013b; Stringaris, Zavos, Leibenluft, Maughan,  & Eley, 2012). Moreover, chronic irritability among youth predicts depression 20  years later (Stringaris, Cohen, Pine,  & Leibenluft, 2009). In short, irritability is likely to be a crucial concept to understanding trajectories from temperamental precursors and later mental disorders in children, adolescents, and adults (Aebi, Plattner, Metzke, Bessler,  & Steinhausen, 2013; Rowe, Costello, Angold, Copeland,  & Maughan, 2010; Stringaris & Goodman, 2009a, 2009b).

Low Fearfulness

Deficits in emotion regulation explain considerable variance in conduct problems and aggression that are displayed in the context of high emotional arousal (Beauchaine, Gatzke-Kopp, & Mead, 2007; Hubbard et al., 2002; Shields & Cicchetti, 1998). A second type of aggression is referred to as instrumental or premeditated. Low levels of fearful inhibition and the presence of callous-unemotional traits predict this type of aggression (Shaw, Gilliom, Ingoldsby, & Nagin, 2003). Moral socialization is partly dependent on negative arousal (i.e., anxiety) evoked by potential punishment. Internalization of social norms occurs when children experience negative arousal when confronted with a forbidden activity. Thus, without fearful inhibition, it is more difficult for a child to develop moral socialization, empathy, and guilt (Moul  & Dadds, 2013; Newman, Patterson,  & Kosson, 1987). In turn, lack of empathy and guilt are believed to underlie psychopathic behaviors. In a large sample of children from Mauritius, Gao and colleagues (2010) found that crime at age 23 retrospectively predicted poor fear conditioning at age 3. Youth with callous-unemotional traits also present characteristics consistent with low levels of fearful inhibition (Barry et al., 2000; Chapter 22). Compared to those with low callous-unemotional traits, children with callous-unemotional traits show a more severe and aggressive pattern of conduct problems and are more likely to engage in instrumental/premeditated forms of aggression (Frick, Ray, Thornton, & Kahn, 2014; Loney, Frick, Clements, Ellis,  & Kerlin, 2003). Furthermore, they exhibit deficits in responses to punishment and in reactivity to threatening stimuli, which also 176

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suggests deficient fearful inhibition (Barker, Oliver, Viding, Salekin,  & Maughan, 2011; Levenston, Patrick, Bradley, & Lang, 2000). Additional support for links between low fearful inhibition early in life, callous-unemotional traits, and severe conduct problems comes from neuroimaging studies. Relative to controls, boys with conduct problems and callous-unemotional traits manifest lower right amygdala activity to fearful faces (Jones, Laurens, Herba, Barker,  & Viding, 2009). Differential amygdala activation may provide an explanation for three characteristics of psychopathy: poor fear recognition, diminished conditioned fear responses, and deficits in reversal learning (Moul & Dadds, 2013). Finally, adolescent boys with conduct problems and low trait anxiety exhibit greater structural compromises in mesolimbic, septo-hippocampal, and anterior cingulate brain regions than do those with conduct problems and normal levels of anxiety (Sauder et al., 2012).

Low Effortful Control

A third pathway from early temperament to externalizing behaviors is related to deficiencies in effortful control. As outlined earlier, effortful control refers to a child’s ability to deploy attentional resources to inhibit prepotent responses in order to regulate behaviors and emotions (Rothbart, 2007). Temperamental dimensions of attentional shifting, focusing, and inhibitory control are critical components of effortful control (Rothbart, Ziaie,  & O’Boyle, 1992). Significant evidence links low effortful control to risk for ADHD-related behaviors (Lemery, Essex, & Smider, 2002). Such findings are consistent with neuropsychological deficits in executive inhibitory control among many children and adolescents with ADHD (Barkley, 1997). In adults, Nigg and colleagues (Nigg, Blaskey, Huang-Pollock,  & John, 2002a; Nigg et  al., 2002b) found that low conscientiousness (a dimension of personality linked conceptually to low effortful control) was associated with inattention symptoms, whereas hyperactive and impulsive symptoms were related to low agreeableness and high negative emotionality (two additional dimensions of personality). Observational data collected from children between infancy and first grade indicate that ADHD is associated with low effortful control, high anger, and hostile behavior (Nigg, Goldsmith,  & Sachek, 2004). In the context of ADHD, comorbid ODD and CD should be addressed to understand the heterogeneity of temperament associations. For instance,

associations between negative affect and ADHD could be attributed to comorbid oppositional defiant problems, whereas findings of low anxiety and low physiological arousal (i.e., low fearfulness) may reflect co-occurrence of conduct problems and callous-unemotional traits among those with ADHD (Raine, 2002). In summary, there are at least three possible pathways from temperamental vulnerability to externalizing behavior. The first involves emotional regulation processes and encompasses traits such as negative emotionality and difficult temperament. Children who present with high negative reactivity and strong reactions to frustration and anger are at increased risk for ODD, nonpsychopathic CD, and hyperactive/impulsive ADHD symptoms. A second trajectory involves low fearful inhibition (i.e., trait anxiety) and is related closely to callous-unemotional traits and psychopathic behavior. Low fearful inhibition results in insensitivity to punishment learning and disrupts development of empathy and guilt. Extensive literature describes links between low fear, callous-unemotional traits, psychopathic behavior, and amygdala dysfunction. A third pathway is characterized by low effortful control and is associated particularly with ADHD and impulsivity. Importantly, although these vulnerabilities may occur in isolation, they frequently co-occur. It is not unusual, for example, for children who are high in negative affectivity to also exhibit low effortful control (indeed, these mechanisms often interact among children with ADHD and comorbid conduct problems who show poor emotion regulation; see Beauchaine et  al., 2007). Foley and colleagues (2008) found that children with ADHD showed significantly elevated scores on negative reactivity, activity, and impulsivity. Furthermore, recent developments identify a dual-process model whereby both a low-fear temperament (callousness, lack of remorse or guilty) and deficient inhibitory control (impulsivity, poor behavioral control, proneness to boredom) are equally important for the emergence of psychopathy among adults (Fowles  & Dindo, 2009). These results indicate that although certain facets of temperament are associated with vulnerability to specific disorders, considerable overlap exists.

Temperament and Psychopathology: End of a Spectrum and/or Vulnerability?

As noted earlier, it is important to consider whether externalizing psychopathology reflects an extreme along a temperamental continuum. Some

argue that only severity distinguishes temperament from disorder for any given behavior. For example, attention is often viewed as a quantitative trait that becomes disordered, as in ADHD, when expressed after crossing an arbitrary threshold. Indeed, similarities between symptoms of inattention and the temperamental dimension of effortful control suggest a continuum (De Pauw  & Mervielde, 2010). In contrast, cognitive and motor deficits characteristic of ADHD are not typically considered components of temperament (Barkley, 1997). Studies conducted with adults show that personality traits explain about half the variation in ADHD symptoms (Nigg et al., 2002b). Mixed results on continuities between temperament and mental disorders prevent definitive conclusions (Rettew, 2013). In addition, associations between temperament and psychopathology are likely to vary by disorder. A  consensus is that temperament may not be enough to explain the complexity of psychopathology, although some traits share phenotypic resemblance with disorders (Rettew, 2013). A large body of evidence shows that temperament interacts with environmental factors, moderating trajectories to psychopathology (see Pathways to Externalizing Behavior above, Muris  & Ollendick, 2005). Interactions between temperamental traits and environmental factors—and interactions among temperamental traits—are complex. The vulnerabilities in question are dimensions of temperament that may not be related directly to core features of externalizing behavior. Nevertheless, when temperamental predispositions interact with adverse social environments, externalizing behaviors may result for some individuals. Here, we detail potential moderators underlying pathways from temperament to psychopathology.

Genetic and Environmental Influences on Trajectories of Psychopathology

Factors that influence temperament and child psychopathology include direct effect of genes, environmental moderation of genetic effects (G×E), and indirect effects of genes through environment (rGE; Lemery-Chalfant et al., 2013).

Direct Effects of Heritability and Genes

Studies that address heritable associations between temperament and behavioral disorders provide considerable evidence of direct effects. For example, Gjone and Stevenson (1997) showed that a common genetic factor accounted for associations between negative affectivity and both attention Krieger, Stringaris

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problems and aggression 2 years later among 7- to 12-year-olds. Similarly, the proportion of covariance between negative emotionality and oppositional defiant and conduct disorder symptoms that was attributable to heritability accounted for 30% and 50% of the variance in symptoms, respectively (Singh & Waldman, 2010). In a separate twin study spanning infancy to early childhood, shared heritable influences accounted for much of the covariance between negative emotionality and externalizing problems (Schmitz et al., 1999). Thus, specific heritable factors appear to influence both temperament and externalizing behavior. Furthermore, molecular genetics research has begun to identify specific genes that influence both individual differences in temperament and vulnerability to psychopathology. For example, the 7-repeat allele of the dopamine D4 receptor gene (DRD4) is associated with individual differences in infant attention (Auerbach, Benjamin, Faroy, Geller, & Ebstein, 2001), mother-reported aggression at age 4 (Schmidt, Fox, Rubin, Hu, & Hamer, 2002), and vulnerability to ADHD (Faraone et al., 2005). Thus, even though temperament represents a set of complex, multifactorial inherited traits, the effects of some specific genes are beginning to be elucidated.

G×E Models

G×E models assume that effects of the environment on developmental outcomes depend on the exposed genotype. When G×E is operative, individuals do not choose environments (Plomin et al., 1977). Rather, their genetic vulnerability renders them susceptible to the environment in which they find themselves. Findings demonstrate consistently that temperament interacts with environmental risk factors to influence later psychopathological outcomes. For example, Crockenberg and Leerkes (2005) reported an association between long hours in nonparental care and externalizing problems 2  years later among infants who were easily frustrated at 6 months. Similarly, Schermerhorn et al. (2013) found that children with temperament profiles including high levels of resistance to control and unadaptability were vulnerable to externalizing problems in the face of chronic family stress. Moreover, negative mothering in the second and third years of life is associated with externalizing problems only among highly negative infants (Belsky, Hsieh, & Crnic, 1998), and maternal sensitivity predicts lower externalizing problems from 178

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2 to 4 years only among children characterized by adverse temperaments (Mesman et al., 2009). Emerging molecular genetics research has begun to identify specific genes that influence both individual differences in temperament and sensitivity to the environment in predicting behavior. For example, Sheese and colleagues (2007) reported that 18to 21-month-old children with the DRD4 7-repeat allele who experienced low-quality parenting were more likely to exhibit high levels of sensation seeking than were children without the 7-repeat allele who also experienced such parenting. Similarly, Bakermans-Kranenburg and van Ijzendoorn (2006) reported an association between maternal insensitivity and externalizing behaviors, but only for children with the DRD4 7-repeat polymorphism.

rGE Models

rGE describes processes through which an individual’s genotype is associated with environmental risk exposures (Knafo  & Jaffee, 2013). rGE has gained attention in the developmental psychopathology literature and emphasizes the importance of both (a)  mutual influences of genes and environment on behavior and (b)  how individuals sometime modulate their environments, either through selection of specific contexts such as deviant peers (active rGE), elicitation of aversive reactions from others (evocative rGE), or when genetic factors that are common to both children and their parents shape the home environment (passive rGE; see Beauchaine  & Gatzke-Kopp, 2013; Plomin, 2013). An important example of rGE concerns effects of parenting styles on temperament and behavior (De Clercq, Van Leeuwen, De Fruyt, Van Hiel,  & Mervielde, 2008; Rothbaum  & Weisz, 1994). For example, previous literature demonstrates that temperamental traits evoke different parenting responses (Lemery-Chalfant et al., 2013). In a retrospective adoption study, Riggins-Caspers et al. (2003) demonstrated that children of biological parents with psychopathology were more likely than those without to experience adoptive parental harsh discipline; these children had more behavior problems as well. Similarly, mothers of irritable infants differ from those of nonirritable infants in both amount and growth trajectory of visual contact, affective stimulation, physical contact, soothing, noninvolvement, and responsiveness to positive signals. Maternal behavior is systematically more positive toward nonirritable compared to irritable infants (van den Bloom & Hoeksma, 1994).

rGE is observed for internalizing behaviors as well. Behavioral inhibition evokes parental overprotection, which then potentiates maladaptive parent–child interactions over time, thus exacerbating the child’s fear of novelty and predisposing anxiety symptoms (Kagan  & Snidman, 1999). With the exception of genetic associations with substance use, molecular genetic literature on rGE is sparse (Jaffee  & Price, 2007). Burt (2009) conducted a study in 132 men who participated in several group activities in the lab and were later asked to judge how much they liked the other members. Men who were heterozygous or homozygous for the G allele of the G1438A polymorphism of the serotonin transporter receptor 2A (5HT2A) gene engaged in more rule-breaking behaviors and were better-liked by their peers than were men homozygous for A allele. The association between the G1438A polymorphism and peer relationships was mediated in part by its effects on men’s rule-breaking behavior. These results represent evidence that genes predispose individuals not only to particular behaviors, but also to the social consequences of those behaviors.

Conclusion

Temperamental traits are biologically based early-developing behavioral and emotional tendencies that are manifested particularly in social interactions and are relatively stable over time and across situations. These characteristics have implications for later development and for vulnerability to psychiatric disorders. Although a consensus regarding the structure of temperament is still lacking, a hierarchical framework seems plausible. This framework suggests a dynamic interplay of approach/ withdrawal systems (high-order factors) and reactive/effortful controls (low-order factors) that leads to different temperamental traits. At least three temperamental trajectories appear to be associated with externalizing behaviors. The first involves emotion regulation processes and encompasses traits such as negative reactivity, emotionality, and temperamental difficulty. Children who present with strong reactions to frustration and anger are at increased risk for hyperactive/impulsive ADHD, ODD, and nonpsychopathic CD. A  second trajectory involves low fearful inhibition (anxiety) and is closely related to callous-unemotional traits and psychopathic behavior. Low fearful inhibition interferes with punishment learning, resulting in lack of empathy and guilt. A third pathway is characterized by low effortful control and is associated with

ADHD. However, one should not interpret such pathways as being orthogonal; indeed, such traits frequently overlap.

Future Directions

In the past several decades, we have witnessed enormous improvements in our understanding of temperament and its potential links to externalizing behaviors. However, several challenges remain. First, the specificity of temperamental pathways remains unclear. As we have seen, temperamental traits are better predictors of broad vulnerability to externalizing problems than of specific disorders. Second, although the neural substrates of temperament are well understood, much work remains to clarify how trajectories of brain development map onto temperamental stability and change. Some disorders are conceptualized as delays in brain maturation (Shaw et al., 2007), and it will therefore be interesting to know how brain maturation affects intra- and interindividual variation in temperament. Third, assessing temperamental traits in early life appears to have important clinical and public health potential (Rapee, 2013); targeting behavior inhibition may reduce rates of internalizing disorders. It would be tempting to design similar low-risk interventions for temperamental traits to test outcomes in externalizing disorders.

Note

1. Variance in any behavioral trait that is attributable to heritability is often referred to as “genetic” in the behavioral genetics literature. However, there are many nongenetic sources of heritability (Beauchaine  & Gatzke-Kopp, 2013), including stress-induced epigenetic effects that alter fetal neurodevelopment and confer vulnerability to externalizing spectrum disorders (Beauchaine et  al., 2011). Thus, we use the term “heritable” instead of “genetic” in this chapter and elsewhere in this volume.

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11

Midbrain Neural Mechanisms of Trait Impulsivity

Aimee R. Zisner and Theodore P. Beauchaine

Abstract Trait impulsivity, defined as a preference for immediate rewards over larger delayed rewards and reflected in actions that are socially inappropriate, maladaptive, and enacted without consideration of consequences, is a highly heritable temperamental vulnerability to all externalizing spectrum disorders. Neuroimaging research indicates compromised mesolimbic function among those with clinically significantly impulse control deficits. Impulsive individuals display chronically low tonic dopamine (DA) levels and blunted phasic DA responses to incentives. Behaviorally, these deficits manifest in continuous searches for immediate, high-intensity rewards (e.g., novelty-seeking, risk-taking, drug use), which serve to temporarily up-regulate a chronically aversive mood state. When mesolimbic neural vulnerability interacts with environmental adversities, such as coercive parenting, deviant peer affiliations, neighborhood risk, and drug use, impulse control deficiencies worsen and progression from attention-deficit/hyperactivity disorder to more severe externalizing spectrum disorders becomes likely. Key Words:  trait impulsivity, externalizing spectrum disorders, impulse control deficits, mesolimbic system, rewards

Introduction

Self-regulation, broadly construed as “the ability to control thoughts, feelings, and behavior” (Petersen & Posner, 2012, p. 82), enables humans to suppress strong affective responses in the service of social affiliation (see e.g., Beauchaine, 2001; Beauchaine, Gatzke-Kopp,  & Mead, 2007; Porges, 1995, 2007), to persist in difficult tasks that yield long-term advantages over short-term reward (see e.g., Sitzmann  & Ely, 2011), and to navigate familial and environmental risk with partial protection from psychopathology (see, e.g., Lengua, 2002; Shannon, Beauchaine, Brenner, Neuhaus, & Gatzke-Kopp, 2007). These and other well-replicated positive outcomes, which span developmental epochs from infancy to adulthood, suggest that self-regulation is a linchpin of adaptive human function (Barkley, 2001; Buckner, Mezzacappa,  & Beardslee, 2010; Eisenberg, 184

Spinrad,  & Eggum, 2010; Shoda, Mischel,  & Peake, 1990). In contrast to positive associations between self-regulation and psychological adjustment, almost all forms of psychopathology are characterized by one or more deficiencies in self-regulatory behavior (see Beauchaine, 2001; Posner & Rothbart, 2000). In this chapter, we focus on midbrain neural mechanisms of deficient self-regulation, which are observed across externalizing spectrum disorders including attention-deficit/hyperactivity disorder (ADHD; e.g., Barkley, 1997; Biederman et al., 2012), oppositional defiant disorder (ODD; e.g., Luman, Sergeant, Knol,  & Oosterlaan, 2010), conduct disorder (CD; e.g., Beauchaine & Gatzke-Kopp, 2012; Frick  & Nigg, 2012), substance use disorders (SUDs; e.g., Gratz  & Tull, 2010; Stoltenberg, Lehmann, Christ, Hersrud,  & Davies, 2011), antisocial personality disorder

(ASPD; e.g., Alcorn et  al., 2013; Swann, Lijffijt, Lane, Steinberg,  & Moeller, 2010), and borderline personality disorder (BPD; e.g., Beauchaine, Klein, Crowell, Derbidge,  & Gatzke-Kopp, 2009; Crowell, Beauchaine, & Linehan, 2009). Importantly, self-regulation is not a unitary construct (see, e.g., Posner  & Rothbart, 2000). Neuroscience research indicates that several interdependent neural systems subserve self-regulatory functions, including (1)  the mesolimbic dopamine (DA) system, discussed herein, which modulates nonvolitional aspects of reward processing, incentive salience, and approach motivation (e.g., Beauchaine, 2001; Robinson  & Berridge, 2008; Schultz, 2002); (2)  the mesocortical DA system, which exerts top-down, often volitional control over subcortically mediated (i.e., limbic) response tendencies (e.g., Carr & Sesack, 2000; Munakata et al., 2011; Steffensen et al., 2008; Séguin & Parent, this volume); (3)  the septohippocampal system, which suppresses behavior in situations of real or perceived danger through induction of involuntary fear and anxiety (e.g., Corr, 2004; Gray  & McNaughton, 2000; Corr  & McNaughton, this volume); and (4) more widely distributed attention networks (see, e.g., Petersen & Posner, 2012). Following from behavioral, psychophysiological, and neuroimaging work conducted in our lab over the past 15 years (e.g., Beauchaine & Gatzke-Kopp, 2012; Brenner  & Beauchaine, 2011; Burns, de Mourna, Beauchaine,  & McBurnett, 2014; Gatzke-Kopp et  al., 2009; Sauder, Beauchaine, Gatzke-Kopp, Shannon,  & Aylward, 2012; Shannon, Sauder, Beauchaine,  & Gatzke-Kopp, 2009) and from extensive research on the neuroscience of behavioral impulsivity (described herein), we have arrived at an ontogenic process perspective on externalizing spectrum disorders (Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013). According to this perspective, heritable compromises in midbrain DA function confer vulnerability to the well-characterized developmental pathway from temperamental impulsivity, expressed very early in life as ADHD, to subsequent ODD, CD, delinquency, SUDs, and ASPD as affected individuals mature (see Beauchaine  & McNulty, 2013; Loeber  & Hay, 1997; Moffitt, 1993), especially when they encounter high-risk environments. In the remainder of this chapter, we (1)  outline behavioral genetics research indicating that a single heritable trait—behavioral impulsivity—confers vulnerability to all externalizing spectrum disorders across the life span, (2) discuss likely midbrain DA

mechanisms of this vulnerability, (3)  briefly consider how midbrain DA deficiency interacts with other neural systems implicated in self-regulation, and (4)  describe how substance use and abuse in particular exacerbate deficiencies in midbrain DA responding by amplifying preexisting vulnerability. Before doing so, however, we discuss alternative definitions of impulsivity and explain how we choose to operationalize the construct.

Defining Trait Impulsivity

The term “impulsivity” usually refers to deficiencies in self-control. In our work, we conceptualize impulsivity as (1) a preference for immediate rewards over larger delayed rewards and/or (2)  actions that are socially inappropriate, maladaptive, and enacted without consideration of consequences (see Beauchaine  & Neuhaus, 2008). Importantly, we consider impulsivity to be on a spectrum of individual difference along which members of the population vary. As with any individual difference, normative variation in impulsivity is not maladaptive; it is expressed in core aspects of personality, such as extraversion, sensation seeking, and novelty seeking (see, e.g., Sagvolden, Johansen, Aase,  & Russell, 2005). In its more extreme expressions, however, impulsivity can be quite debilitating, conferring vulnerability to a number of psychopathological conditions (see Beauchaine  & McNulty, 2013; Neuhaus & Beauchaine, 2013) as we discuss in detail later. In addition to such broad definitions, trait impulsivity is often operationalized more restrictively. Whiteside and Lynam (2001) describe impulsive behaviors as manifestations of four etiologically distinct personality facets, including urgency, lack of planning, lack of perseverance, and sensation seeking. In contrast, Patton and Stanford (1995) parse impulsivity into three facets, including motor impulsiveness (actions without forethought), nonplanning impulsiveness (overemphasis on the present), and attentional impulsiveness (difficulty maintaining focus on stimuli). Similarly, disinhibition is often construed as an inability to suppress immediate urges and may refer to a lack of control in motor and/or cognitive domains (Johansson  & Hansen, 2000). We prefer to use ADHD hyperactive-impulsive scale scores to index trait impulsivity, for three reasons. First, ADHD is among the most heritable of all psychiatric traits (Faraone et  al., 2005)—far more so than most alternative measures of impulsivity. Two recent meta-analyses of twin and adoption Zisner, Beauchaine

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studies indicate that heritable factors account for 73–85% of the variance in ADHD symptoms (Nikolas & Burt, 2010; Willcutt, in press). Second, ADHD scale scores explain far more variance in functional outcomes than do more circumscribed definitions of impulsivity (Neuhaus & Beauchaine, 2013). Finally, ADHD confers significant risk for more serious externalizing problems later in development (see Beauchaine et al., 2010; Beauchaine & McNulty, 2013). Thus, ADHD scale scores afford higher levels of both concurrent and predictive validity than do other measures of trait impulsivity.

Behavioral Genetics of Trait Impulsivity

Extensive behavioral genetics work suggests that a single higher order factor, which can be conceptualized as trait impulsivity (see Beauchaine & Marsh, 2006; Beauchaine & McNulty, 2013), confers vulnerability to all externalizing spectrum disorders (Dhamija, Tuvblad, & Baker, this volume; Krueger et al., 2002, 2007; Tuvblad et al., 2009). This factor analytic structure has been replicated for syndromes that are typically associated with child externalizing psychopathology, including ADHD, ODD, and CD (e.g., Tuvblad et  al., 2009), and those associated with adult psychopathology (e.g., Krueger et  al., 2002), including low constraint (disinhibition), conduct problems, ASPD, and substance dependence. In the Kruger et al. (2002) study, this higher order impulsivity factor was 81% heritable. Although greater environmental adversity increased genetic risk for externalizing behavior in this sample (Hicks, South, DiRago, Iacono,  & McGue, 2009), unique nonshared environmental factors were associated strongly with specific types of externalizing behavior (Krueger et al., 2002). Such findings suggest strongly that impulsivity confers common vulnerability to externalizing outcomes but that environmental risk factors shape this heritable vulnerability into specific forms of externalizing conduct across development (Beauchaine  & McNulty, 2013).

Midbrain Neural Mechanisms of Trait  Impulsivity

DA, a monoamine neurotransmitter, is crucial for processes related to motivation, incentive salience, learning, and motor control. Dopaminergic projections are extensive throughout both subcortical and cortical regions of the brain (for reviews, see Beaulieu & Gainetdinov, 2011; Iversen & Iversen, 2007). Stimulation of DA neurons can elicit very different behavioral responses, such as excitatory 186

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versus inhibitory locomotor activity, depending on neurotransmitter levels, opposing functions of the network stimulated, and differing ligand affinities of excitatory and inhibitory receptors (Calabrese, 2001). Thus, dose–response curves for DA agonists tend to be nonlinear, with low doses inducing different, and sometimes opposing, physiological and behavioral effects than high doses. For example, apomorphine reduces locomotor activity in a dose-dependent manner when administered in low doses, whereas high doses do not continue to induce hypoactivity (Strömbom, 1976). Individual genetic and neurobiological differences, such as DA gene variants and receptor density, also contribute to differential responding to DA agonism/antagonism (Kelly et al., 1998; Volkow et al., 1999).

Dopaminergic Projections

By convention, DA projections that ascend from the midbrain are divided into either three or four neuroanatomical networks (Beaulieu  & Gainetdinov, 2011; Björklund  & Dunnett, 2007; Taber, Black, Porrino, & Hurley, 2012). Cell bodies in the mesolimbic and mesocortical pathways originate in the ventral tegmental area (VTA). Mesolimbic DA neurons project primarily to the ventral striatum (VS), including the nucleus accumbens and the caudate nucleus, but also to certain limbic regions, including the basolateral amygdala. The mesolimbic pathway, which is the primary focus of this chapter, is most strongly associated with motivation, incentive salience, and impulsivity (see, e.g., Gatzke-Kopp & Beauchaine, 2007). The mesocortical pathway includes DA projections throughout the cerebral cortex, including the prefrontal and cingulate cortices. It is associated with error monitoring, executive functions, evaluating incentive magnitude, and maintenance of goal-directed behavior. Across development, the mesocortical DA system exerts increasing inhibitory control over the mesolimbic DA system through extensive reciprocal connections (see Beauchaine  & McNulty, 2013). Importantly, the integrity of these functional connections is compromised among externalizing adolescents (Shannon et al., 2009). Thus, mesolimbically-mediated impulsive behaviors are not suppressed sufficiently by top-down mesocortical influences. More specific prefrontal mechanisms of impulsivity are discussed in a separate chapter of this volume (Séguin & Parent, this volume). In addition to the mesolimbic and mesocortical DA systems, the nigrostriatal pathway projects from the substantia nigra pars compacta (SNc), located

adjacent to the VTA, to the dorsal striatum (comprised of the caudate nucleus and putamen) and is most commonly associated with movement, including motoric dysfunction (e.g., Parkinson’s disease). It is important to note that these dopaminergic pathways are both anatomically and functionally interconnected (Björklund  & Dunnett, 2007; Tisch, Silberstein, Limousin-Dowsey, & Jahanshahi, 2004; Wise, 2009). For example, sizable numbers of DA neurons in both the VTA and SNc project to the dorsal and ventral striata, limbic regions, and the cerebral cortex (Björklund  & Dunnett, 2007). In addition, the VS is not an undifferentiated region of incentive processing. Rather, the ventromedial striatum (comprising the medial olfactory tubercle and medial nucleus accumbens shell), is more responsive to incentives than is the ventrolateral striatum (Ikemoto, 2007). Moreover, the dorsal striatum is not involved exclusively in motor processes (Voorn, Vanderschuren, Groenewegen, Robbins, & Pennartz, 2004). In fact, the ventromedial portion of the caudate nucleus and putamen are regarded as part of the VS due to their functional similarities to other VS structures (Voorn et al., 2004). As noted earlier, the mesolimbic and mesocortical DA pathways are comprised of both feed-forward and feedback interconnections. Bottom-up DA input from the VS, thalamus, and other subcortical structures to cortical brain regions is modulated by top-down circuits, including projections from the anterior cingulate, supplementary motor area, and medial prefrontal cortex (mPFC; Gariano & Groves, 1988; Kunisho & Haber, 1994; Taber & Fibiger, 1995; Plichta et al., 2013). When intact, these feedback connections help guide goal-directed behavior by suppressing impulsivity (see Beauchaine  & McNulty, 2013; Nigg  & Casey, 2005; Shannon et al., 2009). Pharmacologic activation of prefrontal DA decreases DA levels in the nucleus accumbens, a mesolimbic structure (Louilot, LeMoal,  & Simon, 1989). Conversely, decreasing prefrontal DA increases DA levels in the nucleus accumbens. As noted earlier, disruption in this feed-forward/feedback system, as evidenced by altered functional connectivity, appears to be one neural substrate of impulsivity (Tisch et al., 2004).

Other Neurotransmitter Systems

In addition to DA, other neurotransmitter systems are also involved in impulsivity. Midbrain DA neurons and their targets are modulated by multiple neurotransmitter systems, including serotonin, γ-amino butyric acid (GABA), glutamate,

norepinephrine, and endogenous opioids (see Beauchaine, Neuhaus, Zalewski, Crowell,  & Potapova, 2011; Dalley  & Roiser, 2012; Fields, Hjelmstad, Margolis,  & Nicola, 2007; Morgane, Galler,  & Mokler, 2005). For example, although most VTA neurons are dopaminergic, more than a third in the rat are GABAergic, and approximately 2–3% are glutamatergic (Nair-Roberts et al., 2008). Both DA and GABAergic neurons in the VTA receive input from the PFC, the laterodorsal tegmental nucleus, and the lateral hypothalamus. These provide modulatory effects on the nucleus accumbens and the PFC (see Fields et  al., 2007). Serotonergic neurons, which originate primarily in the raphe nuclei, project extensively to brain regions implicated in impulsivity, including the VTA, SNc, PFC, and limbic structures (Lechin, van der Dijs, & Hernández-Adrián, 2006). Moreover, serotonin systems interact dynamically with DA systems, and manipulating serotonin neurotransmission (e.g., through antagonism or tryptophan depletion) affects performance on behavioral measures of impulsivity (see Dalley & Roiser, 2012). The goal of this chapter is not to detail every way in which different neural systems influence one another to affect behavioral outcomes, but rather to focus specifically on empirical findings that link the mesolimbic DA system to impulsive behavior. Additional neural systems are considered in other chapters.

Current State of the Science

As outlined earlier, accumulating evidence implicates low mesolimbic dopamine levels, both at rest (tonic activity) and in response to incentives (phasic activity), as core neural substrates of impulsivity (see Gatzke-Kopp, 2011; Gatzke-Kopp  & Beauchaine, 2007; Sagvolden, Johansen, Aase,  & Russell, 2005). Although early theories mistakenly attributed impulsive behaviors to excessive DA reactivity (see Brenner, Beauchaine,  & Sylvers, 2005), a large body of empirical work conducted in the past 10–15 years implicates reduced tonic and phasic midbrain DA activity in pathological impulsivity (e.g., Scheres, Milham, Knutson, & Castellanos, 2007; Volkow et  al., 2007). Low tonic midbrain DA activity, assessed via positron emission tomography (PET), correlates with trait irritability (Laakso et  al., 2003) and is therefore experienced as an aversive mood state (see, e.g., Beauchaine et al., 2007). In contrast, high central DA activity is associated with positive mood and hedonic capacity (see, e.g., Ashby, Isen,  & Turken, 1999). The chronic Zisner, Beauchaine

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irritability experienced by impulsive individuals is likely to motivate them to seek frequent reinforcers in efforts to up-regulate phasic mesolimbic DA activity, which temporarily improves their aversive mood state (Sagvolden et  al., 2005). In other words, those with chronically low DA levels may engage in excessive reward-seeking behaviors to alleviate feelings of anhedonia, boredom, and irritability. However, these individuals experience relatively low hedonic value from pleasurable stimuli and therefore seek more frequent and extreme incentives to derive hedonic “payoff.” In the presence of other psychological vulnerabilities (e.g., emotion dysregulation) and risk factors (e.g., coercive parenting practices; low-income neighborhoods), impulsive children develop more severe externalizing psychopathology given their tendency to engage in high-risk behaviors (Beauchaine  & McNulty, 2013; Beauchaine et al., 2010).

Animal Studies and Translational Models

Beginning in the mid-1950s, Olds and Milner pioneered research on the neural bases of motivation in rats (Olds  & Milner, 1954). Rats often engage in prolonged periods of operant behavior, such as lever-pressing to the point of exhaustion and starvation in order to receive electrical stimulation to specific brain regions, including the VTA and striatum (see Milner, 1991). Subsequent neuropharmacological studies with animals implicated midbrain DA modulation of the VS as central to reward-seeking behavior. Temporal dynamics of midbrain DA activity and reactivity are associated with both impulsive and reward-seeking behavior. Animal research demonstrated that midbrain DA activity comprises both tonic (baseline) and phasic (event-related) responding and that neural processing of appetitive stimuli depends on both (Schultz, 1998). For example, phasic DA activity in the nucleus accumbens increases following presentation of an unexpected reward, providing a temporal window for organisms to associate specific behaviors with rewarding stimuli (Tremblay, Hollerman,  & Schultz, 1998). Once such behaviors are repeatedly reinforced, increased phasic DA activity propagates backward in time to presentation of cues that signal impending reward rather than presentation of the reward itself. However, DA responses during reward presentation do occur if the reward is larger in magnitude than in previous presentations (Schultz, 1998). In contrast, DA activity is unchanged by rewards that are as good as expected and is reduced 188

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following rewards that are worse than expected. Such findings highlight the importance of phasic DA responding for associative learning (i.e., learning that two events are related) or that one event causes the other (e.g., pressing a lever to receive a reward). According to Sagvolden et  al. (2005), phasic midbrain DA firing is crucial for understanding impulsive behavior. Impulsive individuals possess a hypofunctional DA system—expressed as both low tonic and phasic responding—which reduces the time interval during which reward-related learning associations can be made. According to this formulation, impulsivity is characterized by preference for immediate over delayed rewards due to a general failure to associate behaviors with nonimmediate rewards. Thus, raising intrinsic levels of DA (e.g., through administration of DA agonists) lengthens the interval during which such associations can occur, which serves to reduce impulsive behavior (see Gatzke-Kopp & Beauchaine, 2007). In addition to well-timed and sufficiently intense phasic DA activity, tonic midbrain DA firing must be within a properly “tuned” range for learning reward-related contingencies, and chronically low or high levels of extracellular DA in cortical and striatal regions may disrupt the synaptic plasticity required for this process (Schultz, 1998). Among impulsive individuals, novelty-seeking and sensation-seeking behaviors may up-regulate intrinsically low levels of midbrain dopamine and improve mood (Sagvolden et  al., 2005). As noted earlier, although these attempts may raise DA levels and improve mood temporarily, they do not address the underlying DA deficiency and may become pathological if left unmanaged (Gatzke-Kopp  & Beauchaine, 2007). In contrast, methylphenidate, a DA agonist used to treat ADHD, reduces locomotor activity in rats and produces more regulated behavior in humans, presumably because hedonic capacity is increased and affected individuals need to rely less on their interactions with the external environment to increase striatal activation (Arnsten, 2006). Importantly, methylphenidate normalizes striatal neural responding to incentives among both children and adults with ADHD (Vles, Feron,  & Hendriksen, 2003; Volkow, Fowler, Wang, Ding, & Gatley, 2001) and normalizes frontocingulate underactivity (Rubia, Halari, Mohammad, Taylor,  & Brammer, 2011) and frontostriatal functional connectivity deficits (Rubia, Halari, Cubillo, Mohammad,  & Taylor, 2009) among children with ADHD.

Neuroimaging

Findings from neuroimaging studies, including those conducted with both PET and functional magnetic resonance imaging (fMRI), indicate that DA release from the substantia nigra/VTA to the VS is associated with reward anticipation, such that stimuli that cue for impending reward elicit midbrain and VS activation (Schott et  al., 2008; see O’Doherty, 2004). As in previous animal work described earlier, although receipt of expected rewards does not differentially activate the VS (O’Doherty, Dayan, Friston, Critchley,  & Dolan, 2003), this region is activated by both unexpected rewards (Delgado, Miller, Inati,  & Phelps, 2005) and rewards that are of greater than expected magnitude (Rolls, McCabe,  & Redoute, 2008). Thus, the VS appears to code for differences between predicted and obtained rewards and is important for predicting future reward—particularly during conditions of uncertainty. In contrast, the VS is deactivated during omission of expected rewards (i.e., extinction; O’Doherty et al., 2003) and in response to other forms of punishment (Delgado, Nystrom, Fissell, Noll, & Fiez, 2000). The VS, along with other structures implicated in reward processing, including the amygdala and orbitofrontal cortex (OFC), may index the relative value of a predicted reward, with greater activation correlating with greater reward value (Knutson, Adams, Fong,  & Hommer, 2001) and reduced activation signaling reward devaluation (Gottfried, O’Doherty, & Dolan, 2003). Furthermore, human neuroimaging studies reveal that the VS responds to both primary (e.g., taste rewards) and secondary reinforcers (e.g., monetary incentives; O’Doherty, 2004). Neural responses to anticipated and delivered rewards differentiate those with ADHD from controls. During reward anticipation, VS hypoactivity is observed among individuals with ADHD (e.g., Carmona et  al., 2012; Furukawa et  al., 2014; Hoogman et al., 2011; Scheres et al., 2007; Ströhle et al., 2008). Compared to healthy controls, individuals with ADHD exhibit reduced striatal responding when choosing both smaller, immediate rewards and larger, delayed rewards (Plichta et al., 2009). In contrast, studies that evaluate neural activation during reward delivery are less consistent. In fact, the VS may activate similarly or greater to rewarding outcomes in ADHD (Furukawa et  al., 2014; Scheres et al., 2007; Wilbertz et al., 2012). Aberrant neural responding to incentives among those with ADHD is unsurprising given findings

from single photon emission computed tomography (SPECT) and PET studies, which implicate a compromised midbrain DA system in ADHD (e.g., Vles et al., 2003; Volkow et al., 2007). Children with ADHD also possess smaller ventral striatal volumes, which are associated with severity of hyperactive/ impulsive symptoms (Carmona et al., 2009; Sauder et  al., 2012). Furthermore, methylphenidate and other DA agonists increase extracellular DA levels in the striatal pathway (e.g., Volkow, Fowler, Wang, Ding,  & Gatley, 2001). Thus, pharmacological interventions for ADHD, which increase availability of DA in the striatum, decrease impulsive behaviors by amplifying DA availability to levels experienced by individuals without the disorder (e.g., Hinshaw, Henker, Whalen, Erhardt,  & Dunnington, 1989; MTA Cooperative Group, 1999).

Links to Traditional Externalizing  Disorders ADHD

Trait impulsivity is a defining feature of the hyperactive-impulsive and combined types of ADHD. As noted earlier, ADHD scale scores are more heritable and provide greater concurrent and predictive validity than more circumscribed measures of impulsivity. As expected in a population characterized by impulsivity, children, adolescents, and adults with ADHD exhibit diminished midbrain DA reactivity in response to incentives (see Plichta  & Scheres, 2014; Rubia, 2011). Furthermore, the window for associative learning is shortened in ADHD (Sagvolden et  al., 2005), resulting in greater difficulty learning relationships between one’s behavior and resulting consequences, particularly when consequences do not immediately follow behavior. However, cognitive processes, including error detection, can improve in individuals with ADHD when reinforcement contingencies are presented sufficiently close together (see Luman, Oosterlan, & Sergeant, 2005). For example, receipt of immediate, performance-based rewards increases neural responding to task-relevant stimuli and error in children with ADHD and is associated with improved task accuracy (Rosch & Hawk, 2013). Despite exceedingly high heritability estimates and growing empirical support for impaired DA-mediated reward processing in ADHD, polymorphisms in genes that encode for DA expression account for very little variance in trait impulsivity (see Beauchaine et  al., 2010; Beauchaine  & McNulty, 2013). Such genes also account for little variance in mesolimbic DA function (see Durston, Zisner, Beauchaine

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2010; Faraone  & Mick, 2010; Gizer, Ficks,  & Waldman, 2009; Sharp, McQuillin,  & Gurling, 2009). Instead, as with most complex behavioral traits, impulsivity appears to be an equifinal outcome of a number of Gene × Gene and Gene × Environment interactions (see Gatzke-Kopp, 2011; Nigg, Nikolas, & Burt, 2010). For instance, early-life social and environmental factors, such as childhood abuse, neglect, and nutritional deficiency, may induce epigenetic changes in neural development and functioning associated with trait impulsivity (see Archer, Oscar-Berman, Blum,  & Gold, 2012; Beauchaine et  al., 2011). Moreover, certain genes (e.g., DAT1) appear to be particularly susceptible to epigenetic regulation (Shumay, Fowler,  & Volkow, 2010). For example, DAT1 codes for the dopamine transporter, which moves DA from the synaptic cleft to the presynaptic neuron and is expressed most abundantly in the striatum (Ciliax et al., 1999). Interactions between DAT1 and specific environmental risk factors, such as exposure to tobacco and alcohol in utero, exacerbate liability for ADHD above and beyond main effects of gene polymorphisms (Brookes et  al., 2006; Neuman et al., 2007).

CD

Comorbidity rates of ADHD and CD are exceedingly high (e.g., Gau et al., 2010), and ADHD in preschool confers risk for childhood-onset CD despite largely distinct diagnostic criteria (see Beauchaine  & McNulty, 2013; Beauchaine et  al., 2010; Loeber, Green, Keenan,  & Lahey, 1995). In fact, approximately half of preschoolers who exhibit ADHD and oppositional features develop more serious conduct problems later in childhood (Campbell, Shaw,  & Gilliom, 2000), which suggests that trait impulsivity is a significant predisposing vulnerability to CD. In fact, multiple research labs report difficulty recruiting participants who meet criteria for CD but not ADHD (e.g., Finger et al., 2011; Gatzke-Kopp et al., 2009). Because trait impulsivity is a defining feature of ADHD, and because ADHD predicts development of early-onset CD in the context of environmental risk (e.g., coercive/ineffective parenting, deviant peer group affiliations, neighborhood criminality; see Beauchaine et al., 2010; Patterson, DeGarmo, & Knutson, 2000), it is of little surprise that deficient impulse control and dysfunctional reward processing are reported in early-onset CD (e.g., Finger et  al., 2011; Gatzke-Kopp et  al., 2009; Shannon et  al., 2009; White et  al., 2014). For example, 190

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youth with CD indicate a preference for smaller but immediate rewards over larger delayed rewards (White et al., 2014). Moreover, Finger et al. (2011) reported hypoactivation of the caudate nuclei and OFCs of adolescents with disruptive behavioral problems relative to healthy adolescents during early stimulus-reinforcement learning. These participants also produced increasingly more commission errors in later trials compared to controls, which may suggest that weaker stimulus-reinforcement learning in early trials engendered poorer representations of predicted reward outcomes in later trials. In fact, this research group later reported that, relative to healthy controls, youth with a history of disruptive behaviors exhibit hypoactivation of the caudate to outcomes that are better than predicted but greater caudate responding to outcomes that are worse than predicted (White et al., 2013). This pattern is a hallmark of trait impulsivity and was also reported by Gatzke-Kopp et al. (2009) among externalizing youth in response to nonreward versus reward conditions. Thus, aberrant neural processing of reward is observed in early-onset CD.

ASPD

Adults with antisocial traits exhibit altered neural processing of reward, but both hyper- and hypoactivation of the VS have been reported (see Glenn & Yang, 2012). At least three factors may account for such inconsistencies:  (1)  although impulsivity is a common vulnerability for antisocial behavior, antisociality has less circumscribed biological underpinnings than ADHD and thus encompasses a more heterogeneous group that includes, but is not limited to, individuals with high trait impulsivity that emerged early in life (Beauchaine et al., 2010); (2) experimental conditions of reward anticipation, delivery, and extinction have not been separated adequately (these conditions should be treated separately because individuals on the externalizing spectrum exhibit differential ventral striatal responding under conditions of reward receipt vs. nonreward; see, e.g., Gatzke-Kopp et al., 2009); and (3) similar to findings that implicate hypofunctional DA responses to incentive cues among individuals with ADHD, hyperfunctional DA responses are seen among nonclinical samples with somewhat elevated impulsivity scores (see Plichta  & Scheres, 2014). Regarding this final point, neural correlates of impulsivity among those with antisocial traits may follow a nonlinear pattern, such that neural responding differs for individuals with clinically significant versus subclinical levels of antisociality.

For example, using PET, Buckholtz et  al. (2010) reported that higher impulsive-antisocial traits predicted greater activity in the VS to monetary incentives and stimulant intake among nonclinical community members with no history of substance abuse. Similarly, individuals who commit criminal acts but avoid detection and punitive consequences for their actions may possess superior top-down regulation of midbrain DA input compared to individuals whose criminal acts were detected (Yang et  al., 2005). Future studies may clarify the role of the striatum in antisocial traits through careful experimental design.

Substance Abuse Disorders

The same neural systems that are associated with trait impulsivity give rise to the positive reinforcing properties of drugs of abuse. All drugs that are abused commonly by humans (e.g., alcohol, nicotine, cocaine, amphetamine, opiates) increase synaptic DA levels in the nucleus accumbens, either directly or indirectly, whereas drugs with minimal abuse potential do not (Di Chiara  & Imperato, 1988). In contrast, administration of DA antagonists to the VS or VTA reduces and in some cases extinguishes goal-directed, reward-seeking behavior, including self-stimulation and self-administration of drugs of abuse (see Ikemoto, 2010). According to the incentive sensitization theory of addiction (Robinson  & Berridge, 1993, 2008), drugs that elicit DA surges in striatal regions are reinforcing because these brain regions (1)  code for hedonic value (see earlier discussion) and (2)  become sensitized to the drug during early stages of addiction. Once addiction develops, motivation to seek and use drugs remains despite providing minimal and/or brief subjective pleasure due to neuroplastic changes involving multiple neurotransmitter systems (see Koob, 2011; Robinson  & Berridge, 1993, 2008). With repeated use, drugs of abuse, particularly stimulants, down-regulate tonic mesolimbic function and attenuate DA responding to incentives, including the drug itself, whereas conditioned drug cues elicit robust mesolimbic activation (Volkow, Wang, Fowler, & Tomasi, 2012). The switch from nonaddicted drug use to substance dependence marks a transition of primary neural control over drug seeking and consumption from the VS, which is associated with reward and motivation, to the dorsal striatum, which contains dense DA innervation and is associated with habitualized, compulsive behavior (see Everitt  & Robbins, 2013). Diminished mesolimbic DA functioning perpetuates use due to

tolerance (i.e., increasingly higher doses of the drug are required to experience reinforcement), withdrawal (i.e., dysphoria/anhedonia is experienced when the drug is not consumed), and heightened saliency of drug-related cues (e.g., paraphernalia, environments were the drug was previously consumed), which signal impending relief from withdrawal (Volkow et  al., 2012). Notably, drugs of abuse also reduce prefrontal regulatory control over subcortical regions implicated in reward and negative emotional states and compromise higher order executive functions, such as self-control and evaluation of stimulus saliency (Everitt & Robbins, 2013; Goldstein  & Volkow, 2011). For example, mice with a history of chronically elevated DA neural firing in the nucleus accumbens due to strong stimulant exposure exhibit lower tonic DA activity in this region and weaker connections with the PFC (Thomas, Beurrier, Bonci, & Malenka 2001). Thus, addiction is characterized by sensitization to drug cues, but diminished responses to the drug and other incentives, negative reinforcement (removal of negative psychological states through use), and top-down disinhibition from the PFC all render individuals susceptible to continual use and relapse. Risk for substance abuse overlaps extensively with risk for externalizing disorders, including ADHD, conduct problems, delinquency, and antisocial traits (Krueger et  al., 2002). Deviant peer group affiliations influence initiation of alcohol and drug use, but trait impulsivity, which is highly heritable, is associated with addiction (Belin, Mar, Dalley, Robbins,  & Everitt, 2008; Dick  & Bierut, 2006; Kendler, Schmitt, Aggen, & Prescott, 2008; Viken, Kaprio, Koskenvuo, & Rose, 1999). Although trait impulsivity and ADHD necessarily precede substance use and abuse (Verdejo-García, Lawrence,  & Clark, 2008), this relation may be moderated by concurrent conduct problems (Harty, Galanopoulos, Newcorn,  & Halperin, 2013; Harty, Ivanov, Newcorn,  & Halperin, 2011; Lee, Humphreys, Flory, Liu, & Glass, 2011; Wilens & Morrisson, 2011). Furthermore, most evidence suggests that stimulant treatment for ADHD does not affect a child’s risk for later substance abuse (Biederman et al., 2008; Harty et al., 2011; Mannuzza et al., 2008; Molina et al., 2013). Importantly, trait impulsivity is both a predisposing liability for and consequence of substance abuse (de Wit, 2009; Verdejo-García et al., 2008). Individuals who are impulsive may be at especially high risk for developing substance dependence because the neural substrates of addiction (e.g., Zisner, Beauchaine

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hypofunctionality of the PFC and mesolimbic DA system) overlap with those implicated in impulsivity and thus already occur in impulsive individuals (see Beauchaine  & McNulty, 2013; George  & Koob, 2010; Schoenbauma,  & Shahamd, 2008). For example, reduced mesolimbic DA transporter, D2 receptor, and/or D3 receptor binding is reported in both ADHD (Volkow et al., 2009) and alcoholism (e.g., Laine, Ahonen, Räsänen,  & Tiihonen, 2001). Furthermore, individuals who abuse substances or are at increased risk (e.g., strong family history of substance abuse) exhibit neural patterns characteristic of individuals with trait impulsivity. Both detoxified alcoholics and healthy individuals with a family history of alcoholism (but not a personal history of alcoholism) exhibit diminished activation in the nucleus accumbens to anticipation of incentives, and lower accumbal activation correlates with higher impulsivity scores (Andrews et al., 2011; Beck et  al., 2009). Similarly, in a prospective study of 15- to 18-year-olds with no prior substance use, smaller left nucleus accumbens volumes predicted substance use initiation during a 2-year follow-up period (Urošević et al., 2014), thus implicating reward processing dysfunction as a vulnerability for substance use. Functionally, neurobiological changes caused by substance use impede self-regulation in a population already predisposed to risky and impulsive behaviors. Strong stimulants, which elicit more direct and pronounced effects on the mesolimbic system than do other substances of abuse, may be particularly devastating for this system. For example, individuals with cocaine dependence exhibit reduced DA release in the striatum and reported an attenuated “high” compared to control participants when administered intravenous methylphenidate (Volkow et al., 1997). Such findings support a classic Neurobiological Vulnerability × Environmental Risk interaction dynamic in which individuals with low DA responding are predisposed to substance abuse, which elicits neuroplastic changes that further diminish DA responding (see Beauchaine  & McNulty, 2013).

Developmental Considerations

Although ADHD was once thought to be a child and adolescent disorder, 65% of affected individuals maintain syndromal features well into adulthood (Faraone, Biederman,  & Mick, 2006), even though debate continues over the percentage who continue to surpass strict diagnostic criteria. Among children whose symptoms remit with 192

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age, brain circuits involved in top-down inhibitory control and bottom-up dopaminergic input may functionally “catch-up” to those of unaffected peers either through delayed maturation or alternative, compensatory routes (Vaidya, 2012). For example, Shaw et  al. (2006) reported that children with ADHD have thinner than normal medial PFCs at 9  years, and that children with lasting impairment have PFCs that remain thinner into adolescence compared to those with better outcomes and controls. Similarly, children and adolescents with ADHD may have smaller caudate volumes, which normalize across development as clinical symptoms improve (Castellanos et  al., 2002). Even among typically developing children, the brain undergoes profound cortical and subcortical changes, most notably cortical thinning in the parietal and right dorsal frontal areas and cortical thickening in areas implicated in language in the frontal and temporal lobes. Such neural changes correlate with cognitive maturation across development (Toga, Thompson, & Sowell, 2006). The quality, severity, and persistence of symptoms associated with early neurodevelopmental deficits in DA functioning depend on dynamic transactions between genetic vulnerabilities and environmental risk factors, including prenatal exposure to pollutants, alcohol, cigarettes, and maternal stress; perinatal complications; and postnatal exposure to toxins, poor diet, poor parenting, and maltreatment (see Gatzke-Kopp, 2011; Nigg et al., 2010). For example, interactions between the variable number tandem repeat polymorphism of the DA transporter gene (DAT1) and early parenting predict development of CD symptoms years later (Lahey et al., 2011). Gene × Environment interactions help to explain high comorbidity rates between ADHD and CD, despite CD’s substantially lower heritability (Cadoret, Leve,  & Devor, 1997) and later average age of onset (Kessler et al., 2005). Such interactions imply that targeted interventions, such as those that improve executive functioning, social skills, and emotion regulation, may help control symptom severity, and thus impairment associated with ADHD, when provided during critical developmental periods (Halperin  & Healy, 2011). For example, children with hypofunctional DA systems may be able to overcome behavioral deficits associated with trait impulsivity when exposed to positive environmental influences, such as a consistent and organized disciplinary style that includes predictable, frequent feedback and immediate reinforcers (Sagvolden et al., 2005). Appropriate reinforcement

(ideally combined with pharmacotherapy) in early childhood facilitates an adaptive development trajectory by improving self-regulatory skills while dissuading use of maladaptive coping strategies before bad habits become ingrained.

Controversies and Research Agenda

Despite tremendous progress in understanding the heritability and neural bases of trait impulsivity and its role in mental health and illness, a few notable controversies in this research area persist. These controversies highlight ambiguous areas in need of further empirical investigation. One such controversy is the scope with which midbrain DA deficits underlie disorders across the externalizing spectrum. The position asserted here and elsewhere (e.g., Young et al., 2009) is that low tonic midbrain DA and aberrant DA-mediated responses to incentive cues comprise an underlying vulnerability to all externalizing disorders. Although developments in behavioral neuroscience and neuroimaging have elucidated a prominent role of midbrain DA in trait impulsivity and externalizing spectrum disorders, some research programs emphasize neural and behavioral differences between externalizing disorders, which may obscure this commonality. For example, ascribing “cool” inferior frontostriatal dysfunction to ADHD but “hot” ventromedial orbitofrontal-limbic dysfunction to conduct disorder (e.g., Rubia, 2011) suggests more neural specificity vis-à-vis Diagnostic and Statistical Manual of Mental Disorders (DSM)-defined disorders than our model implies (see Beauchaine  & McNulty, 2013). Notably, however, the medial OFC, which helps code reward value and preference of immediate versus delayed reward (McClure, Laibson, Loewenstein,  & Cohen, 2004), may be hyperresponsive or insensitive to reward contingencies in ADHD (Rubia et  al., 2009; Ströhle et  al., 2008; Wilbertz et  al., 2012; see also Séquin  & Parent, this volume). Furthermore, aberrant caudate activation during reward processing is reported in CD (Gatzke-Kopp et al., 2009; White et al., 2013). Importantly, we adopt an ontogenic process model of externalizing spectrum disorders, in which interdependent individual-level vulnerabilities (e.g., hypoactive mesolimbic DA activity) and equally interdependent contextual risk factors (e.g., coercive parenting, deviant peer group affiliations) interact with one another to increase the likelihood of developing externalizing psychopathologies across the life span (Beauchaine & McNulty, 2013). From this perspective, reward-processing deficits—a biological

vulnerability to ADHD—are amplified by environmental adversity, which contributes to underdeveloped emotion regulation abilities (Beauchaine et al., 2007). Thus, conduct-disordered behaviors may emerge from deficits in “cool” processes transacting with risk factors to exacerbate deficits in “hot” processes. Differentiating ADHD from CD on the basis of neurobiological differences may therefore be misleading when we consider these disorders from a developmental perspective in which trait impulsivity, expressed as ADHD, lays a foundation for ensuing conduct-disordered outcomes. Another controversial area in impulsivity research involves differences in the neural underpinnings of reward anticipation versus reward outcome. Diminished mesolimbic reward responding to incentive cues is well established in ADHD, but results for DA-mediated responses to reward outcomes are far less consistent. As described earlier, the VS may respond similarly among those with ADHD versus controls or respond more to reward outcomes (Furukawa et  al., 2014; Scheres et  al., 2007; Wilbertz et al., 2012), whereas the OFC may be hyperresponsive to reward contingencies (Rubia et  al., 2009; Ströhle et  al., 2008; Wilbertz et  al., 2012). Ströhle et  al. (2008) speculate that their findings of OFC overactivation reflect compensatory mechanisms, whereas Rubia et al., (2009) state that OFC overactivation may reflect hypersensitivity to reward. Such findings provide evidence for differential reward processing for both cues and outcomes in ADHD, but also underscore the need to more fully explore mesocorticolimbic interactions in both typical and atypical populations. Finally, despite being comprised of largely non-overlapping diagnostic criteria, comorbidity between internalizing and externalizing disorders is exceedingly common (e.g., Costello, Mustillo, Erkanli, Keeler, & Angold, 2003; Gilliom & Shaw, 2004). Growing evidence suggests that blunted DA responses and low tonic DA levels underlie avolition and anhedonia—traits that span diagnostic boundaries (e.g., Forbes et  al., 2009; Furukawa et  al., 2014; Meyer et  al., 2001; Volkow et  al., 2007). Unfortunately, internalizing and externalizing disorders are usually not studied together. By studying heterotypic comorbidity (i.e., the presence of at least one internalizing disorder and at least one externalizing disorder within an individual; Angold, Costello, & Erkanli, 1999) explicitly, transdiagnostic neural mechanisms that underlie these disorders can be better understood. For example, anxiety comorbid with ADHD may protect against Zisner, Beauchaine

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impulsivity and response inhibition deficits but worsen working memory (Schatz & Rostain, 2006), yet the neural mechanisms through which ADHD is affected by trait anxiety are poorly understood (for an exception, see Sauder et al., 2012).

Conclusion

Impulsivity is a multidimensional trait that is defined and measured in a number of ways. Here, we define impulsivity as a construct that is highly heritable and captured best by ADHD hyperactive/ impulsive scale scores. Using this approach, substantial translational and neuroimaging research points to impulsivity as a behavioral product of low tonic midbrain DA and blunted mesolimbic responses to incentives. This vulnerability provides the neural basis for development of ADHD and for progression along the externalizing spectrum, beginning with ADHD and escalating to conduct problems, antisociality, and substance abuse. Notably, although ADHD is associated with well-recognized DA impairments, the relation between aberrant DA functioning and other disorders becomes increasingly obscured as one progresses along the externalizing spectrum. This fact suggests that environmental risk factors are stronger etiological agents in disorders that are developmentally removed from ADHD. This pattern has important diagnostic and therapeutic implications; although impulsivity is a common vulnerability to all externalizing spectrum disorders, appropriate interventions applied during critical developmental periods can target and protect against environmental risk factors that advance individuals along the externalizing spectrum (Beauchaine  & McNulty, 2013; Beauchaine et al., in press).

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CH A PT E R

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Prefrontal and Anterior Cingulate Cortex Mechanisms of Impulsivity

Natalie Castellanos-Ryan and Jean R. Séguin

Abstract Impulsivity is implicated in all externalizing disorders. It can be construed as a multifaceted construct involving one or many different cognitive errors/deficits that result from the breakdown in optimal functioning and communication between the prefrontal cortex (PFC) and the midbrain regions. This chapter reviews literature focused specifically on forms of impulsivity in which top-down executive deficits involving primarily the PFC and anterior cingulate cortex (ACC) play an important role. Although a few limitations and controversies in the field remain, there is support for the notion that impulsivity is reflected in several important endophenotypes, including delay discounting, response inhibition, and affective decision making. These processes provide links between brain mechanisms and phenotypic expressions or disorders. Improved understanding of endophenotypes should translate into improved and more personalized approaches to prevention and treatment of externalizing disorders via targeting of specific cognitive control functions. Key Words:  impulsivity, prefrontal cortex, anterior cingulate cortex, externalizing, response inhibition, delay discounting, decision making, executive function

Introduction

Impulsivity has a range of definitions that often include a general failure to plan ahead and an inability to control or regulate emotions and behaviors, especially behaviors that are unduly risky or can result in maladaptive consequences (Crews  & Boettiger, 2009; Dalley, Everitt, & Robbins, 2011; Evenden, 1999). In the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013), impulsivity is reflected in symptoms such as intrusions (interruptions) into other people’s activities and an inability to wait (hasty actions without consideration for consequences). The ICD-10-CM (World Health Organization, 2014), identifies a category of psychiatric disorders “whose essential features are the failure to resist an impulse to perform an act that is harmful to the individual or to others,” in which “individuals typically experience an increased sense of tension prior to the act and then pleasure, gratification or release

of tension at the time of committing the act.” (http://www.icd10data.com/ICD10CM/Codes/ F01-F99/F60-F69/F63-/F63.9). The ICD-10 states that these mental disorders “are characterized by an intense need to gratify one’s immediate desires and failure to resist the impulse or temptation.” (http:// www.icd10data.com/ICD10CM/Codes/F01-F99/ F60-F69/F63-/F63.9). Certainly, impulsive tendencies have been referred to in a number of ways in the personality, behavioral, and psychopathology literatures, such as “acting without premeditation,” “lack of planning,” “excitement seeking,” “low tolerance to boredom,” and “behavioral undercontrol,” among others. Consequently, different measures of “impulsivity” may in fact measure different constructs. Indeed, although general factor models of personality typically identify only one factor for impulsivity, extensive research has been conducted to differentiate between different dimensions of impulsivity, which 201

usually results in between two and four subfactors (Reynolds, Penfold,  & Patak, 2008; Whiteside, Lynam, Miller, & Reynolds, 2005). At a psychological level, impulsivity is often viewed as the result of faulty problem solving or decision making. Optimal decision making is defined as the process of choosing a particular action among a number of alternative options, one that is expected to result in the most beneficial outcome. It may be framed within the context of an executive function (EF) model that covers four phases of problem solving, as adapted from Luria’s work (Luria, 1966), including (1)  problem representation, or an input phase in which a problem is perceived and an attempt is made to understand it; (2) planning, or a processing phase in which alternative options are evaluated; (3)  execution of the plan, or an output phase during which the solution is executed; and (4)  monitoring, or a review phase during which the solution is evaluated and errors detected and corrected. Most agree that basic/core processes such as attention and working memory affect performance at all phases (Carlson, Zelazo, & Faja, 2013). Finally, this problem-solving model emphasizes “cool” decision making (made in emotionally neutral conditions/context); however, problem solving is also sensitive to “hot” motivational cues (or emotional/rewarding contexts; see Zisner & Beauchaine, this volume). Faulty decision making observed among impulsive individuals is thought to result specifically from failures in optimally incorporating temporal factors in decision making (e.g., Kim  & Lee, 2011) or, more simply put, from “errors of not being able to wait” (Diamond, 2013, p. 38). Such errors can result from deficits at different points or phases of the decision-making process. For example, impulsive individuals could make poor choices because of (a) “nonplanning impulsivity” or a deficit in premeditation, in that they value immediate outcomes or rewards and discount the value of delayed rewards (often referred to as temporal/delay discounting—a deficit associated with phase 2 of Luria’s problem-solving model; Green  & Myerson, 2004; Kirby, Petry, & Bickel, 1999); (b) a strong tendency to produce habitual/default actions prematurely, which is a sign of response perseveration or an incapacity to override or stop habitual/default actions (i.e., deficits in response/motor inhibition; related to the third phase of the problem-solving model; Aron, Robbins,  & Poldrack, 2014; Chambers, Garavan, & Bellgrove, 2009; Verbruggen & Logan, 2009); (c) failure to reflect on the consequences of 202

their choices (i.e., broadly defined as a deficit in affective “decision making”; Bechara, 2005; related to phase 4 of the problem-solving model); and/or (d) either incapacity to perceive or attend to important information in their environment that may help them make better decisions; that is, deficits in attention or interference control (Friedman & Miyake, 2004) or deficits in working memory (Hofmann, Schmeichel, & Baddeley, 2012) related to all phases of problem-solving model. In this chapter, we attempt to integrate several models of impulsivity and EF and highlight dissociable deficits and mechanisms that result in impulsive decision making and behavior that are mediated, at least in part, by the prefrontal cortex (PFC) and anterior cingulate cortex (ACC). Thus, we review evidence clarifying the role of the PFC and ACC in the following processes involved in impulse control: (1) delay discounting, (2) response inhibition, (3)  affective decision making, and (4)  basic processes implicated at all stages of problem solving, such as poor attention or working memory. Although it is clear that these dynamic and interrelated functions rely on a network of brain regions as opposed to just one or two, damage to or dysfunction within the PFC (more specifically to the ventromedial PFC [vmPFC], and to subregions such as the orbitofrontal cortex (OFC), lateral PFC—both ventrolateral [vlPFC] and dorsolateral [dlPFC]—and ACC) is associated with different forms of poor impulse control (Arnsten, 2009; Kim  & Lee, 2011; Torregrossa, Quinn,  & Taylor, 2008). In Figure 12.1, we present anatomical locations of the main cortical brain regions discussed in this chapter. In addition to these regions, the striatum and other thalamic and subthalamic brain areas are also implicated in faulty problem solving and poor impulse control (Dalley  & Roiser, 2012; Torregrossa et  al., 2008). For example, the medial PFC and parts of the cortico-striatal loop mediate reward-related behavior and delay discounting (Cardinal, Winstanley, Robbins,  & Everitt, 2004; Dalley et  al., 2011; Dalley, Mar, Economidou,  & Robbins, 2008). Thus, there is agreement that impulse control involves several brain regions and results from an interplay of interconnected brain areas including limbic, striatal, and prefrontal structures (Dalley et  al., 2011; Dalley  & Roiser, 2012). Impulsivity that results from dysfunction in limbic and striatal regions (often referred to as bottom-up dysfunctions or dysfunctions in the “impulsive brain

Prefrontal and Anterior Cingul ate Cortex Mechanisms of Impulsivit y

Pre-SMA DLPFC

Pre-SMA DLPFC

VMPFC and OFC

VMPFC and OFC IFC (or VLPFC)

ACC

Figure 12.1  Anatomical location of prefrontal, anterior cingulate and pre-supplementary motor regions; the main brain regions referred to in this chapter. dlPFC, dorsolateral PFC; vmPFC, ventromedial PFC; OFC, orbitofrontal cortex; IFC, inferior frontal cortex; vlPFC, ventrolateral PFC; ACC, anterior cingulate cortex; pre-SMA, pre-supplementary motor area.

system”) are sometimes conceptualized or studied separately from those resulting from dysfunctions in prefrontal mechanisms (referred to as top-down dysfunctions or dysfunctions in a “reflective system” [Bechara,  2005] or “executive system” [Bickel et  al.,  2007]). However, in reality, these systems and dysfunctions are difficult to dissociate, and poor impulse control results normally from a breakdown in communication within key networks involving these regions (e.g., Shannon, Sauder, Beauchaine, & Gatzke-Kopp, 2009). In this chapter, we attempt to review impulse control problems with an emphasis on dysfunction in top-down control over subthalamic processes and in which prefrontal brain mechanisms figure strongly (for a discussion of limbic and subcortical brain mechanisms involved in these deficits, see Zisner & Beauchaine, this volume). First, we provide a historical context in which studies on impulsivity and studies examining brain mechanisms involved in impulsivity developed. Second, we review studies linking impulsivity and its different facets to traditional externalizing disorders such as attention-deficit/hyperactivity disorder (ADHD), conduct disorder (CD), delinquency, antisocial personality disorder (ASPD), and substance use disorders (SUDs). Third, we review the current state of science that supports the role of the PFC and ACC in different deficits in impulse control. Next, we outline important developmental aspects in the associations among PFC and ACC mechanisms, impulsivity, and externalizing behaviors. Finally, we summarize some of the controversies and limitations in the field, discuss ways to address these, and propose a number of avenues to guide a research agenda.

Historical Context

Over the past 20  years, impulsivity has been increasingly characterized as multifaceted, and interest has emerged toward investigating brain mechanisms behind the different forms of errors or dysfunctions that bring about impulsive behavior, such as deficits in delay discounting, response inhibition, affective decision making, and EF processes of attention and working memory. However, a link between impulse control and prefrontal brain areas was recognized as early as the mid-1800s. The first insights into the involvement of the PFC in impulse regulation came from the documentation of the famous case of Phineas Gage (Harlow, 1848; Van Horn et al., 2012) and other brain lesion and neuropsychological case studies.

Evidence from Early Brain Lesion and Neuropsychological Case Studies

The case of Phineas Gage, a railroad worker who survived a brain injury to his vmPFC (specifically, OFC), is notable because it is the first documented case of impulsive behavior resulting from brain injury. Gage’s case showed that damage to the PFC can result in striking personality changes, with formerly pleasant, socially adapted people becoming impulsive, profane, egoistic, and insensitive to social norms (Van Horn et al., 2012). Although consensus has yet to be reached regarding the precise anatomical boundaries of subregions of the PFC, researchers agree on the vmPFC, which includes the OFC, the dlPFC, and the ACC. Patients with damage to these subregions suffer distinct neuropsychological deficits, many related to impulse control problems. Early studies showed that damage to any of these subregions of the PFC resulted Castell anos-Ryan, Séguin

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in deficits in impulse control or self-regulation. However, some subregions (vmPFC, ACC) are highly interconnected with subcortical limbic areas, including the ventral striatum. Studies showed that damage to these regions could result in additional deficits from those observed after dlPFC lesions. For example, findings from case studies of patients with damage to the vmPFC, such as Phineas Gage, converge in showing that these patients have difficulty in self-restraint and impulse control, particularly in following or obeying social norms and also in failing to regulate their primary physiological drives, sometimes demonstrating aggressive sexual advances and excessive overeating (Wagner & Heatherton, 2010). In contrast, case studies of patients with damage to the dlPFC reveal few problems with social interactions or understanding emotional cues. Instead, they display difficulties in maintaining multiple goals in memory, planning behavior, and inhibiting goal-irrelevant distractions. Through the years, research has revealed that the dlPFC is involved principally in core abilities linked to EF such as working memory, attention, flexibility, and inhibition, all necessary to accomplish goal-directed behaviors (see Miller  & Cohen, 2001; Wagner  & Heatherton, 2010). Finally, the few documented case studies of patients with focal damage to the ACC reveal a loss of motivational drive similar to that associated with damage of the dlPFC but much more severe, as well as difficulties in maintaining goal-directed behavior (e.g., Cohen, Kaplan, Moser, Jenkins,  & Wilkinson, 1999).

Advances in Brain Neuroimaging Technologies

With advances in neuroimaging techniques, researchers gained further insight into brain structure, networks, and functions. Imaging studies showed that the ACC had dense projections to the motor cortex and spinal cord, other areas of the PFC (particularly the dlPFC), and the thalamus and brainstem nuclei, which implicates the ACC in functions related to motor control, cognitive control, affective control, and arousal (Carlson et  al., 2013). Thus, the ACC is a key brain area for impulse control in that it seems to be essential for the willed control of action (Banfield, Wyland, Macrae, Munte,  & Heatherton, 2004) not only in terms of initiating actions, but also in overriding competing, well-established tendencies or overcoming temptations. Neuroimaging studies also confirm the ACC’s involvement in 204

a variety of complex functions including decision making and monitoring (Bush et  al., 2002), performance monitoring (MacDonald, Cohen, Stenger,  & Carter, 2000), detecting and monitoring response conflicts (Gehring  & Fencsik, 2001) and errors (Kiehl, Liddle, & Hopfinger, 2000), and reward-punishment processing (Gatzke-Kopp et al., 2009; Knutson, Westdorp, Kaiser,  & Hommer, 2000). In sum, one of the most consistent findings from functional imaging studies is that the ACC is involved in error monitoring and detection and in detection of conflict among competing responses (for review, see Carter & van Veen, 2007). Neuroimaging studies of the dlPFC and vmPFC also confirm their important role in impulse control/ self-regulation. The dlPFC is directly and densely connected to the ACC, which work together to regulate behavior. Yet, whereas the dlPFC is important for cognitive control by providing top-down input for task-appropriate behaviors (Garavan, Ross,  & Stein, 1999; MacDonald et al., 2000), the ACC is involved in monitoring when cognitive control is necessary and should be implemented (MacDonald et  al., 2000). Neuroimaging studies implicate the vmPFC, particularly the OFC, in processing emotions, reward and punishment (Elliott, Dolan,  & Frith, 2000), self-awareness (Stuss & Levine, 2002), “real-life” decision making (Damasio, 1994), and impulsivity (Booij et al., 2010).

Changes in the Conceptualization and  Measurement of Impulsivity

For most of the past century, there was no consensus among researchers regarding the conceptualization of impulsivity or whether the construct was uni- or multidimensional. Many models and measures were therefore developed that often assessed the construct in different ways. Over the past 20-plus years, consensus has emerged in conceptualizing impulsivity as multidimensional (Evenden, 1999; Sharma, Markon,  & Clark, 2014). At the broadest level, impulsivity can be divided into impulsive action (lack of motor/response inhibition in cool or neutral conditions) and impulsive choice (problem solving in “hot” conditions or when emotional or rewarding stimuli are present). Because of the complex nature of impulsive choice, more recent studies have attempted to dissociate processes related to reward processing from those related to temporal perception or decision making (Castellanos-Ryan et al., 2014). Currently, there are a number of self-report questionnaires and behavioral/laboratory tasks to

Prefrontal and Anterior Cingul ate Cortex Mechanisms of Impulsivit y

assess different facets of impulsivity. Self-report questionnaires assessing impulsive tendencies include the Barratt Impulsiveness scale (Patton, Stanford, & Barratt, 1995); the UPPS Impulsive Behavior Scale (Whiteside  & Lynam, 2001); the Impulsivity, Venturesomeness, and Empathy scale (IVE; Eysenck  & Eysenck, 1980); the Substance Use Profile Scale (SURPS; Castellanos-Ryan, O’Leary-Barrett, Sully,  & Conrod, 2013); the Sensation Seeking Scale (Zuckerman, 1994); the Tridimensional Personality Questionnaire (Cloninger, 1987); and a number of five-factor personality scales (either as low constraint or conscientiousness; McCrae  & Costa Jr, 2004). Scales that assess the DSM hyperactive/impulsive subtype of ADHD are also sometimes used (see Neuhaus  & Beauchaine, 2013; Zisner  & Beauchaine, this volume). It is important to note that although some of the questionnaires mentioned assess sensation seeking, a trait that is sometimes grouped with impulsivity or referred to as a form of impulsivity, there is now substantial research suggesting that these are dissociable traits, with distinct biological and cognitive mechanism and related to externalizing problems in different ways (see Castellanos-Ryan, Rubia, & Conrod, 2011; Castellanos-Ryan et al., 2014). For instance, it is not unusual for sensation-seeking behavior to be planned and premeditated, and, when unplanned or impulsive, it is associated with deficits in response inhibition that stem from a reward processing bias (Castellanos-Ryan et al., 2011) rather than from the cognitive problems involved in impulsivity discussed in this chapter. Different aspects of impulsive action and impulsive choice can also be assessed with a number of behavioral paradigms that can measure impulsivity in both animals and humans. The most common paradigms are described briefly here. Temporal or delay discounting refers to the tendency to value immediate outcomes highly and to discount the value of delayed outcomes or rewards (Green & Myerson, 2004). These tasks stem from a long tradition of research on delay of gratification (Mischel, Shoda,  & Rodriguez, 1989). In most delay discounting tasks, participants are instructed to choose between various amounts of reward (e.g., money) “now” or a larger reward later (e.g., delayed by either a week, month, 2 years, etc.). Although inhibition is necessary for all phases of EF and also refers to inhibitory control of attention (to external stimuli and thoughts), emotions, and

actions/responses, the term inhibition in the experimental literature covered in this chapter is largely operationalized as the control of motor response tendencies. Response inhibition is generally assessed with go/no-go and stop signal reaction time paradigms, which involve a binary decision on each stimulus under time pressure. When presented with a “go” signal, one is required to press a particular button or respond actively in some way; when subsequently presented with a “no-go” signal, one is then required to withhold the prepotent response. In the stop-signal reaction time task, the “no-go” or “stop” cue/signal appears quickly after the presentation of the go signal. For go/no-go paradigms, the main outcome measures used to assess impulsive action or response inhibition are commission errors (also designated, depending on the task, as incorrect hits, false positives, or premature errors), which are also commonly used for the stop-signal tasks. However, the main or most commonly used outcome measure for stop-signal tasks is the stop-signal reaction time (SSRT), which is an estimation of the time a participant takes to inhibit a “go” response (Verbruggen & Logan, 2009). Affective decision-making is generally referred to as an inability to reflect on the consequences of a choice or to deficits in decision making in a motivationally significant context. It is commonly assessed with neuropsychological tasks such as the Iowa Gambling Task and the Cambridge Gamble and Risk Tasks (Clark, Cools,  & Robbins, 2004). During such tasks, participants are instructed to choose cards from one of four decks in any order until the task comes to an end (for an average of 100 trials). They are typically told that some decks are better than others. Each choice involves some cost or benefit, either in the form of monetary loss or gain. Two of the four decks provide small rewards but smaller losses, whereas the other two decks provide large rewards but equally large losses.

Links to Traditional Externalizing Disorders

Externalizing disorders that are traditionally linked with deficits in impulse control or poor self-regulation include ADHD (Rubia et al., 2001; Urcelay & Dalley, 2012), oppositional defiant disorder, CD, and ASPD (Castellanos-Ryan et  al., 2011; Herba, Tranah, Rubia, & Yule, 2006; Moffitt, Caspi, Harrington, & Milne, 2002), and substance misuse and other forms of addiction (Ersche et al., 2013; Koob & Volkow, 2010). Castell anos-Ryan, Séguin

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Performance/Behavioral Findings

Deficits in impulsive action (or inhibitory control), assessed with neuropsychological tasks, are associated with various externalizing behaviors, including ADHD (Lijffijt, Kenemans, Verbaten, & van Engeland, 2005), CD (Hobson, Scott,  & Rubia, 2011), and substance abuse (Kaufman, Ross, Stein,  & Garavan, 2003). A  number of studies have shown that poor performance on EF tasks tapping working memory and selective attention are associated with externalizing problems (Peeters, Monshouwer, Janssen, Wiers, & Vollebergh, 2014; Young et al., 2009). Go/no-go and stop signal reaction studies show that aggressive adolescent males and children with ADHD and CD have reduced response inhibition (more commission errors; Lijffijt et al., 2005; Oosterlaan  & Sergeant, 1998). The association between self-reported impulsivity and CD is partially mediated by poor response inhibition during a stop task (Castellanos-Ryan et  al., 2011). Children with ADHD are slow to inhibit their responses on an SSRT (Castellanos, Sonuga-Barke, Milham,  & Tannock, 2006). However, children with ADHD display slow reaction times in general (Kenemans et al., 2005) and high variability in response and reaction time (e.g., Rubia et al., 2001), which may be more indicative of a general processing-speed deficit in responding to stimuli or attentional impairment than of a specific response inhibition deficit. Finally, although substance use is also associated with poor response inhibition, this association might not be specific because it did not survive control for other externalizing problems (Castellanos-Ryan et al., 2011; Castellanos-Ryan et al., 2014). Substance use problems seem to be more consistently associated with neurocognitive measures tapping impulsive choice, such as delaydiscounting tasks (Bickel, Jarmolowicz, Mueller, Koffarnus, & Gatchalian, 2012; Castellanos-Ryan et al., 2014). Studies also show that adults with SUDs (Bechara, 2005) and adolescents with behavioral problems (Ernst et  al., 2003) perform poorly on affective decision-making tasks. Conduct problems, ASPD, and ADHD are all associated with steep delay discounting (e.g., Acheson, Vincent, Sorocco,  & Lovallo, 2011; Bobova, Finn, Rickert,  & Lucas, 2009). Other studies suggest that individuals with ADHD may be more sensitive to delay aversion than to discounting of rewards per se (Sonuga-Barke, Dalen, & Remington, 2003). 206

Imaging Findings

Functional neuroimaging studies of performance during response inhibition tasks among ADHD participants show reduced responding in frontal (e.g., inferior frontal gyrus [IFG] and medial structures such as the ACC and presupplementary motor area; pre-SMA) and in striatal regions of the brain when performing Go/No-Go and Stop Signal Tasks (Banaschewski et  al., 2005; Pliszka et  al., 2006; Rubia, Smith, Brammer, Toone, & Taylor, 2005b; Suskauer et  al., 2008). However, some researchers have suggested that medial activations may be related more to error detection (a deficit related to the fourth and last phase of the problem-solving model) rather than to response inhibition per se (Chambers et al., 2009; Whelan et al., 2012). Despite some studies showing that CD and ADHD symptoms across adolescence are related additively to working memory function as measured by neuropsychological tests in young adults (Séguin, Nagin, Assaad,  & Tremblay, 2004), few imaging studies have examined specificity of the neurocognitive underpinnings of CD or antisocial tendencies exclusively. Structural and functional imaging studies that have examined CD suggest that it is associated with abnormalities in the OFC, ACC, and superior temporal cortices, as well as in underlying limbic brain regions (see review by Rubia, 2011). Compared with patients with ADHD, patients with CD showed more dysfunction in the OFC, ACC, insula, hippocampus, and superior temporal lobes when performing tasks related to response inhibition, sustained attention, and reward processing (Rubia et al., 2009, 2010). As suggested by performance studies reviewed in the previous section, there is emerging evidence from neuroimaging studies of performance tasks that individuals who are prone to substance use problems can be distinguished from other clinically disinhibited/ impulsive groups based on motivational sensitivity or impulsive choice, rather than on general deficits in response inhibition (impulsive action) or error processing (deficits during phase four of problem solving). For example, high-functioning drug users (presumably without severe personality dysfunction; Yechiam, Stout, Busemeyer, Rock,  & Finn, 2005) and adolescents with pure substance using profiles (Castellanos-Ryan et al., 2011) show impulsivity specifically in reward conditions as opposed to a general tendency toward errors of commission, suggesting that subthalamic mechanisms are more heavily implicated than prefrontal ones. However, this pattern does not rule out the involvement of

Prefrontal and Anterior Cingul ate Cortex Mechanisms of Impulsivit y

prefrontal mechanisms related to reward processing, such as those related to the OFC. Indeed, substance use in early adolescence that is uncomplicated by ADHD or CD is associated with a high left OFC response and low left IFG responding when anticipating reward (Castellanos-Ryan et al., 2014).

PFC functioning—dopamine (DA), serotonin (5-HT), and norepinephrine (NE) (Aron, Dowson, Sahakian,  & Robbins, 2003a; Chamberlain et  al., 2009)—will also be mentioned, and readers may find more about this topic in several excellent reviews (e.g., Dalley & Roiser, 2012; Fitzgerald, 2011).

Current State of Science

Affective Decision Making

Most of the studies just mentioned provide little information about comorbidity, which is highly prevalent among externalizing disorders. The small number of studies that have attempted to investigate psychobiological and brain correlates of comorbidity among externalizing behavior problems show that some facets of impulsivity may be common to all externalizing behaviors, whereas other facets may be associated specifically with only some externalizing behaviors. For example, self-reported impulsivity and response inhibition prospectively predicted variance common to externalizing behaviors, but reward response bias was specifically associated to substance use problems (Castellanos-Ryan et  al., 2011). More recently, Castellanos-Ryan et al. (2014 replicated and extended these findings by showing substantial common variance across ADHD, CD, and substance use, which was associated primarily with measures of impulsive choice. Specifically, common variance across externalizing disorders was associated with delay discounting and low blood oxygen level dependent (BOLD) response in substantia nigra and the subthalamic nucleus, but with high BOLD response in pre-SMA and precentral gyrus during successful inhibition. Although these brain regions are implicated in inhibitory control (Obeso, Robles, Marron,  & Redolar-Ripoll, 2013), recent findings suggest that both networks play specific roles in the motivation for action and the capacity to slow down and evaluate conflicting choices (Frank, Samanta, Moustafa,  & Sherman, 2007). As mentioned earlier, the PFC and ACC are implicated in many cognitive deficits related to impulsivity, and they are interconnected with striatal and subcortical brain regions. Although it is clear that many deficits result from a disruption or imbalance between functioning in subcortical and prefrontal brain regions, we now turn to recent studies that focus on top-down mechanisms and those that elucidate the role of the PFC and ACC in affective decision making, delay discounting, response inhibition, and deficits in attention and working memory. Some studies involving the three neurotransmitters most frequently implicated in impulse control and

A number of studies using the Iowa gambling task and the Cambridge Gamble and Risk Tasks (Bechara, 2004; Clark et  al., 2004) show that the vmPFC plays a key role in reflection, planning and premeditation, and in affective decision-making (Bechara, 2004). The vmPFC encompasses the medial part of the OFC and more ventral sectors of the medial PFC. Sometimes, but not always, the ACC is included as part of the vmPFC. The vmPFC is a highly interconnected brain region that links subcortical brain regions such as the insula (critical for representing patterns of emotional and affective states) with the dorsolateral PFC and hippocampus (key for maintaining an active representation of memory over a period of time). Thus, it is thought to play a crucial role in “triggering affective states from recall and imagination” (Bechara, 2005, p.  1459), which aid in choosing the most beneficial option. Although lesion studies have implicated both the vmPFC and the insula in risky decision making, one study showed that patients with lesions in these brain areas engage in risky decision making for distinct reasons (Clark et al., 2008). Patients with damage to the vmPFC do so because they exhibit an increase in risk taking, whereas patients with damage to the insula do so because of their insensitivity to aversive outcomes and deficits in risk adjustment. Clark et  al.’s (2008) study, and others, show that patients with vmPFC damage display a preference for high-varying outcomes, whereas healthy controls normally display an aversion for outcome variance. It is possible, then, that the vmPFC is involved in preserving the naturally occurring bias, consistently observed in healthy participants, of preferring options that minimize outcome variance. The key role played by the vmPFC in risk taking and affective decision making has also been supported by a number of electroencephalogram (EEG) and imaging studies revealing engagement of the ACC and ventral PFC (e.g., Polezzi, Sartori, Rumiati, Vidotto, & Daum, 2010). Finally, in terms of neurotransmission, although findings of studies on the role of 5-HT (and tryptophan depletion) and DA in risky decision making in humans have been somewhat inconsistent, there Castell anos-Ryan, Séguin

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is evidence that although DA promotes behavioral activation to seek rewards, 5-HT is involved in regulating cognitive biases in decision making (and learning from bad decision outcomes), thus serving to inhibit actions when punishment is likely (Cools, Nakamura, & Daw, 2011; Crockett, Clark, Smillie, & Robbins, 2012; Murphy et al., 2009).

is clear that it is important in impulsive choice because there is evidence that, during the choice phase of delay discounting, there is enhanced DA release from this region (Winstanley et  al., 2009). Finally, acute tryptophan depletion, which results in reduced 5-HT synthesis, yields increased delay discounting in humans (Schweighofer et al., 2008).

Temporal/Delay Discounting

Motor/Response Inhibition

Exaggerated delay discounting is also associated with dysfunction in the PFC (e.g., Olson et al., 2009). Specific areas of the PFC that are implicated in delay discounting include the medial frontal cortex (Bickel, Pitcock, Yi, & Angtuaco, 2009), the dlPFC (McClure, Ericson, Laibson, Loewenstein, & Cohen, 2007), and the OFC (Mobini et al., 2002; Winstanley, Theobald, Cardinal, & Robbins, 2004). Animal single-neuron recording studies suggest that the PFC is involved in multiple computations when making “intertemporal choices” (e.g., making choices based on the magnitude and delay of reward at two or more different times), with one of its main roles being to code and organize signals related to values of individual options (in terms of timing and magnitude of reward; Kim, Hwang, Seo, & Lee, 2009). Such studies suggest that this encoding and organization happens with the help of DA neuronal activation in the PFC, with research revealing that DA neurons in this area change their activity as a function of the magnitude and delay of the expected rewards (Takahashi et al., 2011). Signals related to the delay and magnitude of reward are thought to converge with the encoding of temporally discounted values in the PFC, bestowing it with the ability to monitor and enhance the decision maker’s capacity to resist the temptation to accept immediate reward and pursue long-term benefits (Kim & Lee, 2011). Thus, when individuals choose large but delayed rewards, there is an increase in BOLD activity in the dlPFC (McClure et al., 2007). Activation in the OFC is associated both with steeper discounting (e.g., Rudebeck, Walton, Smyth, Bannerman, & Rushworth, 2006) and with a preference for larger, delayed rewards (Winstanley et  al., 2004). Dalley, Everitt, and Robbins (2011) suggest that the differences in findings could be due to lesions of the lateral versus the medial OFC having opposite effects on discounting (Mar, Walker, Theobald, Eagle, & Robbins, 2011) and to effects of lesions on the learning stages rather than after acquisition is established. Thus, although the exact role that the OFC plays remains unclear, it 208

Many lesion and neuroimaging studies show that the ability to suppress undesirable (or habitual) responses assessed with Go/No-Go or Stop Signal Tasks require the PFC (Aron, Fletcher, Bullmore, Sahakian,  & Robbins, 2003b; Aron et  al., 2014). Functional imaging studies have helped map the brain’s “stop circuit,” which involves the right IFG, the ACC, and the presupplementary and motor cortex (as well as other non-PFC areas such as the basal ganglia; Aron, Behrens, Smith, Frank,  & Poldrack, 2007; Chambers et  al., 2009; Dalley et  al., 2011). Converging evidence from lesion studies, transcranial magnetic stimulation (TMS) studies, and many neuroimaging studies (e.g., Aron et al., 2014; Chambers et al., 2006; Garavan et al., 1999) implicate the right frontal gyrus in response inhibition, with some studies suggesting that inhibitory control is mediated by a right-hemisphere network in which the right IFG plays a prominent role (Chambers et al., 2009). Other studies find that more medial rather than ventral areas of the PFC are implicated in response inhibition. For example, Rieger et  al. (2003) and Décary and Richer (1995) found that damage to dorsomedial PFC, including the ACC and SMA, led to poor inhibition during go/no-go and stop tasks. A  number of studies have also implicated the SMA and pre-SMA in inhibitory impairments, with some implicating the right SMA and pre-SMA (Floden  & Stuss, 2006) and others the left SMA and pre-SMA (Picton et  al., 2007). Regardless of which hemisphere, these findings are consistent with a large body of literature showing that the SMA is important in motor planning and that the pre-SMA is important in updating of motor plans (e.g., Mostofsky & Simmonds, 2008). As with other forms of inhibitory or impulse control, neurotransmitters implicated in response inhibition include DA, 5-HT, and NE (Lamar et al., 2009; Rubia al., 2005a). Rats improve their motor and response inhibition after being administered atomoxetine, a NE reuptake inhibitor (Chambers et  al., 2006; Robinson et  al., 2008). Importantly, NE modulates both DA and 5-HT (see Beauchaine et al., 2011). These and other animal studies (e.g.,

Prefrontal and Anterior Cingul ate Cortex Mechanisms of Impulsivit y

Eagle, Tufft, Goodchild, & Robbins, 2007) suggest that NE, and not DA or 5-HT, is the critical neurotransmitter for motor inhibition. Indeed, studies investigating the role of 5-HT in response inhibition have been somewhat inconsistent, with some studies showing that acute tryptophan depletion increases premature responding (Booij et al., 2006; Dougherty et al., 2007) but that it is unrelated to SSRT (Eagle et al., 2009).

Selective Attention/Inference Control and  Working Memory

Other mechanisms involved in decision making and impulse control are the abilities to (a) focus on important cues in the environment while resisting the intrusion of information that is irrelevant (selective attention) and (b) keep many bits of transitory information in mind so that they can be manipulated (working memory). Areas associated with selective attention are the lateral OFC and dorsolateral (IFG) region of the PFC (Aron et al., 2014). Working memory is frequently associated with activation in dlPFC (Lezak, Howieson, Bigler,  & Tranel, 2012; Petrides, 2000). Although involved in all phases of problem solving, it is associated strongly with response inhibition. Indeed, response inhibition declines with increasing working memory load (Hester, Murphy, & Garavan, 2004). Although activation in the right dlPFC is often observed during response inhibition (Figner et al., 2010; Hester et  al., 2004), the dorsal PFC activation may reflect associated cognitive processes that coincide with response inhibition, rather than response inhibition per se (Mostofsky  & Simmonds, 2008; Simmonds, Pekar, & Mostofsky, 2008). Such cognitive processes include working memory, response selection (Braver, Barch, Gray, Molfese,  & Snyder, 2001), and the active maintenance of stimulus-response rule representations (Brass, Derrfuss, Forstmann, & von Cramon, 2005; Derrfuss, Brass, Neumann, & von Cramon, 2005). Activation in the dlPFC during response inhibition may also be accounted for by attentional processes. For example, some studies show that right dlPFC activation, together with right parietal activation, is associated with sustained attention (e.g., Fassbender et  al., 2004). In another study, Fassbender et  al. (2006) found that there were common activation patterns for both inhibition and attending to cues:  activation in right dlPFC, right cuneus, and right cerebellar tonsil. This study, together with others showing that right-hemisphere frontal-parietal activation is associated with sustained attention

(Shulman et  al., 2010), working memory (Hester, D’Esposito, Cole,  & Garavan, 2007), and other EF components (Yantis et al., 2002), suggests that the dorsal prefrontal and parietal components of the “inhibition network” are associated with more general attentional functions, whereas the IFG performs a more specific response suppression or overturning function (Aron et al., 2014). This finding is further confirmed by studies showing that (1) activation in the dlPFC is higher for “conditional stopping” (i.e., when stopping is required only under certain circumstances, e.g., stop signal points to the right, but not others; stop signal points to the left) than for simple or “unconditional” stopping (i.e., stopping is required whenever the stop signal appears; Jahfari, Stinear, Claffey, Verbruggen,  & Aron, 2010; Swann et al., 2012), and (2) lesions to the dlPFC do not affect stopping when damage to the right IFG is accounted for (Aron et al., 2003b; Clark et al., 2007). Also, when looking within trials at the timing of activation, the dlPFC is active during task cue/instructions and go cues, whereas the right IFG (or vlPFC) is active close to the motor response of inhibition (Swann, Tandon, Pieters, & Aron, 2013). In terms of neurotransmission, although 5-HT is not associated with all forms of impulsive behavior, it is associated with other functions related to the PFC, such as attentional and cognitive flexibility (Clarke, Dalley, Crofts, Robbins, & Roberts, 2004; Lapiz-Bluhm, Soto-Pina, Hensler,  & Morilak, 2009) and working memory (Cano-Colino, Almeida, Gomez-Cabrero, Artigas,  & Compte, 2014; Enge, Fleischhauer, Lesch, Reif,  & Strobel, 2011). This has helped inform the hypothesis that 5-HT generally activates the PFC, whereas NE deactivates it (see Fitzgerald, 2011). Indeed, there are some studies supporting a “deactivating role” of NE on the PFC. In fact, the α2 agonist clonidine, which decreases synaptic PFC NE, decreases impulsivity (Nair  & Mahadevan, 2009; Zhang et  al., 2012). However, there are also studies showing that NE reuptake inhibitors (e.g., desipramine, reboxetine, atomoxetine), which boost synaptic NE, improve impulsivity, inattentiveness, and hyperactivity (Sutherland, Adler, Chen, Smith, & Feltner, 2012; Wilens et al., 2001); improve response inhibition (e.g., slower SSRT; Chamberlain et al., 2011; Robinson et al., 2008); lower delay discounting of reward (Robinson et al., 2008); and improve overall attention (Navarra et al., 2008). These latter studies oppose the hypothesis that NE deactivates the PFC, but it is important to note that these drugs Castell anos-Ryan, Séguin

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boost PFC DA, which may be “activating” the PFC (Fitzgerald, 2011). Concomitant release of a number of neurotransmitters, particularly DA, 5-HT, and NE, should be studied to better understand the neurochemistry of impulsivity.

Developmental Considerations Normative Development of the PFC and ACC and Related Functions

Rudimentary PFC functions begin to emerge toward the end of the first year of life (e.g., Diamond & Goldman-Rakic, 1989) and continue to develop across childhood, adolescence, and even into early adulthood (Blakemore  & Choudhury, 2006; Giedd et  al., 2010). There are at least four key structural changes in the PFC that take place across development:  (1)  myelination (i.e., a layer of myelin, often referred to as “white matter,” is formed around extensions or axons of neurons and acts as an insulator that increases the speed of transmission of electrical impulses from neuron to neuron; Blakemore & Choudhury, 2006; Giedd et al., 2010; Walhovd, Tamnes,  & Fjell, 2014); (2)  the corpus callosum, which connects the two hemispheres, shows peak growth rates between 3 and 6 years in anterior brain regions (Thompson et al., 2000); (3) gray matter volumes display an inverted U-shape pattern of development, gradually increasing (reflecting the creation of new synapses) across childhood and then decreasing (reflecting pruning of synapses shaped by experience) into adolescence and adulthood (Blakemore  & Choudhury, 2006), although a linear decrease from birth has also been reported in recent studies (Brown et al., 2012); and (4) different regions of the PFC develop at different rates, with gray matter volumes reaching adult levels earliest in the OFC, followed by the vlPFC and the dlPFC (Walhovd et al., 2014). These structural changes in the PFC are accompanied by changes in function and neural activation. The two main patterns of changes proposed are “focalization” (i.e., a shift from more diffuse to more focal activation of the PFC; e.g., Durston et al., 2006; Luna et al., 2001) and “frontalization” (i.e., increasing reliance on more anterior regions of the PFC; e.g., Zelazo, Carlson, & Kesek, 2008). Yet others have suggested that activation during EF and response inhibition becomes more evenly distributed across the brain, not necessarily within the PFC (Luna, Padmanabhan, & O’Hearn, 2010). In addition to the development of focalization and frontalization, studies of early developmental changes indicate that delay discounting (or 210

delay of gratification), affective decision making, response inhibition, working memory, and sustained attention increase significantly from ages 3 to 5 years and continue to improve across development (Crone  & van der Molen, 2007; Davidson, Amso, Cruess-Anderson,  & Diamond, 2006; Hongwanishkul, Happaney, Lee,  & Zelazo, 2005; van de Laar, van den Wildenberg, van Boxtel, & van der Molen, 2011). Studies of later developmental changes indicate that although response inhibition, working memory, EF, and the brain circuitry necessary for these functions are in place by adolescence, adolescents are less precise than adults (Luna, Garver, Urban, Lazar, & Sweeney, 2004) and show increased activation in prefrontal regions (medial frontal gyrus, IFG, and dlPFC), which suggests that greater effort is required to perform comparably on these tasks (Luna  & Sweeney, 2001). Thus, what seems to characterize the transition from adolescence to adulthood is not acquisition of new cognitive skills but a change in how the brain operates, from initially relying on more regionalized processing in the PFC to incorporating a broader network of regions that share processing in an efficient and more flexible manner (Luna et al., 2010).

Developmental Models of Externalizing and Impulse-Control Problems and the Brain

The role and function of PFC/ACC brain mechanisms may vary across developmental periods. It is first important to recognize that impulsivity and externalizing problems may be evident in early childhood and persist into adolescence and adulthood. Such individuals follow what was defined by Moffitt (1993) as “life-course persistent” and by Patterson and colleague’s (Patterson, Reid,  & Dishion, 1992) as “early starter” trajectories of externalizing behavior. In contrast, another trajectory is the “adolescent-onset” or “adolescent-limited” pathway, which identifies youth who experience a steep increase in impulse-control and externalizing problems (generally in the form of delinquent or risk-taking behaviors) with the onset of adolescence thought to be due to an increase in sensation seeking during this period (Castellanos-Ryan, Parent, Vitaro, Tremblay, & Séguin, 2013; Steinberg et al., 2008). This rise in sensation seeking observed during early adolescence may result from activation of the ventral striatum (Spear, 2009). The role and development of the ventral striatum and other midbrain regions is covered by Zisner  & Beauchaine (this volume) but is mentioned here because the discrepancy in developmental timing of maturation

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of the PFC and subcortical brain areas may evoke certain forms of impulsivity and externalizing problems (Beauchaine & McNulty, 2013). More specifically, one model posits that the heightened impulsive and externalizing behavior, including substance use and delinquency, observed in adolescence is the product of the interaction between the level of development of two distinct neurobiological systems:  a socioemotional system localized primarily in limbic and paralimbic areas of the brain and a cognitive-control system mainly associated with PFC, specifically the lateral prefrontal, parietal, and anterior cingulate cortices (Steinberg, 2008). According to this model, sometimes referred to as the “dual-systems” and/or the “maturational gap” model, impulsive behaviors and risky behavior during puberty and early adolescence increases due to the relatively rapid development of the socioemotional system, which brings with it increased levels of dopaminergic activity. This increase in dopaminergic activity is hypothesized to lead to increases in reward and sensation seeking (Steinberg, 2008). This contrasts to the relatively slow but steady maturation of the cognitive control system associated with self-regulation and impulse control, which reaches full development only by the end of adolescence or early adulthood (Luna et al., 2001), thus leading to a “maturational gap” or a period of increased vulnerability to risk taking during adolescence. Some indirect support for this model is offered by studies showing that sensation seeking has a curvilinear development through the life span, with scores increasing between ages 10 and 15 years and declining or remaining stable thereafter. In contrast, impulsivity (and related traits such as low conscientiousness) is either stable (De Fruyt, Van Leeuwen, Bagby, Rolland,  & Rouillon, 2006; Tackett, Krueger, Iacono,  & McGue, 2008) or decreases linearly with age (Galvan, Hare, Voss, Glover,  & Casey, 2007; Steinberg et  al., 2008). Human and animal studies also indicate that increases in reward seeking coincide with pubertal development (e.g., Martin et al., 2002; Spear, 2000). Additionally, evidence from behavioral studies of cognitive control demonstrates that performance improves gradually over the course of adolescence and does not peak until late adolescence (Albert  & Steinberg, 2011). Finally, both impulsivity (Verdejo-Garcia, Lawrence,  & Clark, 2008) and reward/sensation seeking (Galvan et al., 2007; Romer, 2010) are correlated with self-reported risk taking and externalizing problems.

Finally, another model, the accentuation hypothesis, suggests that the onset of pubertal development may not be the “trigger” initiating adolescents in substance use, delinquency, or other risky behaviors. Rather, puberty and stress associated with the transition to adolescence may exacerbate previous individual differences in psychosocial functioning (Caspi  & Moffitt, 1991; Rudolph  & Troop-Gordon, 2010).

Controversies and Limitations

Although there is general agreement among most researchers about specific brain areas in the PFC that are important in impulse control, there is still disagreement as to what specific or “specialized” inhibitory or executive function they fulfil. For example, although studies reviewed herein agree that the pre-SMA is a key area involved in inhibitory control (Floden  & Stuss, 2006), others conclude that the pre-SMA is responsible for resolving conflicts between responses or response selection, whereas the IFG is responsible for response inhibition (Aron et al., 2007; Aron et al., 2014). The specific role of the OFC in impulsive choice (e.g., delay discounting) is also unclear (Dalley et al., 2011). Some disagreements and inconsistency in findings may stem from the fact that, so far, much of the evidence reviewed in this chapter comes from lesion and functional magnetic resonance imaging (fMRI) studies, which have several limitations: (1) although lesion studies and imaging studies of normal individuals can shed light on brain–behavior relations, it is clear that functions that appear to be localized are influenced by processing in other cortical areas; (2) the majority of structural and functional imaging studies involve “region of interest” analyses, which restrict the search and analyses to a priori hypothesized regions only; (3)  the subtraction method used in fMRI is imprecise and typically co-measures several cognitive functions other than the target functions; (4)  fMRI results are highly task dependent, making comparability very difficult across studies that use similar but slightly different tasks; (5) most studies are cross-sectional in nature. Although cross-sectional studies can be useful, they cannot inform about developmental trajectories of disorders. Finally, there is controversy/disagreement around the nosology of externalizing disorders and how much change is necessary with respect to the dimensional rather than categorical nature of psychopathology (Hyman, 2010; Jonas  & Markon, 2014; Krueger & Tackett, this volume). There have Castell anos-Ryan, Séguin

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also been disagreements regarding how to best study biological and cognitive correlates of externalizing disorders. Traditionally, most cognitive and imaging studies investigate brain–behavior associations in samples of individuals who are diagnosed with specific disorders, generally disregarding comorbidity or excluding comorbid cases. This is problematic considering that, in practice, comorbidity is the rule rather than an exception. Unfortunately, many studies still report analyses that “control” for another related disorder to evaluate unique effects, a practice that should be interpreted with care because it removes any effects of common factors or mechanisms shared across disorders (see Beauchaine, Hinshaw,  & Pang, 2010)—ultimately doing little to unify the scientific literature on externalizing disorders (see Beauchaine, Neuhaus, Brenner,  & Gatzke-Kopp, 2008). High rates of comorbidity, heritability, and risk factors shared among externalizing disorders have motivated many to question the current way in which we diagnose disorders and analyze brain–behavior associations. Indeed, there is a recent trend in psychiatry to move away from diagnostic categories and toward a more dimensional approach to externalizing problems that is informed by several levels of study (including behavioral, genetic, neural, and cognitive (Cuthbert  & Insel, 2013; Krueger et al., 2002).

Research Agenda and Future Directions

PRE-FRONTAL CORTEX

New advances in this field could be considered to address two main limitations raised earlier:  the lack of a unified and integrative approach to the

study of externalizing behaviors, impulsivity, and their brain correlates and the dearth of longitudinal studies with repeated measures of PFC function and behavior across developmental periods.

A Unified, Integrative Approach to Studying Impulsivity and Its Brain Correlates

A number of different approaches to the study of trait dimensions of psychopathology are currently represented in the literature. One approach involves exclusive focus on empirically derived clusters/profiles based on complex data reduction strategies and model fitting, as suggested by Krueger et al. (2002) and others. Several scientists also propose an externalizing spectrum, which accounts for systematic covariation among more narrowly defined behavioral problems or disorders and is highly heritable (Beauchaine & McNulty, 2013; Krueger et al., 2002). Another conceptualization is the neuroendophenotype approach (Figure 12.2), in which abnormalities in defined brain processes are considered intermediate phenotypes that account for common underlying risk for a variety of potential mental health outcomes that exist along a continuum in the general population (Robbins, Gillan, Smith, de Wit,  & Ersche, 2012). Combinations of new and traditional analytical strategies are also becoming more common. For example, data reduction strategies for cognitive or neural activation patterns can be validated against traditional phenotypes of behavioral undercontrol (e.g., ADHD diagnosis or substance misuse diagnosis) or used to refine new phenotypes (Fair, Bathula, Nikolas, & Nigg, 2012; Whelan et al., 2012). This

BRAIN

IMPULSIVITY (or processes linked to impulsivity)

EXTERNALIZING PROBLEMS

Dorsolateral PFC

BASIC PROCESSES RELATED TO EXECUTIVE FUNCTION

Substance use

(INVOLVED AT ALL STAGES OF PROBLEM-SOLVING)

Ventromedial PFC (inclusive of OFC)

DELAY DISCOUNTING

Ventrolateral PFC or inferior FC

Common variance shared among EPs

RESPONSE INHIBITION

Anterior Cingulate

Conduct, antisocial disorders

AFFECTIVE DECISION MAKING

Pre-SMA Phase 1 Problem representation

Phase 2 Planning/ processing

Phase 3 Execution of plan

ADHD

Phase 4 Monitoring or review

PHASES OF PROBLEM SOLVING

Figure 12.2  Different forms of impulsivity as endophenotypes between PFC and ACC function and externalizing problems. OFC, orbitofrontal cortex; pre-SMA, presupplementary motor area.

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latter approach has merit but may be limited when it does not incorporate sophisticated behavioral (or symptom-level) models and thus fails to capture the extent to which brain variables are also linked to other externalizing behaviors (in addition to ADHD or SUD) and whether they could explain shared variance or comorbidity across disorders. Thus, several of these approaches and analytical strategies could profitably be combined (e.g., the endophenotype approach along with data-reduction strategies for brain data and sophisticated structural equation modeling strategies to model the common and unique variances shared among externalizing symptoms; for an example, see Castellanos-Ryan et  al., 2014). Finally, it is clear that further whole-brain imaging analyses and meta-analyses comparing disorders or symptoms will be necessary to provide a more complete understanding of brain mechanisms involved in impulsivity. Further clarity may be obtained by studies that use new technologies and methods, such as transcranial magnetic stimulation (TMS), and those that include use of radioligands designed to quantify receptor binding for specific neurotransmitter receptors and transporters, or a combination of methodologies, such as combining fMRI, TMS, and other novel structural connectivity methods to examine the functional connectivity and specificity of cognitive and impulse control in the human PFC (Aron et al., 2014; Chambers et al., 2009; Fineberg et al., 2014).

Longitudinal Studies with Repeated Measures of PFC Functioning

One of the main gaps in knowledge and methodological challenges in the study of brain mechanisms involved in impulsivity is the tracking of joint developmental changes in brain and behavior. Longitudinal studies should help clarify differences in neurodevelopmental trajectories that are linked generally or uniquely to different impulse-control problems and disorders. Longitudinal studies would also shed light on relations between deficits in PFC mechanisms/circuitry and onset of impulse-control problems and/or externalizing disorders, thus helping to disentangle issues of temporality and confirming whether deficits in PFC circuitry predate behavioral problems, are a result of these problems, are in reciprocal relation, or are driven by other unmeasured variables. It is currently not understood why some problems and disorders develop earlier than others and how their differential onset relates to the developmental trajectories of the specific PFC circuitries affected.

Two other advantages of the longitudinal approach, compared to the cross-sectional approach, include (a) the derivation of individual slopes from appropriate longitudinal analyses, which are informative for detecting change because each individual provides his or her own control (i.e., one can control for between-individual differences, which is particularly useful when there is large variability between individuals at baseline) and (b) the opportunity to study bidirectional relations within and over time between behavioral and neuroimaging data and how these may predict future behavioral outcomes.

Conclusion

The study of brain mechanisms involved in impulsivity has come a long way since the days of Phineas Gage. Not only have we moved from conceptualizing impulsivity as a unitary construct to a multifaceted one, but advances in methodology and technology have also allowed an increasingly more detailed study of different cognitive deficits involved in impulsivity and their precise neural mechanisms. The sharpening focus of this approach at the conceptual and neurocognitive levels has allowed us to identify key anatomical areas and neural mechanisms involved in impulsivity, such as those related to response inhibition, delay discounting, and affective decision making. There is agreement in the field that a network approach to the study of these mechanisms, using methodology that examines brain connectivity or using a combination of methods, is the way forward in aiding a broader understanding of the brain mechanisms involved in impulsivity.

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Neural Mechanisms of Low Trait Anxiety and Risk for Externalizing Behavior

Philip J. Corr and Neil McNaughton

Abstract High trait anxiety and its neural substrate (the behavioral inhibition system [BIS]) are linked frequently to internalizing disorders. The authors propose that low BIS activity and reactivity (including reduced arousal and reduced attention) contribute to externalizing disorders. They argue that the BIS contributes to externalizing disorders largely through its prefrontal components, with more indirect effects on hippocampus and other subcortical components. BIS contributes to attention-deficit/ hyperactivity disorder (ADHD) and similar symptoms in phenylketonuria (PKU) largely via its prefrontal components; conduct disorder through prefrontal and temporal components; and psychopathy through prefrontal, temporal, and amygdalar components. Furthermore, neurological damage to brain regions involved in more than one externalizing disorder underlie comorbidity among them. The authors propose that frontal and subcortical BIS dysfunction acts synergistically with other dysfunctional neural systems to generate externalizing disorders, and the variation in patterns of neural dysfunction accounts for phenotypic differences across externalizing disorders and their subtypes. Key Words:  behavioral inhibition system, attention-deficit/hyperactivity disorder, conduct disorder, psychopathy, externalizing disorders, anxiety

Introduction

Trait anxiety is a term that is most often linked to internalizing disorders. The most recent description of its key neural substrate, the behavioral inhibition system (BIS), focuses on anxiety as a consequence of high sensitivity of the septo-hippocampal system and related structures (Corr & McNaughton, 2012; McNaughton & Corr, 2004, 2008). By contrast, externalizing disorders are more often attributed to dysfunction in reward systems related to the behavioral approach system (BAS). However, the normal distribution of trait anxiety as a personality characteristic in the population suggests that abnormally low BIS sensitivity should be just as disadvantageous as abnormally high BIS sensitivity. Externalizing disorders, then, may involve either low sensitivity of the BIS, high sensitivity of the BAS (e.g., impulsivity; see Beauchaine, 2001; Beauchaine, Gatzke-Kopp, & Mead, 2007), or 220

both. Importantly, the BIS controls not only behavioral inhibition but also arousal and attention, with key implications for externalizing disorders. Our extension of the voluminous BIS literature to externalizing disorders is speculative in parts. Nevertheless, we believe that the general framework outlined here can illuminate the complex roots of these disorders, advance development of detailed theories of externalizing spectrum disorders, and explain phenotypic variation—including why these disorders share comorbid features.

The BIS

The concept of the BIS (Gray, 1975) originated in the fact that inhibition of behavior by certain types of aversive stimuli (e.g., approach–avoidance conflict) is sensitive specifically to anxiolytic drugs. Gray (1977) reviewed an extensive literature on behavioral effects of anxiolytics (i.e., barbiturates,

alcohol, and benzodiazepines) on a wide range of behaviors assessed during reward, passive avoidance, classical conditioning of fear, escape, one-way active avoidance, two-way active avoidance, frustrative nonreward, discrimination learning, intermittent schedules in the Skinner box, reduction of reward, after-effects of reward, and after-effects of aversive stimuli. Across these diverse paradigms, anxiolytics did not impair simple approach or simple active avoidance behavior but did impair inhibition of prepotent behavior (passive avoidance) produced by approach–avoidance conflict (e.g., in the presence of conditioned signals of punishment). Gray concluded that a single system (i.e., the BIS) mediated anxiolytic-sensitive behavior and anxiolytic-sensitive autonomic reactions. Its key outputs were inhibition of prepotent behavior, increments in arousal, and increments in attention. Defining anxiety in terms of the behavioral effects of anxiolytics was Gray’s “philosopher’s stone” (Corr, 2008). Although this approach ran the risk of tautology, its predictive value has now been confirmed in two ways. First, development of an ethological distinction between fear and anxiety (first extracted as different clusters of response and then identified as reactions to an immediate predator or uncertain threat, respectively) allowed for testing and confirmation of the proposal that anxiety, but not fear, is sensitive to anxiolytic drugs (Blanchard & Blanchard, 1990a, 1990b; Blanchard, Griebel, Henrie, & Blanchard, 1997; for a review, see Gray & McNaughton, 2000). Second, the classical (γ-aminobutyric acid [GABAA]) anxiolytic drugs that were used to develop the theory had side effects, such as anticonvulsant, muscle relaxant, and addictive actions, which may have accidentally given rise to the neural effects fundamental to the theory. However, serotonergic anxiolytic drugs, developed after the theory was proposed, shared none of these side effects yet retained the theoretically key neurophysiological effects of more classical anxiolytic drugs on hippocampal θ rhythm (McNaughton, Kocsis, & Hajós, 2007). They also shared key components of their behavioral profile in animal tests (for a review, see Gray & McNaughton, 2000). The predictive value of hippocampal θ was extended recently to pregabalin (Siok, Taylor, & Hajós, 2009)—a distinct third class of anxiolytics. Furthermore, a human scalp electroencephalogram (EEG) analogue of this anxiolytic-sensitive θ was demonstrated recently. This is a conflict-specific rhythmic activation that is sensitive to both classical and novel anxiolytic

drugs (McNaughton, Swart, Neo, Bates, & Glue, 2013) and thus can be used as a specific measure of BIS sensitivity in humans. The BIS controls processes that ultimately generate anxiety. The BIS inhibits conflicting prepotent behaviors, engages risk assessment, and elicits scanning of memory and of the environment. All of these function to resolve concurrent goal conflict. The paradigmatic example of its action is generation of anxiety by concurrent and equivalent activation of fear (or frustration) and approach systems; that is, approach–avoidance conflict. Goal conflict is resolved by increasing, through recursion in hippocampal-cortical loops, the negative valence of stimuli (held in cortical stores) until resolution occurs through either (a) avoidance, or (b) approach, if safety signals are detected in the concurrent scanning of the environment and memory stores (Gray & McNaughton, 2000). The main EEG signature of this recursive process is the θ rhythm, which is present when humans are engaged in emotionally salient personal rumination (e.g., Andersen et al., 2009; Moore, Gale, Morris, & Forrester, 2006; Moore, Mills, Marsham, & Corr, 2012).

Approach, Avoidance, Conflict, and  Personality

Behavioral inhibition system theory describes “state” interactions among three motivation/emotion systems that control approach, avoidance, and approach–avoidance conflict, respectively (see Figure 13.1). The simple assumption that each of these systems can have a long-term “trait” sensitivity (reactivity) to its inputs (Gray, 1970) resulted in the reinforcement sensitivity theory (RST) of personality (Gray & McNaughton, 2000; McNaughton & Corr, 2004, 2008; for a summary see Corr, 2008). In more formal terms: 1. The fight-flight-freeze system (FFFS) mediates reactions that function to remove the animal from aversive stimuli of all types, conditioned and unconditioned (including frustrative reactions to omission of expected appetitive stimuli). The FFFS mediates fear, not anxiety. Critically, anxiolytic drugs (i.e., those lacking antipanic and antidepressant actions) do not affect the FFFS, but panicolytic drugs do. The associated personality trait is fear proneness, involving a greater tendency to avoidance/ escape. The FFFS is composed of a hierarchy of neural modules. The neural level of a module is determined by the immediacy of threat—more Corr, McNaughton

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–

NOVELTY

BIS +

Rew

–

Anxiety

+

_

BAS

CS-Rew IS-Rew

Conflict detector

+ ATTENTION 1. Environmental scanning 2. External scanning (risk assessment) 3. Internal scanning (memory) + AROUSAL

–

+ +

+

APPROACH

Figure  13.1  Overall relation of the behavioral inhibition system (BIS), fight-flight-freeze system (FFFS), and behavioral approach system (BAS). As indicated, the simplest way to activate the BIS is concurrently to activate the FFFS and the BAS (i.e., face the animal with an approach–avoidance conflict). In this case, both simple approach and simple avoidance will be inhibited and replaced with environmental scanning (in the form of altered attention), external scanning (risk assessment behavior), and internal scanning of memory. Note that all of these scanning operations are aimed at detecting affectively negative information and involve an increase in the salience of such information. As a result, a secondary consequence of activation of the system is normally a shift of the balance between approach and avoidance tendencies in the direction of avoidance. The inputs to the system are classified in terms of the delivery (+) or omission (−) of primary rewards (Rew) or punishers (Pun) or conditional stimuli (CS) or innate stimuli (IS) that predict such primary events. (Adapted from Gray & McNaughton, 2000.)

formally, its defensive distance (Blanchard & Blanchard, 1990a; Blanchard et al., 1997). 2. The BAS mediates reactions that function to bring the animal closer to appetitive stimuli of all types, conditioned and unconditioned (including relief reactions to safety signals). The associated personality traits are optimism, reward orientation, and (especially in very high BAS-active individuals) impulsivity. The BAS also appears to be organized hierarchically (Gray & McNaughton, 2000), but this organization—and its possible behavioral match “appetitive distance”—has not been subjected to the same detailed analysis as the FFFS. 3. The BIS is responsible for detection and resolution of goal conflict in very general terms (approach–approach, avoidance–avoidance, and approach–avoidance; see earlier discussion). Behavioral inhibition system outputs have evolved to resolve conflict by either permitting an animal to enter a dangerous situation (e.g., via cautious “risk assessment” behavior and scanning of memory) or to withhold entrance (e.g., via an increased level of avoidance). The function of BIS-generated behavior is to allow approach in 222

potentially dangerous environments. This process involves blocking both approach and avoidance behaviors when neither are adaptive (this can result in “defensive quiescence,” which is similar to but involves a body posture distinct from freezing) and replacing them with risk assessment behaviors. The BIS is comprised of a hierarchy of neural modules parallel to those of the FFFS (McNaughton & Corr, 2004) and is affected by anxiolytic drugs, including those that lack antipanic and antidepressant actions (Blanchard & Blanchard, 1990b; Blanchard et al., 1997). A schematic of the FFFS, BAS, and BIS and their functional interactions appears in Figure 13.1.

Neurobiology of the BIS

The FFFS, BAS, and BIS each contain complex elements that are hierarchically structured but in a manner that yields integrative simplicity. There is more immediate “quick and dirty” control by lower neural levels and more complex or distant “slow and sophisticated” control by higher levels (LeDoux, 1994). Parallel activation of multiple parts of each system is followed by interactions between modules

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

that “select” a particular level for control of current behavior. Urgent threat activates the periaqueductal gray and results in panic or attack, unless a module controlling escape is activated. If this happens, periaqueductal gray outputs (but not its activation) are inhibited. Similarly, activation of a module controlling avoidance inhibits escape behavior, replacing it with avoidance. Control of defensive behavior by the FFFS and BIS results in the two-dimensional system depicted in Figure 13.2 (McNaughton  & Corr, 2008). The range from top to bottom of the FFFS and BIS maps onto a dimension of “defensive distance” (Blanchard  & Blanchard, 1990a, 1990b). Loosely speaking, this represents urgency of perceived threat. Separation of the FFFS and BIS reflects a dimension of “defensive direction” either to avoid the source of danger (FFFS:  fear) or to approach it cautiously (BIS: anxiety). Defensive Avoidance

A number of components of the BIS are involved in psychological processes implicated in psychiatric disorders. First, detection of simple goal-related conflict is likely to have its main locus in the hippocampus but can involve all levels of the BIS, ranging from the periaqueductal gray, medial hypothalamus, amygdala, septo-hippocampal system, and posterior cingulate to the prefrontal dorsal stream (see Figure 13.2). Second, general attentional processing and arousal are modulated by aminergic neurotransmitter systems, principally acetylcholine and norepinephrine, with avoidance and behavioral inhibition modulated serotonergically. Third, more specific attentional/arousal processes, particularly fear-potentiated startle, are affected by amygdalar function. Critically for psychiatric disorder, output from the BIS increases negative cognitive biases and aversion, sending feedback to whatever aversive goal-linked areas provided the conflict input.

Defensive Distance

Defensive Approach

Prefrontalventral stream

OCD-deep complex fear

Prefrontaldorsal stream

Complex anxietye.g. social

Anterior cingulate

OCD-shallow simple obsession

Posterior cingulate

Obsessional anxietycognition/rumination

Amygdala

Phobiaavoid

+

+ Septo-hippocampal system

+ Amygdala

+

Phobiaarousal

GADarousal/startle

Amygdala

+ Medial hypothalamus

GADcognition/aversion

+

Phobiaescape

Medial hypothalamus

GAD?risk assessment

Periaqueductal gray

GAD?defensive quiescence

+ Periaqueductal gray

Panicexplode/freeze

–

5-HT (NA)

Figure 13.2  The defense system viewed in two dimensions and its relation to anxiety-related disorders. Two parallel streams control behavior when danger is to be avoided or approached, respectively. Each has a hierarchy, with higher levels engaged at greater defensive distance. The separation of the amygdala into separate components and the placement of one component under the septo-hippocampal system is a modification of the equivalent diagram in Gray and McNaughton (2000). All parts of the system receive both fast, poorly digested (dirty) and slow well-digested (sophisticated) sensory information. The lowest level of the system is held to deal with the most basic response to threat: panic. This can be viewed as a normal response, a pathological symptom of other disorders, or the result of primary panic disorder depending on the cause of activity. Activity in response to threat or from other causes should always be seen as distributed across several parts of the system simultaneously both because of reciprocal connections between the structures (double-headed arrows, solid lines) and indirect links resulting from conditioning. The entire defense system receives diffuse serotonergic (5HT) and noradrenergic (NA) inputs (dashed lines). Symptoms should not be equated with disorders, but we have assigned the control of particular symptoms/disorders predominantly to specific areas. GAD, generalized anxiety disorder; OCD, obsessive compulsive disorder.

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We should emphasize that two behavioral processes often linked to anxiety are not controlled by the BIS, as demonstrated by their insensitivity to anxiolytics. The first is obsession/compulsion. Although obsessions and compulsions can generate anxiety, they do so only if the compulsions conflict with other goals. Compulsions themselves (such as handwashing to escape infection) are generally simple avoidance behaviors (Rapoport, 1989) and are controlled by the FFFS (see Figure 13.2). These can, therefore, be normal behaviors, but, when excessive and when they interfere with functional behaviors, they represent obsessive-compulsive disorder (OCD). The second is action-stopping. This can be generated by output of the BIS to the motor system, which leaves activation of conflicting goal representations intact but prevents either of them from capturing the motor system. However, action-stopping can also be generated by stimuli that do not involve the BIS, and therefore it is not affected by anxiolytic drugs (McNaughtonet al., 2013). Action-stopping is often controlled by the inferior frontal gyrus or, under very tight time constraints, the presupplementary motor cortex (Aron, Fletcher, Bullmore, Sahakian, & Robbins, 2003; Floden & Stuss, 2006). There is evidence that the inferior frontal gyrus may be a target of BIS output to generate conflict-related stopping (Neo, Thurlow, & McNaughton, 2011; Shackman et al., 2009).

Personality and Clinical Comorbidity

Extremely high BIS sensitivity should generate levels of trait anxiety that rise to a level of clinical disorder. More specific personality predispositions (e.g., obsessionality, panic proneness) depend on functions of particular component parts of the FFFS or BIS, whereas more general changes in defensive reactions (e.g., neuroticism, emotionality) may arise from the fact that the FFFS and BIS are both modulated by diffuse monoaminergic neural inputs and stress hormones. Long-term variation in this systemwide modulation alters reactivity of all parts of each system. Critically, anxiolytic drugs (treated here as a proxy for a hormone that controls personality) alter defensive distance (i.e., magnitude of perceived threat and thus the level of the BIS that is in control of behavior) generally rather than altering any single module and, thus, any single behavior. That is, they manipulate a factor of anxiety proneness. Benzodiazepines, which have strong anxiolytic properties, operate by modulating sensitivity 224

of GABAA receptors without activating them. The benzodiazepine site is likely to be the target of circulating “anxiety-specific” hormones. Importantly, some benzodiazepines can increase sensitivity to GABA, whereas others can decrease sensitivity. There is evidence for endogenous compounds that bind to benzodiazepine receptors and that may have such a hormonal action (see Gray  & McNaughton, 2000). We might expect, then, that longer term increases in the reactivity of such a system could lead to a personality factor that would influence specific morbidity for generalized anxiety disorder and (unlike changes in the serotonin system) would not affect morbidity for OCD, panic disorder, or depression (although it would affect the extent to which anxiety resulted from and so was comorbid with those conditions). Conversely, longer term decreases in reactivity could provide vulnerability to a range of disorders of insufficient anxiety; namely, externalizing disorders. From this view, both extremely high and extremely low levels of anxiety would be dysfunctional—a proposition consistent with the maintenance of a normal distribution of this trait in the general population. Serotonergic anxiolytics operate by binding to the 5-HT1A receptor. The normal ligand for this receptor is serotonin, which is also released concurrently onto other 5-HT receptors. An endogenous, 5-HT1A-specific hormone is unlikely. Changes in the 5-HT system (similar to effects of serotonin-selective reuptake inhibitors such as fluoxetine [Prozac]) would therefore be expected to produce concurrent variation in both the FFFS (trait fear) and the BIS (trait anxiety), thus generating a factor with broad-ranging effects, such as neuroticism. These proposed effects of endogenous benzodiazepine and endogenous serotonin variation are broadly consistent with clinical and genetic data. For example, structural modeling of patients’ (n  =  8,098) symptoms (Krueger, 1999) extracts a higher order internalizing (e.g., depression and generalized anxiety disorder) factor that breaks down into lower order factors of “fear” and “anxious-misery,” which share about 50% of their variance (as do questionnaire measures of trait anxiety and neuroticism). Similarly, Kendler, Prescott, Myers, and Neale (2003) examined the genetic structure of 10 major psychiatric disorders in a sample of 5,600 twins. Genetic risk for internalizing disorders broke down into an “anxious-misery” factor (i.e., depression and generalized anxiety disorder) and a specific “fear” factor (i.e., animal and

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

situational phobia). Their definition of fear may have been too narrow to capture the full range of FFFS functions, but their data suggest that some anxiety-related disorders are separate from at least some fear-related ones.

The BIS and Externalizing Disorders

Investigators have long argued that establishing links between psychological disorders and temperament/personality factors may be useful in advancing our understanding of diatheses, etiology, progression, prognosis, and treatment (e.g., Costa  & Widiger, 1994; Harkness  & Lilienfeld, 1997; Krueger & Tackett, 2003, 2006; Nigg et al., 2002; Tackett, 2006; Watson, Clark, & Harkness, 1994; Widiger & Trull, 1992; Widiger, Verheul, & van den Brink, 1999). The goal is to isolate and characterize neural processes that underlie these long-term stabilities in behavior (i.e., “personality”) with regard to specific disorders. From the perspective of RST, motivational dysfunctions involved in externalizing disorders should result from distortions of operation of the FFFS, BAS, and/or BIS. Dysfunctional behavior can result from dysfunction of one of these systems in isolation but will also often result from dysfunction of systems acting in combinations (see, e.g., Beauchaine, 2001; Beauchaine, Katkin, Strassberg,  & Snarr, 2001). In conditions involving a pure excess of approach behavior, the BAS is likely to be functionally dominant (see Zisner  & Beauchaine, this volume). However, the BIS is often the core system because it is involved in the regulation of goal conflict detection and resolution. BIS dysfunction causes failure of inhibition of inappropriate behavior, which can be as important as excessive approach in generating externalizing symptoms. Also, as discussed later, the BIS plays important roles in attention, arousal, and cognitive processing (particularly negative cognitive biases). These often play central roles in the clinical presentation of externalizing disorders, and a dysfunctional BIS contributes motivation-driven dysfunction in these processes, thus supplementing dysfunction of primary systems such as attention. All the externalizing disorders appear to involve BIS subprocesses (primarily in the frontal cortex) that normally act together with subcortical subprocesses to comprise “the BIS” as a whole. Although dysfunction or personality variation can be specific to the BIS, its outputs cannot be seen as occurring in isolation from the FFFS and BAS. The BIS is activated when there is goal conflict (i.e., when the BAS

and FFFS are equally activated and require opposite behavior). Variation in or dysfunction of either the BAS or FFFS will therefore shift when the BIS is activated. Likewise, activation of the BIS leads to heightened activity in the FFFS. As discussed later, operations of these separate components can generate the complex motivations, emotions, and behaviors seen in externalizing disorders. We argue that variation in the precise pattern of dysfunction within not only the BIS, but also within the BAS and FFFS, is, in part, what differentiates among externalizing disorders. To substantiate this claim, we need to consider variation in dysfunction, particularly in prefrontal components of the BIS that, until now, have been poorly specified. In particular, its primary prefrontal component—the dorsal stream—is a substantial and highly differentiated part of the human cortex, all of which is represented by a single box in Figure 13.2.

ADHD and Phenylketonuria (PKU)

ADHD is one of the most common childhood disorders (see Hinshaw, this volume). At the broadest level, there are two primary forms:  inattentive (ADHD-IA; e.g., distractibility and difficulty focusing on tasks for a sustained period) and hyperactive/ impulsive (ADHD-HI; e.g., fidgeting, excessive talking, and restlessness). When present together, these symptom sets comprise ADHD combined type (ADHD-CT, which has now been downgraded to combined “presentation” in the Diagnostic and Statistical Manual of Mental Disorders [DSM-5]; see Drabick, Steinberg, & Hampton, this volume). The comparability of processes underlying ADHD symptoms and personality is supported by longitudinal studies that demonstrate that ADHD is fairly stable from childhood to adulthood, with more than half of those diagnosed in childhood continuing to have the same diagnosis in adulthood (Biederman et  al., 1993). ADHD is highly heritable (Biederman, 2005; Faraone  & Doyle, 2001; Hinshaw, this volume). For our purposes, it is instructive to consider phenylketonuria (PKU) in parallel with ADHD. PKU is a well-known, genetically caused metabolic disorder with a radically different etiology (i.e., a double recessive gene) to that of ADHD. However, residual symptoms of PKU after dietary treatment overlap with features of ADHD and include both primary (sensory, motor) and executive (attention, working memory, planning) functions (for review, see Stevenson  & McNaughton, 2013). Antshel and Waisbren (2003) described PKU as showing Corr, McNaughton

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“ADHD symptom expression,” and there is a high prevalence of diagnosed ADHD among children with PKU (Antshel, 2010). Importantly, ADHD and PKU share a common major deficit in failure of inhibitory control, and it is here that the potential contribution of the BIS looms largest. Both disorders have highly overlapping abnormalities of the frontal lobe and of the white matter connections between the frontal lobe and subcortical regions, such as the basal ganglia (Castellanos et al., 2002; Sowell, Toga, & Asarnow, 2000): “The most replicated alterations [in ADHD] … include significantly smaller volumes in the dorsolateral prefrontal cortex, caudate, pallidum, corpus callosum, and cerebellum” (Seidman, Valera, & Makris, 2005, p. 1263). There are indications that reduced volume in dorsolateral prefrontal cortex, middle frontal gyrus, anterior cingulate cortex, inferior parietal cortex, temporal cortex, basal ganglia, and cerebellum remain in place during adulthood (Proal et al., 2011; Seidman et  al., 2011; but see also Amico, Stauber, Koutsouleris, & Frodl, 2011). These structures are involved in motor control, reward processing, executive functioning, and inhibition of motor behavior. Both ADHD and PKU are also associated with disturbances in dopaminergic (see Stevenson  & McNaughton, 2013) and noradrenergic systems (e.g., Pliszka, 1998). We argue that, when integrating the neurology of all the externalizing disorders, reduced cortical dopamine (DA) results in failures to regulate subcortical DA that, in turn, produce impulsivity. Early results suggested increased DA transporter densities in the striatum (Krause, Dresel, Krause, Kung,  & Tatsch, 2000), but, more recently, decreases in the striatum and nucleus accumbens, which may be partially lateralized (Volkow, Swanson, & Newcorn, 2010; Volkow et  al., 2009), have been reported. Reduced DA, expressed both in mesocortical input to the dorsolateral prefrontal cortex and mesolimbic input to the nucleus accumbens, may account for some cognitive impairments in ADHD (Sonuga-Barke, 2005), as well as in PKU. But variations across individuals suggest “that additional pathology … is necessary to account for the large differences in inattention observed” (Volkow, Wang, Newcorn, Fowler, et  al., 2007, p.  1182; see also Volkow, Wang, Newcorn, Telang, et al., 2007). Both ADHD (Beauchaine et  al., 2001; Quay, 1997) and PKU (Stevenson & McNaughton, 2013) have been suggested to involve BIS dysfunction. With the possible exception of some hippocampal 226

damage in PKU and some frontal-hippocampal disconnection in ADHD, the pattern of neurological disturbance suggests that both disorders involve damage to frontal rather than subcortical components of the BIS (Stevenson & McNaughton, 2013). Such relative frontal specificity of the BIS modules involved would be consistent with the common widespread frontal neuropathology in ADHD and PKU detailed in the previous paragraphs. If low BIS sensitivity were the only dysfunction in an individual, we would predict (as illustrated in Figure 13.1) reduced behavioral inhibition (i.e., the capacity to inhibit prepotent goals and to resolve conflict by increased risk aversion), reduced attention (including both environmental and memory scanning), and reduced arousal. This set of symptoms covers a significant part of the description of ADHD-IA. However, there are both positive and negative features of the symptom profile that argue against any simple equation of ADHD-IA with global BIS dysfunction. An example of a positive feature is stopping in the Stop-Signal Task (SST; Aron & Poldrack, 2006; Band, van der Molen,  & Logan, 2003; Logan, Cowan,  & Davis, 1984). It was predicted by the original BIS theory of ADHD (Quay, 1997) that affected children would show a longer stop signal reaction time (SSRT); that is, they would need more time for their inhibitory system to be effective in stopping the prepotent response. This hypothesis has been confirmed directly (Nichols  & Waschbusch, 2004), and is consistent with related symptom variation in a nonclinical sample (Kooijmans, Scheres,  & Oosterlaan, 2000). However, stopping behavior in the SST is not controlled by the BIS because it is not sensitive to anxiolytic drugs, despite their affecting concurrent goal conflict-related EEG activation (McNaughton et  al., 2013). Stopping in the SST is controlled by the right inferior frontal gyrus (Aron et  al., 2003), and, in people with ADHD, reduced activity in this area is linked to both poorer inhibition of “going” in the SST and to poorer inhibition of memory retrieval. The latter is likely to be the result of frontal-hippocampal disconnection (Depue, Burgess, Willcutt, Ruzic, & Banich, 2010). PKU deficits also appear to involve the inferior frontal gyrus, in addition to their similar involvement to ADHD in structures including the dorsolateral executive network (Christ, Huijbregts, de Sonneville, & White, 2010). An example of a negative feature is that as many as a third of children with ADHD have comorbid anxiety. If ADHD and anxiety simply reflect

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

opposite poles of variation in global BIS function, this should not be possible. Comorbidity would be consistent with involvement of the BIS in ADHD if the observed anxiety arose from subcortical components of the BIS (such as the hippocampus and amygdala), whereas behavioral inhibition, arousal, and attention deficits in ADHD arose in prefrontal components (see Sauder, Beauchaine, Gatzke-Kopp, Shannon, & Aylward, 2012; Stevenson & McNaughton, 2013). It is important to re-emphasize that “the BIS” is not unitary. Rather, it represents a set of hierarchically organized processes (see Figure 13.2). In ADHD-IA, then, we appear to have dysfunction of prefrontal components of the BIS, but not of either subcortical components or of the overall modulation of the entire system by endogenous benzodiazepines, serotonin, or noradrenaline. Importantly, prefrontal BIS dysfunction would be combined with: a) reduced DA-related reactivity to reward stimuli; and, b) with dysfunction of additional non-BIS prefrontal and dopaminergic circuits controlling stopping and other functions such as working memory (Beauchaine & McNulty, 2013; Sauder et al., 2012). The combined type of ADHD requires a more complex application of RST (Gomez & Corr, 2014). ADHD-CT is associated not only with relatively low cognitive control (BIS−) but also with high positive and negative emotionality (BAS+, FFFS+; respectively). The combination of BAS+ and FFFS+ results both in the generation of a greater tendency to make responses in the absence of conflict and in much higher levels of motivation under conditions of conflict (when the BAS and FFFS are equally activated). Given a dysfunctional BIS, this strong motivational activation is accompanied by a general inability to switch attention and behavior when a prepotent response is present. In this view, a dysfunctional BIS could be involved in all types of ADHD and PKU. However, it should be noted that, in a standard challenge task such as the SST, the BIS biomarker (McNaughton et al., 2013) would be abnormally low only in those with the ADHD-IA subtype (in which arousal is low). With the CT subtype, the insensitive BIS would be subjected (for any given value of normal stimulus parameters) to a higher level of conflicting BAS and FFFS input than would neurotypical controls; thus, its activation could well be equivalent to or higher than neurotypicals, whereas the capacity of that activation to inhibit relevant behaviors would be subnormal (because these behaviors are

more than normally activated). This process could give rise to the combination of impaired motor inhibition and anxiety. In sum, disrupted frontal components of the BIS could account for a proportion of observed deficits in behavioral inhibition and attention in all types of ADHD and PKU. Conversely, reduced dopaminergic functioning must account for a range of deficits unrelated to the BIS (see Beauchaine  & McNulty, 2013; Beauchaine, McNulty, & Hinshaw, this volume, Zisner  & Beauchaine, this volume). We have linked a number of features of ADHD to some combination of BIS, BAS, and FFFS dysfunction. However, given the extent of frontal pathology, including white matter pathology, other features certainly require more local explanations. Nonetheless, consideration of the operation of the BIS, BAS, and FFFS should reveal core underlying deficits that may help to explain a wide range of specific symptoms (e.g., inattention, hyperactivity, and dysregulation of behavior), as well as their comorbidities with other conditions.

Conduct Disorder (CD)

We have already suggested that ADHD-IA and ADHD-CT both involve BIS− deficits but differ in that the latter have additional BAS+ and FFFS+ deficits. “With respect to externalising disorders in childhood, Quay suggested that ADHD and CD reflect different problems in the functioning of the BAS and the BIS. ADHD is characterised by an underactive BIS, whereas CD is associated with a BAS that dominates over the BIS:  when cues for both reward and punishment are present, CD children focus on cues for reward at the expense of cues for punishment” (Matthys et al., 1998, p. 644; see also Matthys, Vanderschuren,  & Schutter, 2013). Children with comorbid CD+ADHD combine these two patterns to have an even greater imbalance between BIS− and BAS+ (Matthys et  al., 1998). Notably, many studies of CD are confounded by ADHD comorbidity, and comorbid ADHD+CD may be particularly prone to progress to more severe forms of externalizing psychopathology (Beauchaine, Hinshaw,  & Pang, 2010; Beauchaine & McNulty, 2013), including psychopathy (Gresham, Lane,  & Lambros, 2000; Lynam, 1998). In considering the neurobiology of CD, it is therefore important to try, as far as practical, to contrast “pure CD” with “pure ADHD,” given that we can already expect overlap between the two, albeit not as extensive as that between ADHD and PKU. Notably, however, this is very difficult in Corr, McNaughton

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practice because CD often develops from ADHD (see Beauchaine, McNulty,  & Hinshaw, this volume). Matthys, Vanderschuren, and Schutter (2013) reviewed studies that included comorbid ADHD+ CD and linked reduced punishment sensitivity in CD to dysfunction in stress responding, serotonin, noradrenaline, and amygdalar dysfunction (with the latter showing, in addition, reduced gray matter volume); reduced responding to incentives to dysfunction in sympathetic responses, DA (see later discussion), and orbitofrontal cortex (OFC) function; and impaired executive function (cognitive control) to dysfunction of the OFC, superior temporal cortex, anterior cingulate cortex, and posterior cingulate cortex. Pure CD, but not pure ADHD, has been associated with insular, right OFC, occipital cortex, and hippocampal dysfunction (Lockwood et al., 2013; Rubia et  al., 2009b, 2010). Occasional reports of involvement of the anterior cingulate and hippocampus in ADHD could be due either to inclusion of CD cases in the sample or to differences in the tasks used to test the populations. CD appears to show somewhat more involvement than ADHD in temporal and parietal areas (Rubia et al., 2008, 2009a, 2010), whereas ADHD shows somewhat more involvement in the inferior frontal gyrus, the dorsolateral prefrontal cortex, and the posterior cingulate (Rubia et al., 2008, 2009a, 2009b, 2010; but see Lockwood et  al., 2013). Unlike ADHD, CD does not appear to involve the ventrolateral prefrontal cortex (Rubia et al., 2009a, 2009b). DA dysfunction appears to contribute to CD (Matthys et al., 2013). However, there are very few studies that do not involve comorbid ADHD+CD. There is evidence for dopaminergic abnormality as a vulnerability to development of CD among those with ADHD (see Beauchaine  & McNulty, 2013). The DA-4 receptor gene, DRD4, is linked to carefuly selected cases of ADHD combined with CD, but this linkage is not detected in more mixed ADHD samples (Holmes et al., 2002), and DRD4 may interact with the DRD2 gene to generate these effects. Similarly, DAT1 (a DA transporter gene) does not appear to be directly linked to CD (Schulz-Helik et al., 2008) but appears to interact with a lack of parental engagement (whether positive or negative) to predict future CD among children with ADHD (Lahey et  al., 2011). There are also single-gene variants (in, e.g., COMT, necessary for DA synthesis, and DβH, necessary for conversion of DA to noradrenaline) that do not appear 228

to be linked strongly to CD in isolation but show nonlinear synergies in generating CD in combination (Grigorenko et al., 2010).

Psychopathy

The classic picture of psychopathy comprises features of apparently good adjustment (e.g., adequate intelligence, charm) with features of definitely poor adjustment (e.g., behavioral deviance, parasitic existence) and underlying dysfunctions, including behavioral deficits (acting on impulsive whims), cognitive deficits (e.g., poor judgment), emotional and interpersonal deficits (e.g., shallow emotions; lack of empathy, remorse, and shame; and insincerity), motivational deficits (poorly motivated antisocial behavior), and ego distortion (egocentricity). About 1% of the general population and between 15% and 25% of the prison population may have clinically significant psychopathic features (Hare, 1996). Importantly, however, not all or even most individuals with psychopathy are identified and diagnosed, and many live “normal” lives (Babiak & Hare, 2007; Cleckley, 1941). What they have in common is a failure to regulate behavior adaptively—especially in relation to detection of goal conflicts and their appropriate resolution. Thus, on the face of it, psychopathy would appear to involve significant BIS dysfunction (see Fowles, 1980). In common with other externalizing disorders, psychopathy appears to involve frontal dysfunction but may include greater involvement of the temporal lobes. There are increases in callosal white matter and decreases in gray matter in prefrontal cortex (right orbitofrontal, right anterior cingulate, left dorsolateral), the superior temporal gyrus, amygdala, and hippocampus (Müller et  al., 2008; Weber, Habel, Amunts, & Schneider, 2008; Yang & Raine, 2009), which suggests major involvement of frontal-temporal-limbic circuits (Wahlund  & Kristiansson, 2009). Dorsolateral prefrontal, amygdalar, and hippocampal involvements are consistent with a dysfunctional BIS. As with the other externalizing disorders, the BIS is clearly not the only frontal system involved, and amygdala dysfunction appears to extend beyond simple behavioral inhibition in passive avoidance tasks. There is “less amygdala responding and less amygdala-orbitofrontal functional connectivity in response to fearful expressions in youths with psychopathic traits” (Finger et al., 2011, p. 153). As with the other externalizing disorders, dopaminergic alterations are also implicated. Neurochemical and neurophysiological hypersensitivity to d-amphetamine has been observed in

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

the mesolimbic DA reward system among individuals with psychopathy (Buckholtz et al., 2010). However, the effect of d-amphetamine may not be exclusively dopaminergic because d-amphetamine also releases serotonin and noradrenaline (West, Van Groll, & Appel, 1995), and both the serotonergic and noradrenergic systems are implicated in emotion processing (Bijlsma, de Jongh, Olivier, & Groenik., 2010; Hung et  al., 2011) and startle reactivity (Koch, 1999). Similarly, orbitofrontal and caudate responses to reward presentation during passive avoidance are blunted in CD cases with high levels of psychopathy (Finger et  al., 2011). This suggests general hypofunction of the DA system (with resultant increased rebound sensitivity to DA when it is released). Such rebound sensitivity could explain why antisocial individuals are particularly prone to abuse DA agonists (Fridell, Hesse, Jaeger, & Kuhlhorn, 2008). A positive feedback loop has been suggested in which “chronic intake of drugs of abuse produces cortical dopaminergic hypofunction and other changes in cortical neurobiology that lead to impaired ability to gate or modulate subcortical dopamine function” (Jentsch & Taylor, 1999, p. 384; see also our later discussion of externalizing disorders and DA, especially Figure 13.3). Impulse control takes different forms in “primary” versus “secondary” psychopathy (Fowles  & Dindo, 2006; Karpman, 1941, 1949; Lykken, 1995). This distinction suggests that different roles are played by the FFFS and BAS, in addition to the BIS. Primary psychopathy is related to innate fearlessness and impaired socialization (FFFS−). In contrast, secondary psychopathy is related to normal fear sensitivity but reckless and impulsive behavior (BAS+), often leading to increased negative affect, formerly termed “neurotic psychopathy” (Blackburn, 1979; Hare, 1970). As argued by Corr (2010), both subtypes may be characterized by BIS dysfunction, specifically impaired goal conflict detection and resolution processes, which leads to inflexible and maladaptive behaviors that are difficult to change due to response inflexibility when behavior is inappropriately controlled by only one system (e.g., the BAS). The idea that the BIS is defective in psychopathy is not new (see Fowles, 1980), but it has not previously taken into account differentiation between the FFFS and the BIS. The vast majority of previous research has relied on the pre-2000 BIS theory (Gray, 1982), which postulated that the BIS is activated by conditioned stimuli for punishment and

nonreward. This earlier theory was typically interpreted in terms of the BIS serving as the main system mediating most forms of punishment (except the unconditioned variety) relevant to human motivation. As a result, there has been an unfortunate conflation of and confusion between FFFS-fear and BIS-anxiety in psychopathy research. We can now use revised RST to dig deeper into possible motivational roots of psychopathy. Lykken (1995) argued that the fearlessness of primary psychopathy is associated with an underactive BIS coupled with normal levels of BAS reactivity, leading to maladaptive behavior via impaired processing of stimuli associated with potential threats or punishment. Lykken also argued that secondary psychopathy is associated with an overactive BAS but normal levels of BIS reactivity, leading to impulsive and reckless behavior. As a consequence, individuals with secondary psychopathy experience relatively high levels of negative affect given their increased exposure to adverse outcomes. This theory is consistent with experimental research revealing that individuals with primary, or true, psychopathy may be differentiated from nonpsychopathic controls by a number of key features, especially autonomic underreactivity (e.g., as measured by electrodermal activity) to anticipated aversive stimuli (e.g., electric shock). Such data lend support to the hypothesis that individuals with primary psychopathy have an underreactive BIS and are generally low in fear/anxiety (see Fowles, 1980). As noted earlier, differentiation of fear and anxiety in these studies was not made clear. Indeed, Lykken related primary psychopathy to low fear, and then related low fear to the BIS, not the FFFS (see Fowles & Dindo, 2006, p. 13). Research shows that, in general, individuals with psychopathy, when compared with controls, have higher scores on questionnaires designed to assess the strength of the BAS and lower scores on those designed to assess the strength of the BIS (e.g., Book & Quinsy, 2004). For example, among prison inmates (n  =  517 males), Newman et  al. (2005) tested Lykken’s hypothesis by classifying individuals as either psychopathic or nonpsychopathic using the Psychopathy Checklist-Revised (PCL-R; Hare, 2003). Individuals with primary psychopathy had significantly lower BIS scores than their nonpsychopathic counterparts (as predicted by Lykken, BAS scores did not differ). In addition, individuals with secondary psychopathy had significantly higher BAS scores (also as Corr, McNaughton

229

predicted by Lykken), but their BIS scores were more inconsistent. Although these results may be interpreted as showing that individuals with psychopathy (especially primary) are underreactive to aversive stimuli, recent research suggests that their behavior may be better accounted for in terms of goal conflict detection and processing. These data reveal that individuals with psychopathy are not always insensitive to punishment; rather, situational factors play a role (e.g., Newman & Kosson, 1986). In support of this claim, cognitive deficits are found using nonemotional stimuli (Newman, Schmitt, & Voss, 1997). Such findings suggest that individuals with psychopathy are relatively unresponsive to contextual cues that are peripheral to their dominant response set (i.e., primary task), irrespective of whether the task involves emotional stimuli or not (Newman, Curtin, Bertsch, & Baskin-Sommers, 2010). In a major re-evaluation of BIS theory in psychopathy, Wallace and Newman (2008) argued that individuals with psychopathy manifest disinhibition (i.e., decreased ability to regulate behavior to avoid adverse consequences) in situations in which avoidance of an adverse outcome requires overriding a prepotent response or modifying an existing behavioral goal. For those with primary psychopathy, selective attention is not appropriately reallocated in an automatic manner to processing of stimuli that are unrelated to their attentional focus. This conclusion is consistent with the finding that individuals with primary psychopathy do not have a general deficit in attentional focus: they perform as well as controls when task-specific stimuli are within their attentional focus. Rather, they lack the ability to shift their focus of attention when it has been captured by dominant stimuli in the environment. These may well be reward-related stimuli, but the issue is not that this subgroup is necessarily highly reward-oriented (which seems more of a problem with secondary psychopathy). This line of evidence suggests that, for individuals with primary psychopathy, there is an underactive FFFS (thus their low fear) and a dysfunctional BIS (thus their failure to modulate behavior in goal conflict situations). For individuals with secondary psychopathy, on the other hand, there is a dysfunctional BAS (once again with a dysfunctional BIS but perhaps with a normally functioning FFFS). Blair, Mitchell, and Blair (2005) treat the BIS as if it is a single fear system and argue that, like other such theories, the BIS theory fails to account for the fractured relationship of different aspects 230

of defensive behavior in psychopathy. However, they do not take into account either the FFFS/BIS distinction or the fact that both FFFS and BIS are hierarchically organized. Their model of psychopathy treats it as having three main strands:  emotional dysfunction, antisocial and aggressive action, and impaired passive avoidance (Blair et al, 2005, fig. 8.1, p. 111). As with ADHD, these first two strands result from dysfunction of systems other than the BIS. At first glance, a passive avoidance deficit could indicate a general failure of the BIS. However, in generating their neural model of psychopathy, Blair et  al. treat the main prefrontal component of the BIS (the dorsolateral PFC) as intact (which is consistent with a capacity of individuals with psychopathy to undertake planning but ignores the reported deficits in the left dorsolateral PFC) and focus on the amygdala as a primary source of deficit. In this context, it is important to note that one of the primary deficits they use to link psychopathy to the amygdala is fear-potentiated startle. This is a key test on the basis of which the amygdala was included in the BIS (Gray  & McNaughton, 2000). Considerable evidence points toward individuals with psychopathy having both impaired startle potentiation to aversive stimuli (e.g., Benning, Patrick, & Iacono, 2005; Herpertz et  al., 2001; Patrick, Bradley  & Lang, 1993; Vaidyanathan, Hall, Patrick,  & Bernat, 2011) and amygdala dysfunction (see Blair, 2010; Gao, Glenn, Schug, Yang, & Raine, 2009). An impaired BIS means that, in the context of goal conflicts, primary psychopathy will be associated with impaired ability to switch attention and modulate responses and, as a consequence, a failure to learn from exposure to aversive experiences (often such individuals do not even appreciate the significance of such experiences until it is too late). The BIS is unable to resolve this goal conflict for a number of reasons. A dysfunctional BIS that fails to resolve FFFS–BAS conflict (or any other kind of goal conflict) would not provide appropriate cognitive control of executive and attentional resources sufficient to focus on salient stimuli in the environment. In addition, especially in secondary psychopathy where the BAS is overactive, a dysfunctional BIS would also fail to apply an effective brake on inappropriate prepotent behavior. As already discussed, inhibition of prepotent behavior and attentional control are different processes within the BIS. Thus, according to the position adopted here, primary psychopathy is associated with an impaired

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

FFFS and dysfunctional BIS (but a relatively normal BAS), and secondary psychopathy is associated with a hyperactive BAS and dysfunctional BIS. The role played by the FFFS in secondary psychopathy is difficult to discern because higher levels of negative affect experienced by individuals with secondary psychopathy may be entirely proportional to the degree of aversive stimuli they encounter.

The Neurology of Externalizing Disorders

Our review of ADHD, PKU, CD, and psychopathy suggests a partial overlap in terms of both the global symptom clusters and neurology. Much the same specific functional systems are implicated in each, with different degrees of involvement either in terms of their level of dysfunction or neural spread (i.e., the modules of each system that are involved), both from disorder to disorder and from case to case. Similarly, much the same dysfunction in the global dopaminergic modulation of these systems occurs, again with some variation in degree and extent. This situation is summarized in Figure 13.3, which expands on the neurology of ADHD presented by Sonuga-Barke (2005). Variation in the precise pattern of dysfunction (Table 13.1) presents as one or another type/ subtype of disorder. In terms of the primary motivational systems on which we have focused, our global qualitative evaluations suggest differing patterns of contribution of the BAS, FFFS, and BIS to the different observed phenotypes, with some degree of BIS deficit serving as a common factor (Table 13.1: Phenotype; but see also our later discussion of DA). There is a fair degree of matching of these superficial phenotypic suggestions with the reported areas of cortical dysfunction. In particular, BIS dysfunction is suggested to be involved phenotypically in all cases, and, neurally, involvement of more frontal aspects of the BIS is observed with more moderate behavioral problems, with addition of more extensive temporal cortex (particularly superior temporal gyrus), amygdala (BIS-related components of which control fear-potentiated startle), and hippocampal involvement in terms of more problematic behavior. There is also a reasonable match between suggested phenotypic FFFS involvement and the extent to which anterior cingulate cortex and amygdala are involved. One potential exception here is that FFFS+ in the case of ADHD-CT appears to occur with a dysfunctional anterior cingulate cortex. One possibility is that this dysfunction is presenting as excessive output, or loss of inhibition, from the structure; the other

STG

Hippocampus

DLPFC

Thalamus Dorsal striatum

Mesocortical VTA∗

Nigrostriatal Nigra∗

Mesolimbic Ventral striatum OFC/ACC

Thalamus Amygdala

Figure 13.3  Simplified overview of systems involved in externalizing disorders (key components shaded). There is a largely shared direct dysfunction of dopamine (VTA/nigra), OFC/ACC, and higher levels of the behavioral inhibition system (BIS; DLPFC), with more indirect functional involvement of the amygdala and hippocampus. Psychopathy (and to a lesser extent conduct disorder) involves additional dysfunction (dashed boxes) of STG, hippocampus, and the amygdala. Attention-deficit/hyperactivity disorder (ADHD) involves disconnection of DLPFC from hippocampus (dashed arrow), whereas PKU involves hippocampal dysfunction. Dopamine systems have dysfunctional interactions with frontostriatal-thalamic circuits that can vary in detail among the disorders. ACC, anterior cingulate cortex; DLPFC, dorsolateral prefrontal cortex; OFC, orbital frontal cortex; STG, superior temporal gyrus; VTA, ventral tegmental area. (Figure updated from two figures in Sonuga-Barke, 2005.)

possibility is that involvement of anterior cingulate is only occurring when ADHD is comorbid with CD (Rubia et al., 2009b, 2010) and does not contribute to FFFS-related changes. Another apparent exception is that the suggested BAS+ phenotype does not match in direction or pattern what would be expected in relation to the OFC. It may be that, again, dysfunction involves increased (particularly inappropriate) response generation; it may also be that BAS+ is driven more by subcortical (e.g., amygdala) involvement or by fairly directly related to changes in multiple DA systems. Differences between disorders appear to result from variation in the relative involvement of different functional systems (such as the BAS, FFFS, and BIS) that reflect relatively minor variations in the position of the “geographic” boundaries of a relatively continuous area of pathogenesis. For example, the degree of BIS involvement appears to Corr, McNaughton

231

Table 13.1 A Tentative Summary of Relations Between Motivational Phenotype and Neural Source. Diagnosis

ADHD-IA

ADHD-CT

PKU

CD

PSYC-1

PSYC-2

BAS

0

+

as ADHD

+

0

+

FFFS

0

+

as ADHD

0

−

0

BIS

−

−

as ADHD

*

−−

−−

BAS Structures

OFC

0

0

0

−

−

−

FFFS structures

ACC

−

−

−

−−

−−

−−

Amygdala

0

0

0

−

−−

−−

BIS structures

DLPFC

−

−

−

*

−

−

Temporal

−

−

−

−

−

−

STG

0

0

0

−

−

−

Hippocampus

*

*

−

−

−

−

White matter

−

−

−

0

+

+

Insula

0

0

0

−

−

−

MIFG

−

−

−

0

−

−

Parietal

−

−

−

−−

0

0

Phenotype

Other structures

Note that, with the exception of the amygdala and hippocampus, affected subcortical areas have been omitted, as have the posterior cingulate and the cerebellum. Subtypes of both ADHD and psychopathy (PSYC) have been assigned the same structural values (gray areas) because there is insufficient data to delineate their neural differences. Involvement of an area is indicated for major dysfunction (− −), dysfunction (−), minor dysfunction or disconnection (*), no reported involvement (0), and hyperactivity (+). ADHD-IA, ADHD inattentive subtype; ADHD-CT, ADHD combined subtype; PKU, phenylketonuria; PSYC-1, primary psychopathy; PSYC-2, secondary psychopathy; ACC, anterior cingulate cortex; BAS, behavioral approach system; BIS, behavioral inhibition system; DLPFC, dorsolateral prefrontal cortex; FFFS, fight-flight-freeze system; MIFG, medial or inferior frontal gyrus; OFC, orbital frontal cortex; STG, superior temporal gyrus.

involve the spread of the boundary of dysfunction from dorsolateral prefrontal cortex to include the hippocampus and then the superior temporal gyrus and the amygdala. From this view, combinations of patterns of damage shown in Table 13.1 would be frequent and would present as one or another type of comorbidity (e.g., ADHD + psychopathy). Consistent with this view of a topographically variable zone of neural dysfunction, approximately 60% of children with ADHD also have a diagnosis of oppositional defiant disorder and/or CD (see Beauchaine, McNulty,  & Hinshaw, this volume; Beauchaine et al., 2010; Levy, Hawes, & Johns, this volume). Conversely, as many as 70% of those in clinical samples with CD have comorbid ADHD (Beauchaine et  al., 2001). In fact, it appears that “comorbidity between CD and the hyperactive/impulsive subtype of ADHD … represents a particularly virulent condition, characterised by a strong genetic loading, increased rates of aggression, and elevated risks of future antisocial 232

behaviour … and score [high] on measures of psychopathy” (Beauchaine et al., 2001, p. 610; see also Finger et al., 2011, p. 152; Gresham, Lane, & Lambros, 2000). Conversely, most of those with ADHD do not develop psychopathy because the neural level of the BIS involved is different (with primarily prefrontal involvement in ADHD and temporal cortex, amygdalar, and hippocampal involvement in psychopathy) and is combined with differences in other systems (notably opposite changes in white matter tracts such as the corpus callosum). The idea of involvement of only some modules of the BIS in some disorders also accounts for the fact that, in ADHD samples, 30% can have comorbid anxiety disorders (Pliszka, 1998; Spencer, Biderman,  & Wilens, 1999). In such comorbid cases, prefrontal hypofunction related to externalizing disorder may be counterbalanced by subcortical (e.g., amygdalar, hippocampal) hyperfunction in internalizing disorder. The lack of equivalent

Neural Mechanisms of Low Trait Anxiet y and Risk for Externalizing Behavior

anxiety + psychopathy comorbidity would be accounted for, in this model, by the more substantial involvement of subcortical components of the BIS in hypofunction. Interestingly, in this context, the presence of internalizing disorder has been reported to involve a lesser reduction in hippocampal volume than that normally seen with externalizing disorders (Sauder et  al., 2012). This pattern suggests an interaction at this key nodal point of the BIS of descending frontal and ascending subcortical influences. As with the hippocampus and the BIS, comorbid internalizing disorder has been reported to ameliorate reduced volumes of both the putamen and anterior cingulate cortex among those with externalizing disorders (Sauder et  al., 2012). Consistent with this finding, comorbid anxiety appears to normalize the impaired inhibition seen in children with ADHD in the Stop Signal Task (Manassis, Tannock, & Barbosa, 2000). Moreover, effective inhibition is associated with resilience to development of both ADHD and CD in response to psychosocial adversity (Nigg, Nikolas, Friderici, Park, & Zucker, 2007). The comparison of the etiologies of PKU and ADHD (Stevenson & McNaughton, 2013), in particular, suggests that quite different primary insults can result in distorted neural development that qualitatively converges on a common core pathology of particular frontal and temporal lobe structures (i.e., equifinality). PKU and ADHD involve opposite changes in plasma levels of large neutral amino acids but (through different routes) result in similar deficiencies in the brain of the precursors of DA and (perhaps to a lesser extent) serotonin. Similar final common-path arguments can be made for different etiological variants of ADHD (Swanson et al., 2007). Manipulation of DA transmission alters connectivity in default mode (frontal rather than hippocampal) frontoparietal and frontoinsular networks that include many of the structures listed in Table 13.1 (Cole et  al., 2013). Changes in functional connectivity during development could lead to relatively consistent distortions of the affected areas, with the result that early impaired dopaminergic transmission would affect frontal components of the BIS via changes in the default mode network. However, “disordered DA” is not a simple unitary explanation for the pattern of developmental pathology. “The unbalanced presence of dopamine in various areas of the brain can result in a broad spectrum of outcomes, including cognitive, personality and psychiatric deficiencies. … Variations in the COMT, MAOA, MAOB and

DβH genes [can all be linked] to psychopathology in general and conduct problems in particular [and] can interact with each other enhancing nonlinearly the likelihood of a negative outcome” (Grigorenko et al., 2010, p. 160), with the locale and extent of DA disturbance depending of the precise genetic or environmental cause. Separately from the etiological contribution of DA is the issue of the role of the overlapping dopaminergic deficits in externalizing disorders and the related pattern of OFC deficits, which contrasts with our phenotypic designation of BAS+. This issue is complex. Pre- and postsynaptic DA receptors may produce disparate functional outcomes resulting in an inverted-U response relationship, with both low and high DA levels resulting in the same pattern of dysfunction (see, e.g., Plichta  & Scheres, 2014). Similarly, “paradoxical effects are observed, by which drugs improve performance in individuals with suboptimal DA and poor performance but impair performance in individuals with already optimized DA and good performance” (Cools, Sheridan, Jacobs,  & D’Esposito, 2007). Loss of cortical DA can “lead to impaired ability to gate or modulate subcortical dopamine function and [an] associated augmentation in the control of behaviour by reward-related stimuli … reduced basal dopamine turnover … [and a] greater dopaminergic response to stress” (Jentsch  & Taylor, 1999). In externalizing disorders, including drug abuse, there appears to be a “reward deficiency syndrome” in which deficiency in the DA system results in abnormal behavior (impulsivity, drug taking) that tends to result in some restoration of the level of dopaminergic input to the BAS that would normally have been produced by normal behavior (Blum et al., 2000). From this view, the designated BAS+ of Table 13.1 is correct in that the output from the BAS (i.e., approach) is increased as a greater amount of reinforcement is required to deliver normal levels of hedonic tone (in the form of DA). However, we must enter a caveat in that whereas the quantitative output is increased, the qualitative output (in the form of adaptive response selection) is decreased. We can thus reconcile the reduced volume of frontal components of the BAS (which provide more limited machinery for goal selection and so a more restricted range of goal choices) with increased responding to those goals that are selected via reduced DA release into cortical areas and the tendency to opposite reactions of cortical and subcortical control of DA (Jentsch & Taylor, 1999). Corr, McNaughton

233

Conclusion

Overall, externalizing disorders appear to arise from a number of quite different proximal developmental causes via largely similar neural substrates (DA, white matter, large neutral amino acids) that are selectively effective on largely similar prefrontal and temporal lobe circuits. All include damage to some, often frontal, components of the BIS, thereby sharing characteristics of low trait anxiety, at least in terms of higher order goals with relatively large defensive distances (McNaughton  & Corr, 2004). Although only prefrontal modules of the BIS are involved, this allows for comorbidity of externalizing and internalizing disorders, resulting from opposite frontal and subcortical dysfunctions, respectively. In neonates, DA dysfunction appears to include generation of developmental abnormality of the targets of dopaminergic neurons in dorsolateral frontal and temporal regions of the default mode network, including the hippocampus, thus generating hypofunction of the BIS of varying extents. All externalizing disorders also include some type of DA hypofunction that, paradoxically, underlies some forms of impulsivity that can be characterized as a quantitative (but not qualitative) increase in BAS function. Definitions and neural source may be important here because ventral striatal DA synthesis is more positively correlated with extravagance than with simple impulsivity (Lawrence & Brooks, 2014). We argue that the quantitative increase in BAS output is accompanied by impaired function of frontal components of the BAS, which results in a decrease of the functional quality of its outputs. (The same may well be true for FFFS+.) In addition, all externalizing disorders include non-BIS prefrontal dysfunction, which contributes to executive deficits. On the basis of this view, the externalizing disorders share a general class of hypodopaminergicrelated disturbances of neural development including hypofunction of the BIS, coupled with varying disturbances of the BAS, FFFS, and other (particularly executive-related) systems (Table 13.1). In cases where only higher levels of the BIS are compromised, hyperactivity of lower levels appears to provide a degree of counterbalance and potentially contributes to resilience. The BIS as a whole, therefore, appears to be important in relation to the risk of externalizing behavior, but it is clear that, in all cases, its failure of inhibition is acting in tandem with the excessive generation 234

of inappropriate behaviors caused by hypodopaminergic impulsivity. Thus, excessive observed goal approach is the result of both an increased approach tendency and a reduced capacity for negative consequences to generate conflict and so prevent approach.

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CH A PT E R

14

Sex Differences in the Prevalence and Expression of Externalizing Behavior

Robert Eme

Abstract Males exceed females in the expression of almost all externalizing behavior, particularly the most pernicious forms, including chronic physical aggression, violence, and life-course–persistent antisocial behavior. Indeed, unlike most behavioral traits, these forms of aggression are expressed almost exclusively by males. In this essay, the author reviews evidence for inherited and acquired biological vulnerabilities that help explain these large sex differences in aggressive behaviors. Inherited vulnerabilities include the greater probability that boys will exhibit lower levels than girls of biologically based temperamental traits such as self-control, empathy, and fear, which in turn increases the likelihood that they will engage in externalizing behavior. Acquired vulnerabilities include the greater probability that males more than females will experience adverse prenatal, perinatal, and postnatal events resulting in impaired intelligence and executive functions, which also increases the likelihood that they will engage in externalizing behavior. Key Words:  sex difference, externalizing behavior, violence, aggression, biological vulnerability

Introduction

Sex differences are often associated with large phenotypic variation within species (Parsch & Ellegren, 2013). It is therefore not surprising that sex is widely acknowledged as an important factor in understanding individual differences in many behaviors (Stewart & McDermott, 2004). Indeed, as Thomas Insel, director of the National Institute of Mental Health observed, “It’s pretty difficult to find any single factor that’s more predictive for some of these disorders than gender” (Holden, 2005, p. 1574).1 The validity of Insel’s observation has no better illustration than through sex differences in the prevalence of three of the most pathological externalizing behaviors: chronic physical aggression, violence, and life-course–persistent antisocial behavior (LCP). Although males exceed females in expression of externalizing emotions (e.g., anger, contempt, disgust; Chaplin & Aldao, 2013), and in almost all externalizing behaviors and disorders across most modern cultures

(American Psychiatric Association [APA], 2013; Caspi et al., 2014; Martel, 2013a),2 males so far exceed females in the prevalence of chronic physical aggression, violence, and LCP, that these behaviors can be characterized as being almost exclusively male. In this essay, I  focus on biological vulnerabilities that help explain the overwhelming male prevalence of these three externalizing constructs and on externalizing behavior in general. First, however, I outline the historical interest in biological vulnerabilities to sex differences in externalizing behavior, and I present research that supports the contention that (a) physical aggression, violence, and LCP antisocial behavior are the most pathological behaviors on the externalizing spectrum, (b) prevalence rates of these behaviors overwhelmingly favor males, and (c) these behaviors are relevant to the spectrum of externalizing behaviors and disorders discussed through this volume. I conclude with a discussion of a future research agenda. 239

Historical Context

The study of biological origins of sex differences began at least 150 years ago with Darwin’s (1860) anguished exclamation that, “The sight of a feather in a peacock’s tail, whenever I gaze at it makes me sick!” (cited in Buss, 2012, p.  6). The peacock’s resplendent plumage, along with other inconvenient examples in the animal kingdom, seemed to contradict his theory of natural selection because it seemed impossible that this metabolically costly plumage, an open invitation to predators, was an adaptation that arose as a consequence of successful survival (Buss, 2012). Darwin’s “sickness” was cured in 1871 with publication of his evolutionary theory of sexual selection, which explained how such adaptations might arise as a consequence of successful mating (Buss, 2012). The study of sex differences in the brain can be traced to the mid-1800s, when Arnold Berthold removed the testes from roosters and noted that they became less aggressive and lost interest in hens. He concluded that, “The testis acts on the blood and the blood acts on the whole organism” (cited in McCarthy, Arnold, Ball, Blaustein, & De Vries, 2012, p.  2243). In 1959, a seminal study by Phoenix, Goy, Gerald and Young (cited in McCarthy et al. 2012, p. 2243) demonstrated that sex differences in certain behaviors of guinea pigs, and by extension in their brains, were permanently sexually differentiated by the prenatal testosterone surge. This finding led to thousands of subsequent studies in many species (guinea pigs, rats, mice, hamsters, gerbils, ferrets, dogs, sheep, and marmoset and rhesus monkeys) showing unequivocally that sex hormones present in prenatal development have organizational effects that produce permanent changes to brain structures and the behaviors these structures subserve, including aggression (Berenbaum & Beltz, 2011; Hines, 2011). The study of sex differences in neurobiological underpinnings of externalizing behavior had as its most influential beginnings in Quay’s (1993) theory of the psychobiology of undersocialized aggressive conduct disorder, and Moffitt’s developmental theory of conduct disorder (Caspi & Moffitt, 1995; Moffitt, 1993), which proposed a dual taxonomy of life-course–persistent (LCP) and adolescent-limited (AL) antisocial behavior as a way of explaining marked individual differences in the stability of delinquency. The central tenet of the initial formulation of Moffitt’s theory was that AL vs. LCP individuals comprise distinct subgroups with unique developmental trajectories and etiologies (Bushway, 240

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2013; Moffitt, 1993). The LCP subgroup is relatively small (5%–10%), almost exclusively male (Moffit, 2006; Moffitt, Caspi, Rutter, & Silva, 2001; Moffitt et al., 2008), and exhibits antisocial behavior beginning in childhood, which tends to persist into adulthood. The stability of this behavior suggests that LCP delinquency has roots early in life, and is likely to be caused by inherited or acquired neurobiological and neuropsychological variation that occurs more frequently among males, thereby explaining the observed sex difference in prevalence (Moffitt, 2006). The AL subtype refers to a much larger group whose antisocial behavior is limited largely to adolescence. Their delinquent behavior is hypothesized to be caused by a maturity gap (such as a desire for autonomy), not neurobiological/neuropsychological variation, which prompts youth to engage temporarily in social mimicry of LCP antisocial behaviors via peer reinforcement. As these youth mature, they outgrow their motivation for delinquency. Moffitt concluded that to understand the etiology of severe, persistent, antisocial behavior, researchers must focus on LCP cases, and by implication biological vulnerabilities underlying their condition. Evolutionary psychology is now a major theoretical perspective, and evolutionary explanations abound for sex differences in externalizing behaviors (Archer, 2009; Buss, 2012; Campbell & Cross, 2012; Geary, 2010; Kruger  & Fitzgerald, 2012; Martel, 2013a). Also, it is now nearly axiomatic that human behavioral traits “almost always arise from complex transactions between biological vulnerabilities and exposure to environmental risk across development” (Beauchaine  & Hinshaw, 2013, p. xii). Thus, “Over the past decades, there has been a veritable explosion in our knowledge of developmental neurobiology … ” in general (Cicchetti, 2013, p.  v11) as well as in the neurobiological underpinnings of antisocial behavior (Matthys, Vanderschuren,  & Schutter, 2013; Portnoy et  al., 2013; Raine, 2013; Van Goozen  & Fairchild, 2008). This essay is informed by recent advances in evolutionary psychology and developmental neurobiology, which provide insights into how biological vulnerabilities contribute to sex differences in prevalence and expression of externalizing behavior.

Sex Differences in Prevalence of Externalizing Behavior

One of the most remarkable findings in the study of antisocial behavior and criminology is that a small group of individuals (5%–10%, almost all

male) are responsible for over half of all crimes in the United States, and an even greater proportion of index crimes (Moffitt, 2005; Piquero, Jennings, & Barnes, 2012; Vaughn, Salas-Wright, DeLisi,  & Maynard, 2014). The behavioral and collateral consequences of this relatively small group are profound and pervasive, resulting in billions of dollars in terms of justice system, victimization, mental health, and associated costs (Vaughn et al., 2014). Early starting individuals are prone to chronic physical aggression and persistent antisocial behavior that becomes increasingly more violent over time. Moreover, they typically have a number of neurobiological vulnerabilities that contribute to their delinquency (e.g., Raine, 2013). Hence, it is difficult to exaggerate the importance of understanding the biological vulnerabilities of these “few, persistent and pathological” individuals (Moffitt, 2006, p. 571). Because it is essential to document that a putative sex difference in any domain of psychopathology is real prior to discussing its causes (Rutter, Caspi, & Moffitt, 2003), I now present evidence to support the assertion that almost all individuals who engage in the externalizing behaviors of chronic physical aggression, violence, and life-course-persistent antisocial behavior are male.

Human Aggression

Human aggression can be defined as any behavior directed toward another that is carried out with immediate intent to cause harm (Anderson  & Bushman, 2002; Dodge, Coie,  & Lynam, 2006). Aggression that is physical can be defined according to an ethological approach, which lists physical aggressions that occur in agonistic encounters such as “hitting, slapping, kicking, biting, pushing, grabbing, pulling, shoving, beating, twisting, choking,” as well as “threatening to physically aggress, and use of objects and weapons to aggress” (Tremblay, 2010, p. 343). With regard to seriously pathological physical aggression, early and persistent physical aggression is linked concurrently and longitudinally to development of subsequent externalizing problems and delinquency (Zahn-Waxler et  al., 2006). The small group of individuals who increase in frequency and seriousness of physical aggression during adolescence are most likely the same individuals who were on the highest trajectory in terms of frequency and seriousness from early childhood (Tremblay, 2010, 2013). Physical aggression is more common in males at all ages and across all cultures.3 It is most evident in reports from self, peers, and teachers of real-world,

severe aggression, and in male-on-male aggression (Alink et al., 2006; Archer, 2004, 2009; Dodge et al., 2006; Tremblay, 2010, 2013). Effect sizes4 generally range from medium (teacher report) to large (peer) for children (Archer, 2004; Card, Sawalaini, Stucky, & Little, 2008; Hyde, 2005, 2014) and emerge early in life (Archer, 2004, 2009; Alink et al., 2006). Such differences are stable through childhood, adolescence, and adulthood (Dodge et al., 2006; Piquero, Carrriaga, Diamond, Kazemian, & Farrington, 2012). By adolescence, approximately 5% of males engage in chronic physical aggression, whereas female cases are rare (Tremblay, 2010, 2013). For example in a general population sample of 14–17 year old adolescents (n = 1165) and their mothers in the province of Quebec (Canada), the prevalence of aggressive CD was found to be 13% for males and a miniscule 0.4% for females (Romano et al., 2004).

Violence

Violence, which can be defined as behavior that is intended to cause physical and psychological injury, including homicide, assault, robbery, and rape (Farrington, 2007), is the most pathological class of externalizing behaviors. A  sex difference in prevalence of violence typically emerges in middle or late adolescence (Loeber  & Burke, 2011). According to Tremblay (2010, p.  352), “Physical violence of females during adolescence is generally so rare that modeling their developmental trajectories fails.” Similarly, Pinker (2011, p.  104) notes, “The one great universal in the study of violence is that most of it is committed by fifteen-to-thirty-year-old men.” Indeed, violence, in its most extreme forms of same-sex homicide and war, is virtually an exclusive male phenomenon (Archer, 2009; Buss, 2012).

LCP Antisocial Behavior

As discussed previously, LCP antisocial behavior refers to one of two categories in a developmental taxonomy in which individuals follow different trajectories/ pathways of delinquency (Moffitt, 2006). The LCP subgroup is characterized by social, familial, and neurodevelopmental deficits, onset in early childhood, and continuity of delinquency thereafter (Moffitt, 2006; 2007; Odgers et al., 2008). The LCP trajectory has received extensive research support, in which a small group of individuals (5%– 10%) show early antisocial behaviors (i.e., before age 10), persisting into adolescence and adulthood, when antisocial behaviors become far more serious (Barry, Golmaryani, Rivera-Hudson,  & Frick, Eme

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2013; Bushway, 2013; Farrington & Loeber, 2012; Fairchild, van Goozen, Calder,  & Goodyer, 2013; Frick  & Nigg, 2012; Jennings  & Ringle, 2012; Moffitt, 2006, 2007; Moffitt et al., 2008; Tremblay, 2010, 2013). However, as with virtually all forms of mental pathology (Beauchaine, 2013; Hinshaw, 2013), there is an emerging consensus that the distinction between LCP and AL is best conceptualized as a difference in degree (i.e., quantitative/dimensional) rather than a difference in kind (i.e., qualitative/ categorical; see Fairchild et  al., 2013; Waldham  & Lahey, 2013; Walters, 2011). Implications of this understanding are threefold (Fairchild et al., 2013). First, childhood-onset of conduct problems are not synonymous with LCP since 50%–70% of individuals with childhood-onset conduct problems outgrow their difficulties by adolescence (Fairchild et al., 2013). Second, severe antisocial behavior that emerges in adolescence may not be limited to adolescence (Fairchild et al., 2013). Third, the etiology of adolescent-onset antisocial behavior may also involve neurobiological factors, although not as frequent or as severe as those involved in LCP (Fairchild et al., 2013). Importantly, however, such a reformulation of the developmental taxonomic theory does not diminish the importance of the central finding that that there is a small group of early starters who persist in their antisocial behavior, which becomes much more serious over time. Moffitt, Caspi, Rutter, and Silva (2001) reported a very large sex difference based in the Dunedin Multidisciplinary Health and Development study, in which LCP characterized 10% of males and 1% of females (10:1 male/female sex ratio). This finding prompted Moffitt (2006) to describe LCP as “almost exclusively male” (p.  593). Subsequent findings from related research reporting sex ratios ranging from 3:1 to 15:1 are broadly consistent with this finding; and thus it is commonly accepted that female LCP is rare (Fairchild et  al., 2013; Moffitt, 2006, 2007). Divergence in reported sex ratios may result primarily from measurement differences, which result in variations in the severity of the antisocial behavior criteria used to designate an individual as LCP. Moffitt et al. (2001) used criteria that identified the most severely antisocial individuals, who are almost exclusively male. Less stringent criteria (e.g., Odgers et al., 2008) identify less severely antisocial individuals, with correspondingly lower sex ratios (personal communications with T. Moffitt, April 17, 2013, and C. Odgers, April 9, 10, 2013). The rarity of female LCP receives strong support from research in the criminological literature 242

Sex Differences

on career criminality (the criminological equivalent of LCP, Loeber, Capaldi, & Costello, 2013), which reports male/female ratios ranging from 9:1 to 12:1 (DeLisi & Piquero, 2011). The foregoing should not be interpreted to mean that female LCP is non-existent or that its consequences are trivial. For example, although based on criteria less stringent than Moffitt et al. (2001), outcomes of females designated LCP in the Odgers et al. (2008) study were by no means benign. At age 32 these women were experiencing significant mental health, physical health, and economic problems, and a large percentage had engaged in significant antisocial behaviors such as violence toward partner (44.8%), hitting a child (41.7%), and getting into fights (47.1%).

Links to Traditional Externalizing  Disorders

Biological vulnerabilities that contribute to a greater male prevalence in the most serious forms of externalizing behavior are relevant to explaining why externalizing disorders are correlated robustly with one another both concurrently and longitudinally (Lahey  & Waldham, 2012). These longitudinal correlations exemplify the developmental psychopathological concept of heterotypic continuity. Antisocial behaviors show strong stability across time, although the phenotypic form of behavior changes with development (Hinshaw, 2013). Beauchaine, Hinshaw, and Pang (2010, p.  328) note that antisocial adult males “usually traverse a developmental pathway that begins with severe hyperactive/impulsive behaviors as early as toddlerhood, followed by oppositional defiant disorder (ODD) in preschool, early onset conduct disorder (CD) in elementary school, substance use disorders in adolescence, and antisocial personality in adulthood” (see also Beauchaine  & McNulty, 2013; Essay 27, this volume).These correlations suggest that behaviors and disorders on the externalizing spectrum share considerable genetic etiology and neurobiological vulnerability, with unique non-shared environmental influences producing different symptom constellations over time (see Beauchaine  & McNulty, 2013; Beauchaine et  al., 2010; Lahey & Waldham, 2012).

Biological Vulnerability

Beginning with seminal work of Moffitt, most reviewers have concluded that some form of biological vulnerability, manifested in inherited or acquired neurobiological/neuropsychological

variation, and resulting in compromised brain functioning, is involved in the etiology of externalizing behavior (Caspi et al., 2014; Dodge et al., 2006; Gatzke-Kopp, 2011; Hill, 2002; Hinshaw & Lee, 2003; Moffitt, 2006; Peskin, Gao, Glenn, Rudo-Hutt, Yang, & Raine, 2012; Raine, 2013; Rutter, 2003). These vulnerabilities interact and transact with numerous potentiating environmental risk factors, which amplify externalizing behaviors (Beauchaine & McNulty, 2013; Hinshaw, 2013; Neuhaus & Beauchaine, 2013; Essays 11 and 27, this volume). However, establishing that a biological vulnerability has true causal effects on the progression of externalizing behavior, and that males and females intrinsically differ in terms of such vulnerability, is exceedingly challenging (Jaffee, Strait, & Odgers, 2012; Rutter et al., 2003). Most of the literature on vulnerabilities and risk factors for antisocial behavior is based on observational studies (Jaffee et al., 2012). However, since the gold standard for establishing causality is experimentation (i.e., manipulation of the putative vulnerability), which should preclude or potentiate future antisocial outcomes, depending on the direction of the manipulation (Compas & Andreotti, 2013), conducting experiments is indefensible ethically and therefore impossible in practice. Toward addressing this challenge, Rutter et al. (2003) proposed a set of criteria for identifying biological processes that may cause sex differences in psychopathology. If met, these criteria come as close as is possible to experimentation in establishing causality. Prior to a discussion of these criteria, however, a brief discussion of the sex chromosomes in humans (X & Y) is warranted, since all biological sex differences ultimately stem from chromosomal origins (Arnold, 2009, 2012; Bellott et al., 2014).

X and Y Chromosomes

As articulated by Hughes and Rozen (2012), the dominant theory of evolution of sex chromosomes holds that they descended from a once identical pair of autosomes that ceased recombining with one another and thus differentiated. Recombination suppression was selectively advantageous in the nascent Y chromosome because it restricted expression of Y-linked sexually antagonistic genes5 to males. However, this evolutionary strategy also had a disadvantage, as the lack of regular meiotic recombination left the Y chromosome vulnerable to gene loss and evolutionary decay, because the Y chromosome gradually lost its ability to trade defective Y genes for good X genes, except at the extreme ends of both

chromosomes. Consequently, the Y chromosome has lost most of the 1000 or so genes, and is currently thought to have only 52 active protein-coding genes (in contrast to 836 on the X chromosome; Balsara, Faerber, Spinner, & Feudtner, 2013). Two classes of genes remain in the non-combining region of the Y chromosome, termed the male specific region, and they have different functions (Bellott et  al., 2014; Skaletsky et  al., 2003). One class of genes, which are only expressed in the testes and have no homologues (counterparts) on the X chromosome, have a role in testis determination and spermatogenesis. For example, the Sry gene (sex-region determining), initiates differentiation of the fetal gonad into the male testes, which in turn triggers a prenatal cascade of androgen that is unique to males, as discussed below. A second class of genes, which are expressed in all parts of the body and have homologues on the X chromosome, have roles regulating the state of genome and in activation of other genes. Males, with one X chromosome, carry only one allele for most X-linked genes (Migeon, 2007). Females, with two X chromosomes, carry two alleles, which can be the same form (homozygous) or a different form (heterozygous). Male hemizygosity places males at a biological disadvantage compared with females (Migeon, 2007), as also discussed below.

Criteria for Inferring that a Behavioral Difference Is Sex-Linked

The Rutter et al. (2003) criteria are based on a three-level model of biological causes of sex differences. At the first, distal level, some aspect of the genetic difference between sexes (XY vs. XX chromosome complement) is implicated (Arnold, 2009, 2012). Alone, however, this does not explain a sex difference in vulnerability. Rather, such explanations occur at the second level through more intermediate biological causes. For example, being male or female may cause a different pattern of prenatal hormone production, with effects on programming of brain development. At the third level, proximal vulnerability or resiliency that derives from preceding Level 1 and Level 2 effects must be implicated directly in a causal mechanism that predisposes to sex differences in psychopathology. This third level, proximate effect must meet three criteria6 to explain a sex difference in psychopathology, two of which will be adopted in this essay. First, it must provide vulnerability or resiliency to a particular mental disorder. Second, the sexes must differ on the effect. Ultimately, it is also necessary to determine how links between and across levels are mediated (see Figure 14.1 for an example of this model). Eme

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Three level model Level 1 Distal biological cause SRY gene on y chromosome

Level 2 Intermediate biological cause Prenatal androgen surge

Level 3 Proximate effect Male rough & tumble play

Vulnerability to dominance aggression Figure 14.1  Criteria for inferring causality of a sex effect on psychopathology (Rutter et al., 2003). See text for a discussion of activity level as a specific example.

Sex difference in  proximate effect. A sex difference in the third level proximate effect that contributes to greater male prevalence in externalizing behavior could arise in several ways (Lahey et  al., 2006; Martel, 2013a; Zahn-Waxler et  al., 2006, 2008). More boys than girls may (a)  possess the vulnerability, (b)  score higher on the vulnerability, (c)  be affected more adversely by the vulnerability, and/or (d) be susceptible to interactions between the vulnerability and adverse environmental influences. Finally, there may be sex-specific genes that influence a vulnerability that occurs among boys (i.e., a gene × gene interaction; Meier, Slutske, Heath, Martin, 2011). Since most evidence for these various possibilities is focused on (a), I accord this the lion’s share of discussion. Rutter et al. (2003) determined that although no variables met their criteria for proximal vulnerabilities, there were a “range of good leads” (p.  1109). A  decade later, evidence supports such leads. Below I examine them, defined as proximate vulnerabilities that come closest to meeting the two criteria outlined above. In addition, possible biological bases for sex differences in proximate vulnerabilities are also examined. Evolutionary explanations for externalizing disorders have been addressed thoroughly by Martel (2013a) and are beyond the scope of this essay, so they will not be discussed except in those instances when they provide important illumination of proximate vulnerabilities. I  focus specifically on Moffit’s (2006) categories of inherited and acquired neuropsychological variations.

Inherited Neuropsychological Variation Temperament

It has long been known that from early infancy onward, children differ from one another in 244

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constitutionally based reactions to the environment (Kagan, 2013; Rothbart, 2012; Essay 10). Such individual differences, which have come to be known as temperament, are influenced across development by genes, maturation, experience, and gene × environment interactions. Although there is still debate over precise definitions of temperament, temperamental dimensions,7 and the best ways to measure them (Martel, 2013a; Mervielde & De Pauw, 2012), the Rothbart model is most widely used among developmentalists, and organized and directed the field (Eisenberg, 2012). This model, which conceptualizes temperament as including both reactive (i.e., approach, avoidance) and regulatory dimensions (Rothbart, 2012), has been adopted and adapted by Nigg (2006) and Martel (2013a) in their integrative models relating temperament to the development of psychopathology. There is general consensus that extreme expressions of temperamental traits, such as approach (trait impulsivity) and avoidance (trait anxiety) confer vulnerability to psychopathology (Martel, 2013a). Because the Nigg and Martel models of temperament converge on approach, avoidance, and regulatory aspects of temperament as most relevant for understanding sex differences in externalizing behavior, I focus discussion on these dimensions and their subtraits. These three dimensions can be viewed as the top of a hierarchical structural model from which subdimensions emerge (Nigg, 2006). (see Figure 14.2). Approach can be defined as a willingness to approach possible incentive or reward/reinforcement. It subsumes the subtraits of impulsivity and activity level (Martel, 2013a; Nigg, 2006). Avoidance can be defined as a readiness to withdraw in potentially unrewarding or uncertain contexts. It subsumes the subtrait of fear/anxiety level (Martel, 2013a; Nigg, 2006).

Approach

Impulsivity

Withdrawal

Activity level

Effortful control

Fear

Empathy/ affiliation Figure 14.2  Temperamental traits.

Control, which is designated as effortful control in child temperament models (Nigg, 2006), is a construct first introduced by Rothbart and colleagues (Rothbart, 2012; Rothbart & Bates, 2006; Rueda, 2012) to describe self-regulatory aspects of temperament. It can be defined as “the efficiency of executive attention—including the ability to inhibit a dominant response and/or to activate a subdominant response, to plan, and to detect errors” (Rothbart & Bates, 2006, p. 129). Finally, empathy, which in the Nigg (2006) model is an element in the subtrait of affiliation, is influenced by all three superordinate traits. Most of the evidence for various ways in which sex differences in temperament contribute to greater male prevalence rates of externalizing behavior focuses on mean differences between the sexes. Three considerations must be kept in mind when interpreting such mean differences, especially those deemed “small.” First, differences in variability must also be examined, because even when a mean difference is small, there may be a very large sex difference in the tails, or extreme ends of the distribution (Halpern, 2012). Second, a small sex difference in a trait can cause a large difference in externalizing behavior over time through gene–environment interplay, including gene–environment correlation (rGE) and gene × environment interaction (G×E) (Beauchaine & Gatzke-Koop, 2013; Knafo & Jaffee, 2013). Gene–environment correlation refers to processes through which an individual’s genotype influences his or her exposure to certain environments. By selecting their environments, individuals create their own experiences, which are therefore correlated with genetic propensities (Plomin, DeFries, Knopik, & Neiderhiser, 2012). Two forms of rGE, active and evocative, explain how small sex difference may cause a large difference in externalizing

behavior over time. In active rGE, a child with an impulsive temperament may actively seek out risky experiences and environments and thus increase the likelihood of engaging in an externalizing behavior such as illegal substance use (Beauchaine & Gatzke-Kopp, 2013). In evocative rGE, a child’s impulsive temperament increases the likelihood of evoking negative interactions from others, thereby increasing the probability of engaging in oppositional/defiant behavior. It is estimated that a typical child with ADHD engages in an astonishing half a million negative interchanges each year (Pelham & Fabiano, 2008). Thus, an initially small difference early in life may “snowball” during development, creating larger and larger phenotypic effects (Plomin et al., 2012). Gene-environment interaction refers to processes through which environments moderate effects of genes on behavior, or through which genes moderate effects of environments on behavior (see Beauchaine & Gatzke-Kopp, 2013).8 For example, Caspi et al. (2002) reported that the low activity variant of the MAOA gene (MAOA-L) confers vulnerability to violent aggression, but only in contexts of maltreatment, an environmental variable. Thus, maltreatment moderates effects of genetic vulnerability on aggression. Although there was no main effect of MAOA on antisocial behavior, an interaction of very large effect size was found. Males with the MAOA-L genotype, who comprised only 12% of the sample, accounted for 44% of violent convictions. Moreover, 85% of those with the MAOA-L genotype who experienced maltreatment developed some form of antisocial behavior. In fact, conjoint effects of genetically influenced temperamental vulnerabilities and environmental risk factors are often synergistic Eme

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rather than additive (Beauchaine, Klein, Crowell, Derbridge,  & Gatzke-Kopp, 2009). Acting together, they may therefore increase rates of antisocial behavior exponentially (Raine, 2013). Third, to the extent that a child has more than one vulnerability trait and faces a potential cascade of interactive effects with adverse environments, the cumulative impact may also be synergistic (Evans, Li, & Whipple, 2013; Masten & Cicchetti, 2010; Neuhaus  & Beauchaine, 2013). I  now turn to “good leads” for sex differences in temperamental traits that help explain the greater male prevalence in externalizing behavior.

Control

Interest in self-control unites all behavioral and social sciences, and impaired self-control may be the single most important variable in explaining developmental origins of antisocial behavior (Moffitt, 2012; Moffitt et  al., 2011). Among various processes involved in self-control, there is robust empirical support for two dimensions of temperament: trait impulsivity and effortful control (Eisenberg, Spinrad,  & Eggum, 2010; Eisenberg et al., 2013; Martel, 2013a; Nigg, 2006).

Trait Impulsivity

Those who are trait impulsive often engage in reactive undercontrol and rapid approach to rewarding stimuli (Eisenberg et al., 2010, 2013; Martel, 2013a; Nigg, 2006). A commonly accepted definition of impulsivity is “the tendency to act on immediate urges, either before consideration of possible negative consequences or despite consideration of negative consequences” (DeYoung, 2010, p. 487). This broad definition should not be interpreted as indicating that impulsivity is a unitary construct, as current research and theory suggests it comprises several different components that underlie various forms of impulsive behavior (Cross, Copping, & Campbell, 2011; Sharma, Markson, & Clark, 2014). One such trait, designated “disinhibition vs. constraint/conscientiousness,” reflects impulsive behaviors that arise from lack of planning, or failure to persist in the face of challenges. This is related strongly to externalizing behaviors (Sharma et al., 2014), and closely reflects DSM-5 symptoms such as “impatience, difficulty in delaying responses, and frequently interrupting and intruding on others” (APA, 2013, p. 60). Considerable evidence suggests that DSM-derived ADHD scales and similar measures of impulsivity capture an almost entirely heritable 246

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trait (Beauchaine & Gatzke-Kopp, 2013). Thus, impulsivity defined and measured by such scales is an extremely valid latent construct (Neuhaus & Beauchaine, 2013).

Vulnerability

There is widespread agreement that DSM-defined behavioral impulsivity is a major vulnerability to development of more severe externalizing behavior (see Beauchaine  & McNulty, 2013; Farrington  & Loeber, 2012; Frick, 2012; Moffitt, 2012; Neuhaus & Beauchaine, 2013; Tackett et al., 2012; Raine, 2013). This neurobiological vulnerability, described in detail in Essays 11, 12, and 27, interacts with environmental risk factors to promote progression along the externalizing spectrum (see Beauchaine  & McNulty, 2013, Beauchaine et  al., 2010). Children with ADHD and ODD are particularly likely to progress toward and/or engage in delinquent behavior when reared in environments characterized by various psychosocial adversities such as hostile and inconsistent parenting, maltreatment, and neighborhood violence/criminality (see Beauchaine et al., 2013). Although only about half of such children become delinquent, antisocial adult males “almost invariably traverse a developmental pathway that begins with hyperactivity/ impulsivity in toddlerhood, following by ODD in early childhood, early-onset CD in elementary school, substance use disorders in adolescence, and antisocial personality in adulthood” (Beauchaine et al., 2013, p. 481).

Sex Difference

Research on ADHD provides strong support for a sex difference in behavioral impulsivity among children. In epidemiological studies of ADHD—for which referral bias is unlikely—males outnumber females on the hyperactive-impulsive dimension by a ratio at least 2:1, but usually higher not only in the United States but also globally (Erskine et  al., 2013; Nigg, 2013; Rutter et  al., 2003; Willcutt, 2012). When higher clinical cutoffs are used, higher sex ratios favoring males are observed (Szatmari, Offord, & Boyle, 1989).

Biological Basis of Sex Differences

There are at least two possible biological bases for the sex difference in trait impulsivity. First, in the dual-pathway model of ADHD, dysfunction is posited in either the frontostriatal (mesolimbic) pathway, which mediates bottom-up processing of incentive salience including reward responding,

and/or the ventromedial prefrontal (mesocortical) pathway, which mediates top-down executive control of attention and emotion (Arnsten & Rubia, 2012; Castellanos, Sonuga-Burke, Milham,  & Tannock, 2006; Diamond, 2013; Lumen, Tripp, & Scheres, 2010; Raine, 2013; Rubia, 2011; Sonuga-Barke, 2010, 2011; Tomasi  & Volkow, 2012a). Early in life, behavioral impulsivity is mediated by deficiencies in the bottom-up mesolimbic pathway. This dysfunction includes lower tonic mesolimbic dopamine levels and less phasic dopamine responding to rewards (see Sagvolden, Johansen, Aase,  & Russell, 2005). Those with ADHD engage in excessive-reward seeking behaviors—expressed as hyperactivity-impulsivity—in part to up-regulate this underresponsivity, which is experienced as an aversive mood state (Beauchaine et  al., 2013; Matthys, Vanderschuren,  & Schutter, 2013; Neuhaus  & Beauchaine, 2013). Importantly, there appear to be large sex differences in mesolimbic dopamine system dysfunction, which is observed consistently among children with the hyperactive-impulsive and combined presentations of ADHD (e.g., Campo, Chamberlain, Sahakian, & Robbins, 2011; Martel, 2013a; Tomasi & Volkow, 2012a). As reviewed by Martel (2013a), healthy adult males appear to have a lower presynaptic dopamine tone and lower extrastriatal dopamine receptor density and availability compared with healthy adult females. A limitation of these findings is that, to my knowledge, similar studies have yet to be conducted with children. Importantly, deficiencies in the top-down mesocortical system are likely to play and ever-increasing role in the development of externalizing behaviors as children mature (Beauchaine & McNulty, 2013). Indeed, although extensive pruning of prefrontal brain regions responsible for executive function and behavior regulation is observed as normal children mature into adolescence (e.g., Gogtay et al., 2004), such pruning is absent among delinquent boys (De Brito et al., 2009). Thus, a picture emerges in which many delinquent males are doubly vulnerable given deficiencies in both the mesocortical and mesolimbic networks. Second, as previously mentioned, the Meier et al. (2012) study which found sex-specific effects on childhood CD, suggests that one possible mechanism through which a sex-linked vulnerability could emerge is through an interaction of an X-linked gene such as the low activity variant of the monoamine oxidase A gene (MAOA-L) with an environmental variable such as maltreatment,

which greatly increased risk for antisocial behavior in males in the seminal study of Caspi et al. (2002). A recent meta-analysis of 27 studies conducted through August 2012 (Byrd & Manuck, 2014) provides strong confirmation of the Caspi et al. (2002) findings. Thus, despite some null results (see Haberstick et al., 2014 for the most recent example)9 overall, the MAOA-L gene is a consistent biological vulnerability for males, which interacts with maltreatment to increase risk for development of antisocial behavior (Goldman & Rosser, 2014). This vulnerability appears to be due to the association of MAOA-L with “altered neural responses to affective stimuli, including enhanced amygdala reactions to facial expressions of emotion or emotion recall, less engagement of prefrontal regulatory regions, and disrupted functional and effective (top-down) connectivity within corticolimbic circuitry of emotion processing” (Byrd & Manuck, 2014, p. 15). It is possible that early maltreatment exacerbates these neural deficits, thereby increasing impulsive aggression (Byrd & Manuck, 2014; Buckholtz & Meyer-Lindenberg, 2008; Goldman & Rosser, 2014; Meyer-Lindenberg et al., 2006). Because males are approximately three times more likely than females (37% vs. 12%) to have this genotype (Caspi et al., 2002; Eme, 2013; Enoch, Steer, Newman, Gibson, & Goldman, 2010; Haberstick et al., 2005; Sabol, Hu, & Hamer, 1998), there appears to be significant support for a sex-specific genetic effect that increases male risk for antisocial behavior.

Effortful Control

Effortful control is subserved by the executive attention (EA) system, one of three attentional networks, each supported by independent brain circuits (Petersen  & Posner, 2012; Rueda, 2012). Effortful control also includes the abilities to shift and focus attention when needed, and to inhibit inappropriate behavior or activate behavior when one does not want to do so (Eisenberg et al., 2010, 2013). In short, EC is the temperamental disposition to exercise self-regulation when doing so is difficult (Diamond, 2013).

Vulnerability

Low EC is the vulnerability most often linked to externalizing behaviors across the lifespan since by definition impairment in the ability to inhibit inappropriate impulses and follow societal norms is expected to increase the likelihood of externalizing behavior (Tackett et al., 2012). Eme

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Sex Difference

A recent meta-analysis by Else-Quest, Hyde, Goldsmith and Van Hulle (2006) reported a large sex difference in EC disfavoring males (d = –1.01), and greater male variability. Subsequent research supports this sex difference (Else-Quest, 2012).

Biological Basis

As discussed previously, limited evidence points toward to a sex difference in dopaminergic function, which is involved in EC (Martel, 2013a). Moreover, research on sex differences in brain organization has found greater overall anatomical and functional connectivity in individual networks in the adult female than in the adult male brain (Ingalhalikar et  al., 2013; Tomasi  & Volkow, 2012b). These findings have received strong support from the first study to analyze the brain as whole to provide insight into the organization and integration of individual brain networks known as the structural connectome (Ingalhalikar et  al., 2013). The study analyzed the structural connectome in a large sample of 949 (ages 8–22 years; 428 males, 521 females). Results revealed fundamental sex differences in structural architecture of the human brain. During development, male brains are structured to optimize intrahemispheric communication, whereas female brains are structured to optimize interhemispheric communication. These findings suggest that women’s higher brain connectivity may optimize functions that require integration and synchronization across large cortical networks such as executive attention (Tomasi, personal communication, July 13, 2013), which in turn may contribute to their higher level of EC.

Activity Level

Activity level is a subdimension of approach (Martel, 2013a; Nigg, 2006). In turn, activity level is comprised of a number of additional subdimensions, including high-intensity pleasure, as exemplified by rough and tumble play (R&T; Else-Quest, 2012), which is synonymous with play fighting (Geary, 2010; Pellis & Pellis, 2011), and was used originally to describe play chasing, fleeing, and wrestling among rhesus monkeys (Fry, 2005). It is the most common form of play among mammals and typically involves components such as chasing, wrestling, pouncing and jumping on partners, pushing, etc. (Geary, 2010).

Vulnerability

Play fighting is blend of competition and cooperation (Pellis  & Pellis, 2012). It is competitive 248

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in humans, because participants attempt to gain some advantage over one another, such as throwing and pinning the partner to the ground. It is cooperative and thus differs from physical aggression because participants usually do not take advantage of their success in a way that occurs in real fighting. However, despite the cooperative component, play fighting frequently puts males “on the verge of aggression” (Maccoby, 1998, p. 51). In fact, teachers often have difficulty telling the difference between play fighting and real fighting, and see play fighting as problematic because it can turn into real fighting (Blackmore, Berenbaum, & Liben, 2008). Play fighting increases in frequency during the juvenile years and then declines slowly, often merging into real fighting during adolescence (Geary, 2010). The universal distribution of R&T among mammals and among humans across virtually every culture suggests that it evolved to serve the adaptive functions of allowing practice of fighting skills under safe conditions, and establishment and maintenance of immediate or long term social dominance necessary for success in one-on-one competitions, one manifestation of which is physical aggression (Archer, 2009; Buss, 2012; Buss  & Schmidt, 2011; Fry, 2005; Geary, 2010, 2012; Martel, 2013a; Pinker, 2011). From an evolutionary perspective, success in dominance aggression served as a solution to several adaptive problems, including enhancing reproductive success (Buss, 2012).

Sex Difference

Research on activity level has consistently found that boys are more active and impulsive, and more likely to derive pleasure from engaging in high-intensity activities than girls (Else-Quest, 2012). Furthermore, the sex difference in R&T is much larger than for high-intensity-pleasure (Geary, 2010). Although girls do engage in R&T, males do so much more frequently and with more vigor and greater zest. This sex difference is most evident with groups of three or more children when adults are not present. Absent adult supervision, groups of boys engage in R&T three to six times more frequently than girls. The difference emerges by age 3 years, peaks between ages 8 and 10, and increases in roughness as boys enter puberty, where the distinction between play and genuine physical aggression begins to dissolve. Furthermore, unlike younger boys for whom physical aggression is often associated with social rejection, older tough boys who engage in physical aggression in the form of bullying are relatively

popular among both boys and girls (Juvonen  & Graham, 2014; Olweus, 2013). This would be expected from an evolutionary perspective as social dominance hierarchies become established (Geary, 2010). In sum, since males engage much more frequently and with more vigor and greater zest in play fighting than females, it follows that they can be far more easily socialized than females into antisocial forms of real fighting.

Biological Basis

Greater male exposure to prenatal testosterone provides a proximate biological explanation for the sex difference in R&T. As described by Blakemore et al. (2008) and Hines (2011), at 6–7 weeks prenatally the Sry gene on the Y chromosome induces differentiation of neutral gonads into testes. In the absence of the Sry gene, and with involvement of other genes, neutral gonads develop into ovaries at about 12 weeks (Blakemore et al., 2008). The testes begin to produce testosterone prenatally, whereas ovaries do not. This sex difference appears to be maximal between 8 and 24 weeks and then tapers off by birth. Most importantly from the perspective of this essay, this surge affects brain development and therefore behavior. There are thousands of studies across species showing unequivocally that sex hormones have organizational effects because they produce permanent changes to brain structures and the behaviors these structures subserve, including R&T and aggression (Berenbaum  & Beltz, 2011; Hines, 2011, 2013). For example, female offspring of rhesus macaque monkeys treated with testosterone during pregnancy show increased R&T as juvenile animals when compared with offspring of nontreated controls (Hines, 2011, 2013). The experimental nature of animal studies makes it possible to rule out psychosocial environmental confounds, an ever-present source of ambiguity in human studies (see above). The most extensively studied “natural experiment” on the effects of testosterone on human behavior is congenital adrenal hyperplasia (CAH). This is an autosomal genetic recessive disorder that occurs in approximately 1:5000 to 1:15,000 male and female births, which results in increased production of androgen from the adrenal gland. Among females, exposure to excess androgen beginning early in gestation has many of the same effects as testosterone produced naturally by the testes among males during gestation (Blakemore, Berenbaum,  & Liben, 2008).

Although girls with CAH are typically born with partially virilized genitalia (Pasterski et  al., 2011; Hines, 2013), there are rare cases when genital virilization is so severe that girls were mistaken for boys and reared as such (Hines, 2013). Nowadays, the disorder is typically diagnosed at birth, and girls are surgically feminized during infancy, reared as female, and provided ongoing medical treatment to prevent further virilization (Berenbaum  & Belz, 2011). Findings in which CAH females are followed parallel those from research on testosterone among animals. Affected girls exhibit more R&T and more physical aggression compared with their non-affected sisters and unrelated girls who are matched on age and SES (Berenbaum & Belz, 2011; Berenbaum, Blakemore, & Belz, 2011; Hines, 2011; Pasterski, Golombok,  & Hines, 2011; Wong, Pasterski, Hindmarsh, Geffner, & Hines, 2013). Although these data provide support for the influence of the sex difference in prenatal androgen exposure on R&T, there remains a potential confounding influence of parental socialization in humans. If parents treat their CAH daughters differently because of girls’ genital virilization at birth, this difference in parental treatment rather than exposure to androgen could explain at least some of the more male-typical behavior of CAH girls (Wong et al., 2013). However, three lines of research argue against this interpretation. First, severity of CAH in females is correlated positively with masculine behaviors. Furthermore, because genitalia of CAH girls are typically not completely virilized, their prenatal androgen exposure tends to be less extensive than that of a typical boy (Pasterski et  al., 2011). Thus, their behavior might be even more masculinized if their exposure was the same as boys. Second, normal variability in maternal testosterone during pregnancy correlates with greater male typical play in non-CAH females. Finally, although parents report encouraging less girl-typical and more boy-typical toy play in CAH girls, this differential parental socialization is, at least in part, a response to their daughter’s increased interest in masculine play (Wong et al., 2013), exemplifying active gene-environment correlation.

Fear/Trait Anxiety

Fear/trait anxiety represents affective reactivity associated with the temperamental dimension of withdrawal. As an individual difference, fear represents the propensity to passively avoid threat and uncertainty Eme

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(Martel, 2013a; Nigg, 2006). As an emotion, fear comprises experiences one has in the presence of threat (either real or imagined) and is subserved by neural structures that evolved to detect danger and produce responses that maximize the probability of survival (LeDoux, 1996, 2013; see Essay 13).

Vulnerability

There is convincing evidence that low trait anxiety—instantiated peripherally in poor skin conductance conditioning—characterizes adult criminals, individuals with psychopathy, and antisocial adolescents (APA, 2013; Raine, 2013). Poor skin conductance fear conditioning may reflect insensitivity to punishment, as well as reduced anxiety and guilt associated with wrong-doing (Frick & Morris, 2004; Matthys, Vanderschuren, & Schutter, 2013; Van Goozen & Fairchild, 2008). This explanation is supported by the finding that adult criminals, assessed at age 23 years, showed poor skin conductance fear conditioning at age 3 years (Gao, Raine, Venables, Dawson,  & Mednick, 2010; Raine, 2013). In addition to findings of poor skin conductance conditioning among externalizing samples, low electrodermal activity at rest marks vulnerability to externalizing behavior across the lifespan, including ADHD, ODD, CD, ASPD, and both psychopathy and psychopathic traits (Lorber, 2004; see also Beauchaine et al., 2013).

Sex Difference

There is a small sex difference in fear among children, evident in infancy, with girls being more fearful than boys (d = 0.12; Chaplin & Aldao, 2013; Else-Quest, 2012; Else-Quest et al., 2006; Gartstein & Rothbart, 2003; Olino et al., 2013). Evidence for this difference is based mainly on parent reports of their children’s distress or withdrawal from sudden changes or novelty (Chaplin & Aldao, 2013). This difference increases from kindergarten through 6th grade, when girls are twice as likely as boys to be rated by teachers (17.4% vs. 8.6%) as fearful (i.e., “fearful or afraid of new things; is worried, worries about many things; cries easily”; Cote, Tremblay, Nagin, Zoccolillo, & Vitaro, 2002). By adolescence and continuing into adulthood, the sex difference increases to d –0.40 (Else-Quest, 2012), when women are twice as likely as men to have a phobia (APA, 2013). Sex differences in anxiety and phobias are even larger for situations that pose physical threats to bodily integrity, the type of fear most relevant to avoiding physical aggression (Buss, 2012; Campbell, 1999; Campbell & Cross, 2012). 250

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Biological Basis

Low resting heart rate, a measure of autonomic hypoarousal, is the best-replicated biological correlate of conduct problems, aggression, and psychopathy from childhood to adulthood (APA, 2013; Lorber, 2004; Portnoy et al., 2013; Raine, 2013). When measured in childhood, low resting heart rate predicts both adolescent aggression and LCP conduct problems (APA, 2013; Portnoy et al., 2013). It is theorized that autonomic hypoarousal indexes lack of fear or anxiety (Portnoy et al., 2013; Raine, 2013). Furthermore, beginning at age 3 years, a sex difference emerges, with boys having lower resting heart rates than girls, by approximately 6.1 beats per second (d = 0.36) (Raine, 2013).

Empathy

In the Nigg (2006) model, empathy is an element in the temperament subdimension of affiliation, defined as social approach-cooperation. It is viewed as being influenced by all three superordinate dimensions (i.e., approach, withdrawal, control; Nigg, 2006). In the broadest sense, empathy can be conceptualized as an adaptive behavior which includes social responses to various emotional expressions such as pain and fear, and is generally viewed as having two forms (Vachon, Lynam, & Johnson, 2014). Cognitive empathy is the ability to detect or understand emotions, whereas affective empathy is a visceral response that stems from comprehension of another’s emotional state or condition, and is identical or very similar to what the other person is feeling or would be expected to feel (Eisenberg et al., 2006).

Vulnerability

It is commonly accepted that a deficit in affective empathy is a vulnerability to development of externalizing behavior, since individuals with such a deficit may not be deterred from antisocial behaviors by expressions of distress by victims (Campbell & Cross, 2012; Raine, 2013; Vachon et al., 2013). However, a recent meta-analysis challenges this assumption (Vachon et al., 2013). In fact, the relationship between empathy and various forms of aggression (verbal, sexual, physical) is surprisingly weak. For example, empathy and physical aggression are only slightly correlated, r = –0.12. Nevertheless, the true relationship between empathy and aggression may be stronger if empathy is reconceptualized to include more maladaptive, clinical manifestations of callous disregard for the feelings of others and lack of remorse for the misery caused by one’s

actions (Vachon, Lynam, & Johnson, 2013). This suggestion is consistent with the well characterized relationship between callous-unemotional (CU) traits (e.g., lack of guilt and remorse, lack of concern for the feelings of others) and serious conduct problems among juveniles (Frick, Ray, Thornton, & Kahn, 2014). Indeed, among juveniles with severe conduct problems, those with elevated CU traits are at higher risk for severe and persistent antisocial outcomes, even after controlling for severity of conduct problems, age of onset, and common comorbid conditions (Frick, 2012; Frick et al., 2014; see also Essay 21). Thus, an extremely low level of empathy confers vulnerability to externalizing behaviors.

Sex Difference

Boys are more likely than girls to exhibit low levels on various measures of empathy in general, and on measures of the lowest levels of empathy as indexed by CU traits. With regard to empathy in general, reviews by Zahn-Waxler et  al. (2008) and Geary (2010) indicate that male newborns are less responsive to social stimuli and less able to make eye contact than female newborns. Girls 12 to 20 months old respond to the distress of other people with greater empathic concern than boys. Preschool girls, who are more socially aware than infants, exhibit more (a) empathy and prosociality, (b) social skills, (c) remorse after transgression, and (d)  understanding of others’ emotions, problems, and intentions than boys. Greater social sensitivity of girls continues throughout childhood and adolescence. Meta-analytic reviews of sex differences in empathy indicate a medium effect size (d = 0.60) favoring girls on self-reports (Eisenberg et  al., 2006) and a small effect size (Hedges’ g  =  0.13) favoring girls on observational measures of facial, vocal, and behavioral expression (Olino, Durbin, Klein, Hayden,  & Dyson, 2013. Following from these findings, Baron-Cohen (2003, 2011) advanced an Empathizing-Systemizing theory of sex differences, which proposes that on average females have a stronger brain-based drive to empathize than males, and that males on average have a stronger drive to systemize (to analyze or construct rule-based systems). With regard to CU traits, boys score higher than girls on various measures from preschool to adolescence (Essau, Sasagawa,  & Frick, 2006; Ezpeleta, Osa, Granero, Penelo,  & Domenech, 2013; Frick, Bodin,  & Barry, 2000; Frick, personal communication, September 2, 2013; Humayun, Kahn, Frick,  & Viding, 2014; Rowe et al., 2010). For example, in a large sample

of 3687 seven-year-old twin pairs representative of the population in the United Kingdom, 73% of boys versus 27% of girls were rated high on CU traits as assessed by teacher report (Humayun et al., 2014; Viding, Blair, Moffitt, & Plomin, 2005).

Biological

Baron-Cohen (2003, 2011) has proposed that the male fetus’s higher level of prenatal testosterone organizes the brain toward stronger systemizing and weaker empathy. Evidence for this theory comes from the Cambridge Fetal Testosterone Project, in which amniotic fluid obtained through amniocentesis is examined for testosterone. Among children who were followed up through age 11, higher levels of fetal testosterone were correlated with lower frequency of eye contact at 12 months, poorer quality of social relationships at 48  months, and diminished empathy at 48 and 96 months (Baron-Cohen et  al., 2011). Furthermore, because the procedure of amniocentesis probably underestimates quantity and duration of prenatal exposure to testosterone (Geary, 2010), the relationship between prenatal testosterone and empathy may actually be stronger.

Conclusion

First, it should be noted that if the sex difference favoring males on these five temperamental traits were aggregated, it is likely that their power to explain the almost exclusively male character of chronic physical aggression, violence, and life-course-persistent antisocial behavior would be greatly enhanced (Del Guidice, Booth, & Irwing, 2012). Second, one concise way of organizing the preceding discussion of the sex differences in five biologically based temperamental traits, which help explain the greater male prevalence in externalizing behaviors, is to conceptualize them within the framework of two commonly accepted childhood-onset developmental pathways of serious antisocial behavior which tend to be life-course persistent (Frick, 2012; Frick et al. 2014). The first pathway involves problems with emotional and behavioral dysregulation, reflecting the temperamental traits of high impulsivity, low effortful control, and high activity level. The second pathway appears to involve problems marked by callous, unemotional traits, and can be conceptualized as reflecting the temperamental traits of low fear and extremely low empathy, often accompanied by high impulsivity and low effortful control. Greater male levels of low fear, empathy, and effortful control, coupled with higher levels of impulsivity and activity level help explain the greater male prevalence on both pathways. Eme

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Acquired Neuropsychological Variation

Acquired variation in neuropsychological function can occur in at least three ways. First, adverse prenatal, perinatal, and post-natal events can cause traumatic brain injury (Gatzke-Kopp, 2011; Shannon-Bowen & Gatzke-Kopp, 2013; Essay 23). Second, environmental events, especially during fetal and early postnatal development, can produce changes in both function and structure of the brain that influence subsequent development (Beauchaine, Neuhaus, Zalewski, Crowell,  & Potapova, 2011; Lewis, Relton, Zammit,  & Davey-Smith, 2013; Owens & Hinshaw, 2013; Rutter, 2008) as a result of epigenetic effects in which experience “gets under the skin” (Essex et al., 2013, p. 58) and functional changes in neural networks (Neuhaus & Beachaine, 2013). Third, errors that occur during chromosome segregation or DNA replication can result in genetic mosaicism (i.e., cells in an individual’s body have distinct genotypes (Biesecker & Spinner, 2013; Lupski, 2013). Mosaicism, which has long been known to cause medical disorders such as cancer, has now been linked to neurodevelopmental disorders such as epilepsy, autism spectrum disorder, and intellectual disability (Poduri, Evrony, Cai, & Walsh, 2013).

Vulnerability

Among several areas of functioning that acquired neuropsychological impairment affects, two have the most research support: intelligence (i.e., general mental ability) and executive function (Peskin et al., 2012; Essay 22).

Intelligence.

Intelligence can be defined as: “the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience. It is not merely book learning, an academic skill, or test-taking smarts. Rather, it reflects a broader and deeper capability for comprehending our surroundings—“catching on,” “making sense” of things, or “figuring out” what to do” (Nisbett et al., 2012, p. 131). Intelligence is a strong predictor of a wide variety of outcomes in modern life, including academic achievement, job status, income, health self-care, and law-abidingness (Gottfredson, 2008; Hunt, 2011). Literature reviews have consistently identified low IQ as a vulnerability for engaging in antisocial behavior (Farrington, Loeber, & Ttofi, 2012; Loeber & Pardini, 2008; Losel & Farrington, 2012). Furthermore, because low IQ measured as early as age 3 years predicts subsequent antisocial behavior 252

Sex Differences

(Farrington et al., 2012), it represents a vulnerability to externalizing behavior and not merely an effect of externalizing behavior. Intelligence is correlated highly with academic achievement (r = 0.40 to 0.80; Deary, 2011; Gottfredson, 2008; Hunt, 2011; Mackintosh, 2011; Neisser et al., 2012), so low IQ likely affects antisocial behavior at least in part through academic failure (Farrington, 2012). Children who experience academic failure are less likely to be socialized into academic pursuits that direct them away from negative social influences, and more likely to associate with deviant peers who model and reinforce antisocial behaviors (Jaffee et al., 2012).

Executive functions.

As noted above, executive functions (EFs) can be defined as a “family of top-down mental processes needed when you have to concentrate and pay attention, when going on automatic or relying on plain instinct or intuition would be ill-advised, insufficient, or impossible” (Diamond, 2013, p. 136). Three core EFs of inhibition, working memory, and cognitive flexibility are essential for cognitive, social, and psychological development, and hence for success in school and life (Diamond, 2013). Given their critical importance for successful functioning in virtually every major domain in life, and given that they are the most sensitive of all cognitive functions to cerebral insult (Stuss & Levine, 2002), it is not surprising that impaired EF is a major vulnerability for externalizing behavior (Loeber & Pardini, 2008; Morgan & Lilenfeld, 2000; Peskin et al., 2012; Schoemaker, Mulder, Dekovic, & Matthys, 2013, Essay 22). Furthermore, findings that (1) impaired EF, especially inhibition, is associated with externalizing behaviors in preschool children (Shoemaker et al., 2013), and (2) neuropsychological deficits at age 5 predict low self-control and increased vulnerability to conduct problems in early adolescence (Jackson & Beaver, 2013), support the interpretation of impaired EF as a vulnerability to and not just an effect of externalizing behavior.

Sex Difference

Greater vulnerability of males from womb to tomb to a host of biological hazards that increase risk for impaired intellectual and executive functioning is well established (Geary, 2010; May, 2007; Migeon, 2007). Boys (a)  are at greater risk of death or damage from almost all obstetric complications (Balsara, Faerber, Spinner,  & Feudtner, 2013; Baron  & Rey-Casserly, 2010; Geary, 2010; Kent, Wright,  & Abdel-Latif, 2012; Kraemer,

2000; Lawn, Biencowe, Darmstadt,  & Bhutta, 2013; Samara, Marlow,  & Wolke, 2008; Swamy, Ostbye, & Skjaerven, 2008); (b) are more vulnerable and experience greater deficits than females from prenatal exposure to various neurotoxins (e.g., cocaine, alcohol, marijuana, lead; Lewis & Kestler, 2012); (c) are 20% more likely than girls to die in utero, during infancy, and in preschool (Davis et al., 2007; Migeon, 2006, 2007); (d) are more vulnerable than girls to most physical hazards, toxic exposures, infections, and malnutrition (Rutter et  al., 2003); and (e) suffer more physical, social, and cognitive deficits than girls when growing up in poor conditions such as poverty and inadequate health care (Geary, 2010). Unequivocal finding of a greater male vulnerability to a host of adverse biological hazards receives additional support from an evolutionary perspective, which, counterintuitively, conceptualizes this vulnerability as a consequence of adaptive responding to stress (Gatzke-Koop, 2011; Ellis & Del Guidice, 2014; Martel, 2013a). This perspective is based on the proposition that individuals differ in their sensitivity to both negative and positive environments (Belsky & Pluess, 2013; Del Giudice, Ellis, & Shirtcliff, 2011; Ellis, Boyce, Belsky, Bakermans-Kreanenburg, & van Ijzendoorn, 2011; Ellis & Del Guidice, 2014; Ellis, Del Guidice, & Shirtcliff, 2013; Gatzke-Koop, 2011). In the context of social behaviors, individuals who thrive in supportive environments and are therefore sensitive to context may be especially vulnerable to aggression and related outcomes in adverse environments. Such differential sensitivity to context is an example of a broader developmental concept called phenotypic plasticity (e.g., Belsky & Pluess, 2013). From an evolutionary perspective, developmental plasticity, which is conferred through several neurobiological mechanisms (see Ellis et al., 2011), marks an adaptive phenotype, since it allows the organism to be shaped by early experience, conferring increased ability to confront its likely rearing environment (Ellis & Del Guidice, 2014; Ellis et al., 2011). Moreover, since it is likely that this plasticity is specific to different domains rather than a general plasticity to all domains (Belsky & Pluess, 2013), and that there is no “best” strategy for responding to stress because the strategy that is “best” is a function of the parameters of environmental stressors, the two sexes can be expected to differentially sensitive to different domains/kinds of stressors since historically they have faced different adaptive challenges (Ellis et al., 2011; Martel, 2013a). Males

may be generally more sensitive to environmental signals of adversity (Gatzke-Koop, 2011; Martel, 2013a) since from an evolutionary perspective and, in contrast to females, they have had to develop the traits that enable them to successfully adapt to the stress/adversity involved in dominance aggression. Gatzke-Koop (2011) and Martel (2013a) have proposed that this enhanced sensitivity begins in the prenatal period with the enhanced male sensitivity to prenatal stressors being a signal to the developing male fetus that it will be born into a high risk environment, thus suggesting the need for strategies that will facilitate dominance aggression and risk taking (Gatzke-Koop, 2011; Martel, 2013a).

Biological Basis

There are no doubt a host of biological variables that may contribute to greater male biological vulnerability. Four variables have sufficient evidence to qualify as “good leads”:  male hemizygosity for the sex chromosomes, and greater male vulnerability to effects of fetal programming because of greater male developmental immaturity, greater male vulnerability of the placenta to prenatal insults, and greater male vulnerability of the mesolimbic dopamine system to prenatal insults.

The hemizygotic male.

As discussed previously, with one X chromosome, males carry only one allele for most X-linked genes (Migeon, 2007). With two X chromosomes, females carry two alleles, which can be the same form (homozygous) or a different form (heterozygous). Male hemizygosity, coupled with female cellular mosaicism (discussed next) places males at a biological disadvantage compared with females (Migeon, 2007) because they are more vulnerable to X-linked genetic mutations.

Vulnerability to X-linked gene mutations.

All mammals compensate for the sex difference in X chromosomes by transcribing only a single X chromosome in cells of both sexes (Migeon, 2011). This results in female cellular mosaicism because the typical female has a mixture of two distinct cell lines with either only the paternal or maternal chromosome being functional/ active (Migeon, 2007). Mosaicism is the result of a process typically referred to as X inactivation. Inactivation is an evolutionary adjustment of what would otherwise be a harmful excess of X-linked genes in females (Harper, 2011). One Eme

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of the female X chromosomes becomes randomly inactivated early in embryogenesis (Cooper, 2011), and all female mammals are effectively mosaics for two distinct cell lines with either the maternal or paternal X being inactivated (Migeon, 2007).10 Given only one active X chromosome, males with any deleterious gene mutations always experience failure of the function encoded by the gene (Migeon, 2007). However, among female carriers of an X-linked mutation, random inactivation of one of the two X chromosomes results in approximately 50% of their cells having the normal gene on the active X chromosome, with the mutation being on the inactivated X chromosome. Hence, females typically have enough normal cells to perform the function that has been compromised by a defective gene, or at least ameliorate its deleterious effect (Migeon, 2007). As a consequence, males are far more vulnerable than females to a host of X-linked diseases and disorders, and are over-represented in the lower end of the IQ range due to X-linked intellectual disabilities (Johnson, Carothers, & Deary, 2009; Ropers, 2010; Stevenson  & Schwartz, 2009). Given the importance of low IQ as a risk factor for antisocial behavior, this latter vulnerability is of special importance.

Vulnerability to fetal programming.

As discussed previously, greater male sensitivity to environmental adversity is hypothesized to begin in the prenatal period, when fetal programming occurs. Fetal programming refers to adaptations made in utero in response to changes in the fetal environment, which can cause long-lasting alterations in gene structure and function that confer context-dependent risk or resilience (Glover, 2011;Tibu et al., 2014).

Developmental immaturity.

It is a basic developmental principle that a less mature organism is more susceptible to deleterious effects of adverse events than a more mature organism (Tanner, 1990). Furthermore, it has been long known that biological maturation among males is slower than that among females (Rutter et  al., 2003). This difference in maturation, which characterizes many mammals and most primates (Tanner, 1990), first manifests at 4 months prenatally, and results in a 4- to 6-week female advantage at birth, which increases to a 1-year advantage by age 5, and a 2-year earlier onset of puberty (Geary, 2010). 254

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In the largest pediatric neuroimaging study to date (827 MRI scans of 387 participants, ages 3 to 27 years), total cerebral volume among girls peaked 4 years earlier than among boys (10.5 vs. 14.5), and cortical and subcortical gray matter trajectories peaked 1 to 2  years earlier (Giedd, Razhahan, Mills, & Lenoor, 2012). These differences are thought to be caused, at least in part, by higher levels of prenatal testosterone among males (Martel, 2013a).

Vulnerability of the placenta.

The placenta is a one-pound organ that forms inside the uterus and acts as a life support system for the fetus, providing oxygen and nutrients, removing wastes, and secreting hormones (Santrock, 2014). Small blood vessels from the mother and fetus intertwine in the placenta, allowing very small molecules of virtually any substance to pass back and forth between mother and fetus (Santrock, 2013). Through this mechanism, various maternal conditions/experiences can affect fetal development (Khalife et al., 2012). For example, harmful bacteria can pass from the mother’s bloodstream, into the placenta and then into the fetus and thus increase risk for premature birth (Aagaard et al., 2014). Thus, because the placenta provides an interface between mother and fetus, it plays an important role in fetal programming and undergoes changes maintain development under adverse conditions (Khalife et al., 2012). Furthermore, the male placenta is more vulnerable than the female placenta to various prenatal insults such as maternal undernutrition (Eriksson et al., 2009; Khalife et al., 2012). One reason for this may be that boys grow faster than girls from an early stage of gestation, even before implantation, they therefore need more energy (Eriksson et al., 2009; Khalife et al., 2012; Tanner, 1990).11 They adjust to this need by investing less in placental growth, which makes them more vulnerable to various prenatal insults because their placentas have less reserve capacity to adjust to insults (Eriksson et al., 2009; Khalife et al., 2012). Khalife et al. (2012) reported an association between abnormal placental growth and mental health outcomes (ADHD and CD) for boys, but not girls, at ages 8 and 16 years.

Vulnerability of the mesolimbic dopamine system.

Gatzke-Koop (2011) has reviewed research which suggests that the mesolimbic dopamine system (a core neural substrate of trait impulsivity, see

also Beauchaine & Gatzke-Kopp, 2013; Neuhaus & Beauchaine, 2013; Essay 11) reacts to adverse prenatal conditions such as hypoxia with both hyper- and hypo-dopaminergic changes depending upon the severity and chronicity of the stressor. Furthermore, males appear to have a lower threshold than females for responding to events that trigger a dopaminergic change and thus, “the global susceptibility of dopamine neurons to hypoxic conditions may be especially amplified in the context of maleness” (Gatzke-Kopp, 2011, p. 800).

Future Research Agenda

First, the study of biological bases of sex differences in the expression and prevalence of externalizing behaviors needs to become a major focus of research rather than an intriguing curiosity. Although the importance of studying sex differences in brain function relevant to various behavioral pathologies has been underlined by the prestigious Institute of Medicine as an important research agenda since 2001 (Pardue & Wizemann, 2001), and has recently received even more emphasis with development of policies by the US National Institutes of Health, which will require that applicants who apply for funding for preclinical studies involving cells and animals balance the number of males and females (Clayton & Collins, 2014), there still exists a paucity of research on this topic (McCarthy et al., 2012). Gatzke-Kopp (personal communication, January 2, 2014) noted that the National Institutes of Health research model has, for a long time, precluded real study of sexually dimorphic effects since most studies are expected to include both males and females but lack the power to address differences between them. This neglect is all the more egregious with regard to externalizing behaviors given that, as observed by Waldman and Lahey (2013) observed, the sex difference in the prevalence of externalizing behaviors is so large that it is necessary for the field to elucidate its causes to fully understand the causes of conduct problems themselves. Thus, following Martel’s (2013a) recommendation, many more studies are needed that target sex differences in externalizing behavior as a core research question. For example, the National Institutes of Mental Health Research Domain Criteria research agenda (NIMH, 2011) includes a focus on examining sex differences in three of the five domains most relevant to externalizing behaviors: negative valence systems (e.g., fear), positive valence systems (e.g., initial and sustained responsiveness to rewards), and cognitive systems (e.g., effortful control). With regard to this latter

domain, it is instructive to note that NIMH cites as a specific focus “improved explication of the construct of cognitive control (or effortful control), relative to disentangling current controversies regarding ADHD, juvenile bipolar disorder, conduct disorder, etc.” (NIMH, 2011, p. 11). Second, this focus needs to continue to target the potential role of fetal sex hormones on sex difference in externalizing behavior. Baron-Cohen and colleagues (2014), who recently provided the first direct evidence of elevated fetal steroidogenic activity in autism, emphasized the importance of examining the capacity of prenatal hormones to exert fetal epigenetic programming effects, which in interaction with other pathophysiological factors, may help explain “neurodevelopmental conditions that asymmetrically affect the sexes” (p. 1). For example, there are sex differences in a number of processes that affect gene expression, such as DNA methylation, methyl-binding proteins, chromatin modifications, and microRNA expression, which are mediated in part by prenatal steroid hormones (Baron-Cohen et  al., 2014). These processes may yield additional biologically-based explanations for the greater male prevalence in externalizing spectrum disorders. Third, not all sex differences in the brain are hormonally-mediated, either prenatally or postnatally. Rather, factors associated with the fundamental genetic dimorphism, that is, every cell type carries either the complement of the male chromosomes (XY) or female chromosome (XX), are determinants of sex differences in the brain and throughout the body, independent of gonadal hormonal effects (Arnold, 2012; Bellott et al., 2014; McCarthy et al., 2012). For example mouse models, which allow effects of sex chromosomes to be disassociated from effects of gonadal hormones, demonstrate that there are gene(s) on the these chromosomes that contribute to greater male aggression (Arnold, 2012; Gatewood et al., 2006). Of even more significance, there are certain genes (N=12) on the Y chromosome, and their counterparts on the X chromosome, that have roles in regulating the state of the genome and in the activation of other genes (Bellott et al., 2014). Since all genes on the Y chromosome were exposed to evolutionary selection pressures only in males, and since these genes are in the non-combining region of the Y chromosome, they function somewhat differently from their X-linked counterparts. This results in a fundamental sex difference at a biochemical level in every cell throughout the human body (Bellott et al., 2014). Indeed, as the Institute of Medicine presciently proclaimed Eme

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in 2001, “Every cell has a sex” (cited in Pardue & Wizemann, 2001, p. 28). And, because every cell has a sex, “It will surely be of interest to determine whether this dimorphism has a role in diseases, outside the reproductive tract, that occur with greater frequency or severity in males or females” (Bellott, 2014, p. 498). Finally, there is a need for meta-theory that integrates research on biological, psychological, and social factors to provide a framework to guide empirical examination of sex differences in externalizing behavior (Martel, 2013a). An initial attempt has been provided by Martel (2013a). It is anchored in both evolutionary theory of sexual selection and life history theory, focuses on sex difference in two variables (sensation-seeking/impulsivity and disinhibition/ low effortful control), and views these differences as caused by higher prenatal exposure of males to testosterone. This initial attempt provides a fine beginning to what will presumably be an evolving project. Thus, Martel (2013b) provides a number of suggestions for research to test specific hypotheses generated by her integrative framework. For example, research can be conducted to test the hypothesis that males with higher prenatal testosterone levels, particularly those with genetic risk influencing the dopaminergic neurotransmission system and who are also exposed to early prenatal stressors, will be at the highest risk for developing externalizing behaviors.

Notes

1. There is a long history of debate about the use of “sex” vs. “gender” to characterize differences between males and females (Frieze & Chrisler, 2011). These terms are frequently used interchangeably, and the term “sex differences” can simply refer to observed differences between males and females regardless of their causes (Eagly & Wood, 2013). However, since “sex” typically implies biological causes whereas “gender” typically implies experiential or cultural causes (Frieze & Chrisler, 2011), and since the focus of the chapter is on biological vulnerabilities, “sex difference” is the more apt term. 2. Important exceptions to the greater male prevalence in externalizing behavior are adolescent-onset conduct disorder, adolescent and adult onset oppositional defiant disorder, and relational aggression (APA, 2013; Archer, 2009; Waldman & Lahey, 2013). 3. From an evolutionary perspective Buss (2012, p. 325) suggests that there were at least six classes of benefits that would have accrued to males who engaged in aggressive behavior: “co-opting the resources of others, defending oneself and one’s kin from attack, inflicting costs on intrasexual rivals, negotiating status and power hierarchies, deterring rivals from future aggression, and deterring long-term mates from infidelity or defection.” 4. Effect sizes (Cohen’s d. Hedge’s g) are reported according to the commonly accepted conventions of Cohen (1988): .20=small, .50=medium, and .80=large; and Hyde (2014): .10=trivial.

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  5. Sexually antagonistic genes are those with beneficial functions in one sex and detrimental functions in the other (Hughes & Rozen, 2012).   6. Rutter et  al.’s (2003) third criterion, which stipulates that when the effect is included in a meditational model it either reduces or eliminates the sex difference in the pathology being studied will not be considered for two reasons. First, there is strong disagreement with the statistical approach that has been used to assess such mediational effects (personal communication with T. Beauchaine, December 5, 2013). Second, research employing this criterion was rare in 2003 (Rutter et al., 2003) and with few exceptions (e.g., see Raine, Yang, Narr, & Toga, 2011) continues to be so currently.   7. Terms such as “dimensions,” dispositions,” and “tendencies” have been used to describe how temperament affects individual differences in behavior, with “trait” being used most frequently when discussing personality (Rothbart, 2012). These various terms reflect the historically vexing issue of disentangling temperament, which is presumed to develop very earlier in life, from personality, which is presumed to develop more slowly (Nigg, 2006). Several authors have proposed that the term “trait” can be applied to both temperament and personality (Martel, 2013a; Rothbart, 2012). Rothbart (2012) views temperamental traits as a subset of personality traits. I  use the terms dimension and trait interchangeably when referring to temperament.   8. See Beauchaine and Gatzke-Kopp (2013) for discussion of how to decide whether genes moderate effects of environment or whether environments moderate effects of genes.  9. The null findings of this study may be due to the inadequacy of the measure of antisocial behavior (Goldman  & Rosser, 2014). 10. Although the relative proportion of the cell lines that are functional in a female typically results in a 50/50 mosaic (Migeon, 2007), the mosaic may fluctuate depending upon cell type with either the maternal or paternal X being much more likely to be inactivated (Wu et al., 2014). It is also now known that between 5%–25% of genes escape inactivation to some degree and consequently are expressed at twice the level among females as males (Gunter, 2005; Johnson, Carothers, & Deary, 2009). 11. Boys grow faster prenatally to gain body mass and weight such that they are slightly larger and heavier than girls at birth (personal communication with K. Thornburg, June 14, 2014; Tanner, 1990). The velocities become equal at about 7  months postnatally (Tanner, 1990). This faster growth, at first glance, might seem incompatible with greater male developmental immaturity. However, note that developmental immaturity simply means that boys are not as close as girls to their final developmental status (Tanner, 1990). Thus, boys can be simultaneously larger and longer at birth than girls because of faster growth and also be less close than girls to their final developmental status.

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Child Maltreatment and Vulnerability to Externalizing Spectrum Disorders

Adrienne VanZomeren-Dohm, Xiaoyenan Xu, Eric Thibodeau, and Dante Cicchetti

Abstract This chapter presents an overview of how experiences of child maltreatment can pose vulnerabilities to developing externalizing spectrum disorders. The authors review the history of maltreatment research in terms of conceptualization and development of a nosological framework of maltreatment, presenting an overview of early findings relating effects of maltreatment at multiple levels (social, behavioral, cognitive, and neurobiological) to externalizing problems. Next, they highlight current foci of research at genomic and neuroendocrine levels, including the importance of multilevel approaches that are sensitive to developmental factors, including timing and subtype of maltreatment, as well as age and gender effects. Finally, they specify directions for future research, including translation of research findings into interventions that target multiple processes such as emotion regulation and social information processing. Key Words:  maltreatment, externalizing problems, multiple levels of analysis, developmental psychopathology

Introduction

Child maltreatment has been established by extensive research as a severe environmental hazard that can exert pervasive deleterious effects on a wide range of developmental domains (Cicchetti & Lynch, 1995; Cicchetti & Toth, in press; Cicchetti & Valentino, 2006). Maltreatment can be viewed broadly as a failure of the evolutionary expected caregiving relationship to provide basic emotional, physical, and/or psychological needs essential for optimal development (Cicchetti  & Lynch, 1995). Maltreatment may involve acts of omission (failure to provide for the child) or commission (harmful acts inflicted upon the child), with subsequent implications for maturation (Cicchetti  & Lynch, 1993). The experience of maltreatment may initiate a variety of processes that eventuate in various forms of psychopathology. As human development occurs within a complex, dynamic system containing multiple levels (e.g., social behavior, cognitive processing, genetic activity, neural activity, neuroendocrine

functioning) that have bidirectional influences on one another (Gottlieb, 2007), it is important to consider how variation in maltreatment subtype, onset, duration, and severity may affect an individual organism across these diverse levels (Cicchetti & Lynch, 1995). In this essay, we provide an historical account of child maltreatment with regard to development of disorders across the externalizing spectrum, using a multiple-levels-of-analysis framework emanating from a developmental psychopathology perspective. We also discuss the focus and findings of current research and areas necessary for future investigation.

Historical Context Historical Trends in Maltreatment Research

Child maltreatment, characterized broadly as physical, sexual, or emotional abuse or neglect of a child by a caregiver that endangers the child in some way (Barnett, Manly, & Cicchetti, 1993; Giovannoni & Becerra, 1979), represents one type 267

of traumatic, environmental exposure that can have lasting negative effects on development. Throughout history, research trends have varied greatly in their design, methods, and typology. Early clinical studies of maltreatment from the mid-1960s to early 1970s focused on simple description, usually grouping all subtypes of maltreated children together or concentrating solely on physical abuse (Aber & Cicchetti, 1984). These early studies often suggested aberrant emotional development among maltreated children (e.g., ego defects such as withdrawal, indifference to mother, and psychomotor retardation in young maltreated children; shallow relationships in older children; Terr, 1970). However, many of these studies did not include nonmaltreated comparison groups; conducted observations in vast settings such as hospital emergency departments, day care, therapy sessions, and during home visits with parents; and used different techniques of obtaining maltreatment information (Aber & Cicchetti, 1984). Follow-up studies during the 1970s went beyond simple description and attempted to understand transient versus enduring effects of maltreatment (Aber & Cicchetti, 1984). These studies tended to indicate greater developmental delays for maltreated children in all areas investigated, including motor, language, and activities in daily living (Kent, 1976). Although follow-up studies certainly improved the quality of research, major limitations in terms of study design still were not remedied (Aber & Cicchetti, 1984). Theoretically derived studies of maltreatment did not emerge until the early 1980s (Aber & Cicchetti, 1984), and it was not until this time that a systematic approach to investigating the causes, course, transmission, and consequences of maltreatment was undertaken (National Research Council, 1993). Such investigations adopted a developmental perspective by specifying effects of maltreatment on stage-salient developmental competencies; they generally suggested that maltreated children had distorted affective communication behaviors with both peers and caregivers (e.g., Gaensbauer & Sands, 1979; George & Main, 1979, 1980), as well as negative self-concepts and heightened extrapunitive aggression (Kinard, 1980). At this time, investigators were just beginning to employ adequate control groups, distinguish among types of maltreatment, and become more sophisticated in regard to outcome variables of interest (including variations in sequelae) (see Aber & Cicchetti, 1984). Thus, the spectrum of historical research studies on maltreatment ranges from limited, primarily descriptive 268

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clinical studies in the early 1960s to more improved, theoretically derived studies that considered normal development and heterogeneity in outcomes and focused on stage-specific competency or impairment in multiple domains in the early 1980s (Aber & Cicchetti, 1984).

Conceptualization of Maltreatment

In the 1970s, concerns regarding maltreatment often focused on identification of factors influencing parents’ infliction of harm on their children in an effort to predict risk for maltreatment (Cicchetti & Barnett, 1991). Emphasis on causes of maltreatment captures the medical-diagnostic approach during this era (Aber & Zigler, 1981), which narrowly viewed maltreatment as a category of parental psychopathology and focused primarily on physical abuse. Other approaches were concerned with actions that encompass maltreatment (sociological) or intentionality of harmful behaviors by caregivers (legal) (Cicchetti & Barnett, 1991). The research definition of maltreatment considered two strategies by which researchers often conceptualized this variable, including (a) state social-service reports of maltreatment and (b) physical violence (Aber & Zigler, 1981). The varied approaches to defining maltreatment influenced the focus, investigation, findings, and decisions involving societal, legal, and research endeavors, often resulting in discrepant findings and varying estimates of maltreatment (Cicchetti & Barnett, 1991), greatly impeding the ability of research to optimally inform services for maltreated children (Aber & Cicchetti, 1984). Thus, Cicchetti and Barnett (1991) highlighted the necessity of sifting through methodological difficulties in order to generate more precise definitions that could not only promote communication among professionals but also enhance reliability, replicability, and generalizability of research studies on the heterogeneous population of maltreated children. In their call for a nosology of maltreatment, Cicchetti and Barnett (1991) emphasized several sources of heterogeneity within maltreatment, including the spectrum of actions defined as maltreatment, a diversity of causal networks/pathways that may lead to similar outcomes among maltreated children (principle of equifinality), divergent outcomes among maltreated children despite similar circumstances and components of their experience (principle of multifinality), and response to treatment. In their proposal, Cicchetti and Barnett (1991) also laid forth five specific operational criteria including (a) type of maltreatment; (b) detailed

information about seriousness/severity of each incident; (c) identification of the developmental period during which maltreatment began and through which maltreatment persisted; (d) frequency of occurrence of each maltreatment subtype; and (e) type, length, and number of separations or placements outside of the home for the child. With regard to type of maltreatment, five major subtypes of maltreatment were delineated: (a) physical abuse (PA), (b) physical neglect (PN), (c) sexual abuse (SA), (d) emotional maltreatment (EM), and (e)  moral/legal/educational maltreatment (MLE). Physical neglect was further divided into failure to provide (FTP) or lack of supervision (LOS) for the child. Emotional maltreatment was defined as specific parental acts that unambiguously comprised maltreatment and did not fit into any of the other four subtypes. Because EM involves acts that are arguably not easily recognized and therefore more difficult to identify reliably, Cicchetti and Barnett (1991) proposed further guidelines for this subtype of maltreatment: EM incidents would include acts that thwarted a child’s emotional needs for safety or self-esteem. For example, exposure to violent and hostile environments (e.g., domestic violence) that compromise a child’s ability to feel physically and emotionally stable would be classified as EM. With regard to developmental periods, ages at which maltreatment began and through which maltreatment persisted could be categorized as infancy (0–18 months), toddlerhood (19–35 months), preschool (36–59 months), early school (5–7 years), later school (8–12 years), and adolescence (13–18 years). Information regarding developmental period was considered important, as it helps determine whether an act constitutes maltreatment (e.g., leaving a child unattended during adolescence would not constitute neglect whereas leaving an infant unattended would). Frequency/chronicity of maltreatment considers the amount of time the family has experienced maltreatment and is determined using the number of indicated CPS reports over time. For detailed information regarding severity, interested readers are referred to Barnett, Manly, and Cicchetti (1993), which includes explicit recommendations for ranking the severity of each maltreatment subtype (e.g., for sexual abuse, the lowest ranking of 1 = caregiver exposes child to sexual stimuli such as pornography; the highest ranking of 5 = caregiver has forced intercourse or sexual penetration with child or prostitutes the child; pp. 57–60). Specific delineations among maltreatment subtypes, paired with clear criteria regarding when

maltreatment began, how long maltreatment occurred, and how frequent and severe the specific maltreatment experiences were, helps investigators better understand differences among maltreated individuals and variations in outcomes that may be attributable to onset, duration, frequency, severity, and subtype of maltreatment experiences. Such fine-grained information should help researchers identify various pathways and potentially sensitive periods that render an individual more or less susceptible to deleterious effects of maltreatment. The ability to determine particular sensitive periods could shed light on why different disorders emerge in different individuals. In addition to proposing a comprehensive set of criteria to operationalize maltreatment, Cicchetti and Barnett (1991) also called for the utilization of structured classification schemes in research, as well as inclusion of multiple sources of information regarding assessment (e.g., family social worker, interview of various family members, home visit observations, etc.), and identification of maltreatment perpetrators. In addition, recognition that maltreatment subtypes often co-occur, and that research should attain larger sample sizes, was important in the movement toward achieving more accurate empirical data. Last, the importance of reviewing CPS records for describing, substantiating, and optimally classifying maltreatment incidents was emphasized (Cicchetti & Barnett, 1991). This system allowed capturing a larger proportion of variance in causes, etiology, consequences, and intervention outcomes for maltreated children and dramatically improved research on child maltreatment in the years to come. The classification system has been widely-accepted in the research community, and, consequently, its development represents a landmark event in maltreatment research (Barnett et al., 1993; Manly, 2005).

An Overview of Early Findings Relating Maltreatment to Externalizing Behaviors

Concern over the effects of maltreatment on children’s socioemotional development resulted in numerous research investigations in the upcoming years. In general, higher rates of problem behavior characterized by physical aggression, anger, negative affect, noncompliance/disobedience, acting out, and negative responses (avoidance or approach-avoidance behavior) to friendly gestures by adults and peers were reported among maltreated children at various developmental stages (Erickson, Egeland,  & Pianta, 1989; George  & Main, 1979,

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1980; Kaufman  & Cicchetti, 1989; Kent, 1976; Rogosch  & Cicchetti, 1994; Shonk  & Cicchetti, 2001). Maltreated children were also found to have more aggressive fantasies (Reidy, 1977). That is, physically abused children demonstrated more aggressive imagery and overt behaviors in comparison to nonmaltreated children, as assessed using story cards and observation of free play behaviors; both physically abused and neglected children demonstrated more aggression according to teacher ratings. Thus, maltreated children are not only more overtly aggressive, but also have internal influences and desires (“calls for action”; Rieder & Cicchetti, 1989) to use aggression as a social response more than do nonmaltreated children. Moreover, these aggressive characteristics have been demonstrated in contexts outside of the home. With regard to externalizing behaviors, these aggressive calls for action may result in quick assimilation of aggressive information and lead to reactions that are thought to be most immediately adaptive or protective for the child, such as defensive acting-out behaviors (Rieder & Cicchetti, 1989). In addition to aggression and acting out behaviors, higher rates of impulsivity in maltreated children were reported (Rohrbeck  & Twentyman, 1986). Such historical findings suggested that maltreated children struggle with managing certain aggressive feelings and impulses; in fact, difficulties with self-control and emotion regulation have been found in maltreated children (Shields & Cicchetti, 2001). Importantly, management of aggressive impulses is an important developmental task (Cicchetti  & Schneider-Rosen, 1986), and early aggressive and acting out behaviors pose a major risk factor for later delinquency, adult crime, violent offending, and criminal convictions (Farrington, 1989; Moffitt, 1993; Thornberry, Krohn, Lizotte, Smith, & Tobin, 2003; Widom, 1989). Failure to regulate aggressive emotions could predispose maltreated children to dysfunctional social relationships outside the home, such as rejection by peers (Bolger & Patterson, 2001). Indeed, early studies showed that an inability to regulate negative emotions mediates effects of maltreatment on social competence (Shields, Cicchetti, & Ryan, 1994). Studies conducted more recently show that maltreatment is related to emotion dysregulation, and emotion dysregulation is later associated with higher externalizing symptoms that, in turn, contribute to peer rejection and increased externalizing behavior problems (Kim & Cicchetti, 2010). Such behavioral and emotional impairments can set a 270

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problematic cascade into motion that is characterized by delinquency, low commitment to school, peer deviancy training, school failure, and antisocial behavior (e.g., Bolger  & Patterson, 2001; Burt & Roisman, 2010; Dishion & Tipsord, 2011; Rogosch, Oshri,  & Cicchetti, 2010)—features consistent with developmental failures exhibited by maltreated children (Cicchetti  & Valentino, 2006). This evidence provides a stark demonstration of how maltreatment may affect many levels of a child’s development (e.g., emotion regulatory, behavior, and social functioning). Early research on effects of maltreatment on various cognitive domains generally suggested disrupted information processing, social learning, and social cognition. Altered information processing is characterized by distortions in encoding, retrieving, and recalling specific types of information. For example, anger is especially salient for maltreated individuals (Pollak, Cicchetti, Hornung, & Reed, 2000; Pollak & Kistler, 2002; Rieder & Cicchetti, 1989), which is evident in amplified neural reactivity and attention to angry facial expressions among maltreated infants (Curtis & Cicchetti, 2013) and children (Cicchetti & Curtis, 2005; Curtis & Cicchetti, 2011; Pollak, Cicchetti, Klorman, & Brumaghim, 1997). Heightened responses to angry expressions may permit greater threat recognition but at the expense of accurately discriminating amongst other emotions (Pollak & Kistler, 2002). Thus, the asymmetric distribution of attentional and neural resources expended on threat perception and detection can lead maltreated children to have a reduced ability to process other types of relevant information and understand other emotional states, while hypersensitizing them to expressions of anger. In fact, maltreated children have difficulty recognizing emotional expressions accurately (Camras, Grow, & Ribordy, 1983; Pollak et al., 2000), even though they may be particularly likely to recognize and respond to angry or aggressive cues (Dodge, Pettit, Bates, & Valente, 1995; Pollak et al., 1997; Rieder & Cicchetti, 1989). For example, although PA children are better at detecting anger than both neglected and nonmaltreated children, they have trouble recognizing emotions of sadness and disgust, whereas neglected children seem to have the most difficulty recognizing expressions of anger, sadness, and fearfulness (Pollak et al., 2000). Overall, it appears that maltreatment hypersensitizes some children to displays of anger, while creating an overall deficit in accurate detection of a broad range of emotional expressions.

Recognition of emotion serves as the foundation upon which children use social cues and subsequently interpret others’ actions and respond accordingly. When others’ expressions of anger are most attended to and/or the inability to discriminate among other relevant emotions exists, behavioral repertoires may shift. Attribution biases that come from overattention and/or misperception of threat and anger in ambiguous relational situations (Crick  & Dodge, 1994; Dodge, 1993; Dodge  & Schwartz, 1997) prompt maltreated children to react with hostility and violence. A combination of misinterpretation of social information, ‘easy access’ to aggressive responses, and lack of emotion regulation may lead to hostile behaviors—contributing to the documented relation between abuse and peer nominations of aggressive and disruptive behavior for physically abused children (Teisl  & Cicchetti, 2008). In turn, incidents of increased aggression and impulsivity, paired with attribution biases, hypervigilance toward anger, emotion dysregulation, and an inability to discriminate between diverse emotions, contribute to the development of further externalizing problems in maltreated children, exemplifying a positive feedback loop. In the domain of social cognition, maltreated children demonstrate impaired understanding of others’ internal states (Beeghly & Cicchetti, 1994). Early research also suggested that maltreated children have a decreased ability to understand complex social roles when compared to their nonmaltreated counterparts, even after controlling for IQ (Baharal, Waterman, & Martin, 1981). Furthermore, physical abuse and onset of abuse during the toddler years are associated with delays in theory of mind development, above-and-beyond effects of age and SES (Cicchetti, Rogosch, Maughan, Toth,  & Bruce, 2003). Social difficulties that emanate from impaired ability to understand the intentions and internal states of others suggests that impaired social functioning is a particular pathway through which maltreatment contributes to development of externalizing behavior problems in this population. Research on neurobiological vulnerabilities that may set the stage for externalizing problems in maltreated children comes from both animal and human research studies that began toward the end of the 20th century. Animal research emphasized changes in brain structures, such as reduction in neurons and cortical densities, as a result of experiences analogous to human maltreatment. For example, maternally deprived rodents (a proxy for maltreatment) have reduced fiber densities in the prefrontal cortex

(PFC) and show a loss of inhibitory interneurons in cortical regions (Poeggel et al., 1999). These altered brain areas typically play a role in integrating information (Yang & Raine, 2009), complementing research findings previously discussed regarding disrupted cognitive processing in maltreated children. Additionally, changes in the size of certain brain structures have been supported by animal research. Altered sizes of the amygdala, hippocampus, and ventromedial PFC (e.g., Andrews & Neises, 2012; Meaney, 2001) often follow significant stress. These brain areas serve many functions, including emotion regulation (Goldsmith, Pollak, & Davidson, 2008), stress reactivity (Meaney, 2001), and memory formation (Yehuda & LeDoux, 2007). In addition, animal research demonstrates clear effects of parenting on development of neurotransmitter systems (i.e., effects on the norepinephrine system; Kraemer, Ebert, Schmidt, & Mckinney, 1989; decreased dopamine expression and increased stress reactivity; Meaney, Brake, & Gratton, 2002). There is then the possibility of subsequent neurobiological vulnerability to drug abuse and other externalizing problems (Meaney et al., 2002). Valuable information from animal research has helped expand our knowledge base regarding effects of maltreatment on neurobiology and particular ways that experiences of maltreatment may contribute to the development of externalizing problems. Although true experiments cannot be conducted with humans, neurobiological studies with children are consistent with animal research findings. For example, maltreated children have smaller head circumferences than nonmaltreated children (Strathearn, Gray, O’Callaghan,  & Wood, 2001). Additionally, the total area of the corpus callosum was 17% smaller in a group of neglected, sexually, physically, and psychologically abused individuals admitted for psychiatric evaluation in comparison to healthy controls and 11% smaller than nonabused psychiatric patients as assessed by magnetic resonance imaging scans (Teicher et  al., 2004). Structural differences also have been found in the hippocampus (Bremner et  al., 1997, 2003; Stein, Koverola, Hanna, Torchia,  & McClarty, 1997) and the PFC (Carrion et al., 2009; Richert, Carrion, Karchemskiy,  & Reiss, 2006) of individuals who experienced trauma, including maltreatment. These findings suggest the possibility of stunted or otherwise altered brain growth and lack of integration of brain regions for individuals who experience early maltreatment. Furthermore, early investigations indicated that maltreatment

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contributes to alterations in neurotransmitter systems. A  study by Rogeness and McClure (1996) demonstrated that children with emotional disturbances and a history of neglect have lower levels of dopamine-β-hydroxylase (DBH), an enzyme that converts dopamine to norepinephrine, as well as decreased activity of particular functions mediated by the norepinephrine system (e.g., lower systolic and diastolic blood pressure). These findings supported results of animal studies suggesting that maltreatment affects neurobiology in various ways, with major implications for social behavior. Finally, beginning in the late 20th century, researchers began evaluating effects of maltreatment on cortisol activity and reactivity. Hart and colleagues (1995) were among the first to consider the relationship between maltreatment, cortisol, and behavior. When they examined salivary cortisol and social behavior in a group of maltreated and comparison children, cortisol reactivity was correlated positively with social competence and negatively with internalizing behavior. Maltreated children had less cortisol reactivity than control children and also had lower social competence, higher internalizing behaviors, and higher externalizing behaviors (Hart, Gunnar,  & Cicchetti, 1995). This trend demonstrated the link between maltreatment and reduced cortisol reactivity and its relations to impaired social competence. Additional investigations supported general neuroendocrine dysregulation in maltreated children, with divergent patterns of cortisol regulation (which may be reduced or increased) depending on the type of maltreatment (Cicchetti & Rogosch, 2001b; Tarullo & Gunnar, 2006).

Summary

As demonstrated, early studies varied greatly in their focus (description, transient versus enduring effects, and stage-related developmental issues) and definition (medical diagnostic, sociological, legal, research) of maltreatment. These variations affected the ability for research findings to be replicated and generalized (Cicchetti & Barnett, 1991). The creation of a nosological system by Cicchetti and Barnett (1991; see also Barnett et  al., 1993) set forth specific criteria by which to operationalize maltreatment, with far-reaching implications for research and services for maltreated children. In general, historical studies focused on main effects of maltreatment on the domains of behavior, information processing, social learning, social cognition, and, toward the very end of the 20th century, neurobiology (though some earlier investigations 272

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did explore mediators [Shields et  al.,  1994] and biology × environment interactions [Rogeness  & McClure,  1996]). Not until recently has a major focus on neurobiology greatly advanced and expanded to include various interaction effects of biological vulnerability, maltreatment, and the potential for externalizing behavior, as discussed in detail next.

Links to Traditional Externalizing Disorders

Substantial research demonstrates the significance of child maltreatment as a risk factor for a wide variety of externalizing behaviors, including attention-deficit/hyperactivity disorder (ADHD; Ouyang, Fang, Mercy, Perou,  & Grosse, 2008), oppositional defiant disorder (ODD; Pelcovitz, Kaplan, DeRosa, Mandel,  & Salzinger, 2000), delinquency (Smith, Ireland, & Thornberry, 2005), antisocial personality disorder (ASPD; Luntz  & Widom, 1994), and substance use disorders (SUDs; Harrison, Fulkerson,  & Beebe, 1997; Rogosch et  al., 2010). For example, in a matched cohort study of individuals who were maltreated before age 11, male victims demonstrated significantly higher rates of ASPD than matched controls after 20 years (20.3% vs. 10.1%; Luntz  & Widom, 1994). Numerous studies of this kind have been summarized in major review reports (see Kendall-Tackett, Williams, & Finklehor, 1993; Maas, Herrenkohl, & Sousa, 2008; Renner, 2012). In addition, burgeoning research has examined the role of child maltreatment as an exacerbating factor for existing externalizing psychopathology. Comparison studies of maltreated and nonmaltreated children diagnosed with ADHD serve as the most promising illustration of this effect to date. Maltreated girls with ADHD are significantly more aggressive than their nonmaltreated ADHD counterparts as assessed by parent, teacher, and peer reports, as well as objective observational records (Briscoe-Smith  & Hinshaw, 2006). Youth with ADHD who are maltreated in childhood are also more likely to develop SUDs in adolescence (De Sanctis et  al., 2008), and are 3½ times more likely to be arrested than their nonmaltreated ADHD counterparts (De Sanctis et  al., 2012). Future investigations examining maltreatment as an amplifying factor for other forms of externalizing disorders may help delineate secondary effects of maltreatment-related pathology. It is important to keep in mind that links between maltreatment and externalizing problems may

vary as a function of the subtype of maltreatment, although results from related research have been mixed due to a variety of methodological issues. For example, Lansford et al. (2007) followed a sample of 574 children from age 5 to 21 years, and found, using analysis of covariance, that individuals who are physically abused in the first 5 years of life are at greater risk for being arrested as juveniles for violent delinquency. By contrast, Grogan-Kaylor and Otis (2003) used sophisticated regression to reanalyze original data from the Luntz and Widom (1994) study mentioned previously. Their results indicated that PA was not predictive of arrests in early adulthood, whereas neglect was as a significant predictor. A third large-scale longitudinal study suggested that both PA and neglect are associated with violent juvenile petition or violent adult arrest conviction, and, strikingly, their effect sizes are comparable (dneglect = .41, dPA = .45; Mersky & Reynolds, 2007). Differences in measurement of maltreatment and outcome variables, comorbidity of PA and neglect, sample characteristics, unmeasured contextual factors, and choice of statistical techniques may all contribute to discrepant results. Further research using comparable study designs and more reliable measures of key variables and potential contextual moderators in representative samples is needed to yield more robust conclusions. Finally, effects of child maltreatment on development of externalizing problems may also depend on a child’s gender1 or interaction of gender with various maltreatment characteristics. Traditionally, research efforts have been devoted to associations between child maltreatment and various forms of externalizing problems in males (e.g., Caspi et al., 2002; Stouthamer-Loeber, Loeber, Homish,  & Wei, 2001). However, other studies indicate that maltreatment may exert equally strong, if not stronger, effects on females’ externalizing problems. For example, a significant bivariate relationship exists between childhood maltreatment and alcohol abuse in young adulthood for female participants, controlling for parental alcohol and/ or drug problems, childhood poverty, age, and race, whereas no such relationship exists for male participants (Widom, Ireland,  & Glynn, 1995). Another more recent study revealed that PA in the first 5  years of life has direct effects on substance use at age 12 for girls but not boys (Lansford, Dodge, Pettit, & Bates, 2010). Similarly, Burnette (2013) found that PA is a salient risk factor for later ODD symptoms in girls but not boys, controlling for age and SES. Underlying mechanisms of these

gender-moderated effects remain unclear and warrant closer examination.

Current State of the Science Gene × Environment Interplay

Following sequencing of most of the human genome by the early 2000s, Caspi and colleagues (2002) were the first to demonstrate a genotype × environment (G×E) interaction in the human behavioral literature. Of direct relevance to the development of externalizing psychopathology within the context of child maltreatment, a variable number tandem repeat (VNTR) polymorphism in the monoamine oxidase A  gene (MAOA) moderated the association between reports of child maltreatment and indices of antisocial behavior among males. This seminal finding, subsequently supported by two independent meta-analyses (Byrd & Manuck, 2013; Kim-Cohen et  al., 2006), marshaled in an effusion of G×E studies with regard to child maltreatment and developmental psychopathology at large. Current, more sophisticated approaches toward understanding development of externalizing behaviors via elucidation of interactions between the genome and child maltreatment have focused on multigenic, moderated mediation, and epigenetic models coupled with more precise measures of child maltreatment (Bevilacqua et  al., 2012; Cicchetti, Rogosch, & Thibodeau, 2012; Hasler et al., 2012; Kieling et  al., 2012; Perroud et  al., 2010; Sadeh, Javdani,  & Verona, 2013). Using a large sample (N  =  627) of 10- to 12-year-old children participating in a summer research camp, Cicchetti et  al. (2012) found that variants in the serotonin transporter gene-linked polymorphic region (5-HTTLPR), tryptophan hydroxylase 1 (TPH1) gene, and the MAOA gene moderated associations between a prospective measure of child maltreatment and multi-informant indices of antisociality. Using a longitudinal, structural equation modeling design, Davies and Cicchetti (2013) explained, in part, the manner by which 5-HTTLPR exerts moderating effects. That is, they demonstrated that children’s angry responses to maternal negativity mediated an interaction between 5-HTTLPR genotype (l/l) and child maltreatment (measured prospectively) on the development of externalizing symptoms in 2-year-olds. Further expounding on known associations between hypothalamic-pituitary-adrenal (HPA) axis functioning, child maltreatment, and aggression, Bevilacqua et  al. (2012) provided evidence that a

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diplotype (haplotype combination) in the FKBP5 gene, which increases glucocorticoid receptor (GR) resistance, predicted the highest levels of both lifetime aggression and violent behavior in jail for incarcerated adults with a history of physical abuse, compared with those lacking the risk diplotype. In another haplotype study with relevance to neuroendocrine functioning, a two-SNP haplotype in the cAMP response element-binding protein (CREB) gene, known to regulate the expression of thousands of genes including the corticotropin-releasing factor (CRF) gene, demonstrated a haplotype × child maltreatment interaction predicting heightened risk of adult aggression levels (Hasler et al., 2012). Despite these preliminary findings, a number of multigenic studies show mixed results. Of a total of 15 candidate genes including MAOA, 5HHTLPR, catecholamine-O-methyl transferase (COMT), and TPH1, only COMT, passing multiple-test-correction, moderated the association between retrospective child maltreatment and anger expression (Perroud et al., 2010). Furthermore, in one of the largest G×E studies in the maltreatment literature to date, the MAOA gene and the dopamine transporter (DAT1) gene failed to demonstrate moderation of the association between measures of child maltreatment and conduct problems in a sample of more than 5,000 adolescent males from Pelotas, Brazil (Kieling et  al., 2012). As noted in other essays in this volume, small effects sizes of specific genes on behavior are common in psychiatric genetics, so we must continue to evaluate interaction effects in large samples. With continued concentration on elaborating genomics processes involved in adaptation and maladaptation, the maltreatment literature provides some of the most extensive and novel studies on epigenetic regulation in the behavioral sciences (Lutz  & Turecki, 2013; McGowan et  al., 2009; Weaver et al., 2004; Weaver, Meany, & Szyf, 2006). Epigenetic processes refer to physical and chemical alterations in the genome that dynamically influence gene expression, but not as a result of change to underlying DNA sequence per se. Although a sizable number of studies have examined relations between child maltreatment and epigenetic alterations (for a review, see Lutz & Turecki, 2013), only two studies, to date, have related these associations to variation in externalizing behaviors. Beach and colleagues (2011) found that one particular type of epigenetic regulation, DNA hypermethylation (of the 5-HTT gene) mediated the effect of child sexual abuse on antisocial behavior in a sample of 274

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155 women from the Iowa Adoptee Study. Most interestingly, Beach et  al. (2013), using the same sample, demonstrated that the interaction of parental reports of psychopathology (a measure of what the authors termed “genetic load” in this study) and retrospective maltreatment moderated the mediation of child sexual abuse on antisocial behaviors via 5-HTT hypermethylation. These seminal findings may help explain the molecular biology underlying 5-HTTLPR × maltreatment interactions, which may, in part, engender externalizing psychopathology. Certainly, as psychiatric genetics continues to mature, so, too, will its role in the study of child maltreatment and developmental origins of psychopathology. Nonetheless, important multilevel processes involved in development of externalizing behaviors extend well beyond genomics.

Neuroendocrine Functioning

Child maltreatment exerts deleterious effects on a wide range of biological systems essential for health and well-being. In particular, maltreatment affects systems responsible for adaptive stress reactivity (Rogosch, Dackis, & Cicchetti, 2011; Tarullo & Gunnar, 2006). Recent conceptualizations of stress biology have shifted attention toward delineating processes that underlie allostasis, or dynamic regulation of physiological “set-points” as a means to stabilize critical homeostatic systems (Danese  & McEwen, 2012; McEwen  & Wingfield, 2003; McVicar, Ravalier, & Greenwood, 2013). Although adaptive in the short run, allostasis may, over time, eventuate in physiologic wear and tear (i.e., allostatic overload), undermining behavioral functioning across multiple domains. One of the primary mediators of allostasis is activity of the HPA axis. In particular, HPA axis functioning has been of interest to this literature because of its role in development of both internalizing and externalizing psychopathology within the context of adversity (Alink et al., 2008; Alink, Cicchetti, Kim,  & Rogosch, 2012; McCrory, DeBrito, & Viding, 2010; Murray-Close, Han, Cicchetti, Crick,  & Rogosch, 2008). Given the importance of parental buffering of the stress response via sensitive caregiving in early childhood, environmental adversities such as maltreatment may set in motion a path of maladaptive neuroendocrine functioning, eventuating in reduced psychological adjustment (Cicchetti, Rogosch, Gunnar, & Toth, 2010; Davies, Sturge-Apple, Cicchetti,  & Cummings, 2007; Tarullo & Gunnar, 2006). Studies of child maltreatment and variability in cortisol functioning yield mixed findings, in part

because of discrepancies in measurement of maltreatment experiences, concurrent psychopathology, developmental time period, risk and protective factors, and gender effects (Cicchetti, Rogosch, Gunnar, & Toth, 2010; Davies et al., 2007; Doom, Cicchetti, Rogosch, & Dackis, 2013; McCrory et al., 2010). In an early study with a small sample of maltreated and nonmaltreated preschool-aged children, Hart, Gunnar, and Cicchetti (1995) found no difference in median cortisol levels between groups but found that maltreated children displayed blunted diurnal cortisol patterns. The same team, using a larger sample of 131 maltreated and 66 nonmaltreated children, found that depressed, maltreated children, on average, exhibited less of a decrease in cortisol throughout the day, despite approximately the same average morning cortisol levels as nonmaltreated children (Hart, Gunnar, & Cicchetti, 1996). Trickett and colleagues (2010) found that sexual abuse in a sample of 84 females predicted an attenuation of cortisol activity from adolescence into adulthood, even after controlling for co-occurrence of internalizing psychopathology. Subtypes of maltreatment may predict differential cortisol patterns. Cicchetti and Rogosch (2001b) showed no differences between morning or afternoon cortisol between maltreated and nonmaltreated groups overall; however, youth with combined forms of abuse (i.e., those who experienced both PA and SA as well as PN or EM) evidenced significantly higher morning cortisol levels, whereas maltreated children who experienced PA alone showed both lower morning cortisol and a larger drop in diurnal levels relative to nonmaltreated children. Particularly noteworthy was that PA children demonstrated lower overall cortisol levels, which may be related to findings that diminished HPA activity tends to associate with oppositional and aggressive behaviors (Alink et al., 2008; McBurnett, Lahey, Rathouz, & Loeber, 2000; Platje et  al., 2013; Poustka et  al., 2010; Susman, 2006; van Goozen et al., 1998; van Goozen & Fairchild, 2008). Relations between cortisol functioning, externalizing psychopathology, and child maltreatment are complex. Nonmaltreated children with externalizing behaviors tend to demonstrate cortisol underarousal, and histories of abuse and neglect may have disparate effects on this general trend (see Isaksson, Nilsson,  & Lindbald, 2013; McBurnett et al., 2000; Platje et al., 2013; Tarullo & Gunnar, 2006). Cicchetti and Rogosch (2001a) found evidence that may support this notion in boys: whereas both maltreated and nonmaltreated boys showed

cortisol under-arousal combined with clinical levels of externalizing psychopathology, nonmaltreated boys with externalizing problem behaviors exhibited the lowest cortisol levels out of every group. Furthermore, data from a study by Murray-Close et  al. (2008) showed that nonmaltreated children who exhibit physical or relational aggression have a more blunted diurnal cortisol pattern of change compared to maltreated children. This pattern of results may provide evidence of a social push phenomenon in which biological effects on behavior are most pronounced under conditions of relatively low adversity (Murray-Close et al., 2008; Raine, 2002). Still, some studies do demonstrate that hypocortisolism mediates associations between child maltreatment and externalizing psychopathology. In a study of adult female inmates, Brewer-Smyth, Burgess, and Shults (2004) found that higher rates of aggressive criminal behavior were related to reduced morning basal cortisol levels and histories of child maltreatment. In a sample of 190 children aged 12 years, Ouellet-Morin et al. (2011), via multilevel growth curve analysis, provided evidence that retrospective reports of maltreatment were related to blunted cortisol reactivity to the Tier Social Stress Test. Moreover, blunted cortisol reactivity was associated with greater externalizing problem behaviors. Hart, Gunnar, and Cicchetti (1995) found indirect evidence of relations among these constructs, wherein maltreated children scored lower on measures of social competence, and social competence was positively related to cortisol reactivity. Finally, in an attempt to clarify the directionality and relations between maltreatment, HPA-axis functioning, and externalizing psychopathology, Alink et al. (2012) conducted a longitudinal study across 2 years with maltreated and nonmaltreated children who attended a summer day camp. As expected, maltreated youth evidenced higher rates of disruptive/aggressive behavior. Furthermore, disruptive/ aggressive behavior and withdrawn behavior in maltreated children observed in year 1 differentially predicted cortisol levels at year 2, with disruptive/ aggressive tendencies leading to lower morning cortisol levels. Varied and even perplexing findings are to be expected in this literature, given the complex nature of child maltreatment and individual differences in cortisol functioning and risk/protective factors. What is clear, however, is that child maltreatment leads to allostatic overload of the neuroendocrine system, in the form of either hypercortisolism or hypocortisolism, and often these perturbations

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to stress reactivity lead to externalizing psychopathology. Understanding the effects of dysregulated adrenocortical functioning on the probabilistic development of externalizing problem behaviors for maltreated children will continue to be a rich and formative area for exploration. It is critical for research approaches to acknowledge that biological processes do not act in isolation; rather, they are dynamically influenced across all levels of analysis.

Gender Sensitive Designs

Current advances in understanding differential effects of child maltreatment on antisocial outcomes are narrowing in on gender moderated effects. Relative to boys, girls who display aggression are more likely to engage in socially disruptive and indirect forms of negative conduct known as relational aggression (Archer  & Coyne, 2005; Card, Stucky, Sawalani,  & Little, 2008; Crick, 1995). Given gender-specific forms of aggressive tendencies and differential emphasis on relationship components (see Coyne, Archer, & Elsea, 2006; Cullerton-Sen et  al., 2008; Rose  & Rudolf, 2006), it is imperative to keep studies of child maltreatment and externalizing psychopathology gender-informed. Cullerton-Sen et  al. (2008) provide an illustrative example. Using a sample of 410 maltreated and nonmaltreated children ranging in age from 6 to 12 years, child maltreatment was related to higher levels of relational aggression in girls and physical aggression for boys; additionally, girls were particularly susceptible, in terms of developing relational aggression, to a history of sexual abuse. Godinet and Berg (2013) found that effects of child maltreatment on externalizing symptoms for boys was strongest at longitudinal measurement points in close temporal proximity to occurrences of maltreatment. For girls the opposite was true; effects on problem behaviors increased over time, becoming most pronounced later in development. Not all studies show gender moderated effects, however. For example, Topitzes, Mersky, and Reynolds (2012) found no interaction between maltreatment status (ages 0–11  years) and gender in predicting violent crime in a large sample of low-income participants. Continuing to investigate the nature of gender differences may help facilitate early detection of negative trajectories and identify personalized components of preventive interventions. These and other multi-level approaches, which elucidate processes that underlie varied developmental pathways of psychopathology and normative behavior observed in maltreated 276

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children, will play formative roles in future research frameworks.

Developmental Considerations

We believe it is not possible to truly understand the etiology and maintenance of any mental disorder without taking a developmental perspective. Still, relatively few published studies focusing on associations between child maltreatment and externalizing problems have included measures of developmental timing, nor have a majority of investigations considered how timing and heterogeneous characteristics of maltreatment experiences (subtypes, onset, severity, and chronicity) interact to differentially affect progression of childhood externalizing problems. These limited research efforts may partially account for the mixed findings generated. For example, growth modeling results of one study that followed children from kindergarten through the eighth grade suggest that, controlling for gender, the earlier children experience PA (assessed by maternal interview or questionnaire), the more likely they are to suffer from externalizing problems in early adolescence, whereas the same type of maltreatment experienced at later periods is related to less-negative sequelae and higher rates of improvement (Keiley, Howe, Dodge, Bates, & Pettit, 2001). Similarly, another longitudinal study of childhood SA victims (as indicated by forensic medical records) found that children who were sexually abused at a younger age are at a greater risk for multiple forms of psychopathology including conduct disorder and substance abuse disorder (Cutajar et al., 2010). On the contrary, other studies find that individuals who experience maltreatment during adolescence (as opposed to early childhood) demonstrate more externalizing disturbances later on. For example, Smith et  al. (2005) found in the Rochester Youth Development Study that maltreatment experiences during adolescence increased the odds of arrest, general and violent offending, and illicit drug use in young adulthood, controlling for prior levels of problem behavior and various social-economic and demographic variables. Another large-scale investigation that analyzed child protective service records data using Semi-Parametric Group-Based Methodology (SPGM; Nagin  & Land, 1993) further indicated that, regardless of the starting point of maltreatment (early childhood vs. adolescence), children whose maltreatment trajectory includes adolescence are more likely to offend as juveniles than their counterparts whose maltreatment experiences are confined to early childhood (Stewart,

Livingston,  & Dennison, 2008). Extensive debate exists regarding whether earlier maltreatment environments undermine mastery of stage-salient developmental tasks such as self-regulation and thereby lead to subsequent expression of externalizing problems, or whether the greater autonomy individuals gain when entering adolescence makes them more vulnerable to the appeal of illegitimate coping strategies when maltreatment is present (Kaplow  & Widom, 2007). Prospective, longitudinal investigations beginning early in development (e.g., prenatal, infancy) and with multiple, well-defined biological and socio-emotional measures and more clear and precise outcome measures may help to disentangle such mixed findings. A developmental perspective also underscores the importance of delineating etiological mechanisms at multiple levels of analysis through which maltreatment may exert its devastating effects. Classic mediational mechanisms of cognitive and emotional processes, such as disrupted information processing, attribution biases, difficulties with impulse control, and emotion regulation have been illustrated in the Historical Context section of this essay, and an integrative model combining all such factors has been constructed for further empirical testing (Lee & Hoaken, 2007). Moreover, research also has started to explore the dynamic, complex roles of close personal relationships in explaining effects of early maltreatment on later externalizing problems. For example, in a longitudinal investigation, Salzinger, Rosario, and Feldman (2007) studied differential roles of adolescents’ relationships with parents and peers in associations between preadolescent PA and violent delinquency later in adolescence. Adolescent attachment to parents and verbal and physical abuse in relationships with parents during adolescence mediated links between early maltreatment and later violent delinquency. In contrast, attachment to friends was not predictive of the association between child maltreatment and adolescent violent delinquency. However, verbal and physical abuse with best friends in adolescence did serve as a moderator: abusive behavior with best friends exacerbated risk for violent delinquent outcome more for maltreated than for nonmaltreated adolescents. Finally, enlightened by advancing knowledge gained at the level of neurobiological development, the notion that traumatic early experiences may become ‘hardwired’ into major neurobiological systems and alter their functioning in a manner that places the individuals at greater risk for experiencing

psychopathology has gained increasing research interest. Recent neuroimaging studies have begun to examine maltreated children and address methodological issues (e.g., consideration of psychiatric co-morbidities and maltreatment subtypes), yielding promising findings for further investigation. For example, a recent MRI study using surface-based methods found reduced cortical thickness of the anterior cingulate and lingual gyrus in maltreated 12-year-old children compared with nonmaltreated matched controls (Kelly et  al., 2013). This may represent a precursor to gray matter volume reductions identified in adult samples (Kelly et al., 2013). Similarly, Edmiston et  al. (2011) first observed gray matter reductions in hippocampal volumes in adolescents primarily reporting emotional neglect. In the research literature, this pattern of reduction has been frequently found in adult samples with a history of maltreatment but absent in maltreated children. Thus, Edmiston et al. suggested a possible resolution to such discrepant findings by establishing the emergence of this pattern at adolescence (Fisher & Pfeifer, 2011). Furthermore, an fMRI study by McCrory et al. (2013) expanded previous research by detecting that amygdala response to angry faces was associated negatively with age of onset of emotional maltreatment and neglect, and was associated positively with duration of emotional maltreatment among maltreated 12-year-old children versus nonmaltreated matched controls. Moreover, a diffusion tensor imaging (DTI) study, which followed maltreated adolescents without a previous diagnosis of psychiatric illness for up to 5  years, revealed that those who developed SUDs during follow-up had significantly lower fractional anisotropy (FA—a DTI-derived metric where decreased value is usually associated with white matter disruption; Beaulieu, 2009) values in the right cingulum–hippocampal projection than their counterparts who did not develop SUDs (Huang, Gundapuneedi, & Rao, 2012). Although more replication efforts are needed before firm conclusions can be drawn, future neuroimaging will definitely benefit from combining multimodal imaging techniques, controlling confounds of psychiatric comorbidities and medication, differentiating effects of maltreatment subtypes, and attending to neural network levels beyond isolated regions (Hart  & Rubia, 2012). It may also be valuable to adopt neuroimaging methods to compare structural and functional features of brain development among maltreated victims

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with psychiatric conditions, maltreated victims without psychiatric conditions, and healthy controls longitudinally in order to explore potential compensatory processes that confer resilience to maltreatment (Fisher  & Pfeifer, 2011; Hart  & Rubia, 2012). Another hypothesized, more precise mechanism at the neurophysiological level that has gained increasing attention in recent years is that maltreatment experiences may lead to the development of asymmetry between major components of the physiological stress-response system, which is associated with behavioral problems later (Bauer, Quas,  & Boyce, 2002). For example, maltreated youth exhibit greater salivary α-amylase–cortisol asymmetry than their nonmaltreated counterparts (Gordis, Granger, Susman,  & Trickett, 2008), which is associated with parent-reported adolescent aggression (Gordis, Granger, Susman,  & Trickett, 2006). Moreover, females who were sexually abused in childhood had asymmetrical physiological responses to stress (measured by heart rate variability and cortisol AUC response) in late adolescence, and this asymmetrical response predicted antisocial behaviors in young adulthood (Shenk, Noll, Putnam & Trickett, 2010). Another physiological asymmetry under investigation is the difference in the magnitude of activation between right and left frontal activity, measured via electroencephalogram. Greater relative right frontal EEG activity has been found to be related to higher levels of externalizing behaviors by parental report (Santesso, Reker, Schmidt, & Segalowitz, 2006). Maltreated female adolescents exhibited high relative right frontal EEG activity, which remained stable over a 6-month period (Miskovic, Schmidt, Georgiades, Boyle, & MacMillan, 2010). Furthermore, institutionalized Romanian children exhibited a prolonged period of increased right hemisphere activation and a blunted rebound in left frontal activation compared to community comparison children (McLaughlin, Fox, Zeanah, & Nelson, 2011). A comprehensive, integrative framework pertaining to the development of externalizing-spectrum problems in the context of child maltreatment should be established as our knowledge of etiological mechanisms at various levels of analysis expands.

Future Research Agenda and Concluding Remarks Advancing the Current State of the Science

It is clear that maltreatment has pervasive effects across many levels that can contribute to the 278

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development of externalizing problems. In following a developmental psychopathology perspective, research must continue to uncover mediators at multiple levels of analysis and build upon mediational models in order to help specify causal pathways and further elucidate ways in which genes and other biological vulnerabilities interact to affect behavior. As such, integrative, multi-disciplinary approaches to understanding the complexity of externalizing behaviors are absolutely necessary given that risk and vulnerability operate at multiple levels of an individual and his/her environment; single levels cannot fully reveal trajectories to adaptation or maladaptation (Cicchetti & Dawson, 2002). Consideration of variables that cover a broad distance (i.e., variables that span several different levels within the developmental system) and that get closer to the neural level are especially important for future research. By including such variables, the field can move beyond simple G×E studies and include broader biology × environment interaction studies to link genes, neural responses, brain structure, and behavior. In a similar vein, continuing to explore ways through which genetic variation and neural components influence vulnerability to externalizing disorders using multigenic approaches may help identify and target the most vulnerable children, potentially allowing for more effective and personalized intervention (Lester  & Eley, 2013). For example, genotype × intervention interaction (G×I) designs have recently been implemented in a number of interventions aimed at either preventing or reducing problem behaviors in children (Bakermans-Kranenburg, van IJzendoorn, Pijlman, Mesman,  & Juffer, 2008; Brody, Beach, Philibert, Chen, & Murry, 2009; Beach et al., 2009; Brody et  al., 2013; Cicchetti, Rogosch,  & Toth, 2011; Lester  & Eley, 2013). Although results are mixed (see Cicchetti et al., 2011), most G×I studies show that individuals with genotypes known to confer greater sensitivity to the environment (see Ellis, Boyce, Belsky, Bakermans-Kranenburg,  & van IJzendoorn, 2011) tend to respond best to relevant interventions. Although issues surrounding genotype-based intervention selection exist (e.g., the potential for discrimination, the impracticality of genetic screening, etc.; Brody et  al., 2013), further exploration of and sensitive research on personalized intervention is nonetheless warranted given the possibility for enhanced understanding of prevention and the role these studies can play in shaping our understanding of developmental psychopathology.

Finally, conducting prospective, longitudinal studies that repeatedly assess functioning across time will advance our understanding of the multiple pathways and factors that represent vulnerability to the development of externalizing spectrum disorders. Such endeavors could help determine if particular differences (e.g., biological differences in brain structure and/or function) exist between resilient maltreated children and non-resilient maltreated children (Cicchetti, 2013), helping to illuminate resilience pathways and identify future targets for intervention.

Topics for Further Investigation

Translation of research findings into interventions that target multiple processes, and examination of whether interventions can reset biological systems at large as well as affect behavioral and socioemotional outcomes early on, will be a fruitful topic of further investigation. Understanding how to normalize effects of physiological allostatic over-load via interventions requires examination of many levels of analysis across time. Given burgeoning data that environmental adversities such as child maltreatment affect functionality of the genome, which may in part underlie development of externalizing psychopathology (see Current State of the Science), epigenetic changes may serve to inform the field as early indicators of change in intervention efforts aimed at reducing problem behaviors. Furthermore, measuring changes in neuroendocrine functioning may serve a similar role in understanding effects of psychosocial interventions across multiple systems (Cicchetti, Rogosch, Toth, & Sturge-Apple, 2011). Finally, multi-level analysis from the genome to culture will continue to shape models which best predict varied developmental outcomes in contexts of child maltreatment, helping to prevent risk and promote resilient functioning. Certain preliminary findings offer especially promising avenues of exploration. For example, parenting interventions aimed at reducing parental abuse/neglect are effective in reducing externalizing symptoms (Cicchetti, 2006; Fraser et  al., 2013). However, additional investigations are necessary to establish replicability. Research findings also suggest promise in the area of emotion regulation. Emotion dysregulation mediates relations between maltreatment and reactive aggression (Shields  & Cicchetti, 1998), as well as relations between maltreatment and bullying (Shields  & Cicchetti, 2001). Moreover, the ability to demonstrate empathic responding depends on emotion

regulation abilities (Rosenblum  & Lewis, 2003), and emotion regulation facilitates the ability to tolerate negative/distressed affect, which could result in more socially-appropriate, constructive anger reactions (Eisenberg, Fabes, Nyman, Bernzweig, & Pinuelas, 1994). Consequently, fostering emotion regulation may provide children the ability to cope with negative affect, aggressive behavior, and impulsive tendencies. In fact, recent interventions aimed at promoting emotion regulation skills have reduced externalizing problems in impulsive preschoolers that last at least 1  year post-treatment (Webster-Stratton, Reid,  & Beauchaine, 2013). Thus, it may be especially important to tailor this type of intervention for maltreated children and examine whether emotion regulation improvements reduce documented effects of maltreatment on cognitive and behavioral domains that contribute to externalizing problems. It also may be beneficial for intervention programs to focus on promoting social-cognitive processes in maltreated children. Effects of the FAST TRACK intervention on reducing antisocial behavior were mediated by reductions in hostile-attributional biases, subsequently increasing competent response generation to social problems and devaluing aggression (Dodge, Godwin, & Conduct Problem Prevention Research Group, 2013). Conducting similar investigations to determine whether such processes may beneficially operate in maltreated children may prove fruitful for preventing externalizing behavior problems in this population. In conclusion, this essay summarizes the classic and recent literature on child maltreatment and development of externalizing spectrum disorders, drawing heavily from a multiple-levels-of-analysis perspective, which is fundamental to developmental psychopathology. Our understanding of the nature and underlying mechanisms of links between these processes is greatly enhanced by current technological advances in measurement, statistical modeling, and biological assaying. Until relatively recently, studies of psychopathology have focused on a limited range of variables and analysis. Inclusion of gender, neurobiological, and genomic designs will continue to path the direction for more comprehensive research on the developmental correlates, causes, and consequences of maltreatment. It is imperative, however, to keep in mind that no one system acts independently. Rather, bidirectional interactions are the norm, and related dynamics need to be accounted for in future research.

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Acknowledgments

Our work on this essay was supported by awards from the Jacobs Foundation and the Spunk Fund, Inc. Authors would also like to thank the National Science Foundation for their support of the first author.

Note

1. Debate exists in the psychopathology literature over use of the terms “gender” versus “sex” to characterize group differences between males and females. Since biological sex is chromosomal (see Chapter 14), whereas gender is at least in part determined by social roles, sex is used instead of gender throughout this volume when social roles are not measured, and sex differences can therefore not be attributed unambiguously to socialization processes. However, it is customary in the maltreatment literature to use the term gender, not sex, and in this essay we honor that tradition.

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Coercive Family Processes in the Development of Externalizing Behavior:  Incorporating Neurobiology into Intervention Research

James Snyder

Abstract A multilevel social learning and neurobiological model of developmental and behavior change processes related to externalizing spectrum symptoms and disorders is described. Coercive family social processes related to the development of externalizing disorders and which serve as key proximal targets of change in empirically supported social learning family interventions are considered from theoretical and research perspectives. Conceptual models are offered which integrate research on coercive family processes with emerging neuroscience research to account for and enhance the durability and generality of changes in externalizing symptoms and adaptive functioning engendered by social learning family interventions. Extant research that examines the complementary roles of coercive social processes and neurobiological processes as mediators of change engendered by social learning interventions is described and evaluated. Finally, recommendations are made to guide subsequent intervention research in which social learning and neurobiological processes are treated as complementary, multilevel mechanisms of change in social learning interventions targeting externalizing disorders. Key Words:  coercive family process, social learning, family and parenting interventions, neurobiological processes, multilevel mediators of intervention effects

Introduction

Coercion theory has served as a central component of risk models delineating the family’s role in development of antisocial behavior. It has also provided a translational model, informing development of social learning interventions focused on enhancing parenting practices and family relationships to deflect trajectories of antisocial behavior (Reid, Patterson,  & Snyder, 2002). These interventions have demonstrated efficacy and effectiveness, with robust and long lasting effects across developmental periods and social contexts. Despite this productive empirical record, the role of coercive family processes and contributions 286

of associated parenting and family interventions need to be better incorporated and integrated into emerging neuroscience research on development of antisocial behavior. In writing this essay, I have four goals. First, I provide a description of coercive social behavior and family processes from developmental and intervention perspectives. Second, I build conceptual models that integrate research and theory on coercive family processes and family interventions with emerging neuroscience research on development of antisocial behavior. Third, I describe and evaluate research that examines potential complementary roles of coercive family processes and neurobiological processes as mediators of change

engendered by family based social learning interventions targeting antisocial behavior. Finally, I  describe next steps in an agenda for research integrating social and neurobiological processes in parenting and family intervention research on antisocial behavior.

The Construct of Coercion

Coercion refers to use of aversive behaviors by individuals to obtain rewards and access to desired activities, attain status, and avoid or escape aversive control and demands in social relationship contexts (Patterson, 1982). Topographically, coercive behaviors are socially aversive (Snyder, 1983), and include physical threats and aggression, verbal disparagement, opposition and noncompliance, and displays of negative affect. Coercive behaviors can also be more indirect and subtle, including emotional manipulation, third-party character denigration, and rejection or exclusion in social groups. One primary function of coercive behaviors is to turn off and/or head off others’ behavioral demands and expectations (Snyder  & Patterson, 1995). Child tantrums in response to parental limit setting are functional if they terminate parental efforts to enforce those limits. This function reflects escape or avoidance conditioning in which coercive behaviors are shaped and maintained by negative reinforcement contingencies. Coercive behaviors may also be supported by positive reinforcement contingencies insofar as they enable attainment of desired materials, activities, social control, and status. Of course, positive or “skilled” behavior may serve the same functions as coercive behavior: to manage conflict and disagreement, to attain status, to collaborate on some goal, and to access rewards and desired activities. The frequency of coercive behaviors often depends on utility of such patterns relative to that for more positive, skilled alternatives (Snyder  & Patterson, 1995). Coercion is also social process involving two or more persons. Coercive behaviors are evoked by social partners and shaped by their contingent reactions. Because social interaction inherently entails mutual influence, behaviors of both parties are shaped by the other’s behavior during ongoing interaction (Patterson, 1979; Patterson, Reid,  & Dishion, 1992). When a parent gives in to a tantrum, a child is more likely to tantrum again to diminish future parental limit-setting—and a parent is less likely to set limits in order to avoid child tantrums. Coercion, therefore, focuses on

relationship processes rather than on behavior of any one individual.

Coercive Processes and Development of Antisocial Behavior

Humans have a “built-in” capacity for coercive behavior, as witnessed by infants’ cries to gain adult attention and care giving. Coercive behavior doesn’t have to be “learned” in this sense (Tremblay, 2003), and it is not inherently pathological. Infants also have an array of other built-in social behaviors through which to capture adult attention and caregiving, such as mutual gaze, nuzzling, grasping and smiling, which emerge early in life. During development, both coercive and skilled social behaviors become elaborated in topography and shift in frequency as a result of both biological maturation and social learning (Leve, Pears & Fisher, 2002). If such elaboration leads to predominant reliance on coercive means of relating to others, development of competence and access to supportive social relationships are compromised, and risk for antisocial behavior is increased (Reid, & Eddy, 1997). Once coercive behavior is shaped to become increasingly trait-like, it is relatively resistant to change. It is highly functional in terms of generating short-term positive and negative reinforcement. Coercive behaviors become over-learned and automatic as they occur daily in affected families (Dumas, 2005). As a result of ongoing contingency-shaping, irritability and opposition may become almost reflexive relationship tools; coercion is quickly met with counter-coercion, and escalation in intensity of aversive behavior is used to force capitulation (Snyder, Edwards, McGraw, Kilgore, & Holton, 1994). Individuals who increasingly rely on coercive tactics are both architects and victims of coercive environments (Patterson, 1988); they live in and create highly aversive environments. Coercive family processes have been extensively described based on careful observation in naturalistic home settings (for a summary, see Snyder & Stoolmiller, 2002). Coercive family processes increment risk for antisocial development by undermining effective parenting practices, including effective limit setting and discipline, instruction and positive contingences to shape skilled behavior, communication and problem solving, and monitoring (Patterson et al., 1992). Child coercive behavior has its roots in the family, with potential to set off cascades of experiences that further compromise development. It diminishes supportive social relationships, limits access to and success in learning environments, Snyder

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and leads to rejection and exclusion by others (Reid et  al., 2002; Snyder, Reid,  & Patterson, 2003). Individuals may come to actively select social relationships and contexts that provide rewards and confer status for coercive, antisocial behavior (Dishion, Andrews, & Crosby, 1995).

Coercive Social Processes and Family Intervention

Research on coercive social processes has also provided a translational model, informing development of interventions focused on enhancing parenting practices and family relationships in order to alter trajectories of antisocial behavior (Dishion & Patterson, 1999). Interventions derived from social interaction learning (SIL) theory target reductions in coercive family processes and conflict, and enhancement of constructive, supportive social processes using modeling, shaping, and practice with feedback (Forgatch, Bullock,  & Patterson, 2004). The goal of SIL family interventions is to help parents set clear limits, use contingent noncoercive discipline strategies, promote child skills using positive reinforcement contingencies, improve communication and problem solving, and engage in age-appropriate monitoring (Chorpita & Daleiden, 2009; Kaminski, Valle, Filene,  & Boyle, 2008). Randomized control trials have demonstrated both efficacy and effectiveness of SIL parenting and family interventions in addressing a prevalent and costly array of externalizing problems, including aggression, delinquency, and early-onset drug use and sexual activity. Effects of SIL parenting and family interventions on parenting practices and trajectories of child problem behavior are often large, robust, and long lasting (Sandler, Schoenfelder, Wolchik, & MacKinnon, 2011). Understanding for whom, how, and when change is generated, persists, and generalizes (and when it does not) is a critical task for causal tests of theory and for developing increasingly efficacious and effective interventions. Changes in family processes, including reductions in coercion, are thought to serve as proximal mechanisms by which SIL parenting and family interventions have their effects on child and adolescent antisocial behavior (Snyder et  al., 2006). Several longitudinal randomized trials of parent and family interventions indicate that reductions in coercive family processes and increases in constructive family processes mediate reductions in child antisocial behavior (Chamberlain, Price, Leve, Laurent, Landsverk, & Reid, 2008; Dishion  & Patterson, 1999; Eddy  & 288

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Chamberlain, 2000; Forgatch, 1991; Patterson, Forgatch  & DeGarmo, 2010). However, relevant mechanisms, including changes in child neurobiological functioning, through which SIL parenting and family interventions result in long-term, generalized change across behaviors and social contexts are not well understood (Reed et al., 2013). Interventions derived from coercion/SIL theory are based on empirically supported principles and tactics, facilitating their flexible application and adaptation. Evidence suggests that these family and parenting interventions successfully alter an array of child target problems defining antisocial behavior across a range of child characteristics (Beauchaine, Gatzke-Kopp, Neuhaus, Chipman, Reid,  & Webster-Stratton, 2013), parent factors (Wachlarowicz, Snyder, Low, Forgatch,  & DeGarmo, 2012), caregiving/family configurations (Patterson et al., 2010), and socioeconomic and cultural contexts, even when applied in “real world,” nonresearch settings (Michelson, Davenport, Dretzke, Barlow,  & Day, 2013). However, it is likely that children and families benefit to varying degrees. Children (and parents) may be more or less irritable, more or less reactive to aversive events, and more or less sensitive to reinforcement and punishment contingencies, as a result of interplay of genetic or constitutional factors and environmental learning (Beauchaine & Gatzke-Kopp, 2012).

Coercion Theory, Development, and Intervention: A Summary

Coercion theory is fundamentally a behavioral, social learning model of development and change. Coercive and skilled behaviors are shaped, maintained, and elaborated by social stimuli and contingencies encountered during daily interaction in the family and other social contexts. These cumulative social experiences and their sequelae shape trajectories of antisocial behavior from infancy into young adulthood (Reid & Eddy, 1997). SIL family interventions targeting antisocial behavior entail teaching parents to systematically alter evoking stimuli and contingencies during family interaction in ways that support positive, skilled behavior and that diminish coercive behavior of their children. Temporal persistence and cross-setting occurrence of change are explained by stimulus–response generalization models:  As children take their behaviors into new environments, these behaviors are maintained and elaborated insofar as they generate and are supported by social experiences and reinforcement contingencies in those environments. Cross-setting and

-time reinforcement congruence are also generated by behavior evocation and by environment selection. Mediators of cross-time and -setting growth in antisocial behavior reside in social environments. The SIL model asserts that the same change principles apply to family and parenting interventions across a wide range of child, family, and contextual characteristics. These principles entail altering “key” sets of social stimuli and contingencies in parent–child interaction in a manner that is adapted to specific child, parent, and family needs and resources (Dishion & Stormshak, 2007). There is considerable evidence that this adaptation works. However, increased attention to moderators and mediators would promote understanding of when and how to adapt SIL parenting and family interventions to enhance their efficacy and effectiveness (Kazdin, 1999; Kazdin  & Nock, 2003; LaGreca, Silverman, & Lochman, 2009).

Coercion Theory and the Promise of Behavioral Neuroscience

Theory and research have increasingly turned to social neuroscience as a means to better understand the etiology of antisocial behavior and its persistence and progression in frequency, form, and severity (Beauchaine & McNulty, 2013). However, this theory and research have not been systematically integrated with coercion theory or incorporated into SIL intervention research to better understand for whom (moderators), how (mediators), how broadly (generalization), and for how long (persistence) SIL interventions have their effects. Given strong empirical and practical records accorded to behavioral, SIL models, it is legitimate to ask: “What’s to be gained by adding neurobiological mechanisms?” At the most skeptical level, this integration of neuroscience and social interaction/behavioral models may construed as simply adding a reductionistic layer of explanation and reifying antisocial behavior as a brain “disease.” On the other hand, a far more integrated science, linking social and neurobiological processes, could result in gains in both basic science and applied science. In this essay, I examine how neuroscience theory and research may be integrated with, complement, and extend SIL/coercion theory models of parent training and family interventions to better understand how and why SIL interventions have sizeable, lasting, and cross-setting effects on child antisocial behavior. The utility of the marriage of SIL/coercion theory with neuroscience is an open question; however, at a bottom line, the marriage should

explain more variance in change of antisocial trajectories engendered by intervention than “social” models alone. It should also support development and delivery of increasingly efficacious and effective interventions. If these utility criteria are not met, the elegance and intuitive appeal of neurobiological processes as mechanisms of change may be considered as seductive but empty. The goal is to formulate models and research designs and methods that provide data to test the “value added” of a marriage of SIL family and parenting intervention science with behavioral neuroscience. At the outset, a basic assumption in this essay is that a primary function of the brain is to mediate behavioral responses that are adaptive in relation to external environmental opportunities and challenges—the brain is an organ of (environmental) adaptation. How the brain does this depends on transactions among genetic programs for unfolding development, individual genetic variations in that program, and experience-dependent shaping of neurobiological systems. This assumption avoids reductionism and reification, which minimize and marginalize effects of the environment. Two fundamental, practical questions need to be answered from the perspective of SIL family and parenting intervention science. First, to what degree do genetic vulnerabilities, associated neurobiological system functioning, and experience-dependent shaping of neurobiological systems, moderate or set limits on effects of SIL interventions for antisocial behavior? These moderating effects may operate in several ways. Sensitivity and reactivity to environmental stimuli and contingencies, and regulation of cognitive, emotional and behavioral responses subserved by these neurobiological systems, may (a)  shape family interaction and parenting (evocative rGE effects), (b) affect developing children’s responses to parenting and family environments (gating or vulnerability GE effects), and/or (c)  foster behavioral biases that result in environmental selection such as avoiding parental monitoring (passive rGE effect). Second, to what degree do SIL family and parenting interventions result in experience-dependent shaping of neurobiological system functioning? Moreover, to what degree do these neurobiological changes serve as mediators of intervention effects (in addition to changes in family social processes) to better explain (and to ultimately to promote) cross-setting and cross-time cascades of reductions in antisocial behavior, and of enhancement of social-emotional competence, peer relationships, and school performance? It should be noted here Snyder

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that these mediators may not be the same as those most important etiologically (i.e., we should not fall prey to the treatment–etiology fallacy). Not all neurobiological systems are equally malleable across development; brain systems mature at different rates and are differentially affected by experience depending on developmental timing (Giedd et al., 1999). Cumulative and sometimes extreme environmental experiences may shape neural systems in ways that are difficult to reverse with corrective environmental experiences (epigenetic effects; Lester et al., 2011). Interventions may be effective because they alter higher-level or other brain systems to provide a “work around” of more basic or earlier occurring/ less reversible etiological processes that have become canalized in brain systems. This essay primarily focuses on the second question given: the potential mediating role of changes in neurobiological systems with respect to improvements in child antisocial behavior resulting from SIL parenting and family interventions. Specifically, I  describe the manner in which randomized trials of SIL interventions can be used to test whether experience-dependent changes in neurobiological systems serve as mediators of intervention effects on child antisocial behavior, because randomized intervention research provides a powerful vehicle with which to make strong causal tests of neurobiological system involvement in development of antisocial behavior.

Neurobiological Systems, Coercive Family Processes, and Antisocial Development

Three interrelated brain systems most frequently associated with development of antisocial behavior subserve (a) reactivity to aversive and positive environmental stimuli, (b)  sensitivity to positive and negative reinforcement/punishment contingencies, and (c)  regulation of emotional and behavioral responses to environmental events (Beauchaine  & Gatzke-Kopp, 2012). These brain systems are likely to be exquisitely attuned to and engaged by ongoing streams of positive and coercive social stimuli, and contingencies that comprise ongoing social experiences of children with parents, siblings, peers, teachers, and other social agents. First, the mesolimbic dopamine system (DA) is associated with reward seeking, sensitivity to immediate and repeated rewards, irritable responding to frustrative nonreward, and avoidance/escape motivation (Panksepp  & Moskal, 2008). Its associated endophenotype is impulsivity. Individual differences in functional properties of the mesolimbic 290

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DA system appear to have strong genetic underpinnings (Beauchaine & Gatzke-Kopp, 2012). As well as its function in appetitive exploration and reward seeking, this system may also participate in many stress-induced and negative incentive safety-seeking behavioral processes. The meoslimbic DA system (along with its mesocortical counterpart; see later) contributes to behavior selection and shaping; this instinctual affect–action neural system is engaged to energize and guide behavior in response to cues of (anticipated) environmental positive (and perhaps negative) reinforcement contingencies (Panksepp & Moskal, 2008). Based on cumulative environmental experience with such contingencies (perhaps involving higher-level associative networks), cues come to activate this seeking/wanting system in an experience–expectant manner (Panksepp  & Moskal, 2008). Second, the limbic hypothalamic-pituitaryadrenal or LHPA system is associated with innate and learned reactivity and defensive responding to aversive events, including trauma and punishment, and with evaluation of positive/aversive properties of stimuli (Cain & LeDoux, 2008). It also organizes short- and long-term physiological, endocrine, emotional, and behavioral responses to trauma and stress. This system contributes to the neural substrates of fear conditioning, avoidance, and escape; it evidences experience-dependent shaping at the neurobiological level (Cain  & LeDeoux, 2008). Activation of the LHPA system results in release of cortisol from the adrenal cortex, with negative feedback regulation of upstream neural systems. Cortisol activity is critical to stress regulation but operates optimally in response to acute, short-term stress. Exposure to trauma and chronic stress has negative effects on LHPA axis function (“allostatic load”) and may alter functional properties of the amygdala threat response and regulatory medial prefrontal cortex systems, especially in the absence of a supportive care giving environment (Gunnar et al., 2006). Third, the mesocortical DA prefrontal cortical (vmPFC system) receives input from and provides top-down regulation of mesolimbic DA and LHPA systems. Feed-forward and feedback connections between the vmPFC and mesolimbic DA and LHPA systems are central to regulatory responses to environmental stress, challenge, and opportunity (Hetherington, 2011). The vmPFC and associated frontal cortical areas are implicated in executive/cognitive control, as reflected in attention deployment, delay discounting, planning, decision

making, response selection, and response monitoring. There is evidence that functional properties of frontal cortical control system can be enhanced by supportive environmental experiences (Bryck  & Fisher, 2012) and can be diminished by exposure to aversive, noncontingent, unpredictable, and chaotic care giving environments (Hackman & Farah, 2009; Pears, Kim & Fisher, 2008). The mesocortical DA system shows continued maturation from infancy through young adulthood (Casey, Geidd, & Thomas, 2000; Dahl, 2006).

Brain Systems and Social Experience

Reactivity and sensitivity to social–environmental events and responsiveness to social–environmental contingencies are influenced by individual genetically mediated and maturational variations in functional properties of these three neural systems. However, cumulative social experiences also shape structural and functional properties of these neurobiological systems during development (Arnsten, 2009; Helprin  & Schultz, 2006) through axonal elaboration, synaptic pruning and consolidation, regulation of neurotransmitter synthesis and deactivation, alteration in neurotransmitter receptor sensitivity, and long-term potentiation and depression (Baars & Gage, 2007). Experience customizes neural circuits to meet ongoing needs and cumulative environmental opportunities and challenges at an individual level in ways that that transcend genetic encoding per se (Knudsen, 2004). This experience-dependent shaping of neurobiological systems may facilitate, constrain, and bias behavior, learning, and environmental selection in ways that amplify individual differences over time and across contexts. Reactive, regulatory, response selection and monitoring, and approach and avoidance/escape learning functions served by these systems are intimately related and attuned to social stimuli and contingencies comprising coercive and positive family interaction and parenting practices. Thousands of day-to-day social exchanges in the family environment, accumulating over a number of years, are likely to result in experience-dependent shaping of lower-level regulatory as well as higher-level frontal and associative neural systems. These neurobiological systems may also be engaged and altered by SIL parenting and family interventions, as these interventions are designed to (a) promote reductions in the frequency of aversive stimuli (from nattering to trauma) and punishment; (b)  increase positive social bids and reciprocity to enhance constructive

relationship engagement; (c)  increase use of clear instructions and positive reinforcement contingencies to shape constructive, cooperative behavior; and (d)  rely on calm and contingent use of reinforcement loss and attention withdrawal (time out and response cost) to diminish problem behavior. Social interaction learning theory accounts for how coercive processes and reinforcement contingencies shape behavior but does not fully specify mechanisms of those effects. Even in strict behaviorist models, permanent changes in behavior resulting from social experience must ultimately be represented by changes in structural and/or functional properties of neurobiological systems of individuals (Skinner, 1953). Behavioral and SIL approaches have traditionally eschewed such explanations because these changes could not be observed and measured. However, advances in measurement of neurobiological processes provide opportunities to reliably assess changes in neurobiological functions, set points, operating ranges, and regulatory processes that are transactionally shaped by and shape social experience, including involvement in coercive and positive family interaction processes. As a consequence, changes in neurobiological system processes and their functional interconnections may be tested as candidate mediators of change in trajectories of antisocial behavior resulting from intentional and systematic alteration of social environments in experimental longitudinal trials of SIL parenting and family interventions. The proposed integration of neuroscience and SIL parenting and family interventions is multilevel and transactional. It “places” emotional and behavioral regulation in social relationships and not solely in neurobiological systems within the child. Interventions that support responsive, sensitive, contingent, and supportive parenting, and that reduce coercive processes, provide an external environmental experiences that shape child neuroregulatory systems (Fisher, Gunnar, Dozier, Bruce, & Pears, 2006). Parental monitoring, limit setting, and effective discipline limit impulsivity, and along with intentional shaping of skills using positive reinforcement contingencies, may facilitate association of reward-seeking neural system activation with positive environmental contingencies to shape constructive behavior (Dishion & Patterson, 2006). Caregivers’ use of emotion coaching, clear communication, and problem solving during family interaction may shape executive frontal control and attention capacity of children in ways that promote positive child adjustment Snyder

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(Bierman, Nix, Greenberg, & Domitrovich, 2008). Reducing coercive family processes and increasing sensitive parental responding to child distress may promote development of the LHPA system in ways that promote children’s flexible and adaptive stress regulatory capacities (Bakermans-Kranenburg, Van Ijzendoorn, Mesman, Alink, & Juffer, 2008). What is missing from much of SIL parenting and family intervention research is an explicit examination of the multilevel, transactional contribution of both neurobiological and social learning processes to short- and long-term behavior change.

Integrating Neuroscience into Parenting and Family Intervention Research

Addition of neurobiological system change as a mediator of SIL parenting and family intervention effects on child antisocial behavior to already complex design and measurement demands in randomized, longitudinal intervention research may appear daunting. There are several substantial benefits that might be derived from infusing SIL intervention research with measures of neurobiological system functioning related to emotional and behavioral reactivity and regulation (Adam, Sutton, Doane, & Mineka, 2008; Cicchetti & Gunnar, 2008; Tremblay, 2008). First, randomized intervention research would provide clear tests of causal roles of neurobiological system processes in development of antisocial behavior by establishing their role as mediators of effects of environmental experience (including intervention) on behavioral and emotional adjustment. Second, an increased understanding of how changes in neurobiological systems mediate effects of SIL interventions on antisocial behavior may inform new and/or more focused intervention targets and adaptations to enhance efficacy, effectiveness, and durability of those interventions. Third, changes in neurobiological systems subserving emotional and behavioral reactivity and regulation may provide an explanation of cascades of cross-setting, cross-behavior, and cross-time changes that result from SIL parenting and family interventions (Raizada,  & Kishiyama, 2010). Fourth, changes in neurobiological systems (and in parenting and child behavior) that leave a sufficient “footprint” to effect a variety of downstream developmental outcomes (including delayed effects) would provide a useful index of the effectiveness of SIL parenting and family interventions. Fifth, constitutional, prenatal, and genetic factors that moderate effects of SIL parenting and family interventions on child behavior may point to compensatory or “work-around” neurobiological systems 292

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that might be targeted in adapted SIL parenting and family interventions. A heuristic theoretical model and research design, which incorporate the causal role of changes in neurobiological system functioning as mediators of effects of SIL parenting and family interventions on antisocial behavior, appears in Figure 16.1. As shown at the bottom of the figure, the model postulates a multimediator sequence of SIL parenting and family intervention effects on child antisocial behavior. The proximal mediator of SIL parenting or family intervention involves reductions in coercive processes and negative reinforcement, use of clear limits and punishment by reinforcement loss, and increases in positive engagement and use of contingent positive reinforcement during family interaction (Snyder et  al., 2006). As described above, the meditational role of these changes in family interaction in effecting reductions in child antisocial behavior has been documented empirically in a number of randomized intervention trials of SIL parent training and family intervention. Changes in parent–child interaction engendered by SIL interventions persist for at least 2 years (Reed et al., in press) and mediate substantial reductions in child antisocial behavior for up to 8 years after intervention (Patterson et al., 2010). As a second step in the proposed model, reductions in coercive and increases in positive family processes are hypothesized to provide systematic social environmental experiences that cumulatively and iteratively shape neurobiological systems of children through experience-dependent and -expectant processes. Changes in parenting practices and parent–child relationships would mediate effects of intervention on changes in children’s neurobiological system functioning. To establish that changes in neurobiological system functioning have a causal role in short- and long-term change processes and generalization of positive effects of intervention on child antisocial behavior and social competence, effects of changes in coercive and positive family processes on child behavior must also be mediated, at least in part, by changes in neurobiological system functioning. As shown in the top portion of the figure, tests of this sequential mediation model require sophisticated design and measurement tactics. Randomization of target children who are at risk for or display antisocial behavior to SIL parenting/ family intervention versus a comparison control is needed to make strong inferences about causality. Using a multioutcome longitudinal design, the key constructs of family interaction/parenting,

Randomization: Parenting intervention or control Longitudinal assessment Baseline

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Change in child AS (BL to 24 months) Figure 16.1  A proposed model and experimental research design to integrate neurobiology with coercion/social learning theory of antisocial behavior.

neurobiological system functioning, and child antisocial behavior would be measured on repeated occasions, with timing of measurement selected to reflect expected timing of changes in mediating processes and child behavior outcomes. Ideally, these constructs would be measured using a multi-informant and -method approach to minimize measurement error and reduce problems of multicollinearity, including observation of parent–child interaction. It would also be useful to include a normative community group of children and parents with which to compare the intervention and control-groups on levels and change in family processes, neurobiological processes, and child outcomes, providing opportunities to assess whether interventions change parenting, child behavior, and neurobiological outcomes to levels within normal ranges. This may appear to be a daunting task. However, extant randomized trials of SIL parent training and family intervention have successfully addressed and incorporated all of these design and measurement features and provided analyses, which demonstrate meditational relationships (e.g., Patterson et  al., 2010), except for inclusion of changes in child neurobiological system functioning over time. Measurement of malleable cognitive and behavior performance indicators that closely reflect functioning in candidate neurobiological systems (e.g., performance on

Go No-Go tasks, Stroop tasks, the Trails Test, etc.) may serve as practical proxies for more expensive and technically demanding indicators of neurobiological system functioning (e.g., ERP, fMRI, cortisol assays, PEP, RSA, etc.; Beauchaine, 2012). Realization of this research agenda depends on identification of reliable and valid measures of neurobiological system functioning that are sensitive to change. There are several criteria to be met in identifying candidate neurobiological measures (Eddy, Dishion, & Stoolmiller, 1998). First, candidate neurobiological process associated with risk for antisocial behavior should be malleable to experience. Second, the nature of experience-dependent change in neurobiological functioning that would reflect improvements in child antisocial behavior would need to be specified and measured reliably. Third, cumulative environmental experiences engendered by SIL parenting and family interventions hypothesized to result in changes in target neurobiological systems would need to be specified and measured reliably, as well.

Research on the Role of Neurobiological System Processes as Mediators of Effects of SIL Parenting Intervention on Child Antisocial Behavior

Some initial progress has been made in integrating neurobiological indices of change into research Snyder

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examining effects of parenting and family intervention on development of antisocial behavior. Several exemplars of programmatic research are described in the following sections, organized around the three neurobiological systems that have been associated both with family processes targeted for change in SIL parenting and family interventions and with risk for development of antisocial behavior. At the outset, it is recognized that these three neural systems act in concert and are functionally interdependent, so that measurement of responses of any one neurobiological system is also likely to reflect its interaction with other systems.

Mesocortical DA Prefrontal Systems

Prefrontal cortical systems provide malleable neurobiological targets for SIL parenting and family interventions. These systems and their reciprocal connections to lower, limbic neuroregulatory systems continue to develop into late adolescence. They are closely associated with emotional and behavioral self-regulation, selection and monitoring of instrumental responses, and attention deployment in relation to environmental events. Deficiencies in executive frontal control are also associated with increased risk for an array of externalizing behavior problems. Lewis, Granic, and colleagues have reported a series of studies that examine effects of SIL parent training and child self-regulation training on child conduct problems and on neural activity reflecting prefrontal cortex executive functions (Granic, Meusel, Lamm, Woltering,  & Lewis, 2012; Lewis et  al., 2008; Woltering, Granic, Lamm,  & Lewis, 2011). In the first study (Lewis et  al., 2008), an empirically supported intervention combining SIL parent training and child self-regulation training (Goldberg & Legget, 1990) was provided to families with children referred for clinic-level conduct problems. Evoked response potentials (ERP N2) in dorsal and ventral regions of the PFC were measured during an emotion-evoking Go No-Go task, both before and after the intervention (ERPs are transient voltage fluctuations generated by a large population of localized neurons that are time-locked to discrete environmental events; see Davies, Segalowitz,  & Gavin, 2004). Children were classified as improvers or nonimprovers based on pre- to post-intervention change scores on parent and teacher reports of child conduct problems. Improvers showed better performance accuracy on the Go No-Go task, and reduced activation in inhibitory N2 ERP in the ventral region of the PFC after intervention relative 294

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to nonimprovers. No significant group differences were found for the N2 ERP in the dorsal region. Reductions in N2 in ventral region may indicate that children who improved as a result of intervention needed to exert less effort to regulate emotional arousal, reflecting reductions in perceived “threat” and defensive responding. These findings were partially replicated in a second treatment sample (Woltering et  al., 2011) of children with clinic-level conduct problems using a similar design and method. Changes in PFC ventral and dorsal N2 from before to after intervention were assessed during the Go No-Go task. Children classified as improvers on conduct problems after the SIL parenting intervention showed better Go No-Go task performance relative to nonimprovers and decreased N2 activation after the intervention. Changes in N2 and in externalizing problems in the improver group were correlated (r  =  .23, p < .07). A  recent cross-sectional study of the improver group (Granic et  al., 2012) found that child conduct problems were associated with less flexible parent–child interaction patterns and that flexibility in parent–child interaction patterns were associated with greater dorsal to ventral activation in the PFC during the Go No-Go task. Findings in these studies are consistent with the hypothesis that changes in neural systems of executive frontal control mediate effects of SIL parent and child interventions on reductions in child antisocial behavior, and that these changes in frontal control may be linked to patterns of parent–child interaction. However, the lack of a nonintervention control group and random assignment preclude strong causal inferences, and the pre-/post-intervention assessment design obviated strong tests of any (sequential) meditational role of changes in family processes and executive frontal control on reductions in conduct problems. Indeed, ideally mediator processes would be measured at an intermediate point in between pretreatment and post-treatment evaluations (Kraemer et al., 2002). Research in developmental and cognitive neuroscience documents the linkage of child disadvantage (as reflected in low family SES and poverty) to increased risk for an array of negative social, emotional, and academic outcomes (Shonkoff, 2010). It also links child disadvantage to deficiencies in self-regulation and attention deployment systems in the PFC and the anterior cingulate gyrus (Lipina  & Posner, 2012). Effects of disadvantage on child development outcomes appear to be mediated, at least in part, through family and

other social-environmental processes. Randomized trials of SIL parenting and family interventions for disadvantaged children indicate improvement in children’s long-term antisocial behavior problems, mediated by changes in parenting practices (Beauchaine, Webster-Stratton,  & Reid, 2005). These findings suggest that effects of SIL parenting and family interventions may be mediated by changes in children’s PFC neural systems. Using a randomized trial design, Neville and colleagues (Neville et al., 2013) assessed effects of an empirically validated SIL parent training program (Reid, Eddy, Fetrow  & Stoolmiller, 1999), combined with attention- and emotion-regulation child training exercises, relative to a child training-only and to a control group (children in all three groups were enrolled in Head Start programs). Evoked response potentials in the PFC to probe stimuli during attended and unattended auditory stories were used to assess selective attention before and after intervention. In addition, observations were made of parent–child interactions, and teacher and parent reports of child behavior problems and social skills were collected before and after intervention. Children in the combined parenting and child attention training intervention showed greater improvements in ERP measures of selective attention, more turn taking and scaffolded learning exchanges during parent–child interaction, reduced parent distress, improved child social skills, and decreased child conduct problems relative to children in the child attention training-only and control groups. These findings suggest that PFC neural systems for selective attention are malleable in response to SIL parenting and child attention training, but only if interventions are embedded in daily activities in the natural social environment—in this case, the family. Stand alone, child-focused “brain training” interventions appear to be less effective and have limited durability and generalization (Rabipour & Raz, 2012). These findings are, again, consistent with the hypothesis that change in executive frontal control may mediate effects of SIL parenting interventions on child antisocial behavior, and that changes in executive frontal control may be linked to patterns of parent–child interaction. Random assignment in this study supports stronger inferences about causality, but explicit tests of mediation of intervention effects on child outcomes by changes in parent–child interaction and by increases in child selective attention as indexed by ERP were

not available in this pre-/post-only intervention design. Using a postintervention-only randomized design, Bruce, McDermott, Fisher, and Fox (2009) examined electrophysiological and behavioral measures of frontal lobe functioning during a flanker task reflecting cognitive control and response monitoring of foster children who were randomly assigned to a SIL Multi-dimensional Therapeutic Foster Care (MTFC) or to foster care as usual (FCAU), with a low income non–foster care community comparison (CC) group. MTFC trains foster parents to provide high rates of warmth and positive reinforcement for constructive behavior, and effective consequences for negative behavior. Three sequential evoked response potentials (ERPs), generated by frontal structures in response to congruent and incongruent trials on the flanker task were measured, including (1) error-related negativity (ERN; a negative ERP that is accentuated following performance errors), which assesses initial response monitoring; (2); error-related positivity (Pe; a positive ERP following the ERN) reflecting continued response monitoring and error detection, and (3)  feedback-related negativity (FRN), which reflects responsiveness to explicit feedback and motivation (rather than internal feedback with the ERN). For foster children in the MFTC condition and children in the CC condition, amplitudes of ERN, Pe, and FRN were significantly different than those in the FCAU group in response to negative feedback for errors on the flanker task. These data indicate MTFC may correct frontal deficits in response monitoring to external feedback about errors that may result from adverse conditions leading to foster care placement. Results of this post-only research study should be contextualized in other research by Fisher and his colleagues (described in the next section) that also demonstrate group differences in behavioral outcomes and disruptions in foster care placement derived from randomized comparisons of MFTC and FCAU, of which the sample in Bruce et  al. (2009) comprises a subset. Inclusion of a community comparison group suggests that not only are frontal lobe functions changed as a result of the MFTC intervention but that these changes result in neural functioning similar to children who are not in foster care. However, linkage of observed changes in ERPs to short- and longer-term behavioral change, and to specific social environmental experiences generated by the SIL intervention, is less clear. Snyder

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LHPA Stress Regulatory Systems

Children who experience adverse social and physical environments, including traumatic events, maltreatment, and chronic stress, are at increased risk for a range of negative health and behavioral outcomes, including disruptive and antisocial behavior (Shonkoff, 2010). Effects of such “toxic stress” are particularly apparent in the absence of supportive parenting. The experience of extreme or repeated adverse social environmental events may also result in functional changes in LHPA neurobiological systems, in associated amygdala threat responses, and in medial prefrontal cortex regulatory systems (Gunnar et al., 2006). Several research programs have assessed whether LHPA dysfunctions resulting from adverse experiences can remediated by SIL family interventions. Using a randomized trial design, Brotman et al. (2007) assessed whether a combined SIL parenting and child intervention resulted in changes in cortisol reactivity to a challenging peer group entry task among preschool children who were at high risk for antisocial behavior. The rationale for a focus on cortisol reactivity was derived from research indicating that blunted LHPA activation (manifested as reduced cortisol reactivity) may result from chronic exposure to noncontingent and coercive parenting, and increase risk for child antisocial behavior (vanGoozen, Fairchild, Snoek,  & Harold, 2007). Relative to a no-treatment control group, children in the SIL intervention condition showed a stronger cortisol response just prior to the stressful peer entry situation, but there were no treatment group differences in post-stressor cortisol response or in diurnal cortisol patterns. These findings are consistent with the hypothesis that the LHPA system, as indexed by cortisol reactivity, can be altered by SIL family interventions. In two other reports, Brotman and colleagues (Brotman, Gouley, Chesir-Teran, Klein, & Shrout, 2005; Brotman et al., 2005) also found that the SIL intervention reduced harsh discipline, increased parental responsiveness and stimulation/warmth, enhanced child social competence, and positive engagement with peers, and reduced child aggression during interaction with parents and peers. More complete meditational models were then tested by the Brotman research group. Positive intervention effects on parenting and child aggressive behavior persisted at a 16  month follow-up (Brotman, Gouley, Huang, Rosenfelt,  & Klein, 2008). Effects of the SIL intervention on child aggression at follow-up were partially mediated 296

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by changes in parenting practices (Brotman et  al., 2009). Finally, changes in post-intervention cortisol reactivity resulting from the SIL intervention fully mediated effects of the intervention on subsequent aggression among children whose parents showed low warmth at baseline (O’Neal et  al., 2010). However, the correlation between increases in parental warmth and cortisol reactivity in the intervention group was not significant (r = .24, p = .21). Thus, a double mediation model involving both changes in parenting and cortisol responses was not fully supported. Using a younger sample of 1- to 3-year-old children with significant conduct problems, Bakermans-Kranenburg et  al. (2008) examined whether a parenting intervention focused on use of sensitive discipline, responsiveness to child needs, and reductions in coercive reactions would decrease diurnal cortisol levels after intervention. This study also assessed whether the DRD4 VNTR polymorphism would moderate effects of the intervention on cortisol levels. Children whose parents received the intervention showed lower mid-day cortisol than children in the control group, but only if they had a 7-repeat DRD4 allele. The authors suggest that children with the DRD4 polymorphism may have poor state regulation and thus are more sensitive to environmental/intervention as well as being at high risk for persisting conduct problems. A  separate, earlier report indicated that parents in the intervention group showed increases in effective parenting after the intervention, relative to the control group (Van Zeijl et al., 2006). Using a randomized longitudinal design, Fisher and colleagues assessed whether a combined SIL parenting and child intervention led to changes in diurnal LHPA axis activity among 3- to 6-year-old children in foster care (Fisher, Stoolmiller, Gunnar, & Burraston, 2007). Children who experience disruptions in caregivers, neglect, and maltreatment, may show a blunting of LHPA axis responding, as indicted by low morning cortisol levels and flat cortisol levels across the day (Fisher et  al., 2006; Shea, Walsh, MacMillan,  & Steiner, 2004). The SIL intervention was designed to increase foster parents’ warmth, responsiveness, contingent positive reinforcement, and use of non–harsh limit setting in daily interactions with their foster children. This parenting intervention was complemented by therapeutic play groups offering similar environmental experiences. The SIL intervention prevented reductions in morning cortisol and increasingly flattened morning

to evening cortisol patterns that were apparent over time (morning waking and before bedtime) for children in the foster care control group, and resulted in cortisol patterns similar to those of children in a non–foster care community control. This group also reported that the SIL intervention has positive effects on child behavior. Relative to foster children in the control group, those whose foster parents received the SIL intervention evidenced more secure and less avoidant attachment patterns across 12 months after initiation of intervention (Fisher  & Kim, 2007), opposite to attachment patterns observed in the nonintervention foster care control group. Children in the SIL intervention group also experienced fewer placement transitions (Fisher, Burraston, & Pears, 2005; Fisher & Kim, 2009), and the SIL intervention mitigated dysregulation of diurnal patterns of LHPA cortisol secretion associated with those transitions (Fisher, VanRyzin,  & Gunnar, 2011). Fisher and Stoolmiller (2008) also reported that low morning child cortisol levels were predicted by levels of stress foster parents reported in managing their foster child’s behavior, and that the SIL parenting intervention reduced caregiver distress related to child misbehavior. Collectively, these findings indicate that LHPA dysregulation may be associated with high levels of child emotional distress and disruptive behavior, which challenge foster parents’ willingness and capacity to provide ongoing supportive care. It also suggests that supportive, positive parenting may diminish LHPA dysregulation even when challenging caregiving transitions occur. This series of studies indicates that the SIL parenting intervention has effects at multiple levels, including reduced caregiver stress in the face of challenging child behavior, less suppression of diurnal cortisol, enhanced attachment patterns, and fewer placement disruptions. There also appear to be relationships across these multilevel outcomes. Focusing on infants in foster care, Dosier, Peloso, Lewis, Laurenceau, and Levine (2008) and Dosier, Peloso, Lindheim, Gordon, Manni et  al. (2006) assessed whether an intervention designed to increase caregiver nurturance and responsiveness, and to reduce negative reactivity to infant distress, altered child cortisol reactivity to a strange situation stressor. Caregivers of these infants were assigned randomly to the intervention or a control group, with addition of a community control group. Cortisol responses were assessed before and after the stressor, via a post-intervention only measurement. Infants of caregivers in the parenting intervention

showed lower cortisol levels before but not after the stressor. In addition, cortisol levels of infants in the intervention group were not different from those of infants in the community comparison condition. No other child or parent behavior changes were reported. This study again indicates that parenting interventions may promote regulatory processes in the LHPA axis.

Mesolimbic Dopamine Motivational System

To my knowledge, no parenting or family intervention study has reported changes in mesolimbic dopamine functioning as a mediator of intervention effects on child externalizing and antisocial behavior. There are a number of studies in developmental psychopathology suggesting that risk for ADHD or progression of ADHD to conduct problems depends on the interaction of parenting quality (rates of parental praise, aversive behavior, and expressed emotion) with child genetic polymorphisms associated with dopamine regulation (DAT1, MAO-A, DRD4) (e.g., Li & Lee, 2012, 2013; Sonuga-Barke, Oades, Psychogiou, et  al., 2009). One randomized trial study of parent training for children with ADHD also found that child genetic polymorphisms on the DAT1 moderated effects of the intervention of ADHD and conduct problem symptoms (van den Hoofdakker et al., 2012). Although SIL interventions cannot alter genotypes, parenting interventions appear to modify their behavioral expression. Randomized trials indicate that SIL parenting interventions are effective in managing impulsivity, inattention, and hyperactivity of children with ADHD, and in reducing development of associated conduct problems (e.g., Hinshaw et  al., 2000; Sonuga-Barke, Daley, Thompson, Laver-Bradbury,  & Weeks, 2001; Webster-Stratton, Reid,  & Beauchaine, 2011), especially if delivered early in development. More recently, Beauchaine and colleagues (2013) assessed whether effects of combined SIL parenting and child intervention (relative to control) on child outcomes were moderated by cardiac pre-ejection period (PEP). Pre-ejection period reactivity to incentives provides a downstream sympathetic nervous system indicator of mesolimbic dopamine activity. No Intervention × PEP moderator effect on child outcomes was found, but children with lengthened PEPs at rest, and reduced PEP reactivity to incentives, had higher conduct problems scores before and after intervention, even though they responded to the intervention as well as those with more normal PEP activity and reactivity. Taken Snyder

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collectively, these studies suggest that the degree to which mesolimbic dopamine processes can be altered by SIL parenting and other environmental interventions may be limited. Neurobiological mediators of the effects of these interventions on ADHD and conduct problems may occur at other neurobiological levels in a “work-around” fashion, by improving frontal executive control processes, by altering reactivity in the LHPA threat response system, or by engaging associative learning processes that link activation of the dopamine motivational system with more constructive behavioral responses to environmental challenges and opportunities.

Summary

A number of randomized longitudinal trials of SIL parenting and family interventions that systematically alter everyday social environments of children, and enhance their self-regulation and adjustment, also appear to alter neurobiological processes involved in emotional and behavior reactivity and regulation. This pattern of findings is consistent with the hypothesis that changes in children’s social environments may result in experience-dependent and -expectant shaping of neurobiological system functioning in ways that are associated with enhanced self-regulation and adjustment. Ascertainment of neurobiological changes in randomized trials of SIL parenting (and child) interventions builds on the strong empirical base demonstrating efficacy of those interventions in terms of child behavior change, and the meditational role of changes in social environments in effecting those changes. Systematic changes in children’s natural social environment may provide the repeated, ongoing experiences (Masten, 2001) needed to shape neurobiological processes, perhaps more so than time-limited, specialized “brain training” interventions. Extant, empirically supported SIL parenting and family interventions that systematically change the social environment of children appear to have multilevel effects on child responding at psychological, emotional, behavioral, and neurobiological levels. The degree to which these multilevel effects represent complementary versus redundant change processes, or are transactionally and sequentially linked in terms of the casual chain of change, remain unclear. A number of social experiences are targeted for change in SIL parenting and family interventions. These include reductions in aversive social events and coercive mutual engagement, noncontingent 298

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and unpredictable consequences, punishment involving application of aversive stimuli, and chaotic routines. Targets also include increases in contingent and sensitive responding, positive mutual engagement, intentional and careful shaping of self-regulated/constructive behavior using positive reinforcement, careful instruction and clear rules, discipline focused on reinforcement loss, emotion coaching, and good communication and problem solving. Which of the multiple social environment changes engendered by SIL parenting/family intervention serve as a source of changes in specific versus multiple neurobiological systems awaits further research.

Integration of Coercion Theory, SIL Parenting Interventions, and Neuroscience: Promise and Challenges

A more complete understanding risk and protective factors that shape trajectories for antisocial behavior ultimately entails delineation of events and processes that occur at social-environmental, psychological, and neurobiological levels of analysis. Interventions at one or more of these levels provide opportunities to probe causal and sequential aspects of these developmental processes. As a specific variant of social learning and behavioral theory, coercion theory has provided a rich model for understanding development of antisocial behavior. It has strong empirical support in observational and longitudinal studies and has led to formulation of an array of efficacious and effective SIL parenting and family interventions. Thus, integrating assessment of neurobiological processes as mechanisms of change in randomized longitudinal trials of SIL parenting and family interventions for antisocial behavior holds considerable promise, complementing the current focus on changes at social environmental, psychological, and behavioral levels. Multiple potential benefits may be derived from this integration. These include adapting SIL interventions to have positive effects across a broader range of individuals and developmental periods, ascertaining new targets and tactics to effect greater change, creating indices of the sufficiency of intervention, and gaining better understanding of persistence and generalization of change. In fact, initial research integrating neurobiological measures into randomized trials of SIL parenting and family intervention in order to understand change processes supports the feasibility and promise of a multilevel approach.

However, there remain substantial challenges that suggest being “circumspect in heralding the clarity of insight that adding physiological and neurobiological measures into preventive interventions will provide” (Cicchetti  & Gunnar, 2008, p. 440). There is a continuing need to better understand exactly what environmental experiences of what duration are needed to effect change in which among various neurobiological processes at specific periods in development. Decades of research derived from coercion/SIL theory have successfully specified a number of observable social environmental-experiential variables that are linked to antisocial behavior and are targeted explicitly for change in SIL parenting and family interventions. The manner in which these various social environmental experiences map onto functional changes in one or more of the various neurobiological systems associated with antisocial behavior has not been well articulated. More thorough and systematic examination is needed, and randomized control trial SIL parenting and family interventions provide a strong vehicle to help accomplish this task. This research agenda entails a number of other complications. There are multiple pathways to the various symptom clusters and progressions characterizing disruptive, externalizing, and antisocial behavior, each of which may involve a different combination of environmental and neurobiological processes. Ways in which environmental and neurobiological processes contribute transactionally to antisocial behavior are also likely to depend on developmental period. There are significant measurement issues, especially in identifying indices of environmental, behavioral, and neurobiological processes that are sensitive to change. Even as valid and reliable measures of change are identified and applied, correlations among processes at environmental, neurobiological, and behavioral levels are likely to be modest given various temporal and situational determinants of those processes and their operating parameters and ranges. These correlations may be increased by careful selection of tasks and conditions under which neurobiological processes are measured, perhaps to better reflect social environmental conditions thought to engage those processes. Extant SIL parenting and family intervention studies that have measured and tested neurobiological variables as candidate mediators of change in antisocial behavior support the feasibility and potential of the proposed integrative model. SIL

parenting interventions appear to have multilevel effects, including those exerted on neurobiological processes, and there are indications that changes in processes at different levels may be correlated. However, these investigations have not yet fulfilled criteria for demonstrating multi-sequential and transactional social environmental and neurobiological mediation of intervention effects on antisocial behavior. Moreover, they have not demonstrated that addition of neurobiological processes as mediators explain more variance in trajectories of antisocial behavior. The reasonable skeptic behaviorist would be intrigued, but would need more evidence. As Holmes said to Watson, “Data, data, data, … I can’t make bricks without clay” (p. 289, Conan-Doyle, 1892).

References

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Friendship and Adolescent Problem Behavior: Deviancy Training and Coercive Joining as Dynamic Mediators

Thomas J. Dishion, Hanjoe Kim, and Jenn-Yun Tein

Abstract This chapter examines the interpersonal influence of friendships in the amplification of problem behavior during adolescent development. Two dynamic influence processes are described: deviancy training and coercive joining. The actor–partner interdependence model (APIM) framework is applied to videotaped observations of adolescent friendships, looking at selection and influence processes underlying the amplification of problem behavior through deviancy training and coercive joining dynamics. As such, it is revealed that antisocial youths tend to bring in antisocial friends, a finding consistent with results from several other studies. Together, selection and influence processes contribute to the degree of deviancy training and coercive joining. The amount of observed deviancy training predicts future antisocial behavior, whereas coercive joining uniquely predicts escalations to more serious forms of violence. Implications of these findings are discussed for developmental and intervention science, with the explicit goals of preventing and reducing problem behaviors in childhood and adolescence. Key Words:  externalizing spectrum behaviors, adolescence, friendship, coercion, deviancy training

A Developmental Perspective on Peers

A critical role of caregiving in early childhood is to establish children’s prosocial routines for social interaction with peers (MacDonald & Parke, 1984). As children emerge into the school environment, poor academic and interpersonal skills can lead to marginalization by the conventional peer group (Coie & Kupersmidt, 1983; Dishion, 1990; Dodge, 1983). Marginalization as early as elementary school initiates a pattern of affiliating with other children with problem behavior, which in turn results in reduced opportunities to learn and practice critical prosocial skills for the formation of enduring and healthy friendships (Hartup, 1996). Two identifiable peer interaction dynamics are associated with changes in antisocial behavior in childhood and in adolescence. The first is coercion, when, as early as preschool, children’s “winning” conflicts

with peers leads to increases in aggressive behavior in later interactions (Patterson, Littman,  & Bricker, 1967). The second is contagion of problem behavior as a function of peer-group friendships. Contagion describes the spread of problem behavior through friendships. Deviancy training is one contagion mechanism that involves reinforcement of rule-breaking behavior and norms through laughter and positive affect (Dishion, Spracklen, Andrews, & Patterson, 1996). A second recently identified process of contagion is coercive joining (Dishion & Van Ryzin, 2011), which occurs when individuals join in an effort to inflict harm on others. The first opportunity to engage in extended peer interactions and to establish friendships comes at around age 5, when a child begins to attend public school. In most public schools, the majority of peer interactions unfold in unstructured settings 303

with only a modicum of adult oversight and coaching. Coercion on the playground and deviant peer influence are observable in the first grade and are only modestly correlated with parent–child interactions (Dishion, Duncan, Eddy, Fagot,  & Fetrow, 1994). Research by Snyder and colleagues provided insight into the role of peers at elementary school entry and the growth of covert and overt antisocial behavior during childhood. They developed a protocol for measuring deviant peer influence among 5-year-olds at school (Snyder et al., 2005), and they found that deviancy training and coercion could be identified in peer interactions on the kindergarten playground. As expected, coercive exchanges were associated with growth in overt forms of antisocial behavior in the ensuing year. Parents of children who engaged in deviancy training with peers on the playground noted the children’s increasing overt and covert forms of antisocial behavior during a 3-year period. This research was the first to suggest a possible carryover from peers to the family early in development. It had previously been assumed that the general direction of influence was from family to peers (Patterson, Reid, & Dishion, 1992). Deviancy training on the kindergarten playground and coercive exchanges with peers also predicted teacher ratings of covert and overt antisocial behavior in the second and third grades (Snyder et al., 2008). These findings suggest that organization and structure of informal play settings in public elementary schools are key to learning antisocial behavior patterns that are prognostic of later aggression and violence. Young children with a variety of problem behaviors who learn and practice “antisocial grammar” (i.e., being tough wins; deviant behavior is funny) begin to self-organize into deviant peer groups in early adolescence (Dishion, Patterson, Stoolmiller, & Skinner, 1991). The onset of puberty signals a set of biosocial changes (see Dahl, 2004) that underlie a focus on reward and regression in self-regulation, which may account for the movement of youths into peer groups. Some researchers have called this process a flight to deviant peers (Elder, 1980). Children who have experienced marginalization at home and school are particularly vulnerable to self-organizing into peer clusters. Dishion and colleagues tested this hypothesis in two ways. First, gang membership by age 14 was predicted only by school-based indicators of adjustment at entry into middle school. Specifically, we examined whether peer rejection and acceptance (sociometrics), self-reported antisocial behavior, and academic performance in the sixth grade predicted later gang 304

Friendship and Deviancy Training

involvement. As expected, the study revealed that high levels of antisocial behavior, peer rejection, and academic failure predicted later gang involvement for both males and females. Interestingly, both peer acceptance and rejection predicted later gang involvement for males, suggesting that males who progress to gangs are “controversial”—both liked and disliked (Dishion, Nelson, & Yasui, 2005). The contribution of ethnicity to future gang involvement was trivial when accounting for middle school adjustment in the sixth grade. In later research, we broadened our analysis of deviant peer clustering in early adolescence to include family relationships and socioeconomic status (SES) as predictors. We also speculated about the evolutionary function of deviant peer clustering. Specifically, being marginalized and rejected is one of the major stressors of the human condition (Eisenberger, 2011). A key tenet of an evolutionary perspective is that adaptation must increase the likelihood of a surviving gene pool; that is, it must have evolutionary value. Fast and slow life history strategies are thought to reflect an evolutionary adaptation to early stress. The fast strategy involves having more children sooner, making it more likely to lead to surviving gene pools under stressful conditions. However, the central adaptation has to do with female pubertal development, which is hypothesized to emerge earlier under stressful family conditions in childhood (Belsky, Bell, Bradley, Stallard, & Stewart-Brown, 2007; Belsky, Steinberg, & Draper, 1991). In the model described in the following paragraph, we proposed that deviant peer clustering at puberty is a more salient evolutionary adaptation for a marginalized group in that it is a strong predictor of adolescent sexual activity (Capaldi, Crosby, & Stoolmiller, 1996; French & Dishion, 2003). We tested an evolutionary model of deviant peer clustering in which marginalization in school and the family during the sixth, seventh, and eighth grades predicted gang involvement by age 14. Moreover, gang involvement was hypothesized to predict sexual promiscuity at age 16 (number of partners, frequency of sexual acts, high-risk sexual behavior). Sexual promiscuity was hypothesized to mediate the covariation between deviant peer clustering at age 14 and number of children by age 22. The model fit the data and fit equally well for males and females and for African-American and European-American youths (Dishion, Ha,  & Véronneau, 2012). Interestingly, SES of the family remained a strong predictor of gang involvement and of number of children by age 22.

The link between gang involvement in adolescence and increased problem behavior is quite clear in that when youths join gangs, problem behaviors increase, and, when they leave the gang, problem behaviors decrease (Thornberry, 1998). However, the relationship dynamics that account for these increases have been studied only recently. Several scientific advances are associated with drilling down to the interaction patterns that account for the influence of peers on increases in problem behavior during adolescence (see Fiske 1986, 1987). First, it is possible that peer interaction is an epiphenomenon of genetic vulnerability, with no causal influence on later development. A  careful study of real-time interactions (i.e., microsocial patterns) advances science such that it can specify the real-world conditions of behavior. This level of specificity facilitates our understanding of social and neurocognitive mechanisms through which genetic vulnerability is expressed. Second—and relatedly—understanding genetic and social processes linked to developmental change empowers the design of precise and effective interventions (prevention and treatment) aimed to promote health and well-being among youths and reduce problematic behaviors. Third, a better understanding of the microsocial underpinnings of friendship influence might suggest what kinds of interventions and educational practices may inadvertently do harm. Key to studying microsocial patterns is to empirically establish dynamic mediation (MacKinnon, 2008). The first author’s research into the study of microsocial dynamics of adolescent friendships focused on a sample of boys from a suburban community. We were surprised to find very little negativity or aggression in friendship interactions (Dishion, Andrews, & Crosby, 1995). Only a handful of the 204 boys in the study participated in coercive exchanges with friends during an observation task. Consequently, we focused primarily on the effect of positive reinforcement on youths’ discussions of deviant and normative talk. In the first set of studies, we applied the matching law (McDowell, 1988) to direct observation data and found that the relative rate of reinforcement for deviant talk (relative to normative talk) accounted for overall levels of deviant talk in the dyad (Dishion et al., 1996). The average duration of a dyadic deviant talk bout (i.e., how long the deviant topics continued for the dyad) was a good proxy for deviancy training in the friendships (Granic & Dishion, 2003).

When we applied a dynamic systems framework to the data, we found that those friendship interactions that were well organized and predictable (low entropy) and had longer deviant talk bouts were those that were most likely to promote antisocial behavior from early adolescence (age 13–14) to early adulthood (age 23–24). It appeared as though a subgroup of males learned a “deviant subculture” in which their friendships were formed. The better they knew the rules of the deviant subculture, the more predictable and organized their interactions. These findings fit well with criminological theories that fall under the rubric of differential association theory (J. F.  Short, Jr., 1957; J. F.  Short, 1990; Short & Strodbeck, 1965). Later microsocial studies of deviancy training built on these findings by improving the conceptualization and measurement of friendship quality. Piehler began studying skillful social interactions in friendship alongside deviant talk. Youths were observed in their homes interacting with a friend, and undergraduate students coded the videotaped interactions using the Norm Topic Code (Piehler & Dishion, 2005). As in previous research, Piehler found that dyadic mutuality in the friendship, coupled with deviant talk, predicted future increases in problem behavior. We later extended this finding by incorporating the construct of coregulation, which we believed might facilitate deviancy training. Coregulation involves two individuals who cooperate to have a smooth and cohesive conversation and interaction. Piehler systematically coded coregulation in videotaped interactions of more than 800 friendship dyads. As had been noted in previous research, highly coregulated dyads with high levels of deviant talk were most likely to escalate in substance use from adolescence to early adulthood (Piehler & Dishion, 2014). Thus, there was ample evidence of friendship influence on problem behavior, and it appeared that youths who were better at connecting with peers in terms of deviant talk were more likely to increase some forms of problem behavior, especially substance use. However, these studies have a fundamental confound:  we cannot properly talk about influence when we do not control for the specific characteristics of the youth and her/his friend over time. Fortunately, we were able to apply the actor–partner framework to these data.

Actor–Partner Independence Models

The actor–partner interdependence model (APIM) is a framework used to disentangle the Dishion, Kim, Tein

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Actor Outcome T1

Actor Outcome T3

e1

Partner Outcome T3

e2

Dyadic Relationship Dynamic T2 Partner Outcome T1

Figure 17.1  Actor–partner framework for studying relationship dynamics as a mediating mechanism.

effects of each individual within a dyad on the actor as well as on the partner. One of the challenges in the analysis of dynamic mediation is disentangling observed behavior of the actor and partner in real time. In social interaction, reciprocity is the strongest rule of behavior (see Cairns, 1979). In our longitudinal friendship study (i.e., Piehler & Dishion, 2014) that involved youths from three middle schools, 175 of the total sample of 800 participants had selected friends who were also participants in the study. This enabled us to use an APIM framework to analyze friendship influence on both antisocial behavior and violence (see Figure 17.1). We thus considered the dyads to be indistinguishable (interchangeable; Kenny, Kashy,  & Cook, 2006) in that the estimated effects and variability of the “actor” were indistinguishable from those of the “partner,” which is reflected in equality constraints in structural equation modeling.

At ages 16 and 17  years and again at ages 22 and 23  years, the youths were asked to complete self-report surveys on a variety of antisocial behaviors. We aggregated the 2 years (16–17 and 22–23) to yield a young-adult antisocial score for the adolescent and friend. The models were computed in Mplus 7 (Muthén  & Muthén, 1998–2012); statistically significant (p < .05) unstandardized effect coefficients are shown in Figure 17.2. Note that the standardized estimates were equal between the actor and partner effects. Equality of the standardized path coefficients could be obtained only when variances of the variables were invariant (Olsen & Kenny, 2006). As expected, there was stability in youths’ antisocial behavior from ages 16–17 through early adulthood. Also, the adolescent’s and the friend’s levels of antisocial behavior jointly predicted the duration of deviant talk bouts as observed in the videotaped interactions. In turn, the duration of deviant talk bouts predicted the adolescent’s and the friend’s reported antisocial behavior during a period of 5  years. It is noteworthy that behavior in a relatively brief (40 minute), contrived, and videotaped interaction predicted antisocial outcomes, controlling for prior levels of the youth’s behavior, thus suggesting influence by peers. These data support the hypothesis that friendship interactions influence progression of antisocial behavior from adolescence through early adulthood.

Deviancy Training as a Dynamic Mediator

From past levels of antisocial behavior, we hypothesize that the duration of deviant talk bouts in the friendship discussions would mediate prediction of future antisocial behavior for the actor and the partner. That is, the tendency to engage in deviant talk within the friendship amplifies problem behavior to early adulthood (see Figure 17.2).

Adolescent Antisocial Behavior Age 16–17

Friend Antisocial Behavior Age 16–17

Directly Observed Mediating Mechanism .22∗ .01∗

.01∗

Duration of Deviant Talk Bouts Age 16–17 .22∗

1.8∗

Adolescent Antisocial Behavior Age 22–23

e1

.28∗ 1.8∗

Friend Antisocial Behavior Age 22–23

e2

Figure 17.2  Actor–partner framework for deviancy training as a mediating mechanism for friendship influence on antisocial behavior.

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Adolescent Antisocial Behavior Age 16–17

Friend Antisocial Behavior Age 16–17

Directly Observed Mediating Mechanism .22∗ .34∗

.34∗

Coder Ratings of Coercive Joining Age 16–17 .22∗

.64∗

Adolescent Violence Age 23–24

e1

.28∗ .64∗

Friend Violence Age 23–24

e2

Figure 17.3  Actor–partner framework for coercive joining as a mediating mechanism for friendship influence on violence.

Coercive Joining as a Dynamic Mediator

In a now-classic presidential address to the Society of Criminology, Short (J. F.  Short, Jr., 1998) alerted the scientific audience to a new demand in the study of crime. The nation was facing an unprecedented level of violence in communities. In past research about gangs and delinquency, gang violence was often episodic and short lived, with relatively long periods of senescence between episodes. Later, the structure and activities of gangs changed and violence escalated. Gangs became more organized and widespread, with continuous violence, and communities reorganized away from urban sectors that had become sources of fear and trepidation (J.F. Short, Jr., 1998). Gangs now often promote violence through their mutual interactions and community conferral of social status to gang members. For example, in gang-ridden communities, adolescents gain social status as they begin carrying weapons (Dijkstra et  al., 2010). In his (1998) presidential address, Short suggested that we continue to integrate the macrolevel study of crime with microsocial analysis among individuals in an effort to account for escalations in violent behavior. When we shifted our program of research from a suburban to an urban community, we also noted major differences in friendship interactions among some youths in the study population. In contrast to suburban adolescent friendships, some urban adolescent friendships could be characterized as abrasive and harsh, with youths treating one another with a mix of coercion and control to attain dominance. It was as though the rules of friendship were different for these youths. Rather than joining and sharing a common interest within a friendship of typically developing adolescents, these friendships were inherently coercive. Many youths were dressed in gang attire and used gang-associated mannerisms.

We came to term the process we witnessed coercive joining. Coders rated these friendship interaction behaviors globally. The actor and the partner were rated on the extent to which they promoted abuse of or violence toward individuals other than the friends, tried to dominate or control the interaction, and were disrespectful of their partner by calling her/him names or using profanity. We hypothesized that coercive joining in adolescent friendships facilitates progression from aggression to violence (Dishion  & Van Ryzin, 2011). In research that included 998 adolescents (Project Alliance 1), 85% of the sample participated in videotaped interactions with their best friend. To examine friendship quality, each youth was asked to report about his or her satisfaction with the friendship, pleasant activities shared with the friend, and the extent to which the friend agreed about basic friendship issues. In a model that included antisocial behavior at ages 16–17 years, coercive joining, and friendship quality, all three predictors were significant with respect to prediction of serious violence by age 22–23. An interaction effect was found between friendship quality and coercive joining; specifically, youths with the lowest friendship quality and the highest levels of coercive joining were the most violent 5  years later. It is worth noting that gang involvement at age 13–14 years predicted violence nearly 10  years later (β  =  .19, p < .001), controlling for effects of antisocial behavior at age 16 (β = .24, p < .001) and coercive joining at age 16 (β = .34, p < .001). Approximately 46% of the variance in later violence was accounted for by early gang involvement, coercive joining, and early antisocial behavior (Dishion & Van Ryzin, 2011; Van Ryzin & Dishion, 2012). As shown in Figure 17.3, we applied the APIM framework to analysis of the dynamic mediation effect of coercive joining on violence. In these analyses, we Dishion, Kim, Tein

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also used the subset of friendships that involved a youth and a friend who were both participants in our longitudinal study. Coercive joining at age 16–17 years and violence at age 23–24 reflected construct scores. For violence in early adulthood, the measure comprised arrests for violent acts (robbery, assault, etc.) plus both self-reported and parent-reported aggression and violence. The measure of coercive joining was estimated by using coder ratings of dominance efforts in the relationship, abusive talk (hurting others, hate talk), and swearing at each other. Findings are summarized in Figure 17.3. Using unstandardized effect coefficients and tests of significance, we found that antisocial behavior at age 16 years predicted violence in both the adolescent and the friend at age 23. As might be expected, the youths’ antisocial behavior jointly predicted their engagement in the coercive joining process. As in the APIM deviant training model, coercive joining demonstrated in the videotaped interaction uniquely predicted escalation from antisocial behavior to violence by early adulthood for both the youth and the friend. These findings support the hypothesis that coercive joining is a dynamic mediator between antisocial behavior and progression to more serious violence by early adulthood. The results suggest a process through which youths socialize each other to become dangerous people, a process that seems to involve mutual training in coercion, dominance, and dangerousness. Gang involvement appears to be the primary context in which these relationships are established and maintained.

Implications Theoretical Implications

A growing body of research indicates that many forms of problem behavior in childhood and adolescence are amplified in the context of peers. From an ecological perspective, it is difficult, if not impossible, to disentangle how families, parenting practices, schools, and communities interact with peer influences (Dishion  & Patterson, 2006). Children are not distributed randomly into peer groups so that selection factors are salient. In addition, social policies and economic exigencies play major roles in determining how much time individual children spend with peers. Work inspired by an ecological perspective on peer influences reveals a family–peer mesosystem that potentially accounts for success in middle school (Véronneau  & Dishion, 2010), the influence of peers on drug use in early adolescence (Kiesner, Poulin,  & Dishion, 2010), peer influences on problem behaviors in adolescence 308

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(Laird, Criss, Pettit, Dodge,  & Bates, 2008), and the continuance of antisocial behavior from adolescence to young adulthood (Dishion, Nelson, & Bullock, 2004). Collectively, these findings suggest that when parents withdraw their efforts to manage adolescent behavior, youths self-organize into peer groups. For many adolescents, the amount of time a youth spends with friends remains relatively stable through late adolescence, yet high-risk adolescents increase the number of hours spent with peers to late adolescence (Dishion et al., 2004). In a study of high-risk adolescents, we used daily telephone assessments to evaluate parents and youths regarding displays of affection, discussing the day’s activities, eating meals together, and hours spent with peers unsupervised by adults. The correlation between youth and parent report was strong (average r = .40), and we were able to create a multiagent construct for each experience. When regressing the family report of these daily experiences assessed during a period of 2 weeks to growth in substance use during the next 3  years, the number of hours per day spent with youths without a supervising adult predicted growth in problem behavior during that period (Dishion, Bullock, & Kiesner, 2008). An important distinction is often made in social network analysis between selection and socialization. Selection involves choosing friends that are similar to oneself, whereas socialization is the process of influence. When youths are not monitored and lack adult guidance, it is likely that both selection and socialization effects amplify problem behavior (Dishion, 2013). Freedom to wander the streets and to present oneself as deviant is likely to increase the number of deviant friends (Stoolmiller, 1994), which in turn results in sharp increases in the problem behavior of that group. The behavior itself may function as an entry card (i.e., selection effects), such as having drugs, running drugs, or doing a “hit” on a youth of a rival peer group. In this chapter, we discussed two dynamic mechanisms that encourage development of problem behaviors: antisociality and violence. As observed in a previous longitudinal study, deviancy training is less prognostic of violence than is coercive joining (Dishion, Véronneau, & Myers, 2010; Van Ryzin & Dishion, 2012). Thus, despite comorbidities among various forms of problem behavior, there seems to be some evidence for special peer-related training that supports movement from antisocial behavior to increasingly serious forms of violent behavior (see Beauchaine  & McNulty, 2013; Biglan, 2015) during the developmental period when aggression

is generally abating. These findings suggest a need for concern about policies and practices that lead to poverty and deterioration of neighborhood and school communities. These contexts are explicitly degrading and marginalize youths at school entry. By the time youths begin middle school, many have already adopted a posture of defiance and seek like-minded peers to resist caregiver and adult efforts to socialize them. These groups, if left unattended, can grow into training venues for serious crime and violence. A classic problem of criminological theory is the age–crime curve (Hirschi  & Gottfredson, 1983), which refers to the sharp spike in criminality and antisocial behavior during adolescence. The problem is that most theories of crime fail to explain the temporary increase of problem behaviors during adolescence. In previous work, my colleagues and I  outlined a model that attempts to integrate the study of peer influence processes to explain increases in adolescent problem behavior. We argue that self-organization into deviant peer clusters is a social adaptation to attenuated family relationships and marginalization within the community, most often at school, motivated by changes linked to puberty. With puberty comes sexual motivation and other shifts in self-regulatory dynamics (see Dahl, 2004). Our data strongly suggest that deviant peer clustering is the best predictor of sexual activity by age 16 (Dishion, Ha, et al., 2012). In this model, problem behavior is an outcome of deviant peer clustering, as are sexual promiscuity and number of offspring. From an evolutionary perspective, we proposed that marginalized youths adapt by assuming a “fast” life history strategy, suggesting that the age–crime curve is a result of evolved sexual selection dynamics. In this sense, adolescents become wired to attend to strategies that promote their position in the peer group at large if they are left uncared for by parents and teachers. Individual differences in self-regulation, broadly defined, moderate this tendency (Gardner, Dishion,  & Connell, 2008; Goodnight, Bates, Newman, Dodge, & Pettit, 2006). Inclusion of self-organizing peer groups as an adaptive strategy within an evolutionary framework is a potentially important extension of coercion theory (Dishion, 2014) and developmental theory of externalizing disorder. As other theorists suggest, self-regulation and long-term planning may be hijacked in favor of more immediate, reward-seeking perspectives (see also Beauchaine  & Zalewsky, in press). Frankenhuis and de Werth (2013) make a similar point regarding the findings about patterns

of social cognitive functioning, vigilance, and executive control of youths raised in violent neighborhoods. It makes more sense for youths raised in stressful or dangerous circumstances to develop a cognitive capacity to detect (e.g., Dodge  & Coie, 1987) and respond to and/or avoid aggression. In stressful environments, executive control and planning may be muscles that are not often exercised in favor of the capacity to stay safe or take advantage of immediate reward opportunities. However, we hypothesize that social marginalization is prognostic of early mortality and, less so, stressful family environments. Throughout the course of human and nonhuman primate history, not being connected to a tribe, clan, or social group may be even more harmful to future well-being than having low family resources.

Intervention Implications

Outcomes described in this chapter can be prevented by use of empirically supported interventions. For example, compelling evidence indicates that parenting interventions reduce antisocial behavior among children and adolescents (Dishion et al., 2014; Forgatch  & Patterson, 2010; Henggeler  & Schaeffer, 2010; Van Ryzin  & Dishion, 2012; Webster-Stratton  & Reid, 2010; Zisser  & Eyberg, 2010). Although there is less evidence to support that parenting interventions reduce deviant peer involvement (exempt the following:  Dishion, Bullock,  & Granic, 2002; Forgatch  & Patterson, 2010), still, in an overall sense improvement in parenting is likely to reduce antisocial behavior, which in turn will decrease deviant peer involvement. There is also evidence that school environments can be restructured to reduce the prevalence of deviancy training and aggression in elementary school, as reflected in innovative prevention work at Johns Hopkins University. Using the Good Behavior Game to change contingencies for aggression in the first year of intervention has been shown to prevent long-term patterns of aggression (e.g., Kellam, Reid,  & Balster, 2008). In addition, the Coping Power program, which focuses on youth self-regulation, produces reductions in aggressive and antisocial behavior (e.g., Lochman, Boxmeyer, Powell, Barry,  & Pardini, 2010; Lochman, Burch, Curry, & Lampron, 1984). However, recent analyses of the group versus individual versions of this program vary somewhat in terms of effectiveness. Even when group interventions are very closely supervised, group interventions tend to have less effect than the same content delivered in an individual Dishion, Kim, Tein

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format (Lochman, Dishion, Powell, Boxmeyer,  & Qu, under review). Evidence also exists that deviancy training and coercive joining are dynamic processes that compromise the effectiveness of interventions or, worse, cause interventions to be iatrogenic. In previous research, we found that random assignment to interventions that aggregate high-risk youths have overall negative effects on later substance use and delinquent behavior (Dishion, McCord, & Poulin, 1999). Moreover, videotaped intervention sessions suggested that deviancy training among peers in the group during unstructured parts of the session was driving the iatrogenic effect (Dishion, Poulin,  & Burraston, 2001). A  comprehensive review of the literature about peer aggregation suggested that not all interventions and programs that aggregate antisocial youths produce negative effects, but evidence for diminished effect sizes does exist (Dodge, Dishion, & Lansford, 2006). Prevention of bullying in school settings is another emerging area of research that requires further attention. Although using strategies that focus primarily on adults has been effective (see Olweus, 1991), many bullying programs that are focused on children are ineffective (Merrell, Gueldner, Ross,  & Isava, 2008) or actually increase the problem (Ellis et  al., 2012). One oversight in many bullying programs is the status gained by being aggressive. Several investigators have acknowledged that aggressive behavior in elementary school (Cairns, Cairns, Neckerman, Gest, & Gariépy, 1988; Rodkin, Farmer, Pearl, & Van Acker, 2000) and behaviors such as carrying weapons lead to greater status (Dijsktra et al., 2010). In addition to bringing attention to bullying coalitions (i.e., coercive joining), explicitly discouraging these kinds of peer interactions may be helpful in the effort to prevent bullying and later violence.

Conclusion

Two decades of research to identify dynamic mediation processes that account for the significant influence of peers on emerging delinquency suggests specific mechanisms that account for peer contagion effects. The next stage of research will be to develop and test interventions that directly target these interaction dynamics for prevention and treatment. To date, the peer environment is too often an afterthought. New data suggest that peers in general, and friendship dynamics in particular, are crucial targets and, if unattended, can have potentially 310

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strong effects on the development and maintenance of antisocial behavior.

Acknowledgments

The work described in this chapter was funded by grant DA07031 from the National Institute on Drug Abuse to the first author.

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CH A PT E R

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Neighborhood Risk and Development of Antisocial Behavior

Wesley G. Jennings and Bryanna Hahn Fox

Abstract Psychologists have long understood that certain individual traits, including low self-control, are consistent and robust predictors of antisocial and criminal behavior. Nevertheless, it is equally clear that crime is more concentrated in disadvantaged neighborhoods, which suggests that community-level factors also influence criminal behavior. In recognition of these multi-level influences and processes, the authors consider the interactive effects of individual-level traits and community/neighborhood-level factors on antisocial and criminal behavior. Policy implications and directions for future research are also discussed, with the aim of encouraging interdisciplinary explanations for a problem that a growing body of research suggests has interdisciplinary origins and perhaps interdisciplinary solutions. Key Words:  neighborhoods, communities, risk factors, multi-level factors, aggression, crime

Introduction

For quite some time, psychologists and psychological criminologists have recognized that certain individual differences, including low self-control/impulsivity, callous and unemotional traits, negative emotionality, cognitive defects, and poor conditionability, are consistent predictors of antisocial and criminal behavior (Meier, Slutske, Arndt,  & Cadoret, 2008). However, it is also acknowledged that crime is more concentrated in disadvantaged neighborhoods, which suggests that community-level factors also affect criminal behavior (Ingoldsby  & Shaw, 2002). Criminal and antisocial behaviors have therefore been explained using one of two major frameworks:  individual theories or community-based theories. Individual-level theories are the primary tradition of psychologists and psychiatrists interested in criminal behavior (Lynam et  al., 2000). Neighborhood- and community-based theories, on the other hand, tend to be linked to sociological criminologists and generally target social processes and structural factors as the mechanisms underlying aggregate crime rates (Lynam et al., 2000).

These contrasting perspectives have each garnered considerable attention and support in their relevant field but have rarely been evaluated or integrated into a unified theoretical framework to explain antisocial and criminal behavior (Lynam et al., 2000). Importantly, however, studies that merge the two traditions generally yield increases in explanatory power, so there is growing interest in integrating individual- and neighborhood-level factors when examining criminal and antisocial behavior. In this chapter, we review the strengths and weaknesses of using only individual- or community-level factors in research, we summarize findings from studies that combine the two perspectives, and we suggest paths for future integrated theoretical research.

Individual-Level Theories of Antisocial and Criminal Behavior

Individual-level theories of criminal and antisocial behavior have roots in early 19th-century psychology, where certain characteristics and traits were theorized to make individuals more likely to commit crime (Binder, 1987). After more than 200 years of research, psychologists and psychological 313

criminologists have identified several individual differences that consistently predict criminal behavior. These 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). Among these, impulsivity may be the strongest predictor of criminality and antisociality, and it is the centerpiece of several psychological and criminological perspectives on criminal and antisocial behavior (see, e.g., Cleckley, 1941; Gottfredson & Hirschi, 1990; Hare, 1980; Moffitt, 1993b). For instance, Lynam (1998) found that early hyperactivity/impulsivity, combined with conduct problems, predicts chronic offending and psychopathy. Furthermore, Gottfredson and Hirschi (1990) noted that low self-control is a significant risk factor for aggression, violence, delinquency, and chronic offending. Each of these theories have been replicated in numerous studies, in varying locations, and using a variety of analytical techniques (Jennings & Reingle, 2012). However, two issues consistently emerge from research using individual-level variables to predict antisocial and criminal behavior. First, there are “a bewildering number of constructs referring to poor ability to control behavior” (i.e., impulsivity; Farrington, 2005, p. 179). Second, although impulsivity is the most widely supported trait linked to criminality, a substantial portion of variance in criminality remains to be explained by this and other individual-level constructs (Farrington, 1993; Tonry, Ohlin, & Farrington, 1991).

Neighborhood-Level Theories of Antisocial and Criminal Behavior

In general, scholars who examine neighborhood effects (e.g., Bursik & Grasmick, 1993) associate geographic concentration of socioeconomic disadvantage with social disorganization of neighborhoods. In turn, neighborhood disorganization is reputed to adversely affect individuals and families. In contrast, other scholars suggest that concentrated effects of poverty cannot be explained fully without accounting for familial and individual-level constructs and processes (Gephart, 1997). Although social disorganization theory (as 314 Neighborhood Risk

originally formulated) sought to explain crime at the community-level, there are empirically based reasons to expect that social disorganization theory may also affect individual behaviors. Simcha-Fagan and Schwartz (1986) suggested this possibility when they offered the following statement: An aggregate-level theory cannot be expected to provide a comprehensive explanation of individual delinquency or criminality, it should clarify those contextual effects which, independently of or in interaction with the adolescent’s predisposition, affect individual behavior. (1986, p. 688)

Jencks and Mayer (1990) reviewed the literature on effects that social compositional measures (e.g., poverty) have on a host of developmental outcomes, including educational attainment, cognitive skills, criminal activity, and economic success. They noted that research is, at best, generally mixed regarding the influence of average socioeconomic status (SES; in the aggregate) on the likelihood of an individual’s chances for planning to go to college, attending college, and/or graduating from college, as well as its effect on criminality. More recently, Klebanov et al. (1997) offered an extension of research on the influence of neighborhood effects on crime by testing whether such effects are mediated and/or moderated by family processes such as maternal warmth, cognitive stimulation in the home, maternal coping style, availability of social support, and physical environment. They found positive effects of living in a high-SES neighborhood on academic achievement and verbal ability and negative effects on behavior problems. In addition, they found negative effects of living in heterogeneous neighborhoods on verbal ability scores, which were mediated by quality of the home learning environment. In addition, Brooks-Gunn et al. (1993) demonstrated neighborhood effects on child IQ, the likelihood of becoming a teenage parent, and dropping out of school. Thus, neighborhood-level effects are both direct and indirect, with a noticeable proportion of their (indirect) effects operating through individuallevel and family factors. Simcha-Fagan and Schwartz (1986, p.  671) argue that structural characteristics have effects on organizational networks among members of the community and that these networks can subsequently affect an individual’s ability to develop and foster informal social ties. For example, individuals who rent rather than own property are less likely to have a stake in the community where they live and are subsequently disinclined to participate in

neighborhood organizations. Similarly, renters may be less likely to care about the maintenance or upkeep of their dwelling because they do not intend to be there for any extended period of time. In this regard, their lack of investment may lead to neighborhood incivility and social disorganization (Wilson  & Kelling, 1982), which are both correlates of crime (Sampson, 1995). Public housing units, which are typically overcrowded, are also likely to foster a sense of “anonymity” among neighborhood residents. Failure of individuals to get out of their units and seek informal social ties with their neighbors may be a consequence of violence and fear of crime (Roncek, 1981; Sampson, 1995). Indeed, Sampson and Groves (1989) note that high rates of residential instability are associated directly with delinquency and crime. Research also indicates that neighborhood effects on individual outcomes in early and middle childhood are different across race/ethnicity. At age 3 years, for instance, the benefit of affluent neighbors (vs. middle-SES neighbors) on children’s IQ scores is stronger for white children compared with African-American children (Brooks-Gunn et  al., 1993). Similar findings are found among children at ages 5 and 6  years for IQ scores, verbal ability, and reading achievement scores (Chase-Lansdale, Gordon, Brooks-Gunn,  & Klebanov, 1997; Duncan, Brooks-Gunn, & Klebanov, 1994). All of these studies also demonstrate that having low-SES neighbors is associated with early childhood behavior problems. In a seminal sociological study, economists Ludwig, Duncan, and Hirschfeld (2001) conducted a randomized experiment using predominately minority families living in Baltimore public housing projects to evaluate the causal effects of neighborhoods on criminal behavior. Families who participated in the study, all of whom had similar demographic characteristics, were assigned randomly to one of three conditions, including (1)  a group that received Section 8 vouchers and were mandated to move from the housing projects to low-poverty neighborhoods (the experimental condition), (2) a group that received Section 8 vouchers to move to any available private housing, and (3) a group that did not receive vouchers and stayed in public housing (the control condition). Results indicated that boys in the experimental condition whose families moved to low-poverty/middle-class neighborhoods were arrested for half as many violent crimes as their counterparts in the control group who stayed in public housing (Ludwig et al.,

2001). Interestingly, families with Section 8 vouchers not mandated to live in low-poverty areas still chose to live in neighborhoods with poverty rates only slightly better than public housing, and yet the crime rate for boys in these families was similar to those who lived in low-poverty neighborhoods. These findings suggest that although neighborhood factors influence crime rates, there may also be individual-level factors that explain why individuals who live in “good” or “better” neighborhoods still commit crime.

Interactions Among Individual- and Neighborhood-Level Factors

Although both psychological and sociological theories are supported by considerable empirical evidence, neither perspective can alone explain why both individual- and neighborhood-level factors predict criminal and antisocial behavior. As Tonry, Ohlin, and Farrington (1991) aptly stated, “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). Still, very few studies have included both community- and individual-level factors in the models, despite the convincing argument that “more is to be gained from the linking of the individual and the ecological approach to the study of crime than from their continued separate developments” (Wikström  & Loeber, 2000, p.  1110; see also Farrington, Sampson,  & Wikström, 1993; LeBlanc, 1997; Reiss, 1986; Tonry et  al., 1991; Wilcox, Sullivan, Jones, & van Gelder, 2014). Early studies that evaluated combined effects of individual- and community-level factors on criminal and antisocial behavior tended to focus on how neighborhood-level conditions are related to broad between-individual differences, such as demographic or family characteristics. For instance, in their study of aggression among elementary school children, Kupersmidt et al. (1995) found that neighborhood features are significantly but weakly correlated with individual rates of juvenile delinquency, controlling for family-level factors. Similarly, Lindstrom (1995) found a negative relation between positive family interaction and juvenile offending, but also found that the relation depends on community context (because it is much stronger in disadvantaged neighborhoods). Furthermore, children in low-SES neighborhoods and single-parent households are more aggressive than children in high-SES neighborhoods and single-parent households Jennings, Hahn Fox

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(Kupersmidt et  al., 1995). In addition, although race predicted juvenile offending, it only did so in low-SES neighborhoods (Peeples & Loeber, 1994). Although early studies demonstrated combined influences of neighborhoods and broader individual-level risk factors on antisocial and criminal behavior, they did not assess more distinct individual-level features such as personality traits or developmental features (Leventhal & Brooks-Gunn, 2000), and they did not account for more subtle neighborhood contexts, such as criminal opportunity and other situational factors (Lynam et al., 2000). More extended research was needed, given the theoretical supposition that individual-level factors, such as impulsivity, modify an individual’s vulnerability to “risky” environments (Bohman, 1996; Mednick, Gabrielli, & Hutchings, 1984; White, Moffitt, & Silva, 1989). Unfortunately, no study had examined the interaction of impulsivity and neighborhood effects on criminal or antisocial behavior. Similarly, in early studies, neighborhood effects were assessed primarily using SES and general indicators of social disorganization as opposed to more nuanced aspects of criminal opportunity that may cause variation in crime rates within a community (Cohen, Felson, & Land, 1980). For instance, Mischel (1977) suggested that situations exist along a continuum from “weak” to “strong,” depending on the intensity of behavioral cues that induce standardized expectations regarding the appropriate response to the situation. Funerals are an example of strong situations because it is highly expected for attendants to be serious and somber, and expression of individual reactions in the situation is generally suppressed. Weak situational contexts have highly ambiguous behavioral cues, such as social gatherings, where there are few restraints and expectations on standardized behavior. Weak contexts allow for greater expression of individual-level features because no particular behavioral response is dictated by the circumstances, whereas “strong” situations should eliminate individual-level factors because the context provides such a strong pressure for all individuals to behave in a uniform way (Mischel, 1977). Put differently, Shakur (1993) stated that, “being in a gang in South Central Los Angeles … is the equivalent of growing up in Grosse Pointe, Michigan and going to college: everyone does it” (p. 138). Once again, although studies assessed effects of criminal opportunity on offending, none combined this factor with individual-level features to predict criminal and antisocial behavior. 316 Neighborhood Risk

These omissions were noted by Lynam et  al. (2000) when they conducted the first prospective longitudinal study to examine interactive effects of impulsivity and neighborhood context on offending. Using a sample of boys aged 13–17 years who participated in the Pittsburgh Youth Study, Lynam et  al. found that the relation between impulsivity and self-reported criminal behavior was stronger for boys in lower SES neighborhoods than for boys in higher SES neighborhoods. To explain these findings, the authors hypothesized that impulsive boys avail themselves of criminal opportunities in disorganized neighborhoods and that these opportunities do not exist in higher SES neighborhoods (Lynam et al., 2000). In a similar study that was also based on Pittsburgh Youth Survey data, Wikström and Loeber (2000) examined whether neighborhood context moderated the influence of impulsivity, hyperactivity, and attention problems on delinquency while controlling for criminal risk and protective factors such as guilt/morality, parental supervision, school motivation, peer delinquency, and attitudes on antisocial behavior. Neighborhood context had a nonsignificant influence on boys with multiple risk factors and few protective factors for offending, whereas youth with few risk factors and multiple protective factors were most influenced by neighborhood context (Wikström & Loeber, 2000). Furthermore, neighborhood factors had little influence on individuals’ rates of serious offending, and neighborhood context was nonsignificant for early-onset offending youth (Wikström & Loeber, 2000). These findings once again suggest that there is an interactive effect between individual-level risk factors and neighborhood context, one in which youth with more individual-level risk factors tended to offend regardless of context and criminal opportunity and those with fewer risk factors tended to commit crimes only when suitable contextual factors (i.e., criminal opportunity) presented themselves. In other words, there appears to be an interactive effect of individual-level criminal potential and neighborhood-level criminal opportunity on the incidence of antisocial and criminal behavior. Wikström and Loeber’s study expanded on the findings of past research (e.g., Lynam et al., 2000) because it showed that neighborhood context matters less for those with high risk factors for offending (because they are more inclined to commit crime regardless of context), whereas neighborhood context has more of an impact on those with fewer individual-level risk factors for criminality.

Integrated Individual- and Neighborhood-Level Theories of Antisocial and Criminal Behavior

As an outcome of studies showing interactive effects of individual and contextual risk factors on criminal behavior—particularly impulsivity and neighborhood characteristics—a more integrated theoretical perspective of criminal behavior has emerged. Researchers in criminology have begun to draw on psychological and sociological principles and new methodological techniques, such as multilevel modeling, to understand how neighborhood contexts may moderate how self-control develops and influences antisocial and criminal behavior. Leventhal and Brooks-Gunn (2000) argued that development of self-control is influenced in part by the socialization processes that take place in communities. Those who live in neighborhoods with stronger informal social control, in which the community residents are vested in their community and have concern for their children, tend to develop more self-control than do those who reside in neighborhoods with less informal social control and concern for children. Similarly, Wikström and Sampson (2003) suggest that community socialization and collective efficacy have direct influences on self-control and allow for more criminal opportunities, which in turn increases the likelihood of criminal behavior. For instance, children who live in neighborhoods with lower collective efficacy (i.e., lower informal social control and social cohesion) may have less support and fewer positive influences outside the home to help them develop self-control while also having increased opportunities to engage in criminal behavior in their neighborhoods (Sampson, Raudensbush, & Earls, 1997; Wikström & Sampson, 2003). To test whether neighborhood characteristics have direct influences on development of self-control, Pratt et al. (2004) used data from the National Longitudinal Study of Youth to evaluate the perceived collective efficacy of sampled youth’s neighborhoods, their parents’ monitoring and discipline levels, and youths’ individual levels of self-control. Results suggest that neighborhoods have nearly the same-sized effect on development of self-control as do parents, who had previously been deemed a far more influential factor than the community (Pratt et al., 2004). Additional studies have examined whether individual traits, such as impulsivity, vary according to neighborhood influences and context. Using data collected from more than 20,000 16-year-old

American youth in the National Longitudinal Study of Adolescent Health (Add Health) study, Vazsonyi, Harrington Cleveland, and Wiebe (2006) assessed how concentrated disadvantage in neighborhoods (represented by school clusters) affects the relationship between impulsivity and antisocial behaviors including aggression, violence, and general delinquency. Results indicate that although impulsivity is related to antisocial behavior, neighborhood context appears to influence levels of impulsivity/deviant behaviors in theorized directions. Specifically, aggression and violence increased with neighborhood disadvantage, whereas nonviolent delinquency was actually found to decrease (Vazsonyi et  al., 2006). Furthermore, impulsivity was found to be highest for males and females in higher SES neighborhoods and decreased as neighborhood SES lowered (Vazsonyi et al., 2006). These findings are in contrast to Wikström and Sampson’s (2003) proposed relationships among neighborhood context, impulsivity, and deviance and also with results of the Lynam et al. (2000) study, which showed that deviance and impulsivity both increase as neighborhood disadvantage increases. Because Vazsonyi and colleagues’ findings are atypical, it should be noted that the Add Health dataset uses US Census data to acquire information on neighborhood SES and does not contain measures of social processes or context. Additionally, the impulsivity index may have been problematic because it did not assess critical behaviors associated with low self-control or the inability to delay or control reactions (Gibson, Sullivan, Jones, & Piquero, 2009). Thus, this study may have tapped a different construct than intended, and more research on the topic should be conducted. In this regard, Gibson, Sullivan, Jones, and Piquero (2009) used data from the Project on Human Development in Chicago Neighborhoods (PHDCN) to examine whether neighborhood features affect development of self-control beyond the influences of parenting and individual traits. This study differs from past research in that both structural and social conditions of neighborhoods were included in the PHDCN data, and multi-level models were used to assess the effect of neighborhoodand individual-level factors on delinquency (Gibson et al., 2009). Findings suggest that some aspects of neighborhood structure initially appear to influence self-control, yet after controlling for individual traits of the youth and factors relating to neighborhood selection, neighborhoods are no longer related to self-control. These results differ from Wikström Jennings, Hahn Fox

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and Sampson’s (2003) hypothesis that low collective efficacy relates to higher rates of low self-control and morality among youth in the neighborhood. Yet youth are frequently exposed to other social contexts beyond the neighborhood, such as schools; thus, it is possible that additional community factors may influence development of self-control beyond the neighborhood context (Osgood & Wikström, 2006). The authors suggest exploring places that youth spend the majority of their time to further understand the influence of community factors beyond neighborhood contexts (Gibson et al., 2009). Jennings et al. (2011) provided a methodologically rigorous examination of the role of individualand neighborhood-level risk factors for predicting aggression among youth. Using a sample of more than 5,000 middle school youth who participated in Project Northland Chicago (PNC; Komro et al., 2004, 2008), a large-scale group-based randomized controlled trial of an alcohol preventive program designed for multiethnic urban youth, Jennings et al. applied a series of hierarchical linear regression models to assess unique and simultaneous effects of individual- and neighborhood-level risk factors. They used a scaled measure representing neighborhood problems derived from parents’ responses to questions such as “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?” Positive responses predicted youth aggression in the sixth, seventh, and eighth grades. This neighborhood-level risk factor maintained its statistical significance (prior to adjusting for baseline physical aggression) after accounting for a host of individual-level risk factors, including alcohol use, peer alcohol use, lack of adult supervision, and depression. In addition, age, sex, race, being born in the United States, and coming from a two-parent household were significant predictors of youth self-reported aggression. Ultimately, these models demonstrated that both neighborhood- and individual-level factors uniquely and jointly influence adolescent aggression. 318 Neighborhood Risk

Most recently, Jennings et  al. (2013) evaluated the efficacy of an integrated theoretical model for predicting offending frequency and involvement in violence from ages 10 to 50 years among a cohort of 411 South London males from the Cambridge Study in Delinquent Development (CSDD). Using a series of count-based and logistic regression models, Jennings et al. reported an association between low resting heart rate and life-course offending. This link was significant, over and above an index of 12 individual risk factors measured at ages 8–10; of 15 environmental risk factors measured at ages 8–10; of participation in team sports measured at age 16; and of smoking, binge drinking, impulsivity, and body mass index (BMI) measured at age 18.1

Policy Implications

The growing body of research on interactive effects of neighborhood factors and individual traits has several implications for policy and prevention. In general, studies reviewed in this chapter suggest that neighborhoods with more social control and social cohesion have a positive, even protective, effect on youth by helping develop their self-control and preventing criminal behavior from occurring. Furthermore, it appears that even more impulsive and callous youth in “protective” neighborhoods may be less likely to commit crimes compared to in other contexts due to greater monitoring and less opportunity for crime in their communities (Meier et  al., 2008). Conversely, neighborhoods with less informal social control may increase the risk of youth developing impulsive behaviors (because less supervision and mentoring from those outside the family is present) while increasing criminal opportunities. An example of how research findings can affect policy comes from the Moving to Opportunity for Fair Housing Demonstration (or MTO) program, developed by the US Department of Housing and Urban Development (HUD) in 1994 after the success of the Gautreaux Program, which was in operation in Chicago for almost three decades. Rosenbaum (1995; see also Rosenbaum & Popkin, 1991) presented quasi-experimental evidence that Gautreaux youth who moved to the suburbs greatly increased their educational attainment and job opportunities compared with those who remained in poor urban areas. Informed by this empirical evidence and the notion that residential mobility may benefit individuals from disadvantaged neighborhoods, the MTO program became operational in

five selected sites (Baltimore, Boston, Chicago, Los Angeles, and New York City). Prior MTO evaluations indicated that residential relocation reduced violent arrests for males and improved overall health among children who moved from public housing compared with those who remained in public housing (Katz, Kling,  & Liebman, 2001; Ludwig et  al., 2001). Katz et  al. (2001) also reported reductions in criminal victimization among individuals who moved out of Section 8 housing. In addition, Leventhal and Brooks-Gunn (2003) provided evidence that MTO programs had profound effects on mental health outcomes among individuals and families who moved out of public housing. More specifically, parents who moved reported less distress than those who remained, and boys who moved (especially those between ages 8 and 13 years) reported significantly less depression, anxiety, and/or dependency problems than their counterparts who were still in public housing. Most recently, Kling et  al. (2007) revealed considerable mental health and education benefits for adults and females but also noted adverse effects for males who received vouchers (see also Orr et al., 2003). There are significant implications for crime prevention policy and practice from this new line of research. Evidence suggests individual traits that confer vulnerability to antisocial and criminal behavior—once thought to be entirely learned or inherited at birth—can be enhanced or dampened as a result of contextual and environmental factors relating to neighborhoods (see, e.g., Katz et  al., 2001; Leventhal  & Brooks-Gunn, 2003; Ludwig et  al., 2001; Meier et  al., 2008); thus, preventative strategies become far more feasible in many ways. Although it is often difficult, and in some cases unethical or impossible, to attempt to change individual traits (particularly when related to neurological deficits and genetic expressions), the fact that neighborhood features may be altered to help reduce negative behaviors from occurring is an extremely encouraging thought. Although new research in the field of neurobiology (e.g., Fisher & Kim, 2007; Fisher  & Stoolmiller, 2008) has suggested that certain interventions may actually alter an individual’s neurobiology (which may in turn influence personality and other risk factors for offending) because the causal mechanisms underlying offending behaviors are highly complex and far from being explained fully by any theory currently available, more work still needs to be conducted to effectively translate these findings and theories into practice.

However, public policy similar to the MTO program can be developed to target neighborhoods with higher levels of criminal opportunity and to potentially hinder individuals with low to moderate antisocial potential from committing crime in their communities. Although the costs of such programs may initially deter policy makers from implementing such programs on a large-scale basis, the benefits of preventing crimes in those communities that implement such programs, as well as considering the number of the youth who will not commit crimes because they are no longer enveloped by neighborhoods with significant criminal opportunity, are paramount.

Conclusion

In short, it is becoming increasingly clear that antisocial and criminal behaviors are not the product of community- or individual-level factors alone. Instead, there is an interactive effect between the social/structural contexts of neighborhoods and both innate and learned individual differences that, in concert, produce crime and other antisocial behavior. Although research that unites more traditional psychological and sociological theoretical perspectives has been relatively rare in criminology, as support for the combined effect of communities and individual traits continues to grow so should the number of studies that examine both place and person to explain criminal behavior. As Wilcox, Sullivan, Jones, and van Gelder (2014) recently stated: The strong lines of delineation between these theoretical camps were once useful to criminology as they helped to emphasize distinctiveness of various approaches. However, such delineation has likely outlived its usefulness. (p. 16)

Directions for Future Research

Consequently, future research should continue to develop and test theoretical models that incorporate community- and individual-level factors into a single cohesive, comprehensive, and uncomplicated framework. For instance, David Farrington proposed the integrated cognitive antisocial potential (ICAP) theory in 2003, which unites ideas from several mainstream perspectives such as strain, labeling, learning, social control, and rational choice theories to explain antisocial and criminal behaviors. The ICAP theory suggests that crime occurs when an individual’s antisocial potential combines with a certain level of criminal opportunity that is based Jennings, Hahn Fox

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on community-level situational and contextual factors. Although use and testing of ICAP is still in its infancy, results of research testing the theory have provided empirical support for the integrated model (see Sullivan, 2006; van der Laan, Blom, & Kleemans, 2009). Relatedly, situational action theory, developed in 2004 by P.  -O. Wikström, is a comprehensive model that suggests, in short, that individual differences such as morality and emotion interact with environmental temptations and provocations (i.e., opportunity) to influence the courses of action an individual perceives to be available in a given situation. If alternative options for an action are perceived, the ultimate decision on whether to commit a certain action (such as a crime) depends on additional internal traits, such as level of self-control, and external factors, such as deterrence (Wikström, 2004). Although complex, this theory integrates many of the most significant individual- and neighborhood-level risk and protective factors into one model. The theory has also been tested on several occasions, often by Wikström and colleagues, and has generally been considered an effective model (Wikström & Treiber, 2009). Recently, Pamela Wilcox and colleagues (2014) proposed an integrated approach to examine both offending and victimization that is based on an individual’s personality traits and the criminal opportunity presented in a given context or situation. This perspective suggests that criminal–victim propensity (derived from an individual’s personality) can have direct effects on offending and victimization or indirect effects when interacted with situational crime opportunity (Wilcox et al., 2014). In an initial test of this, personality had a modest direct effect on offending, whereas environmental opportunity was related strongly to both offending and victimization (Wilcox et  al., 2014). However, personality had a significant indirect influence on offending when mediated through criminal opportunity contexts such as delinquent peer association (Wilcox et  al., 2014). In other words, individuals with certain “high-risk” personality traits were more likely to seek out opportunities with individuals who “fit” with their personality, thus making them more likely to commit crime (Wilcox et al., 2014). Taken together, the development and tests of these integrated theories are highly encouraging for a new interdisciplinary approach to explain antisocial and criminal behavior. More research and development of these and other integrated theories can help us understand the combined effects 320 Neighborhood Risk

of neighborhoods and individual traits on criminal behavior and encourage interdisciplinary explanations for a problem that a growing body of research suggests has interdisciplinary origins.

Note

1. The 12 individual risk factors were (1)  low junior school attainment, (2) daring disposition, (3) small height, (4) low nonverbal intelligence, (5) nervous/withdrawn boy, (6) high extraversion of boy, (7)  high neuroticism of boy, (8)  psychomotor impulsivity, (9)  dishonest, (10) unpopular, (11) troublesome, and (12) lacks concentration/restless; the 15 environmental risk factors were (1)  harsh attitude/discipline of parents, (2) teen mother at birth of her first child, (3)  behavior problems of siblings, (4)  criminal record of a parent, (5)  delinquent older sibling, (6)  large family size, (7)  poor housing, (8)  low family income, (9)  parental disharmony, (10) neurotic/depressed father, (11) neurotic/depressed mother, (12) low socioeconomic status, (13) separated parents, (14) poor supervision, and (15) high-delinquency rate school.

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Sampson, R. J., & Groves, W. B. (1989). Community structure and crime:  Testing social-disorganization theory. American Journal of Sociology, 94, 774–802. Sampson, R.  J., Raudenbush, S.  W.,  & Earls, F. (1997). Neighborhoods and violent crime: A multilevel study of collective efficacy. Science, 277, 918–924. Shakur, S. (1993). Monster: The autobiography of an L. A. gang member. New York: Penguin Books. Simcha-Fagan, O.,  & Schwartz, J.  E. (1986). Neighborhood and delinquency:  An assessment of contextual effects. Criminology, 24, 667–703. Sullivan, C. J. (2006). Early adolescent delinquency: Assessing the role of childhood problems, family environment, and peer pressure. Youth Violence and Juvenile Justice, 4, 291–313. Tonry, M., Ohlin, L.  E.,  & Farrington, D.  P. (1991). Human development and criminal behavior. New York: Springer-Verlag. van der Laan, A.  M., Blom, M.,  & Kleemans, E.  R. (2009). Exploring long-term and short-term risk factors for serious delinquency. European Journal of Criminology, 6, 419–438. Vazsonyi, A.  T., Harrington Cleveland, H.,  & Wiebe, R.  P. (2006). Does the effect of impulsivity on delinquency vary by level of neighborhood disadvantage? Criminal Justice and Behavior, 33, 511–541. White, J. L., Moffitt, T. E., & Silva, P. A. (1989). A prospective replication of protective effects of IQ in subjects at high risk for juvenile delinquency. Journal of Consulting and Clinical Psychology, 57, 719–724. Wikström, P.  -O. (2004). Crime as an alternative:  Towards a cross-level situational action theory of crime causation. In J. McCord (Ed.), Institutions and intentions in the study of crime:  Beyond empiricism (pp.  1–38). New Brunswick, NJ: Transaction. 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: Guilford. 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: Simon and Schuster. Wilson, J.  Q.,  & Kelling, G.  L. (1982, March). Broken windows:  The police and neighborhood safety. Atlantic Monthly, 29–38.

CH A PT E R

19

Incarceration and Development of Delinquency

Sytske Besemer and Joseph Murray

Abstract In this chapter, the authors provide an overview of theories and empirical studies on the relation between incarceration and reoffending and describe effects of parental incarceration on criminal behavior of children. How does incarceration affect offending behavior when a person leaves prison? Rigorous scientific evidence is lacking, but available empirical data suggest that incarceration has either a null or criminogenic effect on reoffending. How are children of those incarcerated affected by their parent’s prison sentence? Although several theoretical mechanisms of adverse effects have been suggested, including social transmission, economic and psychological strain, and stigmatization, research addressing such mechanisms is scarce. Available evidence points to parental incarceration as a risk factor for poor adjustment outcomes among children, with effects partly caused by caretaker stress and weakened family bonds. Key Words:  delinquency, crime, prison, reoffending, incarceration, parental incarceration, intergenerational transmission

Introduction

In the past few decades, rates of incarceration have increased exponentially in the United States, and to a lesser extent in many other high-income countries. One in every 99 Americans is currently in prison, and excluding children and the elderly, nearly 1 in 50 people is behind bars on any given day (Gottschalk, 2006; Tonry, 2001; Walmsley, 2011; J. Warren, 2008). Although it is often assumed that incarceration reduces criminal behavior, people who have been incarcerated show high recidivism rates, with 60% reoffending within 3 years (Langan & Levin, 2002). A  handbook on the development of externalizing behavior would not be complete without examining effects of incarceration on offending behavior. Does incarceration really reduce reoffending? Might incarceration cause increases in offending when a person leaves prison? How are children of incarcerated individuals affected by their parents’ prison sentence? The central aim of prisons is punishment through deprivation of liberty. Retribution “ensures

that the guilty will be punished, the innocent protected, and societal balance restored after being disrupted by crime” (Bradley, 2003; p. 31). A second aim of imprisonment is to reduce crime through an “incapacitative effect.” In other words, people who are currently incarcerated are unable to commit crimes in their communities. A third aim of imprisonment is to deter people from committing future crimes. Specific deterrence refers to the possibility that incarcerated people will try to avoid the experience again (Ashworth, Hirsch,  & Roberts, 2009). In contrast, general deterrence refers to the notion that threat of imprisonment deters most people from committing crime in the first place, even without experiencing incarceration themselves. Fourth, prison sentences aim to rehabilitate offenders, over and above any deterrence effect. Both specific deterrence and rehabilitation are most relevant to the topic of this essay (how imprisonment affects individual development), so we discuss these concepts in detail below. Given 323

space constraints, we do not discuss how macro level differences in imprisonment rates affect crime, or whether general deterrence or incapacitation prevent crime (for overviews of such studies, see Bottoms  & Hirsch, 2010; Cullen, Jonson,  & Nagin, 2011; Durlauf  & Nagin, 2011a, 2011b; Nagin, 2013). It is important to realize that incarceration has broader effects than just on offending behavior, which is our focus herein. How incarceration affects mental health of prisoners has been the focus of much attention (Fazel  & Baillargeon, 2011; Fazel  & Seewald, 2012). Clear (2008) describes how incarceration involves a “host of destabilizing consequences—housing changes, school maladaptations, welfare problems, and strains on relationships” (p.  105). Spending time in prison also adversely affects one’s chances of obtaining employment and building a legitimate career. Raphael (2014) describes how former inmates face several challenges, including (a)  less extensive work histories, (b)  expression of behaviors developed inside prison that are unsuitable for workplaces outside prison, and (c)  stigma against ex-offenders by employers. Furthermore, incarceration is concentrated in disadvantaged and black communities in the United States, and contributes to poverty (Alexander, 2010; DeFina  & Hannon, 2013; Western, 2006, Wakefield  & Wildeman, 2014). Already disadvantaged communities are affected negatively by the revolving flux of a large proportion of their adult males in and out of prison. Clear (2007, 2008) argues that concentrated incarceration in communities is likely to increase crime and decrease public safety. Incarceration does not only affect the incarcerated individual, but also his/ her family and the broader community (Clear, 2007, 2008; Comfort, 2007; Murray, 2005; Wakefield & Wildeman, 2014). Although we recognize these various collateral effects on innocent “quasi-inmates” (Comfort, 2003; p. 103), we focus only on incarcerated individuals and their offspring. Our aims in writing this essay are to provide an overview of how incarceration affects individuals’ offending behavior and to describe how incarceration affects the offending of children of incarcerated parents. First, we discuss historical developments in the use of incarceration, and theories about links between (parental) incarceration and development of offending behavior. Next, we summarize the latest empirical research on reoffending by ex-prisoners and offending by prisoners’ children. Although this handbook 324 Incarceration

investigates a range of externalizing problem behaviors, in this essay we focus on criminal behavior, as both a possible cause and consequence of incarceration for individuals and their offspring.

Historical Context

Incarceration is the harshest punishment in all high-income societies apart from the death penalty, which is used only in the United States. This has not always been the case; offenders used to be sentenced to several forms of corporal punishment and public humiliation such as the pillory or stocks, and prison was used to hold offenders before their trial, or while awaiting their punishment. This policy changed in the early 19th Century when the United States saw an increase in so-called penitentiaries, where offenders were held to separate them from criminal influences in their environments. Haney (2005) describes how, during subsequent decades, “the emerging discipline of psychology certainly contributed to the idea that confinement was an appropriate mechanism of social control and, in this sense, helped solidify the prison form” (p. 71). Gottschalk (2006) argues that “issues of crime and punishment were integral to the early political development of the United States” (p. 52), and discusses how prison became a common institution in American society (Gottschalk, 2006; Rothman, 1971). From the 1930s to the 1970s, the United States experienced a relatively stable rate of imprisonment. Every year about 110 per 100,000 people were incarcerated (Blumstein & Cohen, 1973; Gottschalk, 2006). By the 1970s, use of incarceration as criminal punishment appeared to be declining (Mitford, 1973; Nagin, Cullen, & Jonson, 2009; Scull, 1977; Sommer, 1976). However, for the past four decades the United States has increased its reliance on incarceration dramatically. By 2007, about 2.3 million adults were held in federal or state prisons or in jail on any given day. With a current adult population in the United States of just under 230 million, this translates to an incarceration rate of 1 in every 99.1 adults, representing a tenfold increase since 1970 (J. Warren, 2008). Although not quite as dramatic as in the United States, many other high-income countries, including the United Kingdom and the Netherlands, have also increased their use of incarceration (Hofer, 2003; Walmsley, 2011), as have some low and middle-income countries such as Brazil (Murray, Cerqueira, & Kahn, 2013). Our discussion in this essay focuses on the United States, because this country has the highest imprisonment rate in the world, and the highest absolute number of prisoners (Walmsley, 2011). Also, most research on the

consequences of imprisonment for prisoners and their children has been conducted in the United States. There are several possible explanations for the rapid increase in use of incarceration in the United States. Public opinion and social values became “increasingly intolerant of offenders and, concomitantly, tolerant of prison expansion” (Kruttschnitt, 2005; p. 146). Because of an increase in crime and dissatisfaction with contemporary crime policies, there has been a general shift in focus from rehabilitative to more punitive justice policies since the 1970s. States started to adopt punitive offense-based policies instead of policies with an offender orientation based on rehabilitation and individualized sentencing (Tonry, 2009; R. K.  Warren, 2007). Furthermore, there was a reallocation of resources from spending on social welfare to procedural justice, which led to mentally ill patients being fed into prisons (and onto the streets), rather than being cared for in mental health system hospitals as they used to (Cullen & Gilbert, 1982; Cullen & Jonson, 2014; Rotman, 1990). Moreover, the United States embarked on several domestic wars:  the war on crime, the war on drugs, and the war on gangs, increasingly moving populations into the criminal justice system, including drug users and low-risk offenders (Howell, Feld, & Mears, 2012). During this time, many politicians also realized the enormous potential of communicating “get tough on crime” messages through the media. On October 2, 1982, for example, Ronald Reagan declared in a national radio broadcast that, “Drugs are bad, and we’re going after them … we’ve taken down the surrender flag and run up the battle flag, and we’re going to win the War on Drugs” (Reagan, 1982). Such messages grossly oversimplied a complex problem and ignored the social and mental health issues involved in drug taking. Together, politicians and the media fed the idea that crime was out of control, which led politicians to propose increasingly tougher sentences, often to win elections, a concept called penal populism (Bosworth, 2010; Pratt, 2007). Mandatory sentencing policies such as the California Three Strikes and You’re Out Laws further increased the prison population. Garland (2001) described the culture of control that developed, in which the primary aim was to avoid risk, and putting people in prison seemed to be a safe solution to crime. None of these factors explains the rise in incarceration rates completely; most likely they cumulatively and interactively caused the massive

increase in incarceration in the United States (for more information see e.g. Beckett, 1997; Currie, 2013; Gottschalk, 2006; Harcourt, 2006; Tonry, 2004; Zimring & Hawkins, 1991; Bosworth, 2010). Regardless of the multifactorial roots, the result is (a) greatly increased imprisonment rates, particularly for drug-related offenses that would not formerly have led to incarceration; and (b) even greater concentration of impoverished and ethnic minority individuals in U. S. prisons.

Incarceration Experience

Serving a sentence in a contemporary prison involves more than just taking away an individual’s freedom. To understand how incarceration might affect an individual and his/her children we describe some elements of the incarceration experience. Although the explicit aim of incarceration is to deprive liberty without harming prisoners (see above), imprisonment nearly always harms individuals who are involved (Clear, 1994; Irwin  & Owen, 2005). Sykes (1958) described five pains of incarceration:  deprivation of liberty, loss of goods and services, loss of heterosexual relationships, loss of autonomy, and loss of security. The latter four losses—in contrast to deprivation of liberty—are not the main purposes of imprisonment but are consequences of prison life that exert considerable influence on persons concerned. According to Liebling and Maruna (2005), “fear, anxiety, loneliness, trauma, depression, injustice, powerlessness, violence and uncertainty are all part of the experience of prison life” (p. 3). Goffman (1961) described prison as a total institution, which he defined as, “a place of residence and work where a large number of like-situated individuals, cut off from the wider society for an appreciable period of time, together lead an enclosed, formally administered round of life” (p. xiii). Goffman pointed out that because of the characteristics of prison life, prisoners experience a so-called mortification process, through which they lose their personal identity. Irwin and Owen (2005) describe harms of imprisonment in terms of the low priority given to healthcare; psychological damage due to loss of agency and assaults on the self; anger, frustration, humiliation, degradation, and feelings of injustice because of rules perceived as unfair; and economic exploitation such as disproportionately expensive telephone policies. Outsiders generally have no idea what a prison looks like. Tom Shannon (1996) writes: “If you walked into this prison through the front gate you would be amazed at the flower and plant display,

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dozens of massive flower beds, in full bloom, and wonderful colours. We cons see them as we arrive, and don’t see them again, till we leave. Again, these are just for show, for V.I.P. visitors. If you then walk towards the back of the prison where the wings are, you would see the prison proper. Austerity is the word. Dull brick, huge fences, and bundles of razor wire” (p. 187).

This austere environment, combined with the repetitive life inside prison, has a profound effect on prisoners’ psychological states (Hairgrove, 2000): “The innocuous taste of my world, devoid of any emotional nourishment, is gradually overpowering. Bleak and colorless walls, insipid disgusting meals, and phlegmatic, timeworn daily routines, staked upon overcrowding; unadultered trivia by unwarranted and irrational guardian harassment all collaborate to numb my faculties. In tiny surreptitious doses, anaesthesia is dripped into my heart—a formerly complacent heart that is slowly beginning to resemble my dreadful surroundings” (p. 147).

For children of those who are incarcerated, the experience of having a parent inside can vary widely depending on criminal justice and prison policies. Almost all children whose parents are removed by incarceration experience grief and loss. Hissel, Bijleveld, and Kruttschnitt (2011) describe how children have nightmares, worry about their parent(s), and feel lonely. Letters, telephone calls, and visits to prison can be vital to maintain relationships but are highly restricted. The experience of prison visits depends on the policies involved but can often be intimidating and frightening for children. Hissel et  al. (2011) describe the visiting procedures for children in the Netherlands, a country characterized by relatively liberal criminal justice policies and prison visiting procedures (Kruttschnitt  & Dirkzwager, 2011), with the following quote from a child with an incarcerated mother: “I didn’t see mummy immediately. We had to go through a door, then we had to wait till that door was closed before the next door could open. We had to sign in and then we had to put everything in a safe except for small change … Then we had to push a button and wait until the door was opened again. We went upstairs where we had to wait till the door was completely locked. Only then did we come in the room where mummy was. Mummy had to sit at the table precisely on the spot where something was around her leg so that she couldn’t pass stuff to us.

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After the visit we had to go through the steel door. It’s weird that your mum is in prison with all those steel doors. Then you get the feeling that your mother is a criminal, but actually she isn’t. How it feels? I don’t know, I can’t really explain. Weird” (p. 356).

It is important to realize that many children have been exposed to a number of situations that pose risk for healthy development even before their parent was incarcerated. Indeed, many have experienced parental substance abuse, parental mental illness, child abuse and neglect, poverty, and/or frequent moving (Phillips, Burns, Wagner, Kramer, & Robbins, 2002). Some children are present when their parent is arrested, which is traumatic (Arditti, Lambert-Shute, & Joest, 2003). During and after incarceration, children often experience changes in caregiver and house moves (Hissel et al., 2011). It is difficult to remain in contact with an incarcerated parent, because phone calls are expensive (and hardly a substitute for face-to-face contact), and parents are often incarcerated many miles away. Sometimes, of course, parental incarceration can improve stability in children’s lives if children come from particularly chaotic, disadvantaged backgrounds that are influenced by parental antisocial behavior (Giordano, 2010; Hissel et al., 2011).

Theoretical Framework Incarceration and Reoffending

Several theoretical perspectives suggest different ways in which incarceration can affect individuals’ offending behavior after release from prison. Theories can be divided broadly into two types, including (1) those that predict a decrease in offending after incarceration, suggesting either deterrent or rehabilitation effects; and (2) those that predict an increase in offending after incarceration, or a so-called criminogenic effect. Theories predicting criminogenic effects can be subdivided into those emphasizing (a)  learning environments within prisons; (b)  labeling theory, which focuses on the enduring stigma of incarceration; and (c) defiance theory. After we discuss these theoretical views on links between incarceration and reoffending, we consider theories regarding effects of parental incarceration on children.

Deterrence

The idea of prison as a specific deterrent comes from the economic model of crime in which individuals weigh costs and benefits of offending in a

rational choice process (G. S. Becker, 1968). Possible incarceration for a crime contributes heavily to the cost side of the equation, hypothetically reducing the probability of offending. Although this model is straightforward, in reality the costs of offending are not always clear. Offenders often do not know what sentences they would face for different crimes, and the cost of possible incarceration depends on perceived unpleasantness of the experience for the individual. Furthermore, people recently convicted of a crime might believe their chances of being caught again soon are lower due to the “gambler’s fallacy” (Clotfelter & Cook, 1993; Gilovich, 1983; Pogarsky & Piquero, 2003). Thus, a prison sentence might decrease the offender’s estimation of the risk of being caught again. Although rational choice models generally predict deterrent effects of incarceration, there are contingencies to this prediction, depending on perceived certainty and unpleasantness of punishment. Other factors that may alter the influence of deterrence on reoffending are characteristics of the individual such as levels of self-control and impulsivity. Some theorists predict that deterrence will have less influence on individuals with low self-control, because they are less able to delay gratification and take “rational action” (Gottfredson  & Hirschi, 1990; Nagin  & Paternoster, 1993; Nagin  & Pogarsky, 2001; Piquero  & Tibbetts, 1996). Others argue that individuals with low self-control will in fact be more influenced by deterrence processes, because low self-control indicates a high crime propensity, which is a necessary condition for situational factors to either encourage or deter criminal behavior (Hirtenlehner, Pauwels, & Mesko, 2014; Pogarsky, 2007; Tittle  & Botchkovar, 2005; Wikström, Oberwittler, Treiber,  & Hardie, 2012; Wright, Caspi, Moffitt, & Paternoster, 2004).

Rehabilitation

Another way prison might reduce offending is if it is designed to teach new skills or change problematic behaviors, thereby fostering non-criminal lives. If prison aims to reduce reoffending, a prison sentence should include opportunities for incarcerated individuals to acquire personal as well as practical skills. Education and vocational training can increase the chances that released offenders earn income through legitimate means (MacKenzie, 2002). Personal skills training might be targeted at drug addiction, emotion regulation, and/or violence reduction, thereby reducing risk of reoffending

(Cullen  & Jonson, 2011). Unfortunately, with increasing imprisonment rates, money spent on education, training, and other rehabilitation programs has decreased, and prisoners often do not have opportunities to foster skills necessary to prevent reoffending (MacKenzie & Armstrong, 2012).

Criminal learning environment

Effects opposite to rehabilitation might take place within prisons, which are often viewed as universities of crime. Prisons are communities in which individuals live together, sometimes for extended periods of time, creating a prison culture with associated cultural values (Crewe, 2005, 2009; Irwin & Cressey, 1962). Such values often support crime and are transmitted through daily interactions among incarcerated individuals. Gang membership and a violent street culture are imported into U.S.  prisons, so gang life often continues while individuals are incarcerated (Carroll, 1988; Irwin, 1980, 2005; Irwin & Cressey, 1962; Jacobs, 1977; Mears, Stewart, Siennick,  & Simons, 2013; Wacquant, 2001). Not only can people acquire or strengthen criminal values while incarcerated, they might develop or learn practical skills for committing certain crimes (Hawkins, 1976; Steffensmeier  & Ulmer, 2005). This relates to notions of peer deviancy training and contagion proposed by Dishion (Dishion  & Dodge, 2005; Dishion, Andrews, & Crosby, 1995; Dishion, Eddy, Haas, Li,  & Spracklen, 1997). When children and adolescents spend time with and are affiliated with antisocial peers, they have considerable opportunity to shape and refine deviant attitudes and behaviors. This happens through modeling, reinforcement, and mutual discourse. For example, Dishion et  al. (1996) demonstrated that affective exchanges such as laughing during conversations about rule-breaking behavior establish problem behavior as a common ground (antisocial) activity. These processes of affiliation and deviancy training are associated with both onset and escalation of antisocial behavior. Similarly, among juvenile as well as adult prisoners, affiliation with other antisocial people in prison might exacerbate the development of antisocial behavior.

Labeling theory

Labeling theory also predicts an increase in offending as a consequence of incarceration. Classic labeling theory proposes that people act in accordance with labels attached to them by society (H. S. Becker, 1963; Lemert, 1967; Scheff, 1974, Besemer, Murray

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1984). This can be a crucial factor leading from “primary deviance” (experimentation with delinquent activity) to a more persistent criminal life course. Revised versions of labeling theory recognize two major theoretical perspectives about how a person who is labeled “delinquent” might increase his or her criminal behavior (Bernburg, 2009; Paternoster  & Iovanni, 1989; Sampson  & Laub, 1997). First, being labeled might influence a person’s self-perceptions, attitudes, and beliefs, and increase his or her association with delinquent peers (H. S.  Becker, 1963; Bernburg, Krohn,  & Rivera, 2006; Lemert, 1967; Matsueda, 1992; Paternoster  & Iovanni, 1989). Incarceration might then cause increases in criminal behavior as a result of conforming to the stereotype of a “con.” Second, the stigma of a criminal label might block conventional, non-criminal opportunities, and thereby push people into a criminal lifestyle. For example, people might have trouble finding a stable job when they have a criminal record (see Murray, Blokland, Farrington,  & Theobald, 2014; van der Geest, 2011). In the Rochester Youth Survey, Bernburg and Krohn (2003) found that being convicted had a negative effect on educational attainment, which in turn increased offending behavior. This finding supports the idea of blocked conventional opportunities following a criminal label. Previous studies on labeling effects demonstrate considerable influence of convictions on subsequent criminal behavior (Bernburg  & Krohn, 2003; Farrington, 1977; Farrington, Osborn, & West, 1978; Murray, Blokland et al., 2014; West & Farrington, 1977). Recently, scholars have suggested that undesirable labeling effects might depend on offenders’ characteristics and on the type of criminal justice sentence received. For example, labeling might have a stronger effect with younger offenders, for whom personality and behavior are presumably more malleable (Bernburg et al., 2006). Bernburg and Krohn (2003) write that, “Structural location, such as race or social class, may provide people with differential means to resist deviant labeling in the face of official intervention” (p. 1290). Cullen and Jonson (2014) hypothesize that labeling is stronger when sanctions are punitively oriented. They describe how inappropriate punishments that do not target dynamic risk factors that are changeable, such as a person’s education or housing situation, have stronger labeling effects. Furthermore, Cullen and Jonson (2014) suggest that negative labeling effects are more likely to occur with low-risk or first time offenders, who 328 Incarceration

are at lower probability of continued criminality in the absence of criminal justice interference.

Defiance theory

Defiance theory, proposed by Sherman (1993, 2010, 2014), is related to labeling theory. It predicts harmful effects of traditional criminal justice sanctions but focuses on different mechanisms. Sherman stresses the importance of emotions and legitimacy for effectiveness of a sentence. Defiance theory is thus also related to reintegrative shaming theory (Braithwaite, 1989). According to this theory, punishment should be aimed to “shame the act, but not the actor” (Sherman, 2014; p. 156). When offenders define a prison sentence as unfair and feel excluded from the society that punished them, they may develop pride that results in an increase and/ or persistence of their criminal behavior. Sherman predicts that defiance occurs under four necessary conditions: (1) the offender perceives a punishment as unfair, (2) the offender feels alienated or is poorly bonded to the person or sanctioning agency, (3) the offender perceives the sanction as stigmatizing and targeted at his person instead of at his law-breaking act, and (4) the offender does not acknowledge the shame that the punishment caused him to suffer. Sherman (2014) states that “it may be sufficient to say that in the absence of fair treatment, offenders did not feel obligated to obey the law. This is not about anger; it is about a failure of the state to persuade its citizens to comply with a rule of law for the state’s sake, as well as the citizens” (p. 155).

Parental Incarceration

Different theoretical perspectives suggest varying outcomes for children during and following parental incarceration. One perspective suggests that parental incarceration causes increases in children’s own offending behavior because of social and economic strains and stigma. A second perspective recognizes that children of incarcerated parents are at elevated risk, but explains this risk entirely in terms of incarcerated parents being a non-random sample of serious offenders, who are likely to transmit offending behavior to their offspring, either genetically or through environmental mechanisms such as harsh parenting. This perspective holds that parental incarceration itself does not cause increases in children’s offending behavior but that the antisocial behavior producing the offending is the stronger causal mechanism. A third perspective predicts improvement in life situations of some children

when their parents are incarcerated, because an antisocial or abusive influence is removed.

Undesirable effects of parental incarceration

There are several different mechanisms through which parental incarceration might have undesirable effects on offspring offending behavior. Criminological strain theory holds that economic and psychological stress motivate criminal behavior as a means to alleviate strain. Classical strain theory focuses mainly on economic strains, suggesting that poverty and the inability to achieve wealth, a dominant cultural goal in many high-income societies, leads to increased offending (Cohen, 1955; Merton, 1938). Robert Agnew (1992) reformulated strain theory to include emotional and psychological goals, hypothesizing that emotional strain or distress could lead to offending behavior. Strain theories are relevant for considering effects of parental incarceration, as parental incarceration almost inevitably produces economic and psychological strain for children involved. Incarceration often causes a loss of income and financial difficulties for families (Bloom  & Steinhart, 1993). Sometimes families receive financial support from the government, but this varies widely depending on the country (Murray, Bijleveld, Farrington, & Loeber, 2014). Moreover, visiting incarcerated relatives, making phone calls, and writing letters can be costly and lead to additional economic strain (Arditti et al., 2003; Ferraro, Johson, Jorgensen, & Bolton, 1983; Morris, 1965). Social strain arises when children lose a parent to prison because they lose “human and social capital” (Hagan & Dinovitzer, 1999; p. 124). A remaining single parent, or other caregiver, may be unable to devote sufficient time and energy to his or her children, especially if suffering from the loss of a partner during incarceration. Or, if children already live in single-parent families, they experience a change in their primary caregiver and sometimes enter foster care. Increases in maternal incarceration are the main cause of the doubling of children in foster care between 1985 and 2000 in the U. S. (Swann & Sylvester, 2006). Through reductions in parental involvement during incarceration, children might become more likely to use peers as role models, which might in turn lead to increased delinquent behavior (Hagan & Dinovitzer, 1999; Warr, 2002). Parental incarceration can also involve traumatic or stressful experiences leading to psychological strain. Some children are present when their parent

is arrested. Imagine a house raid in which a parent is carried away, possibly in handcuffs. Prison visits can be frightening, because of the security precautions and restricted possibilities for contact during the visit (Nesmith & Ruhland, 2008). Separation from a parent might cause attachment problems, a risk factor for a wide range of externalizing and internalizing behaviors (Bowlby, 1969, 1973, 1980). Children whose parents are incarcerated experience grief but cannot always express this openly. Furthermore, they are often not told of the true whereabouts of their incarcerated parent (Hissel et al., 2011). Children of incarcerated parents might also experience labeling and stigmatization. Prisoners’ children can experience stigma and related bullying and teasing, which might increase problem behavior (Hagan & Dinovitzer, 1999). Institutions, including the police force, might be biased against prisoners’ families and be more likely to arrest prisoners’ children (Besemer, Farrington,  & Bijleveld, 2013; Farrington, 2011). Such processes relate to labeling theory, which posits that people behave and are treated differently according to the label society attaches to them. Stigmatization can also lead to emotional distress or strain. Hissel et al. (2011) note that children often lie about the mother’s whereabouts: “Children at school ask “where is your mother?” Sometimes I say I don’t know and sometimes I don’t answer.’ Q: ‘How do you feel about them asking that?’ CHILD: ‘I don’t like it. Sometimes I’m a bit ashamed … ” (p. 355).

No effect of incarceration: Intergenerational transmission

Alternatively, the correlation between parental imprisonment and offspring offending could be spurious. That is, imprisoned people, who are often the most persistent and serious criminals (Murray  & Farrington, 2008; Murray  & Murray, 2010), may not transmit effects of incarceration per se, but rather the antisocial tendencies that result in incarceration. Such theories suggest stronger intergenerational transmission of criminality the more severe parental antisocial behavior is (Besemer, 2012; Farrington, 2011; Thornberry, 2009; van de Rakt, 2011). Thus, the association between parental imprisonment and offspring offending may be explained by genetic mediation and/or disadvantaged social backgrounds of prisoners’ children. This “selection perspective” (Hagan & Dinovitzer, 1999) Besemer, Murray

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assumes “that imprisoned parents and their children are already different from parents and their children who are not imprisoned, prior to the imposition of a prison sentence” (p. 128). Murray and Farrington (2008) proposed that “parental criminality, parental mental illness, and other environmental risks before parental imprisonment might cause child behavior problems, rather than parental imprisonment itself ” (p.  163) It is a major challenge for empirical research to test whether parental imprisonment has true causal effects on children over and above background risk factors. In fact, definitive tests of directions of causality may be impossible, given that parents cannot be assigned randomly to incarcerated and non-incarcerated conditions.

Positive effects of parental incarceration

In some cases, parental imprisonment may have positive effects on children involved. Hagan and Dinovitzer (1999) suggest that, “there obviously are cases involving the imprisonment of negligent, violent, and abusive parents where the imprisonment of the parents benefits the children by removing serious risks of current and future harm” (p. 123). For children in such environments, parental incarceration might increase their social capital and reduce social strain (Hissel et al., 2011; Jaffee, Moffitt, Caspi, & Taylor, 2003). In addition, after a parent is incarcerated, social support organizations may have contact with families for the first time. As mentioned above, families who experience parental incarceration often have to cope with various problems such as unemployment and both financial and housing problems (Ezinga & Hissel, 2010; Ezinga, Hissel, Slotboom, & Bijleveld, 2009; Murray, 2005). This might go unnoticed for some time, but parental incarceration could initiate social service intervention and support, which could improve children’s social and emotional development and reduce social strain. Although these perspectives offer a more positive view of the effects of parental incarceration, Hagan and Dinovitzer (1999) state that “it is more likely imprisonment is harmful to children even in dysfunctional families, because imprisonment will more often compound than mitigate pre-existing family problems” (p. 125). To summarize, there are several theoretical reasons why, on average, parental incarceration may have undesirable effects on children’s development and their own antisocial behavior. However, these effects are likely to be contingent on a complex 330 Incarceration

interplay of factors that are in place both before parental incarceration takes place, including genetic and social mechanisms of intergenerational transmission, and during parental incarceration, when quality of alternative care may be critical. For some children, the influence of an antisocial parent in the home prior to parental incarceration may even mean that parental incarceration represents a period of respite for the family, and decreased child problem behavior.

Current State of the Science Incarceration and Reoffending

Although incarceration is widely used in the United States, and although the literature on effects of incarceration on reoffending is growing, rigorous scientific knowledge is still lacking (for overviews see Gendreau, Coggin,  & Cullen, 1999; Nagin et  al., 2009; Villettaz, Killias,  & Zoder, 2006). The problem, once again, is that specifying causal effects of incarceration on offending is difficult if not impossible without a randomized experimental design. Such are extremely difficult to realize, however, because of practical, political, and ethical issues. To date, there have been only eight studies in which offenders were assigned “experimentally” to custodial versus non-custodial sentences (Barton & Butts, 1990; Bergman, 1976; Green  & Winik, 2010; Killias, Aebi,  & Ribeaud, 2000; Killias, Gilliéron, Villard,  & Poglia, 2010; Loeffler, 2013; Nagin & Snodgrass, 2013; Schneider, 1986; van der Werff, 1979). One of these was quasi-experimental, or a natural experiment, in the Netherlands (van der Werff, 1979, 1981). Three of these were natural experiments in the United States using imprisonment sentencing disparities among judges (Green  & Winik, 2010; Loeffler, 2013; Nagin  & Snodgrass, 2013). Schneider (1986) reviewed four randomized controlled trials where juveniles were assigned either to restitution or to traditional dispositions such as detention. One of the four trials took place in Boise, Idaho, and compared juveniles who were assigned randomly to one of two conditions:  (1)  being detained in a local detention facility, or (2) paying either a monetary restitution or completing community service. Differences in reoffending between the two groups were small and not significant. In the detention group 59% of 95 youths had one or more subsequent contacts in the court, compared to 53% of 86 youths in the restitution group.

Another experiment was carried out in Switzerland in the Canton of Vaud (Killias et  al., 2000, 2010). The Directors of Corrections were interested in testing effects of community service vs short prison sentences (up to 14  days). From July 1993 until the end of 1995, 123 offenders who volunteered for the study were assigned randomly to one of these two punishment options. Community service consisted of unpaid work such as working in nursing homes for the elderly, hospitals, or schools, and also working in forests and cleaning natural resource areas. After 2  years, ex-prisoners had a higher arrest rate and had developed more unfavorable attitudes toward their sentence and the criminal justice system (Killias et  al., 2000). In a longer-term follow-up after 11  years, reoffending rates did not differ across groups. Interestingly, however, those who were assigned to prison were better off on some outcomes after 11  years. They complied better with tax regulations and were comparable with those who served community service in terms of employment history and marital status (Killias et  al., 2010). The authors concluded that short prison sentences did not appear to be damaging in the long term. It is important to emphasize, however, that offenders were only incarcerated for up to 14 days. Thus, no conclusions can be drawn about effects of longer sentences. A natural experiment on effects of imprisonment occurred in the Netherlands when, upon celebration of the marriage of Princess Beatrix in 1966, Queen Juliana remitted sentences for a large group of offenders (van der Werff, 1979, 1981). This enabled comparison of convicted offenders who had their sentences remitted with a group of offenders who served their sentences normally. The latter group had either already served their sentence by the time the Royal Order was announced, or was sentenced after the Order (March 10, 1966). This led to a methodologically unique situation since the groups were comparable on background and other factors. Van der Werff (1979) compared recidivism rates for traffic, property, and aggressive offenders, according to whether or not their sentences had been remitted. There were no significant differences between offenders who did and did not serve their full term of imprisonment in the proportion reconvicted within 6 years, in the number of convictions, or in the speed with which they reoffended. Three studies have used a feature of the criminal justice system, where cases are assigned randomly to judges, to estimate incarceration effects on reoffending. The studies exploit the fact that judges

vary substantively in their use of incarceration as punishment, and in lengths of prison and probation sentences they hand out. Because offense cases are assigned randomly to judges in certain jurisdictions, examining outcomes for offenders assigned to different judges (and hence with different chances of imprisonment) can be used to study effects of incarceration. Green and Winik (2010), Loeffler (2013), and Nagin and Snodgrass (2013) all made use of this situation and investigated whether rearrest rates differed for offenders assigned to more or less punitive judges. All three studies concluded that there was little evidence that sentencing disparities were associated with rearrest rates. Taken together, results from this collection of studies (Barton  & Butts, 1990; Bergman, 1976; Green  & Winik, 2010; Killias, Aebi,  & Ribeaud, 2000; Killias, Gilliéron, Villard,  & Poglia, 2010; Loeffler, 2013; Nagin  & Snodgrass, 2013; Schneider, 1986; van der Werff, 1979) suggest that incarceration does not reduce, but may instead increase, recidivism among ex-prisoners, although the evidence is very weak. Nagin et al. (2009) also reviewed matching studies, regression based studies, and non-experimental studies of prison effects. Most of these point to criminogenic effects of incarceration, but some suggest specific deterrence effects under particular conditions. As discussed above, deterrence effects may be weak because costs of offending are generally not obvious to offenders—i.e., offenders do not know what sentence they would face for certain crimes. However, Helland and Tabarrok (2007) demonstrate that imminent and extremely tangible threats of life imprisonment reduce reoffending. Helland and Tabarrok (2007) investigated the effects of the California Three Strikes and You’re Out Law. They compared two groups of offenders: (1) individuals convicted of two “strikable” offenses, and (2) individuals who had been tried for a second “strikable” offense but were convicted of a non-strike-eligible offense. The imminent threat for the first group was the prospect of imprisonment for life if they committed just one more offense. The second group did not face this same threat because they had been convicted of only one “strikable” offense. The second group had a 20% higher arrest rate compared to the first group, suggesting that a specific deterrence effect was at work. In the second study, Drago, Galbiati, and Vertova (2009) investigated the effect of Italy’s Collective Clemency Bill, which released more than 20,000 prisoners in 2006. Prisoners were released on the condition that individuals convicted of Besemer, Murray

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a different crime within 5  years of release had to serve the residual of the original sentence on top of the new sentence. Prisoners with higher residual sentences—and thus longer potential incarceration terms—had a lower subsequent recidivism rate. In both this investigation and that of Helland and Tabarrok (2007), individuals knew exactly what was going to happen if they committed another crime. Apparently, this clear information after incarceration did reduce recidivism, lending support for the idea of specific deterrence in these circumstances. Summarizing the empirical evidence on the deterrent effects of incarceration on reoffending, Cullen and Jonson (2014) state that, “the empirical literature—with a few exceptions here and there—is an economist’s nightmare. The “rational human” does not seem to be very responsive to criminal justice efforts at specific deterrence” (p. 72). Exceptions seem to be when the incentive to not reoffend is extremely tangible and clear to the individual. On balance, limited empirical evidence points toward general criminogenic effects of incarceration, supporting labeling theory, although high-quality research on specific labeling mechanisms is lacking. One illustration of labeling effects can be seen in a study by Sampson and Laub (1993), following the original Glueck study of 1,000 men. There was no direct effect of time incarcerated on later self-reported crime, but longer incarceration periods predicted more job instability, which in turn predicted more criminal behavior in adulthood. Lanctôt, Cernkovich, and Giordano (2007) found that incarceration did not predict adult crime when taking into account juvenile delinquency, but did predict social difficulties such as depression and drug-related problems. Despite decreased spending on rehabilitation programs, criminologists continue to evaluate such efforts in hopes of finding humane and effective solutions to reducing reoffending. We cannot review rehabilitation research extensively in this essay, but will mention key conclusions. Cullen and Jonson (2014) review several meta-analyses of intervention research (Andrews & Bonta, 2010; Lipsey, 1992, 2009; Lipsey  & Cullen, 2007; Lipsey  & Wilson, 1998; Lösel, 1995; McGuire, 2002). Across all studies (including those of ineffective programs), a 10-percentage point difference in recidivism is associated with rehabilitation programs, relating to a positive yet small effect size (r) of around .10 to .12. In addition, significant heterogeneity of effects was observed: some interventions are ineffective, whereas others reduce offending strongly and 332 Incarceration

consistently (Andrews & Bonta, 2010; Lipsey, 1992, 2009; Lipsey  & Cullen, 2007; Lipsey  & Wilson, 1998; Lösel, 1995; McGuire, 2002). Rehabilitation appears to be most effective when programs follow three principles of effective correctional treatment (Andrews, 1995; Andrews & Bonta, 2010; Andrews et al., 1990; Gendreau, 1996; Gendreau, Little, & Goggin, 1996; Gendreau, Smith, & French, 2006; Smith, Gendreau, & Swartz, 2009). First, risk factors for crime can be static or dynamic. Static factors such as race and sex cannot be changed, but dynamic factors such as a drug addiction, antisocial attitudes, and association with criminal others can. The need principle states that interventions should focus on addressing these dynamic criminogenic needs. Second, treatment is effective only when it focuses on and is responsive to these criminogenic needs in a behavioral way. This is called the responsivity principle. The most effective treatments are cognitive-behavioral interventions focused on changing antisocial attitudes, cognitions, personality orientations related to recidivism (Cullen  & Jonson, 2012). The third principle of effective correctional treatment is the risk principle, which states that interventions such as prison sentences should be given only to high-risk offenders, because they have many criminogenic needs that can be easily targeted. Low-risk offenders, in contrast, are actually more likely to stop offending if they do not become involved in the justice system through prison sentences. In short, the position is that prison does not always involve rehabilitation programs, and not every treatment is effective in reducing reoffending, but programs following these three principles are more likely to reduce reoffending (Andrews et al., 1990; Andrews & Bonta, 2010; Gendreau et al., 2006).

Parental Incarceration

The state of research on effects of parental incarceration on children is similar to that on incarceration effects on reoffending of individuals: high-quality research using experimental methods is lacking. Murray, Farrington, and Sekol (2012) conducted a systematic review and meta-analysis, and concluded that the most rigorous studies demonstrate that parental incarceration is related to increased risk in offspring antisocial behavior. In focusing on studies that either accounted for levels of parental criminality before incarceration (such as the number of convictions or serious convictions of the parent) or deviant behavior of the children before parental incarceration, they found

that children with incarcerated parents had a 10% increased risk for antisocial behavior compared with their peers. Although parental incarceration clearly is a risk factor for delinquency among offspring, it is unclear whether the prison sentence actually causes increases in offspring delinquency, given the difficulty in accounting for all prior risk factors (Murray, Bijleveld et al., 2014). We therefore cannot say what the intervening mechanisms are in explaining why children of incarcerated parents have an increased risk of offending. A major push forward in elucidating these effects can be achieved using repeated observations of children and their family and peer environments both before and after parental incarceration, as recently done in the Pittsburgh Youth Study (Murray, Loeber, & Pardini, 2012). Here, boys who experienced parental incarceration were matched to other boys on a range of background variables, including parental criminality, using propensity scores. Then, changes in boys’ behaviors were compared between the parental incarceration group and the propensity-matched control group. Boys whose parents were incarcerated showed significantly increased levels of youth theft compared with matched controls whose parents were not incarcerated. Additionally, Murray et al. (2012) investigated mediating mechanisms linking parental incarceration and increases in youth theft. Parenting and peer delinquency processes after the incarceration explained about one half of the effects of parental incarceration on this outcome. Stigmatization processes, which might explain much of the remaining variance, were not measured. In the absence of randomized experiments, such investigations of how behaviors change through time, combined with matched controls, are vital to increase knowledge on mediating mechanisms explaining links between parental incarceration and offspring offending (Murray, 2010; Murray, Farrington,  & Eisner, 2009).

Developmental Considerations

We consider two important developmental issues regarding effects of incarceration on offending. First, young people’s brains and levels of morality are still developing until well into their 20s (see Séguin and Parent, this volume). Second, crime and delinquency decrease naturally with age for the vast majority of individuals, so one should consider how useful it is to incarcerate offenders for lengthy periods. This should be taken into account when processing young people in the criminal justice

system given potential damage to these developing individuals. Although physical maturity usually occurs in the earliest years of adolescence, and many aspects of intellectual reasoning around age 18, executive functions, including impulse control, long-term planning, and verbal memory, are not fully mature until around age 25 (Prior et al., 2011; Steinberg, 2008). Modern neuroimaging studies demonstrate that the brain undergoes profound development during adolescence, especially in frontal and prefrontal regions that subserve executive control over behavior (e.g., Blakemore & Robbins, 2012; Casey, Jones, & Hare, 2008; Gogtay et al., 2004; Steinberg, 2008). The prefrontal cortex is involved in behaviors such as planning, inhibiting inappropriate behavior, and perspective taking. Deficits in these skills correlate with offending behavior. Moreover, the limbic system, related to reward processing for risk taking, is thought to be hyper-sensitive in adolescence (see Beauchaine and Séguin, this volume). Adolescents experience elevated reward responsiveness relative to adults, and “adolescence is strongly associated with an increase in risk-taking, sensation-seeking, and reckless behavior” (Dahl, 2004; p. 3; see also Ernst & Fudge, 2009; Pfeifer & Allen, 2012; Steinberg, 2008). This combination of an underdeveloped regulatory system and excess reward seeking behavior has been called the “greatest mismatch in development” (Burnett, Sebastian, Cohen Kadosh,  & Blakemore, 2011; p.  1660; see also Casey et al., 2008). In their edited book From Juvenile Delinquency to Adult Crime, Loeber and Farrington (2012) discuss the period between mid-adolescence and early adulthood (roughly ages 15 to 29 years). They conclude that “many juvenile delinquents tend to stop offending in late adolescence and early adulthood and that this increase is accompanied by a decrease in their impulsive behavior and an increase in their self-control” (p. 3). Compared with adults, juvenile offenders “are immature, have greater impulsivity, lower self-control, poorer cognitive abilities, less appreciation (for) the consequences of their actions, and a generally poorer ability to make decisions” (Loeber & Farrington, 2012; p. 355). High criminal activity in the late teens and early 20s and the subsequent decrease in crime with age are clearly visible in so-called age-crime curves. An example of such a curve is shown in Figure 19.1, which presents data from the Cambridge Study in Delinquent Development, a longitudinal study of the development of offending and antisocial behavior that Besemer, Murray

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Figure  19.1  Example of an age-crime curve, data from the Cambridge Study in Delinquent Development original men and male siblings.

started with 411 boys born around 1953 (for more information, see Farrington et  al., 2006; West, 1969; West  & Farrington, 1973, 1977). Criminal convictions for these boys and their family members were searched in the Criminal Record Office in London. The graph shows the percentage of these boys and their male siblings who were convicted of an offense at each age. Research suggests that this curve averages multiple trajectories. Moffitt (1993, 2003, 2006) originally suggested that the overall age-crime curve masks two groups: so-called life-course persistent and adolescence-limited offenders. The smaller group of life-course persistent offenders have an early onset of antisocial behavior because of an interaction between neurological difficulties and adverse environments. They persist in this problem and criminal behavior throughout their lives because of the cumulative effect of these neuropsychological and biological difficulties and criminogenic environments (Monahan, Steinberg, Cauffman, & Mulvey, 2013). According to Moffitt’s original theory, the peak in crime in the late teens and early 20s is accounted for primarily by adolescent-limited offenders who start offending during adolescence and desist when they reach adulthood. Moffitt (1993) explains how teenagers are “trapped in a maturity gap” (p. 687); physically they are mature, but psychologically and socially they are viewed as immature adolescents. By showing delinquent behavior and mimicking life-course persistent offenders they aim to bridge this maturity gap. With increasing age and social responsibilities (e.g., jobs, relationships), the motivation to engage in delinquent behavior diminishes. 334 Incarceration

Recently, Moffitt acknowledged that there might be additional groups: boys who were highly aggressive as children, who show low level but chronic delinquency and personality disorders during adolescence and adulthood; and an adult-onset group (Moffitt, 2006; van Koppen, 2013; van Koppen & De Poot, 2013; Zara & Farrington, 2010). Empirical research on Moffitt’s typology is mixed:  different studies find different offender groups (see e.g. Skardhamar, 2009). However, even if there is no clear (dual) taxonomy or distinctively different types of offenders, the idea that a small group of offenders persists in crime and is responsible for a majority of offending, has been demonstrated in many studies (Blumstein, Cohen, Roth, & Visher, 1986; Farrington et al., 2006; Wolfgang, Figlio, & Sellin, 1987). In most jurisdictions, however, legal adulthood begins at age 18. Thus, people at this age (and in some countries from even younger ages) can be convicted and imprisoned with long sentences, including life. It is debatable whether it is useful to hold young people responsible for their actions when it is known that brain systems implicated in vulnerability to criminal behavior have not matured completely. Scott and Steinberg (2003) argue that young people’s immature judgment—regarding perceptions of risk, organization and self-management, and appreciation of future consequences—distinguishes them from adults, and is a basis for reduced criminal responsibility among youth (Steinberg, 2009). Only relatively recently, in 2005, the U.S. Supreme Court concluded that juveniles’ reduced culpability prohibits the death penalty (Roper v. Simmons, 2005). Until 2012, however, 42 states still permitted life

without parole sentences for any offender regardless of age and 27 states required mandatory sentences for crimes such as murder and rape (Howell, Feld, & Mears, 2012). Practically, this meant that children as young as age 12 years could be put in prison for the rest of their lives (Human Rights Watch, 2005). In 2012 the Supreme Court ruled that it was unconstitutional to mandate life without parole for offenders under age 18 years (Miller vs. Alabama, 2012). Interestingly, in its deliberations, the Supreme Court drew on neuroscientific research on the developing brain (Steinberg, 2013). It is a positive development that the legal system is being influenced by scientific evidence on brain and behavior development. Logical next steps are to consider reduced culpability for offenders into their early 20s, and to raise the minimum age of referral to adult court to age 21 or 24 years (Loeber & Farrington, 2012). Another reason to question the use of prison sentences for juveniles is that environments have strong effects on brain development (see Beauchaine and Hinshaw, this volume; Blakemore, 2008, 2010; Blakemore  & Robbins, 2012; Dahl, 2004). The brain is extremely adaptable and malleable during adolescence. Do we want to lock young people away and let prison be the environment in which they develop? Dmitrieva, Monahan, Cauffman, and Steinberg (2012) demonstrated that incarceration (but not residential treatment) was associated with a short-term decline in responsibility and temperance—the ability to control impulsive and aggressive behavior. Prison does not appear to be the best environment to encourage natural desistence out of crime. As Loeber and Farrington (2012) argue, it would be better to address those developmental needs and offer programs to develop “capabilities associated with successful transitions to adulthood, such as life skills, education and vocational and educational training” (p. 354). This leads back to the earlier mentioned rehabilitation goals of incarceration. If young people are to be incarcerated, rehabilitation efforts are vital from a developmental perspective. In “criminal career” research, the natural downward trend in participation in crime from the late teenage years is well established, at least for a majority of youth (see above). Ninety percent of all juvenile offenders desist from crime by their mid-20s (Farrington, 1986). Even life-course persistent offenders show downward trends in criminal behavior from early adulthood onward (Laub & Sampson, 2003; Sampson  & Laub, 2005). Moreover, the

so-called estimated residual criminal career length (the length of time between the current offence and the estimated last offence of an individual) is generally shorter than the prison sentence offenders receive (Kazemian, 2006; Piquero et al., 2001). Thus, many prisoners, if they had not been incarcerated, might have stopped offending long before their prison sentence ended. Because offenders generally desist with age, long prison sentences may not have lasting incapacitation effects.

Controversies

The topic of incarceration effects is highly controversial. Empirical evidence is not conclusive but seems to point to null or criminogenic effects of incarceration on reoffending, with detrimental effects for the next generation. Why has imprisonment not been significantly reduced in light of such findings? As we describe in our introduction, incarcerating and sentencing offenders has several goals in addition to reducing reoffending. Prison sentences might have a general deterrent effect. Perhaps incarcerated individuals might not stop offending, but people who have not previously offended will hesitate to offend because of the sentence they might receive. Research generally shows that the certainty of receiving a punishment is more important in deterring people to commit crime than length or severity of a sentence (Nagin, 2013). We also mentioned incapacitation as a goal of prison sentences: while people are incarcerated they are less likely to commit crime. However, long-lasting incapacitation effects do not seem likely given the natural desistence out of crime with age. Another goal of sentencing offenders is retribution. Offenders have to be seen to be punished for what they have done wrong. Even with these various motivations for imprisonment, it should be possible and reasonable to decrease use of incarceration, particularly in countries with extremely high incarceration rates such as the United States. For example, retribution requires proportionate sentencing. At the moment, punishment is far from proportionate in the United States. Many disproportionate sentences evolved during the war on drugs. An interesting example is the difference in sentencing between powder and crack cocaine. Although the same drug pharmacologically, sentencing for crack cocaine is 18 times more severe than sentencing for powder cocaine—and until 2010 was 100 times more severe (Hatsukami  & Fischman, 1996; Reinarman  & Levine, 1997; Spade, 1996, Fair Sentencing Act Besemer, Murray

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of 2010). Possession of 28 grams of crack cocaine yields a 5-year mandatory minimum sentence for a first offense; it takes 500 grams of powdered cocaine to prompt the same sentence. It is important to realize that African Americans more often use crack cocaine, whereas Whites more often use powdered cocaine. This contributes to considerable disproportionality in sentencing. Indeed, racial and ethnic minorities are overrepresented in all stages of the juvenile and adult criminal justice system—from arrest to incarceration (Feld, 1999; Hawkins  & Kempf-Leonard, 2005; Hawkins, Laub, Lauritsen, & Cothern, 2000; Howell, 2003; Liebman, Fagan,  & West, 2000; Snyder  & Sickmund, 2006; Tonry, 1994, 2009). Blacks have the highest imprisonment rates in the country, seven and three times higher than Whites and Latinos (Guerino, Harrison,  & Sabol, 2012), and they are being treated differently than Whites (Case, 2008). For example, even though rates of drug use by Black youth are no higher than those of White youth (Centers for Disease Control and Prevention, 2006), Black youth are arrested and processed disproportionately in the system for drug offenses. Michele Alexander (2010) calls this disproportionality the “new Jim Crow”. It is outside of the scope of this essay to fully discuss the historical development of this phenomenon, but it is clear that the U. S. has a troubled history with incarceration of different ethnic groups. As Hemmens and Stohr (2013) describe “American prisons have been shaped from the beginning by race as they were created in the context of a racist society” (p. 113). This has an enormous effect on African Americans, perpetuating intergenerational inequality (Wakefield & Wildeman, 2014), destroying and impoverishing communities (Clear, 2007), and in almost every state, a felony conviction leads to loss of civil rights and disenfranchisement (Uggen, Shannon, & Manza, 2012). Even if prison only had a goal of proportional retribution, current U.S.  mass incarceration policies do not fit this goal. People are being incarcerated for extremely long periods, especially for drug offenses that are considered minor in other jurisdictions. The research discussed in this essay highlights the need to show the general public and politicians that incarceration can have criminogenic effects and poses additional risk for children of incarcerated parents.

Research Agenda and Future Directions

As reviewed in this essay, the quality of research on incarceration and development of delinquency 336 Incarceration

is low, and subsequent efforts face major hurdles. We recommend more experimental and longitudinal studies of within-person change to study effects of incarceration on development of criminal behavior, including those that use “natural experiments”, or sophisticated statistical models such as propensity score matching. We need more randomized controlled trials, since research conducted to date is inconclusive. We have discussed the practical, political, and ethical constraints of such studies, but these barriers may be less absolute as they first seem (Killias & Villetaz, 2008). For example, the experimental study by Killias et al. (2000, 2010) used a naturally occurring circumstance in which a Swiss legal provision introduced new forms of punishment for a limited period of time. It shows that the difficulties relating to experiments can be overcome and opportunities to conduct experiments should be taken whenever possible. There are still research budgets and it would be best to spend such money on experimental and longitudinal studies. In addition, given potential criminogenic effects of incarceration in a number of cases, we suggest focusing resources on prevention. Many scholars have shown the merits of prevention programs. Lösel (2012), for example, suggests that “Cognitive behavioral treatment (CBT), structured therapeutic communities, multisystemic approaches, and basic education showed mostly positive outcomes … . Purely punitive or deterrent measures showed zero or even negative effects” (p. 197). It appears as if prison and criminal justice policies have too often eschewed viable theory or valid empirical data. Welsh and Farrington (2012) argue that it is vital to overcome “the short time horizons of politicians” (p. 130), and to balance crime prevention and crime control. Farrington (2013) discusses how cost-benefit analyses can greatly help to encourage policy makers and practitioners to choose effective programs and develop policies based on scientific evidence. Several reviews demonstrate monetary benefits of developmental prevention programs— “interventions designed to prevent the development of criminal potential in individuals, especially those targeting risk and protective factors discovered in longitudinal studies of human development”— outweigh their monetary costs (Farrington, 2013, p.  295; see also Dossetor, 2011; McIntosh  & Li, 2012; Nagin, 2001; Roman, Dunworth,  & Marsh, 2010; Welsh  & Farrington, 2000; Welsh, Farrington, & Sherman, 2001). Monetary benefits can derive from wide-ranging effects, including

those related to improved health (e.g., decreased use of public health care), education (e.g., high school completion, enrollment in college or university), employment (e.g., increased wages, tax revenue for governments, decreased use of welfare services), and of course crime reduction. In Washington state, for example, Aos and colleagues investigated the monetary costs and benefits of crime prevention programs (Drake, Aos, & Miller, 2009; Lee et al., 2012). They showed that the benefit-to-cost ratio was 4.36 for multisystemic therapy and 10.42 for functional family therapy. Following from this research, Washington State decided to invest in several evidence-based crime prevention programs and to reduce the increase in their prison building program. This is an excellent example of reducing crime and incarceration and saving money based on valuable scientific research. Over the past decades there has been a “vigorous campaign of incarceration, pursued in the name of punishing and incapacitating wrongdoers, with a corresponding lack of concern over the environments from which they had come and the particular conditions of confinements to which they were being sent” (Haney, 2005, p.  73) From ’human, moral, and economic perspectives, it seems more defensible to pay attention to these environments and spend money on preventing criminal behavior rather than excluding and incarcerating ever more offenders.

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Cognitive and Emotional Vulnerabilities to Externalizing Spectrum Disorders

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Externalizing Behaviors and Attribution Biases

Anne-Marie R. Iselin, Allison A. McVey, and Colleen M. Ehatt

Abstract In this chapter the authors first review the historical context of attribution biases, particularly within the social-information processing framework. They then discuss empirical evidence on relations between attribution biases and externalizing disorders including aggression. The kinds of attribution biases as well as the strength of the influence of such biases vary across disorders, with the strongest relations between hostile attribution biases and aggression. Current research indicates that hostile attribution biases are causal mechanisms in externalizing disorders, especially aggression. They discuss evidence on how hostile attribution biases may develop from early life exposure to harsh environments. Future research on hostile attribution biases will likely investigate underlying biological processes, the role of emotions, and the influence of culture. Key Words:  cognitive biases, social-information processing, attribution biases, hostile attribution bias, aggression, externalizing disorders

Introduction

Some of the most common social interactions provide great insight into the types of information that capture people’s attention, and the multitude of ways this information can be interpreted and acted upon. Consider a driver who races past you and cuts you off abruptly on the highway. Some people may interpret this behavior as an indication that the person is in a hurry to get to something important. Others may interpret the behavior as an indication that the driver did it purposefully and is a jerk. The first person is likely to shrug off the driver’s behavior, hardly bothered by the situation, whereas the second may tailgate aggressively, fueling antagonism. Models of attribution biases provide theoretical frameworks for investigating and understanding diverse human responses to social interactions such as these. Empirical knowledge gained from these models has improved our understanding of correlates and treatment of externalizing behaviors. We begin our discussion with a review of

the historical context of attribution biases from the social-information processing (SIP) framework.

Historical Context Social-Information Processing

Early information-processing theories (Bruner, 1957) proposed that people trim and simplify vast arrays of perceptual information by paying select attention to particular aspects of stimulus arrays, and subsequently assigning information they gather into a category, or mental representation of abstractly related information. For example, in your kitchen you may perceive a cluster of smooth, curved, oblong, yellow objects, which you categorize as bananas. The basic process of categorization also applies to more complex social interactions, where we classify people into categories with varying degrees of abstraction, also called schemas (Mischel, 1979). We may classify someone more generally as “kind” or “hostile,” or more specifically as “an uptight control freak,” or “an intelligent 347

student interested in understanding conduct problems.” How we process perceptual information and how we classify people varies as a function of our own characteristics as well as characteristics of our environments (e.g., Bargh, Lombardi,  & Higgins, 1988; Mischel, 1979). Another important aspect of how people process and interpret information is how accessible a category is (e.g., Bruner, 1957; Shiffrin & Schneider, 1977; Srull & Wyer, 1979). Recurrent exposure to and activation of a category makes it more accessible, consequently increasing the likelihood that a person will use it to interpret information, particularly ambiguous information (e.g., Bargh  & Pietromonaco, 1982; Bargh et  al., 1988). Consider a child whose parent’s primary interpersonal style is infused with anger, hostility, and aggression, so the child is exposed repeatedly to this category of adults. When the child attempts to make sense of a confusing interaction with another adult, a “hostile” interpretation is most accessible to him/her. Bargh and colleagues refer to these as “chronically accessible constructs” (e.g., Bargh et al., 1988). These early cognitive and social-cognitive information processing principles have strong parallels within the SIP framework—the most prominent model of attribution biases related to child development (Crick & Dodge, 1994). Details of the SIP model are crucial to understanding the concept of attribution biases, and influences they have on people’s social adjustment. According to the SIP model (Crick & Dodge, 1994), human approaches to social interactions involve a relatively stable underlying catalog of social information, as well as dynamic mental processes that occur in the moment. Throughout life, humans interpret and store a wide range of information from social situations they encounter. Akin to previously mentioned cognitive information-processing principles, as this information accumulates it becomes unwieldy, so people distill its breadth into heuristics that help determine how they interact with others. Such “social rubrics” are often referred to as schemas or scripts, and powerfully influence mental processes and behavioral decisions of individuals who are engaged in real-time social exchanges. The SIP model comprises six processes that occur online, in the immediacy of a social encounter (Crick & Dodge, 1994). In the first process, we attend selectively to, and mentally translate, cues that occur in the interaction. Here, one can attend to either internal (e.g., emotional states) or external (e.g., the other person’s facial expressions) cues. In the second process, we interpret these cues using 348 At tribution Biases

a variety of possible strategies, which may include generating mental images, analyzing causes, ascribing intentions, assessing goal attainment, evaluating predictions and expectations, and deducing meaning for oneself and others. In the third process, we clarify what goals we seek to obtain from the social exchange. We may confirm goals that were desired at the outset of the exchange, which are likely to be linked to our long-term catalog of social rules and knowledge (e.g., ingrained beliefs that one should always “save face” in front of peers). We may also alter our goals or generate new goals based on unique characteristics of the social interaction. Goal clarification is marked by emotional states (and management of these states) that guide us to produce specific outcomes (e.g., feelings of guilt may promote an approach goal to make amends with that person). In the fourth process, we generate an array of possible responses to the social exchange. When the social situation is familiar, we may generate responses directly from memory. When the social situation is less familiar, we are likely to generate new responses contingent upon cues from the immediate environment. In the fifth process, we appraise possible responses based on what we expect for outcomes, how well we believe we can carry out each response, and our ideas about how appropriate each response is. In the sixth process, we select a response and act upon it. Thus, during social exchanges people engage many SIP processes. Interactions among these processes are cyclical rather than linear, and many processes can occur in parallel. For instance, we may be encoding information while simultaneously discerning intentions of others and evaluating what has occurred in past experiences. Also, recall that we bring a relatively stable underlying catalog of information to each social encounter. This long-term store of information reciprocally influences the nature of the online SIP processes just reviewed. Faulty or maladaptive SIP across any or all of these six processes is associated with social maladjustment (e.g., Dodge, Pettit, McClaskey, & Brown, 1986; Dodge & Tomlin, 1987; Gouze, 1987; Pettit, Dodge, & Brown, 1988; Quiggle, Garber, Panak, & Dodge, 1992; Slaby & Guerra, 1988). Furthermore, the more maladaptive SIP processes one exhibits, the more externalizing behaviors are observed (Lansford et al., 2006). In this chapter, we focus on attribution biases—which are evident in the second process of the SIP framework. This process requires us to interpret social cues; one method of doing so involves ascribing intentions within the social

exchange (e.g., identifying what the cause or reason was behind someone’s behaviors). Attribution biases occur when we make inaccurate attributions of intent, particularly in ambiguous social situations. Ambiguity in the social exchange is an important characteristic of the environment that influences how people process the array of perceptual information impinging upon them. It is in the presence of ambiguity that people are most likely to default to heuristics or categories that are readily accessible (e.g., Bruner, 1957), yet potentially erroneous as in the case of attribution biases. These biases may be self-blaming as in depressive attribution biases, or other-blaming as in hostile attribution bias (e.g., the example at the start of the chapter in which a driver is interpreted as a jerk who is intentionally cutting you off). Most research on relations between attribution biases and externalizing disorders has focused on hostile attribution biases. Those with hostile attribution biases display them consistently over time, with correlations across 4  years ranging from 0.35 to 0.38 (Dodge, Pettit, Bates,  & Valente, 1995).

Emotion and Social-Information Processing

Before discussing the literature supporting relations between externalizing behaviors and attribution biases, it is important to discuss the role of emotion within the SIP framework. Many externalizing disorders are associated with failures in emotion regulation (e.g., Cole, Zahn-Waxler, & Smith, 1994b; Eisenberg et al., 1996; Mullin & Hinshaw, 2007), especially regulation of anger (Blake  & Hamrin, 2007; Gilliom et  al., 2002; Kerr  & Schneider, 2008; Shortt, Stoolmiller, Smith-Shine, Eddy, Sheeber, 2010). Crick and Dodge (1994) note that emotions are a vital aspect of SIP because they can influence accessibility of responses as well as enhance and revitalize one’s goals (e.g., guilty feelings promote an approach goal to make amends). They acknowledge, however, that the influence of emotions is not particularly well integrated in their model. Accordingly, investigators have sought to clarify relations between the SIP model and emotion regulation/dysregulation. Lemerise and Arsenio (2000) propose a theoretical model that outlines the role of emotions within the SIP framework. They suggest that emotions are important in formation and maintenance of attribution biases, in formation of goals, and in recognition of intentions and goals of others. Moreover, deficits in SIP are exacerbated under heightened emotional states (Dodge  & Somberg, 1987), and

emotions are related to the kinds of attributions of intent we make (Nelson  & Coyne, 2009). For example, children attribute more hostility to those they consider enemies (those who are associated with negative feelings in their memory) than they do to those they consider friends (Peets, Hodges, Kikas,  & Salmivalli, 2007). In short, emotions clearly play a role in the kinds of attributions made in social exchanges.

Links to Traditional Externalizing Disorders

Attributional styles are associated differentially with traditionally categorized externalizing disorders and problem behaviors. They are crucial not only in understanding the development of these disorders, but also in knowing whether the disorder might persist or desist over time and in response to interventions. In the sections that follow, we highlight some of the research evidence on the role of attributional styles in externalizing disorders across the lifespan. Consistent with the normative developmental progression of externalizing disorders (e.g., Beauchaine, Hinshaw,  & Pang, 2010; Beauchaine & McNulty, 2013), we present first the evidence on attention-deficit/hyperactivity disorder (ADHD), followed by oppositional defiant disorder (ODD), conduct disorder (CD), and finally externalizing disorders in adulthood, including antisocial personality disorder (APSD) and psychopathy.

Attention-Deficit/Hyperactivity Disorder

In the absence of co-occurring conduct problems, ADHD is likely not related to hostile attribution bias as defined within the SIP framework. Sibley, Evans, and Serpell (2010) compared 27 adolescents with ADHD only and 18 controls with no ADHD diagnosis, and did not find differences between the groups on mean levels of hostile attribution bias. Similar null results were found in a comparison of girls with ADHD (n = 140) vs. controls (n  =  88) (Mikami, Lee, Hinshaw,  & Mullin, 2008). As we describe immediately below, however, a growing body of research suggests that other attribution biases may be related more specifically to ADHD. It is possible that these non-SIP attribution biases are developmentally less severe deficits in information processing than are hostile attribution biases that are more common among aggressive individuals. This proposition parallels the normative developmental progression of externalizing disorders described by Beauchaine and McNulty (2013), but remains untested. Iselin, McVey, Ehat t

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One of the more commonly examined attribution biases among individuals with ADHD is the positive illusory bias (PIB)—a tendency to rate one’s competence as higher than actual competence as evaluated by external criteria (e.g., other informants, objective tests). For example, although children with ADHD are rated by impartial observers as less effective in social interactions than controls, they rate themselves as more successful (Hoza, Waschbusch, Pelham, Molina,  & Milich, 2000). Several investigations have shown PIB among those with ADHD, with few exceptions (see Owens, Goldfine, Evangelista, Hoza,  & Kaiser, 2007, for a summary). Importantly, even though observers rate those with ADHD lower than those with ADHD rate themselves, self-ratings still fall within a qualitatively negative range (Swanson, Owens, & Hinshaw, 2012). The primary measure of PIB is typically a discrepancy score—a standardized difference score between the individual’s rating of him/herself and external ratings of that individual. Some have critiqued the use of discrepancy scores in PIB research because such scores may not fully explicate the nature of these relations. After disaggregating discrepancy scores into their component parts, scores on objective measures and informants’ assessment of competence are better predictors of impairment 5  years later than are self-ratings (Swanson et al., 2012). The notion of a PIB among individuals with ADHD is intriguing but should be interpreted with caution. Another attribution bias that may be associated with ADHD is the self-serving bias. This occurs when individuals credit their successes to internal qualities or personal achievement, but blame shortcomings and failures on situational demands or factors beyond their control. For example, a student might credit earning a good grade on a test to his own hard work and studying, but later blame a poor grade on a quiz because it had trick questions. There is some evidence that children with ADHD demonstrate self-serving biases (Carlson, Pelham, Milich,  & Hoza, 1993; Pelham et  al., 2002), although, many children in these samples had comorbid externalizing disorders, making it difficult to discern specificity of this bias to ADHD. It is possible that positive illusory and self-serving biases associated with ADHD are developmental precursors to later hostile attribution biases. An important aspect of self-serving biases is the tendency to blame external factors on a negative outcome, which is an equally important aspect of hostile attribution biases. However, with self-serving 350 At tribution Biases

biases the tendency to place blame externally is not yet paired with any negative intent on the other person’s part. For some people, their self-serving biases may be paired repeatedly with more hostile experiences and hostile perceptions of external factors, possibly leading to development of hostile attribution biases. This assertion is consistent with theories on the developmental progression from ADHD to ODD, CD, and ASPD (e.g., Beauchaine et al., 2010; Beauchaine  & McNulty, 2013). About half of youth with ADHD escalate to more severe externalizing disorders, in part through negative and coercive interactions with others (see Snyder, this volume). Repeated conflict and hostile interactions may explain how self-serving biases are eventually paired with perceptions that other people have negative intentions, ultimately giving rise to hostile attribution biases. However, the developmental progression from self-serving biases to hostile attribution biases remains to be tested empirically. There is also evidence that ADHD is associated with hostile attribution biases when it is comorbid with conduct problems (e.g., Andrade et al., 2012; Milich  & Dodge, 1984) and peer rejection (e.g., Moore, Hughes, & Robinson, 1992). For example, children with ADHD and CD are more likely to report hostile attributions to open-ended questions when compared with children with ADHD alone (Milich  & Dodge, 1984). In contrast, Matthys, Cuperus, and Van Engeland (1999) found no differences in mean levels of hostile attribution bias to forced-choice questions when comparing boys with (1) CD or ODD, (2) ADHD only, (3) ADHD and ODD or CD (4) normative controls, and (5) psychiatric controls. Such discrepant findings may emerge because of different response formats used to probe attribution biases. Asking for open-ended explanations for others’ behavior may more readily reveal differences in attribution biases because individuals with ADHD and comorbid aggression are more likely to self-generate hostile attributions given that such schemas are more easily accessible to them (Milich & Dodge, 1984). When offered a list of possible explanations for other’s behavior, accessibility to hostile schemas is less influential, so differences in attribution biases may disappear when comparing individuals with ADHD only to those with comorbid aggression (Matthys et al., 1999).

Oppositional Defiant Disorder

Hostile attribution biases pertain directly to ODD, since they are referenced explicitly in DSM 5 diagnostic criteria for the disorder (American

Psychological Association, 2013). For example, “often blames others” and “spiteful and vindictive” suggest hostile attributions and perceptions. Coy, Speltz, DeKlyen, and Jones (2001) evaluated social-cognitive processes of clinic-referred, preschool-aged boys with ODD (vs boys without ODD) over a 2-year period. Among clinic-referred boys, hostile attributions predicted diagnostic status 1 year later. Boys who reported having hostile attribution biases in addition to ODD were more likely to continue to meet criteria for ODD and other disruptive behavior diagnoses in the following year. In contrast, boys with ODD who did not demonstrate hostile attribution biases were less likely to meet criteria for ODD, or were diagnosed with only ODD and no other disruptive behavior disorder. These findings suggest that hostile attribution biases play a role in persistent behavior problems and possibly in the development of additional externalizing disorders over time.

Conduct Disorder

Children with CD hold more hostile attribution biases than controls. For instance, Dodge, Price, Bachorowski, and Newman (1990) examined relations between hostile attribution biases and CD among incarcerated juvenile offenders. Prison-staff members’ ratings of conduct problems were correlated positively with hostile attribution biases. Furthermore, based on clinical interviews, youth diagnosed with DSM-III (American Psychological Association, 1980) undersocialized aggressive CD exhibited more hostile attribution biases than those diagnosed with socialized CD or no CD at all. Based on a comprehensive review of the CD literature, Frick and colleagues (Frick, Ray, Thornton, & Kahn, 2014) propose that children with CD and normal levels of callous-unemotional traits (CU) may be more prone to hostile attribution biases as compared with those with CD and elevated levels of CU traits. Callous unemotional traits, derived from the psychopathy literature (Cleckley, 1976; Hare 1970; see Castellanos-Ryan and Séguin, this volume), involve failures to experience guilt or empathy, and heartless treatment or manipulation of others (Frick, O’Brien, Wootton, & McBurnett, 1994). Frick et al. (2003) found that hostile attributional biases were more common among boys with CD who had lower levels of CU traits. Based on this evidence, hostile attribution biases may be associated more strongly with conduct problems in the absence of emotional-affective deficiencies. Although more research is necessary, it is plausible

in theory that hostile attribution biases are more prominent in more environmentally-mediated etiological pathways of CD, compared with more genetically-mediated pathways associated with CD in the presence of elevated CU traits (Frick et  al., 2014). This proposition, however, requires further empirical testing. Some youth’s conduct problems bring them into contact with the legal system, and research has examined relations between attribution biases and delinquency. Slaby and Guerra (1988) examined relations between delinquency and hostile attribution biases by comparing aggressive adolescents who were incarcerated in a maximum security youth facility with two community samples of adolescents classified as either low or high on aggression. Incarcerated aggressive youth ascribed to more hostile attributions when compared with both high and low aggressive community youth. However, these findings were qualified by some sex differences. Among males, incarcerated aggressive and high aggressive youth had similarly high levels of hostile attributions, and both scored higher than low aggressive youth. Among females, however, incarcerated aggressive youth held the highest levels of hostile attribution and were significantly different from low and high aggressive community youth (whose attribution biases did not differ). Furthermore, among adolescent male offenders in a medium security prison, those with more hostile attribution biases committed more violent interpersonal crimes (Dodge, Price, Bachorowski,  & Newman, 1990).

Aggression

Several decades of research indicate strong relations between attribution biases and aggression (Dodge, 2006), a symptom observed in many children and adolescents with CD. Considerable evidence for this relation comes from the Child Development Project—a longitudinal study of 585 community children from multiple sites and cohorts (Dodge, Bates,  & Pettit, 1990). Children were enrolled in the CDP at age 5 years, and their aggression and SIP styles have been tracked longitudinally for over 20  years. Several investigations of these children demonstrate that hostile attribution biases predict levels of aggression over time. For example, observed aggressive behaviors, recorded by objective trained observers in kindergarten, were predicted by hostile attribution biases assessed several months earlier (Dodge et al., 1990). This predictive relation, measured across multiple informants (teacher, peers, Iselin, McVey, Ehat t

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observers), persisted even after accounting for prior levels of aggression (Weiss, Dodge, Bates, & Pettit, 1992). Furthermore, hostile attribution biases continued to predict both externalizing behaviors and clinically significant conduct problems into Grades 3 and 4 (Dodge et  al., 1995). Even more impressive, externalizing behaviors, reported by mothers in Grade 11, were predicted by attribution biases measured in kindergarten, controlling for prior levels of externalizing behaviors (Lansford et al., 2006). Orobio de Castro, Veerman, Koops, Bosch, and Monshouwer (2002) performed a meta-analysis to examine relations between hostile attribution biases and aggression. Their analysis included 41 studies comprising over 6,000 children. The weighted average effect size of the relation between aggression and hostile attribution biases was r  =  0.17. This is a small effect size by Cohen’s (1988) standards. Nevertheless, nearly all studies included in their meta-analysis found positive relations between aggression and hostile attribution bias. Larger effect sizes occurred among studies that used clinical samples, did not control for intelligence, and included only boys. Since that meta-analysis, numerous other investigations have demonstrated that individuals with high levels of aggression are more likely to attribute hostile intentions to others. This relation has been supported across a variety of different samples and ages, including normative children (Crick, Grotpeter,  & Bigbee, 2002), normative young adults (Miller & Lynam, 2006), children in special education (Orobio de Castro, Slot, Bosch, Koops,  & Veerman, 2003), and clinic-referred children (MacBrayer, Milich,  & Hundley, 2003). Interesting moderators of this relation may include dispositional anger and inhibitory control (Reunions  & Keating, 2010). In contrast to these findings, other research has not found evidence of a relation between aggression and hostile attribution biases among detained females (Marsee  & Frick, 2007). Although most research indicates the relations between aggression and hostile attribution bias are strongest among boys, more research, especially among females, is needed to fully explicate sex differences (or similarities). It is important to note again the importance of situational ambiguity. When compared with nonaggressive children, aggressive children more accurately identify others’ hostile intentions when the intent is truly hostile (Dodge,  & Somberg, 1987; Dodge, Murphy, & Buchsbaum, 1984). Attribution biases of aggressive children may therefore be specific 352 At tribution Biases

to their erroneous attributions of hostility when the other person’s intent is benign (Dodge, 2006).

Subtypes of aggression

Researchers often classify aggression into subtypes based on its function (i.e., “why”) and form (i.e., “what”; Little, Jones, Henrich,  & Hawley, 2003; Ostrov & Crick, 2007; Prinstein & Cillessen, 2003). Functionally, aggression may be either reactive (i.e., retaliatory) or proactive (i.e., instrumental). Reactive aggression is often described as “hot headed,” whereas proactive aggression is often described as “cold-blooded.” Hostile attribution biases may be differentially related to reactive vs proactive aggression. Using meta-analysis, Orobio de Castro et al. (2002) compared studies in which general aggression was examined with those in which reactive aggression was examined (i.e., studies that excluded children exhibiting proactive aggression). Contrary to their predictions, the aggregate effect size across four studies of purely reactive aggression was substantially smaller than the effect size across thirty-seven studies of general aggression. Subsequent research has produced similarly equivocal findings. For example, in a normative sample of young adults, Bailey and Ostrov (2008) found that hostile attribution biases were related to reactive aggression. In contrast, within a high-risk, low income adolescent sample Arsenio, Adams, and Gold (2009) found only a marginal effect based on teacher’s ratings of reactive aggression. Teacher-rated proactive aggression was unrelated to hostile attribution biases. Among a normative sample of young adults, both reactive and proactive aggression were related to hostile attributions (Miller  & Lynam, 2006). The generally equivocal nature of these findings may be due in part to the fact that reactive and proactive aggression are often correlated on the order of r = 0.60 to 0.70 (e.g., Dodge et al., 1997; Miller  & Lynam, 2006). Expecting hostile attribution biases to differentially predict reactive and proactive aggression may be unreasonable given this overlap. Different samples and raters of aggression could also affect the strength and nature of relations between hostile attribution bias and reactive aggression. Aggression is also subtyped based on physicality. Physical aggression involves overt threats and physical harm to others. Examples include intentionally spilling a drink on someone and hitting them with a ball on the playground. When exposed to such provocation, young boys often react in kind with physical aggression (Nelson, Mitchell,  & Yang, 2008).

In general, reactive physical aggression is associated with hostile attribution biases of a physical nature in response to physical provocation (Bailey  & Ostrov, 2008; Crick et  al., 2002). Such findings remain significant after controlling for sex and for other subtypes of aggression. However, effect sizes are small. Importantly, other research indicates that among normal children, physical hostile attribution biases predict physical aggression only among boys (Nelson et al., 2008). In contrast to physical aggression, relational aggression does not involve physicality. Rather, an individual who engages in relational aggression might intentionally not invite the person to a party, or ostracize a classmate at school. When provoked relationally, many individuals react in kind (Crick et  al., 2002; Bailey  & Ostrov, 2008; Yeung  & Leadbeater, 2007). This tendency may be associated with greater hostile attribution biases of a relational nature especially among girls (Godleski & Ostrov, 2010), but also after controlling for sex and other forms of aggression (Bailey  & Ostrov, 2008). In cross sectional data, hostile attributions partially mediate the path from relational provocation to relational aggression (Yeung  & Leadbeater, 2007). However, this finding does not account for longitudinal pathways. Other research indicates that relational hostile attribution biases are not associated with relational aggression among girls (Crain, Finch,  & Foster, 2005), or in samples comprised of both boys and girls (Nelson et  al., 2008). In a normative sample of both boys and girls, hostile attribution biases were related to relational aggression only within the entire sample; a relation that disappeared when examined across sexes (Mathieson et  al. 2011). In short, there is not yet consensus on the specificity of relations between hostile attribution bias and relational aggression. Similar to reactive and proactive aggression, physical and relational aggression share considerable overlap (rs = 0.63 to 0.87; Crick and Grotpeter, 1995; Crain et al., 2005), so it may be unreasonable to expect unique relations. Recent research is consistent with this notion. In one study, hostile attribution biases in response to relational provocation were related to both relational and physical aggression (Werner, 2012). In a longitudinal study, hostile attribution biases in response to physical provocation predicted both relational and physical aggression (Godleski  & Ostrov, 2010). There may also be undetected moderators of relations between hostile attribution biases and relational aggression. Some especially intriguing

candidates include experiences of relational victimization and emotional sensitivity, which moderate relations between hostile attribution bias and relational aggression among girls (Mathieson et al., 2011).

Antisocial Personality Disorder and Psychopathy

To our knowledge, research has yet to examine associations between hostile attribution biases and ASPD. Nevertheless, much of the literature reviewed above may be relevant to individuals with ASPD, given considerable diagnostic overlap with CD in particular (e.g., aggression, CD diagnosis before the age of 15 years). Similarly, few research studies have examined relations between hostile attribution biases and psychopathy among adults. One of the first studies of this sort tested this relation among incarcerated adult males (Serin, 1991). Individuals who scored high on psychopathy (>28 of 40 on the Hare Psychopathy Checklist-Revised) were more likely to report hostile attributions than individuals scoring at or below 28. In another study of incarcerated adult males, simple correlations indicated that hostile attribution biases were associated with higher psychopathy scores among Caucasians (Vitale, Newman, Serin,  & Bolt, 2005). In contrast, hostile attribution biases were associated with higher psychopathy scores among African Americans only when depressive attribution biases (i.e., internal, stable, global attributions) were included in the analysis (Vitale, et  al., 2005). Clearly, much more research is needed on adult samples to understand how hostile attribution biases relate to externalizing spectrum disorders.

Current State of the Science

Current research supports the notion that hostile attribution biases are causal mechanisms in externalizing behaviors, particularly aggression (Dodge, 2006). Some of the most compelling evidence comes from treatment studies. For example, Guerra and Slaby (1990) randomly assigned incarcerated male and female adolescent offenders to either (1)  cognitive mediation treatment targeting several of the SIP stages, including development of benevolent attribution biases in place of hostile ones; (2) a generic skill development group; or (3) a no-intervention group. Relative to the two control groups, adolescents assigned to cognitive mediation were less likely to exhibit hostile attribution biases. The same youth were rated by staff as less aggressive, Iselin, McVey, Ehat t

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less impulsive, and more flexible than youth in the basic skills and no-treatment groups. In a similar study, Hudley and Graham (1993) examined effects of an intervention that trained children to minimize hostile attribution biases. Aggressive and non-aggressive low income African-American children were assigned randomly to treatment, attention-training only, and control conditions. Treatment children had lower hostile attribution biases than children assigned to the control and attention training conditions. This positive treatment effect also translated into fewer aggressive behaviors. Sukhodolsky, Golub, Stone,  & Orban (2005) randomly assigned children to receive treatment targeting SIP skills or a different treatment targeting other aspects of aggression (i.e., appropriate responding to provocative situations). Children who received SIP-focused treatment demonstrated significantly fewer hostile attribution biases than those in the other treatment condition. Receiving treatment was associated with substantial to complete recovery in aggression and conduct problems for over 50% of the sample; these improvements persisted for 3 months after treatment. Even more persuasive evidence comes from a comprehensive, multiyear, multisite intervention program that targeted SIP skills in a sample of children at high risk for conduct problems (Conduct Problems Prevention Research Group, 1992). Compared with controls, children randomly assigned to receive treatment showed significant decreases in their levels of hostile attribution biases in elementary school. Treatment improvements lead to reductions in antisocial behaviors in adolescence and these positive intervention effects were mediated by changes in attribution biases (Dodge, Godwin,  & The Conduct Problems Prevention Research Group, 2013). Attribution biases are key mechanisms on which researchers and clinicians should focus to predict and thwart aggression and its progression into more antisocial behavior (Dodge, 2006). The focus of this chapter has been exclusive to the role of attribution biases. There is an equally vast and compelling evidence base demonstrating that deviant processing at other stages within the SIP framework plays an important role in understanding externalizing problems (e.g., Fontaine, Burks, & Dodge, 2002) and aggression (e.g., Dodge, Laird, Lochman, Zelli,  & Conduct Problems Prevention Research Group, 2002). Furthermore, deviant processing across all stages may have cumulative effects when predicting later conduct problems. Lansford 354 At tribution Biases

et  al. (2006) classified the degree of youth’s deviant SIP patterns based on whether problems were evident at early (encoding and intent attributions), late (selecting instrumental goals, formulating aggressive responses, making positive evaluations of aggression), both early and late, or none of the steps in SIP. Children who were placed in these classifications in kindergarten had different mean levels of teacher- and mother-reported externalizing behaviors in Grade 11, even when controlling for past externalizing problems. Children with deviant SIP in both early and late steps had especially high levels of externalizing behaviors relative to youth with the other three SIP patterns. Using classifications of SIP patterns measured in Grade 8 to predict Grade 11 externalizing behaviors produced parallel yet stronger results. It is therefore important to bear in mind that externalizing disorders may be better understood by considering attribution biases in conjunction with other deviant SIP patterns.

Developmental Considerations

Dodge (2006) makes several intriguing assertions about the development of attribution biases, based on translational science. He claims that aggression and hostile attribution biases are inherent to all people. Aggression, in particular, emerges early in life and is regulated by existence of benign attributions that develop through socialization processes with others. According to this perspective, hostile attribution biases are a failure to learn more benign attributions. While several specific mechanisms may lead to failures to develop benign attributions (Dodge, 2006), we highlight the role of exposure to harsh environments and the role they play in development of hostile attributions (Dodge, 2011; Dodge  & Pettit, 2003). Early in life, children’s first and most recurrent social encounters are with parents, and through these encounters (whether through direct experience or observation) children learn how to interpret and process subsequent social exchanges. When parenting is harsh or children incur maltreatment, they begin to build social schemas in which others act out of malevolence, not benevolence. Various terms for schemas have been used (e.g., working models, Bowlby, 1980) and theories about how they develop are described in more detail elsewhere (Baldwin, 1992). Repeated exposure to harsh parenting and/ or maltreatment reinforces these malevolent schemas, strengthening a working model that other people have malicious intentions. Recall that the SIP framework proposes that schemas are stored

within a long-term catalog that interacts in reciprocal ways with the cognitive processes engaged during immediate social exchanges. Thus, in real-time social exchanges, aggression and hostility are more accessible given a schema in which the world acts with antagonism. Because of the salience of parents in early life, it is theorized that schemas developed from negative interactions with them will be particularly persistent and lead to problematic biases across a variety of developmentally-relevant social contexts and relationships across the lifespan (e.g., parental exchanges in early childhood, peer exchanges in adolescence, and intimate partner exchanges in young adulthood; see Dodge, 2011). It is important to note that this is a simplified version of more complex accounts of how externalizing disorders develop via deficits across all stages of SIP. More comprehensive reviews of the dynamic and complex relations among sociocultural factors, biological predispositions, and cognitive processes that lead to conduct problems are discussed elsewhere (e.g., Dodge & Pettit, 2003). Research on negative parenting styles supports the notion that exposure to harsh environments encourages development of attribution biases. For example, Gomez and colleagues examined the relative roles of attribution biases, perceived maternal support (e.g., warmth, affection), and maternal control (e.g., inconsistent discipline) in predicting child aggression. Children who perceive their mothers as more controlling and less supportive make more attributions of hostile intent than children who perceive their mothers as less controlling and more supportive (Gomez  & Gomez, 2000). Interestingly, children’s perceptions that their mothers are supportive attenuate the relation between high maternal control and hostile attribution biases. This finding is especially interesting because it suggests that positive parenting may offset children’s hostile attribution biases, presumably by providing the child with positive interpersonal exchanges that support the development of benign attributions. In a longitudinal design, Gomez, Gomez, DeMello,  & Tallent (2001) assessed attribution biases measured 1  year after they assessed children’s perceptions of maternal control and support. Children who perceived their mothers as more controlling and less supportive made more attributions of hostile intent; children who exhibited more hostile attribution biases were also rated as more aggressive by their teachers. Attribution biases mediated relations between harsh parenting and aggression. Importantly, a counterfactual model that tested

aggression as the mediator of relations between harsh parenting and hostile attribution biases was not supported. Maltreatment exhibits similar effects as harsh parenting. Based on data from the Child Development Project (discussed previously), Dodge et al. (1990) found that physical abuse early in life (by age of 5 years) was related to having more hostile attribution biases which predicted observed aggression 6  months later (while controlling for several important confounds). Subsequent research indicates deficits across all SIP stages, including hostile attribution biases, mediate the relation between physical abuse and externalizing problems (Dodge, et al., 1995; Weiss et al., 1992). Importantly, these relations persist into young adulthood. Pettit, Lansford, Malone, Dodge,  & Bates (2010) found that harsh physical discipline in the first 5 years of life predicted violence toward peers in young adulthood (mid-20s); problematic SIP in peer relations mediated this relation. Finally, negative attachment patterns with parents may play a similar role as harsh parenting and maltreatment (Bascoe, Davies, Cummings, Sturge-Apple, 2009; Cassidy, Kirsh, Scolton, & Parke, 1996).

Research Agenda and Future Directions

The most intriguing research to emerge over the next few decades will likely uncover biological processes that subserve attribution biases. Investigations into hormonal, physiological, neural, and genetic factors that underlie attribution biases are ripe for future investigation. As outlined by Dodge (2011), compelling theories and preliminary evidence support the likely fruitfulness of such endeavors. However, more immediate future research may emerge in the following two domains.

Emotions

Emotion dysregulation plays an important role in the development of externalizing disorders across childhood and adolescence (see Beauchaine, Gatzke-Kopp,  & Mead, 2007; Eisenberg, Spinrad, & Eggum, 2010; and Frick & Morris, 2004, for reviews). Intriguing research questions could examine developmental relations between emotion regulation and attribution biases. Attribution biases occur early on during social exchanges, when children encode and interpret cues from the interpersonal interaction. Negative attribution biases may therefore induce strong emotional reactions to interpersonal exchanges. Attribution biases may also initiate a breakdown in emotion regulation strategies, triggering hostile rumination Iselin, McVey, Ehat t

355

or dysregulated expressions of anger. It is also plausible that those with poor emotion regulation are more inclined to attribute hostile intentions to other people’s ambiguous behaviors. Children with poor emotion regulation may be more likely to perceive others as enemies and therefore be quicker to attribute hostile intentions to their behaviors. Research on temporal relations between attribution biases and emotion regulation could further explicate unique developmental pathways of externalizing disorder. For instance, research suggests that attribution biases and emotion regulation might play less of a role in the developmental pathways of children with severe conduct problems and elevated callous-unemotional traits while playing more of a role in the developmental pathways of children with severe conduct problems with more normative levels of callous-unemotional traits (Frick et al., 2014; Frick  & Viding, 2009). As noted previously, relations between attribution biases and CU traits have not been demonstrated consistently, which highlights the importance of future research on these interrelations.

Culture

It is important to examine the influence of culture when investigating SIP and adolescent adjustment (Dodge  & Pettit, 2003; Lansford et  al., 2005). Theories presented and evidence outlined in this chapter for attribution biases are derived from predominantly U.S.  samples of children. However, there are notable exceptions. An interesting study conducted among adolescents in Germany supports the relation between physical aggression and hostile attribution biases (Möller & Krahé, 2009). Studies such as these are important to advance our understanding of the universality (or lack thereof ) of hostile attribution biases and their role in externalizing disorders. Future research should garner evidence on stability of hostile attribution biases across cultures, the nature and strength of hostile attribution biases as mechanisms underlying externalizing disorders, and effects of treatments targeting attribution biases as mediators of intervention outcomes. Fortunately, large cross-cultural datasets on children’s adjustment are emerging (Lansford et  al., 2005), and will offer evidence we need to understand whether relations between attribution biases and externalizing disorders exist in similar ways across different cultures with different norms and values. Such evidence should facilitate development of culturally-sensitive interventions for 356 At tribution Biases

youth with externalizing disorders, enhancing the impact of treatments around the globe.

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CH A PT E R

21

Callous-Unemotional Traits and the Development of Externalizing Spectrum Disorders

Farrah N. Golmaryami and Paul J. Frick

Abstract Conduct disorder (CD) is a pattern of externalizing behavior that includes violations of the rights of others, and of major societal norms. As with other externalizing disorders, those with CD experience problems with the self-control of emotions and behaviors. However, the causes of these problems in self-control vary across individuals with CD. In this essay, we review research suggesting that the level of callous-unemotional traits (i.e., absence of guilt, callous lack of empathy, failure to put forth effort in important activities, deficient or superficial emotions) is an important psychological dimension that may help to define subgroups of children and adolescents with CD, and possibly other externalizing disorders, who show different etiological processes underlying their behavior problems. These developmental pathways have important implications not only for causal theories and research but also for developing more effective treatments for externalizing disorders. Key Words:  Conduct disorder, callous-unemotional traits, causal theory, treatment, externalizing

Introduction

Serious antisocial behavior, which violates the rights of others (e.g., aggression, destruction of property) or violates major age appropriate norms and rules (e.g., deceitful behavior, truancy, running away from home), is a critical component of the externalizing spectrum. This is made explicit in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; American Psychiatric Association, 2013) in which conduct disorder (CD) is placed under the broad heading, “Disruptive, Impulse Control, and Conduct Disorders.” All disorders within this category are “conditions involving problems in the self-control of emotions and/or behaviors” (p.  461, APA, 2013). As also explicitly noted in the DSM-5, the underlying causes of problems in self-control can vary greatly across individuals within a given diagnostic category. Accordingly, there has been significant research focused on advancing our understanding of the most common 360

causes of problems in self-control exhibited by children and adolescents with CD, and on using this understanding to design more effective treatments for this disorder (Frick, 2012). Research on CD has been helpful in documenting a wide array of dispositional vulnerabilities (e.g., biological, emotional, cognitive) and environmental risk factors (e.g., peer, familial, societal) that render children susceptible to serious conduct problems (see Frick & Viding, 2009; Moffitt et al., 2008, for reviews). Recognizing the number and diversity in types of vulnerabilities and risk factors is important for causal theories, because it suggests that causal models focused on any one or even a few types of risks are unlikely to be sufficient for explaining development of CD. Recognizing the number and diversity in types of vulnerabilities/risk factors is also important for treatment, since treatments that focus on any one or even a very few types of risks are likely to be insufficient for effectively treating a

large number of children with CD (Frick, 2012). However, the number and array of different types of vulnerabilities and risk factors also make it difficult to develop coherent and integrative theories to guide research and treatment. In this essay, we attempt to develop a coherent and integrative causal model for CD based on how high one scores on measures of callous-unemotional (CU) traits and how these traits serve as a marker for different developmental pathways to CD. This approach has its historical roots in past research on antisocial adults (Cleckley, 1976; Hare, 1993) and antisocial youth (McCord  & McCord, 1964; Quay, 1964) that have focused on the person’s affective and interpersonal style, rather than on the type of antisocial behavior displayed, to designate important subgroups of antisocial individuals. In this essay, we review findings from research that has used this method for defining subgroups of children and adolescents with CD who differ in their vulnerability/risk profile. We also provide a theoretical framework for explaining how these characteristics can be translated into different explanations for the problems in self-control across subgroups of youth with CD, and possibly other externalizing disorders as well. Finally, we highlight several controversies surrounding the use of this developmental model for understanding the causes of CD, including its implications for tailoring treatment approaches to the unique problems in self-control underlying the behavior problems across subgroups of children and adolescents with CD.

Historical Context

CD was first recognized as a mental health condition in the Second Edition of the Diagnostic and Statistical Manual of Mental Disorders (APA, 1968). In the DSM-II, CD was conceptualized as one of three possible reactions to negative environmental factors:  runaway reaction, unsocialized aggressive reaction, and group delinquent reaction. Thus, from its first inclusion as a psychiatric diagnosis, there was recognition that there are subgroups of persons with CD who can be differentiated by the both the behaviors they display and by the causes leading to those behaviors. In the next edition of the DSM (DSM-III; APA, 1980), there were several changes to how CD was conceptualized and defined. Etiological explanations were removed, and for the first time, diagnostic criteria consisted of very specific antisocial behaviors. Furthermore, a different method for subtyping persons with CD was introduced. This

method differentiated among those who were (1) aggressive or not aggressive and (2) “socialized” (e.g., have lasting friendships, feels guilt/remorse) or “undersocialized.” Subsequent research on the undersocialized aggressive subtype demonstrated that such children and adolescents experienced poorer adjustment in juvenile institutions, and were more likely to continue to show antisocial behavior into adulthood compared with other youth with CD (Frick & Loney, 1999; Quay, 1993). In addition, the undersocialized-aggressive subtype was more likely to show certain neurobiological correlates to their antisocial behavior, such as low serotonin levels and autonomic irregularities (Lahey, Hart, Pliszka, Applegate,  & McBurnett, 1993; Quay, 1993; Raine, 2002). However, there was also considerable confusion over core defining features that might differentiate undersocialized-aggressive CD from other subgroups of antisocial youth, with some definitions focusing on the child’s affective style (e.g., lack of guilt and remorse), and others focusing on the child’s peer group (e.g., whether or not the child committed antisocial acts alone or in groups; Quay, 1993). As a result of this confusion and resulting lack of consistency in research findings, the DSM-III method of subtyping was replaced in the next edition of the manual with a distinction based on the developmental timing of CD onset (APA, 1994). This included two subtypes:  childhood-onset CD (i.e., symptoms emerge prior to age 10)  and adolescent-onset CD (i.e., symptoms emerge age 10 or later). Such a developmental distinction was based on an extensive body of research showing that the two subgroups of youth with CD often had very different outcomes. Specifically, childhood-onset cases were more likely to have problems that persisted into adulthood and were more likely to be aggressive (Moffitt, 2006; Odgers et al., 2008). As a result, the distinction based on the timing of onset largely subsumed the aggressive subtypes included in the previous edition of the manual (Moffitt, 2003). After publication of the DSM-IV, more research supported the childhood-onset/adolescent-onset distinction (Moffitt et  al., 2008). However, research also supported an additional distinction. Specifically, research integrated the affective dimension that combined aspects of (1) the construct of psychopathy, as identified in a long history of research on antisocial behavior in adults (see Hare  & Neumann, 2008), with (2)  affective components of undersocialized-aggressive CD, as identified in research with children and Golmaryami, Frick

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adolescents (Quay, 1993). Together, these define a callous and unemotional interpersonal style (Frick, 2009), which, when dichotomized, is displayed by approximately 20% to 40% of children and adolescents with CD (Kahn, Frick, Youngstrom, Findling,  & Youngstrom, 2012). The construct comprises a lack of guilt/remorse, a callous-lack of empathy, a failure to put forth effort on important tasks, and a shallow and deficient affect (Frick, 2009; Kimonis, Fanti, Goldweber, Marsee, Frick,  & Cauffman, 2013). These traits are included as a specifier for the diagnosis of CD in the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; APA, 2013). A  person who meets criteria for CD and shows two or more CU traits can be given the specifier “with limited prosocial emotions.” In the remainder of this essay, we summarize research related to this method for subtyping youth with CD.

CU Traits and Externalizing Spectrum Behavior Callous-Unemotional Traits and Antisocial Behavior

Frick, Ray, Thornton, and Kahn (2013) reviewed 118 studies (70 cross-sectional and 48 longitudinal) of associations between CU traits and antisocial behavior (i.e., conduct problems, aggression, and delinquency). Among these studies, 105 (89%) reported that CU traits were significantly associated with measures of externalizing behavior, although the strength of the associations varied greatly (–0.15 to 0.84) across studies, with an average correlation of 0.33. Furthermore, CU traits are associated with earlier onset of severe conduct problems (Dandreaux & Frick, 2009; Silverthorn, Frick,  & Reynolds, 2001) and with a more stable pattern of conduct problems (Frick, Stickle, Dandreaux, Farrell,  & Kimonis, 2005; Rowe, Maughan, Moran, Ford, Briskman,  & Goodman, 2010). For example, Rowe et  al. (2010) studied a population-based sample (N = 5,326) of children and adolescents, ages 5 to 16, and reported that children and adolescents with both CD and elevated CU traits were over 5 times as likely to persist with a diagnosis of CD three years later, compared with youth with CD without elevated CU traits. Thus, CU traits are related to earlier age of onset of conduct problems, more aggression, and more stable conduct problems. Such findings lead to the important question of whether or not non-normative CU traits are associated with 362 Callous-Unemotional Traits

important outcomes, even controlling for the more severe, aggressive, and early-onset conduct problems. Frick et  al (2013) reviewed 30 studies reporting that CU traits are associated with antisocial outcomes, even when controlling for conduct problem severity (most including aggression), with four studies examining aggression separately from other conduct problems. Another five studies reported that CU traits are associated with more severe outcomes, controlling for age of onset. In one notable example, McMahon et  al. (2010) studied a large high-risk community sample (N = 754) and reported that CU traits assessed in 7th grade predicted adult antisocial outcomes (e.g., arrests, antisocial personality symptoms), controlling for number of conduct problems, childhood-onset of serious conduct problems, and symptoms of attention-deficit/hyperactivity disorder in grade 7.  In short, not only are CU traits associated with severe, aggressive, and stable conduct problems, they also predict risk for antisocial outcomes controlling for other indicators of severity.

Callous-Unemotional Traits and Attention-Deficit/Hyperactivity Disorder

Importantly, CU traits are also correlated with impulsivity and ADHD (Christian, Frick, Hill, Tyler,  & Frazer, 1997; Frick, Bodin,  & Barry, 2000). This is not surprising given that both CU traits and ADHD are common among children with a childhood-onset CD (Frick  & Viding, 2009; Moffitt, 2003). However, as with the association with conduct problems, CU traits predict poorer outcomes among impulsive individuals, including those with ADHD. Frick et al. (2013) reviewed 25 studies showing that CU traits are associated with more severe antisocial outcomes, controlling for impulsivity or ADHD. Furthermore, in a sample of children with combined ADHD and conduct problems who were enrolled in an outpatient treatment program, CU traits were negatively related to 9 of 14 measures of treatment outcome (Haas et al., 2011). Even controlling for levels of conduct problems, CU traits were associated negatively with staff ratings of social skills and problem-solving, and were positively associated with negative behaviors while in time-out. Thus, the poor treatment response in children with ADHD and CU traits did not appear to be solely related to the more severe conduct problems displayed by these children.

Current State of the Science: Different Correlates to Antisocial Behavior with and without Significant Levels of Callous-Unemotional Traits

Based on the research reviewed in the previous section, it appears that CU traits are related to more severe, stable, and impairing patterns of behavior problems in children and adolescents with externalizing disorders. However, as noted previously, it is highly likely that there are many different causal pathways leading to the problems of self-control exhibited by children and adolescents with externalizing disorders. Thus, another important question is whether or not CU traits may also help to define different etiological pathways to externalizing disorders. In this section, we review research on the different correlates of serious conduct problems among those with and without elevated CU traits that may implicate different causal processes underlying the conduct problems in the two subgroups (see Frick et al., 2013, for comprehensive review).

Genetic and Biological Correlates

A number of twin studies have examined the strength of the heritable influences on CU traits, with estimates ranging from 42% to 68% (Bezdjian, Raine, Baker,  & Lynam, 2011; Blonigen, Hicks, Kruger, Patrick,  & Iacono, 2006; Viding, Blair, Moffitt,  & Plonin, 2005). Furthermore, behavioral genetic studies indicate that CU traits and conduct problems have both shared and unique genetic influences, supporting at least partially different etiological underpinnings (Bezdjian, Raine, Tuvblad,  & Baker, 2011; Larsson, Andershed,  & Lichtenstein, 2006; Taylor, Loney, Bobadilla, Iacono, & McGue, 2003; Viding, Frick, & Plomin, 2007; Waldman, Tackett, Van Hulle, Applegate, Pardini, Frick, & Lahey, 2011). Viding et al. (2005) reported some of the strongest behavioral genetic findings suggesting unique causal factors for conduct problems among those with and without elevated CU traits. Specifically, in a large (N = 7,374) population-based study of 7-year-old twins, they found that heritable influences on childhood-onset conduct problems were considerably greater among those who scored high on teacher-reported CU traits (81%) than for those who scored in the normal range of such behaviors (30%). Thus, there appears to be relatively strong heritable influences to CU traits and to the conduct problems displayed by those with elevated levels of these traits. Such findings make it important to investigate possible genetic polymorphisms and biomarkers that may

at least partly account for the genetic risk for CU traits. Two studies have provided evidence to suggest that CU traits in children and adolescents may be related, at least in part to catechol-O-methyltransferase (COMT) polymorphisms (Fowler et  al., 2009; Hirata, Zai, Nowrouzi, Beitchman,  & Kennedy, 2012). COMT is an enzyme that metabolizes catecholamines including dopamine and norepinephrine. In a study of 162 children and adolescents (ages 6–16  years), CU traits were associated with two polymorphisms on the oxytocin receptor (OSTR) gene (Beitchman et  al., 2012). Given the relatively few molecular genetic studies conducted to date, along with the discouragingly low proportions of phenotypic variance accounted for by single polymorphisms for almost all mental-health conditions, no conclusive statements can be made about the role of specific genetic polymorphisms in the development of CU traits. However, COMT polymorphisms deserve further study, given their associations with conduct problems in past research (Moffitt et  al., 2008). OSTR polymorphisms should also be a focus of additional research, given oxytocin’s role in affiliation and in the recognition of emotions in others (Campbell, 2010). Research has also indicated that, consistent with their behavioral manifestations of restricted emotions, children and adolescents with elevated CU traits show biological indicators of blunted reactivity to certain emotional stimuli. For example, youth with both CD and elevated CU traits show lower magnitudes of heart rate change to emotionally evocative films compared with youth with CD but normative levels of CU traits (Anastassiou-Hadjicharalambous & Warren, 2008; de Wied, van Boxtel, Matthys,  & Meeus, 2012). CU traits are also negatively related to skin conductance reactivity when responding to peer provocation (Kimonis, Frick, Skeem, et al., 2008). Children with CU traits show blunted cortisol reactivity to experimentally-induced stress (Stadler et al., 2011). Furthermore, functional imaging studies show that children and adolescents with both conduct problems and CU traits exhibit lower right amygdala activity in response to fearful faces in comparison to normal control children (Jones, Laurens, Herba, Barker, & Viding, 2009; Marsh et al., 2008; White et al., 2012). Thus, across multiple biological indices and using various paradigms to assess emotional responding, children with elevated CU traits show a more blunted physiological response to certain Golmaryami, Frick

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emotional stimuli. Importantly, this blunted biological response is either not found in children with conduct problems and normative levels of CU traits or, in some cases, the opposite pattern of emotional responsiveness is found in those with normative levels of CU traits (Frick et  al., 2013). For example, boys with conduct problems and normative levels of CU traits showed an enhanced amygdala response following exposure to fearful faces and an enhanced amygdala response during a theory of mind task (i.e., a task requiring taking the perspective of others) compared with controls (Sebastian et al., 2012; Viding et al., 2012). In addition to these indicators of blunted emotional responses to emotional stimuli, functional brain imaging studies have reported differences in the brain’s response to punishment among children with both conduct problems and elevated CU traits. For example, children and adolescents with conduct problems and elevated CU traits demonstrate abnormal responses within the ventromedial prefrontal cortex during punishment of reversal errors, compared with normal controls (Finger et  al., 2008). Furthermore, White, Brislin, Meffert, Sinclair, and Blair (2013) reported that the association between increases in punishments and increases in activity in dorsal anterior cingulate cortex and anterior insula activity was weaker for adolescents with higher levels of CU traits. Importantly, these functional brain imaging studies have compared only children and adolescents with conduct problems and elevated CU traits to normal controls. Thus, unlike the findings for biological indices of emotional responding, the specificity of these results to those youths with conduct problems and elevated CU traits is not clear.

Cognitive and Affective Correlates

These biological differences in responses to emotional stimuli and punishment in children and adolescents with elevated CU traits are supported by research using other paradigms. A number of studies demonstrate that children and adolescents high on CU traits show abnormalities in their responses to cues to punishment during various learning paradigms. For example, children and adolescents with high levels of CU traits are less responsive to punishment when they have to respond to an increasing ratio of punished/rewarded responses and this reward-dominant style is not displayed by other youth with conduct problems (Barry, Frick, Grooms, McCoy, Ellis,  & Loney, 2000; Fisher  & Blair, 1998; Frick et al., 2003). Furthermore, youth 364 Callous-Unemotional Traits

with CU traits are less responsive to gradual punishment schedules compared with other youth with conduct problems (Blair, Colledge,  & Mitchell, 2001). Adolescents with elevated levels of CU traits are less sensitive to punishment in the presence of peers (Muñoz-Centifanti  & Modecki, 2013) and are more likely to underestimate the likelihood that they will be punished for misbehavior (Pardini, Lochman, & Frick, 2003) compared with controls with conduct problems but normative levels of CU traits. In addition, youth with elevated levels of CU traits show other cognitive differences compared with other children and adolescents with conduct problems. These cognitive characteristics may result from the various types of punishment insensitivity and resultant problems in socialization (Dadds  & Salmon, 2003). For example, children and adolescents with serious conduct problems and elevated CU traits endorse more deviant values and goals in social situations, such as viewing aggression as a more acceptable means for obtaining goals, blaming others for their misbehavior, and emphasizing the importance of dominance and revenge in social conflicts (Chabrol, Van Leeuwen, Rodgers, & Gibbs, 2011; Pardini, 2011; Pardini et  al., 2003; Stickle, Kirkpatrick, & Brush, 2009). Deviant values and goals could also be related to deficits in empathy displayed by youth with CU traits. By definition, CU traits involve a lack of empathic concern for others. However, children and adolescents show more consistent deficits in affective empathy (i.e., experiencing negative emotions due to the harm caused to others) than in cognitive empathy (Frick et al., 2013). This distinction between affective empathy and cognitive empathy appears to be important for not only distinguishing youth with CU traits from other children and adolescents with serious conduct problems, but also for distinguishing youth with CU traits from those with autism spectrum disorder (ASD). For example, Jones, Happe, Gilbert, Burnett, and Viding (2010) reported that boys with conduct problems and elevated levels of CU traits showed less affective empathy for victims of aggression compared with normal control boys, boys with ASD, and boys with conduct problems who were normative on CU traits. However, they did not differ from normal controls on cognitive perspective taking or theory of mind tasks, whereas boys with ASD showed poorer performance on these cognitive tasks. Importantly, boys with conduct problems and normative levels of CU traits did not differ from normal controls on

any of the empathy or perspective-taking measures, suggesting that the problems in affective empathy were specific to those with significant levels of CU traits (see Schwenck et al., 2012, for similar results). Children with conduct problems with elevated CU traits also show low levels of fear and anxiety compared with children with conduct problems but with normative levels of CU traits (Frick et  al., 2013). For example, in a longitudinal study of 1,862 girls who were ages 5 to 8 years at the initial assessment (Pardini, Stepp, Hipwell, Stouthamer-Loeber,  & Loeber, 2012), girls with CD who showed elevated CU traits exhibited fewer anxiety problems 6 years later compared with girls with CD but with normative levels of CU traits. In another notable study that used a population-based sample (N = 7,000), fearless temperament at age 2 predicted both CU traits and conduct problems at age 13 (Barker, Oliver, Viding, Salekin, & Maughan, 2011). In follow-back analyses, children at age 13 who were high on both conduct problems and CU traits showed lower fearful responses to punishment cues at age 2 compared with those high on conduct problems but without elevated CU traits. This latter finding provides an important link between low fear and the insensitivity to punishment that are both exhibited by children and adolescents with elevated CU traits. Perhaps the most consistent emotional difference between conduct problem youth with and without elevated CU traits is that children and adolescents with CU traits are impaired in their responsiveness to and recognition of cues to fear and sadness in others. These impairments are found in studies across a wide age range and across diverse methods for assessing emotional responsiveness (see Frick et al., 2013). Critically, this research is also consistent in suggesting that children and adolescents with severe conduct problems but normative levels of CU traits show no deficits in their recognition of emotions in others (e.g., Blair, Colledge, Murray, & Mitchell, 2001; Dadds, El Masry, Wimalaweera, & Guastella, 2007; Fairchild, Stobbe, Van Goozen, Calder,  & Gooyer, 2010; Stevens, Charman,  & Blair, 2001) and show enhanced emotional responsiveness to distress cues in others (Kimonis, Frick, Fazekas, & Loney, 2006; Kimonis, Frick, Muñoz,  & Aucoin, 2008; Loney, Frick, Clements, Ellis,  & Kerlin, 2003). Such findings are consistent with the findings reviewed previously showing opposing activation in the amygdala in response to distress cues in children with conduct problems high on CU traits (decreased activation) versus those with normative

levels of CU traits (increase activation) (Viding et al., 2012). Thus, there is strong biological and behavioral evidence to suggest that CU traits distinguish among subgroups of children and adolescents with severe conduct problems who show different emotional characteristics. A  study by Willoughby, Waschbusch, Moore, and Propper (2011) suggests that these differences in emotional processing may be evident early in life. Five-year-old children (N = 178) with high levels of parent-reported CU traits and symptoms of oppositional defiant disorder (ODD), which often precedes CD, were less soothable and show less negative reactivity to the still-face paradigm (i.e., parental face showing no emotion or interaction with infant), as infants (6  months), compared with those with symptoms of ODD but normative levels of CU traits.

Social Correlates

In contrast to the relatively large amount of research on dispositional differences between conduct-problem children and adolescents with and without CU traits, there is much less research documenting differences in the social context of these groups (Frick et al., 2013). The most consistent finding is that harsh, inconsistent, and coercive discipline is more strongly associated with conduct problems in youth with normative levels of CU traits relative to youth with elevated CU traits (Edens, Skopp,  & Cahill, 2008; Hipwell, Pardini, Loeber, Sembower, Keenan, & Stouthamer-Loeber., 2007; Oxford, Cavell,  & Hughes, 2003; Pasalich, Dadds, Hawes, & Brennan, 2012; Wootton, Frick, Shelton,  & Silverthorn, 1997; Yeh, Chen, Raine, Baker, & Jacobson, 2011). On the other hand, low warmth in parenting appears to be more highly associated with conduct problems in youth with elevated CU traits (Kroneman, Hipwell, Loeber, Koot,  & Pardini, 2011; Pasalich et  al., 2012), although this latter finding is not found as consistently (Falk & Lee, 2012; Hipwell et al., 2007). These findings suggest that conduct problems may be related to different types of socialization practices depending on the presence of elevated CU traits. Also, it is important to note that most studies linking parenting to conduct problems have (1)  not used designs that can detect gene-environment correlations (i.e., environmental risk that is associated with genetic vulnerabilities) and (2) been correlational. In the few longitudinal studies that have tested potential bidirectional effects of parenting and child characteristics, CU traits have been more Golmaryami, Frick

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predictive of changes in parenting over time than parenting has been predictive of changes in CU traits over time (Hawes, Dadds, Frost, & Hasking, 2011; Muñoz, Pakalniskiene, & Frick, 2011). CU traits are also associated with several aspects of emotional relationships between parents and children, including disorganized attachment styles (Bohlin, Eninger, Brocki,  & Thorell, 2012; Fite, Greening,  & Stoppelbein, 2008; Pasalich, Dadds, Hawes,  & Brennan, 2012). Dadds et  al. (2007) reported that boys with high CU traits exhibited deficits in their attention to the eye region of faces compared with boys low on CU traits and, in particular, make less eye contact with both their mothers and fathers in social interactions, compared with those lower in such traits (Dadds, Allen, et al. 2012; Dadds, Jambrak, Pasalich, Hawes,  & Brennan, 2011). Dadds, Allen, et al. (2012) reported that, in addition to less eye contact, children with conduct problems and elevated CU traits show lower levels of both verbal and physical affection with mothers during a task where the mother said she loved the child and showed affection to him or her. These differences were in comparison to both normal controls and children with conduct problems who were normative on CU traits. There is even more limited research on the peer relationships of children and adolescents with CU traits. In one of the few studies of the quality of friendships of youths with CU traits, Muñoz, Kerr, and Besic (2008) reported that, in a community sample of adolescents ages 12–15 years, those with significant levels of CU traits had as many friends as other adolescents, but their friendships were less stable, and viewed by the youth high on CU traits as more conflictual. The most consistent finding on peer relationships of children and adolescents with elevated CU traits is that these youth are more likely to affiliate with delinquent and antisocial peers compared with those with conduct problems but with normative levels of CU traits (Goldweber, Dmitrieva, Cauffman, Piquero,  & Steinberg, 2011; Kimonis, Frick  & Barry, 2004; Pardini  & Loeber, 2008). It is unclear from this research, however, to what extent the association with deviant peers influences the severity of antisocial behavior in youth with elevated rates of CU traits, or how influential youth with CU traits are to the antisocial behavior of their peers. To begin to address this issue, Kerr, Zalk, and Stattin (2012) used peer network analyses in a large (N = 847) community sample of adolescents to test the effects of both the target adolescents’ levels of 366 Callous-Unemotional Traits

CU traits and their peers’ levels of CU traits on the association between antisocial peers and delinquency. Results suggest that the antisocial behavior of the adolescent with elevated CU traits was not strongly influenced by deviant peers but was highly influential on the antisocial behavior of his or her peer group.

Developmental Considerations: Alternative Pathways to Antisocial Behavior

Based on this research, it is clear that conduct problems in children and adolescents have different genetic/biological, cognitive-affective, and social correlates depending on whether or not they are displayed with elevated CU traits. These differences could implicate different causal processes underlying the development of antisocial behavior as a function of the level of CU traits. Following from this research, we have outlined a theoretical model to account for differences in the two groups by specifying different developmental processes underlying a child’s tendency to act in ways that violate the rights of others, or that violate age appropriate expectations across the subgroups of youth with CD (Frick et al., 2013; Frick & Viding, 2009). Our model builds on the well supported distinction made by Moffitt (2006), Patterson (1996) and others (Aguilar, Sroufe, Egeland,  & Carlson, 2000; Nagin  & Tremblay, 1999) between serious conduct problems that emerge early in childhood and those that emerge coinciding with the onset of adolescence. Specifically, these authors suggest that adolescent-onset of serious conduct problems seems to be related to problems in identity development during adolescence, whereas the childhood onset of serious conduct problems seems to be related to deviations in developmental mechanisms which are more enduring and are likely to cause problems across different developmental stages. We build on this model by suggesting that there are least two common pathways within the childhood onset-group leading to different types of enduring vulnerabilities, one involving problems in conscience development defined by a callous and unemotional interpersonal style and a second involving problems in emotion and behavior regulation. In the first of these two pathways to childhood-onset CD, we propose that children and adolescents with elevated CU traits have a temperament (i.e., fearless, insensitive to punishment, low responsiveness to cues of distress in others) that can interfere with normal development of conscience and place the child at risk for a particularly severe

and aggressive pattern of antisocial behavior. This contention is consistent with a number of theories for development of guilt, empathy, and other prosocial emotions. For example, Kochanska (1993) and Dadds and Salmon (2003) both proposed that anxiety and discomforting arousal that follow wrong-doing and punishment are integral to development of an internal system that functions to inhibit misbehavior, even in the absence of a punishing agent. These authors propose that fearless children with deficits in their emotional responses to punishment may not experience this “deviation anxiety,” which normally aids in the development of conscience. Blair and colleagues (Blair, 1995; Blair et al., 2001) contend similarly that a critical process in development of empathic concern is the ability to encode emotionally valenced stimuli. Fearless children may show problems in encoding emotional stimuli and, as a result, may not experience this negative arousal to others’ distress as strongly as other children, leading to problems in empathic concern and perspective taking. In short, there are a number of theories for how punishment insensitivity and/ or lack of arousal to the distress of others can lead to problems in development of empathy and guilt. These problems in development of conscience can make a child more willing to act in ways that hurt others, especially if there is potential for gain. They may also make a child more difficult to socialize to follow rules and other norms of society. Children in the second childhood-onset pathway—those with normative levels of CU traits—show a different pattern of dispositional vulnerabilities and environmental risk factors that is not consistent with deficits in conscience development. Available research suggests that children with severe conduct problems but normal levels of CU traits do not show deficits in empathy and guilt; in fact, these children often show high rates of anxiety, and appear to be highly distressed by effects of their behavior on others. This ties research on CU traits to a number of studies that examine effects of individual differences in anxiety among those with CD (e.g., Jensen et al., 2001; Walker et al., 1991). For example, comorbid anxiety is associated with smaller reductions in gray matter volumes in mesolimbic, septohippocampal, and anterior cingulate brain regions among adolescents with CD (Sauder, Beauchaine, Gatzke-Kopp, Shannon,  & Aylward, 2012). This finding supports the contention that CU traits mark different etiologies among externalizing children. Differences in etiology are further supported by research reviewed above in which

conduct problems among those with normative levels of CU traits show weaker heritabilities, and stronger environmental mediation via hostile and inconsistent parenting. Furthermore, this group is highly impulsive and shows high rates of emotional reactivity, suggesting that their conduct problems may be related to deficits in cognitive and/or emotional regulation of behavior. Poor inhibitory control and strong emotional reactivity, combined with inadequate socializing experiences, could lead to problems regulating behavioral and emotional responses. These problems could result in the child’s propensity to commit impulsive and unplanned aggressive and antisocial acts for which they may be remorseful afterward but may still have difficulty controlling in the future.

Current Controversies and Future Research Directions Research Methods for Studying Developmental Pathways to Externalizing Disorders

The different developmental pathways to CD proposed in this essay are just one possible explanation for the different profiles of risk factors associated with CD in those with and without elevated CU traits. Future research should compare predictions of this model against other potential explanations, and it should test whether there are other pathways that could explain the problems experienced by a significant number of youths with CD. However, this way of conceptualizing CD as a heterogeneous outcome that results from different causal processes across subgroups of youth has important implications for how research is conducted. Research should no longer focus simply on documenting what dispositional vulnerabilities and environmental risk factors are associated with CD, or which vulnerabilities/risk factors account for the most or the most unique variance in measures of antisocial behavior, aggression, or delinquency. Such methods assume that CD is a unitary outcome and could lead to inconsistent and even misleading results on the causal processes underlying this externalizing disorder. To illustrate this problem, findings reviewed above suggest that children with CD and elevated CU traits show opposite patterns of emotional reactivity to signs of fear and distress in others, whether this is studied through brain imaging, psychophysiological paradigms, or behavioral measures. That is, those high on CU traits show reduced emotional reactivity to distress cues, whereas those low on CU traits show heightened Golmaryami, Frick

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levels of emotional reactivity to distress cues. If the association between CD and emotional reactivity is studied without considering the moderating role of CU traits, the opposing patterns could cancel each other out and lead to erroneous conclusions about the role of emotional reactivity in the development of CD. In short, future research on CD needs to employ research methods appropriate for studying an outcome that may be the result of different causal processes (Frick, 2012). The importance of considering CU traits when studying vulnerabilities to CD, and in the theoretical model proposed in this essay, should not be confused with the issue of whether or not CU traits themselves are better considered a continuous dimension in which high rates reflect quantitative deviations from normal development, or a discrete taxon in which extreme rates stem from different causal factors than those that operate in the general population (Edens, Marcus, & Vaughn, 2011; Vasay, Kotov, Frick, & Loney, 2005). In either case, non-normative levels of CU traits could still designate different causal pathways to serious conduct problems. Relatedly, the theoretical model proposed in this essay does not imply that the only acceptable method for studying the role of CU traits in development of serious conduct problems is to form subgroups of children and adolescents with CD based on their levels of CU traits, and then compare groups on etiologically important variables. In fact, a number of studies of CU traits have tested potential interactions between CU traits and serious conduct problems in predicting theoretically important correlates, such as dysfunctional parenting (Wootton, Frick, Shelton, & Silverthorn, 1997) and amygdala activation (Sebastian et  al., 2012). In these studies, conduct problems demonstrated different associations with parenting and amygdala activation at different levels of CU traits, both of which were measured continuously. One issue with this latter approach of testing interactions between continuous measure of CU traits and conduct problems, however, is that there are often significant deviations from bivariate normality between predictors, which could violate assumptions of many parametric approaches to statistical analyses. Specifically, both CU traits and serious conduct problems often have highly skewed distributions, especially in community samples, and the overlap in distributions is typically asymmetrical. That is, very few children show high rates of CU traits without elevations on serious conduct problems, yet there is a significant number of children with serious conduct problems 368 Callous-Unemotional Traits

who show normative levels of CU traits (Frick et al., 2000). As a result, this has led to the recommendation of greater use of person-centered analyses that are less influenced by the non-normal distributions and more directly address the issue of differences across subgroups of youths with serious and impairing conduct problems (Frick, 2012).

Importance of Callous-Unemotional Traits in Those without Serious Conduct Problems

To date, the vast majority of research on CU traits has been conducted with children and adolescents who also show high rates of antisocial behavior (Frick et  al., 2013). Thus, another controversial issue is whether CU traits provide important information about the adjustment of children and adolescents who do not show serious conduct problems (Rutter, 2012). This is an important issue because, although high levels of CU traits in the absence of conduct problems are rare in large representative community samples (Fontaine, McCrory, Boivin, Moffitt,  & Viding, 2011; Frick et  al., 2000), this may not be the case in samples with high rates of early trauma and deprivation (Kumsta, Sonuga-Barke, & Rutter, 2012). Furthermore, CU traits may predict problems in adjustment, even in children without CD. For example, Moran, Ford, Butler, and Goodman (2008) reported results from a large (N  =  5,770) and nationally representative sample of children and adolescents (ages 5–16) indicating that CU traits predicted behavioral and emotional problems 12 and 24 months later, even in the absence of significant levels of CD symptoms. Finally, CU traits may designate unique patterns of poor self-control for children with other externalizing disorders, like ADHD. Musser, Galloway-Long, Frick, and Nigg (2013) reported that children with ADHD without a diagnosis of CD showed different patterns of autonomic responding (both sympathetic and parasympathic) to emotional stimuli depending on the presence of significant levels of CU traits. Importantly, these findings were found in a sample of children in which participants were excluded if they met criteria for CD, and the differences in autonomic responding remained even controlling for number of conduct problems. Using this methodology, children with ADHD without elevated CU traits displayed increased sympathetic activity during exposure to various emotional stimuli, whereas children with ADHD and elevated CU traits displayed reduced parasympathetic reactivity and reduced sympathetic activity across the emotional tasks. Thus, both ADHD groups had

altered patterns of autonomic functioning, but in somewhat different forms compared with typically developing children. In summary, there is promising evidence that elevated levels of CU traits may designate impaired individuals with different patterns of emotional responding, even in the absence of CD or significant conduct problems, but this proposition requires further testing.

CU traits was r = 0.24. Given the modest correlation across raters, it will be important for research to investigate and compare the utility of the different methods of assessment and determine optimal methods for combining ratings across informants (White, Cruise & Frick, 2009).

Need for Advances in the Assessment of Callous-Unemotional Traits

Frick et  al. (2013) reviewed 24 studies that investigated the response to treatment of children and adolescents with CU traits and they reported that, of the 20 studies that compared outcomes, 18 (90%) indicated that the group high on CU traits showed poorer treatment response. For example, in the juvenile justice system, youth with CU traits are less likely to participate in treatment, show lower rated quality of participation in treatment, and are more likely to reoffend after treatment than those low on these traits (Falkenbach, Poythress, & Heide, 2003; Gretton, Hare,  & Catchpole, 2004; O’Neill, Lidz, & Heilbrun, 2003; Spain, Douglas, Poythress & Epstein, 2004). Similarly, in inpatient psychiatric hospitals, children with CU traits are more likely to have longer lengths of stay and experience more physically restrictive interventions (e.g., higher rates of seclusion and physical restraint) during hospitalization (Stellwagen  & Kerig, 2010a; Stellwagen & Kerig, 2010b). Thus, research suggests that youth with CD and CU traits present quite a treatment challenge. However, there is evidence that these youths can respond positively to certain intensive treatments (Kolko  & Pardini, 2010; Waschbusch, Carrey, Willoughby, King, and Andrade, 2007; White, Frick, Lawing,  & Bauer, 2013). In addition, research suggests that if interventions are tailored to the unique emotional, cognitive, and motivational styles of children and adolescents with CU traits, treatments can be effective. To illustrate this, Hawes and Dadds (2005) reported that clinic-referred boys (ages 4 to 9 years) with conduct problems and CU traits responded as well as other children with conduct problems to the part of a parenting intervention that focused on teaching parents methods of using positive reinforcement to encourage prosocial behavior. In contrast, only children without elevated CU traits showed added improvement with the part of the intervention that focused on teaching parents to use more effective discipline strategies. This outcome would be consistent with the reward-oriented response style that, as reviewed previously, appears to be characteristic of children

Given that CU traits may designate important subgroups of children and adolescents with externalizing disorders, it will be important for future research to test optimal methods for assessing this construct. Such assessments may be more difficult to do reliably and validly than for the assessment of other externalizing constructs. Rather than focusing on the child’s behavior, as is the case for diagnostic criteria for ODD, CD, and ADHD, CU traits focus on the child’s interpersonal and affective characteristics. Thus, assessment requires a higher level of inference from the assessor. Furthermore, rather than simply documenting the occurrence of the indicators of CU traits, the assessor must determine if this is the way the child or adolescent “typically” behaves and interacts with others; that is, across multiple relationships and settings (APA, 2013). Specifically, to assess the behavioral symptoms of CD, the assessor needs to document if a behavior has ever occurred over a specific time frame (e.g., past 6 or 12 months). In contrast, to assess CU traits, characteristics should be shown persistently over a specific time, and the assessor must determine that they are not an occasional occurrence in certain situations (e.g., with siblings, in a therapeutic setting, with law enforcement professionals). Assessment of the behavioral symptoms of ADHD also requires the assessor to document that the behavior is not confined to certain situations but there is no suggestion that the behavior must be present in most, if not all, situations. In research conducted to date, CU traits have typically been assessed by rating scales completed by multiple informants (Frick et  al., 2013). On these scales, items related to CU traits are rated in terms of how well they describe the child’s typical pattern of emotional and interpersonal functioning. Furthermore, as with other types of child psychopathology (De Los Reyes & Kazdin, 2005), there appears to be only modest agreement across different raters. For example, Frick et  al. (2013) reported that the average correlation across raters of

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with CU traits. Similarly, Caldwell, Skeem, Salekin and Van Rybroek (2006) demonstrated that adolescent offenders with CU traits were less likely to reoffend after release from incarceration when treated using an intensive treatment program that utilized reward-oriented approaches, targeted the self-interests of the adolescent, and taught empathy skills. This research suggests that certain intensive interventions can reduce the level and severity of externalizing behaviors (e.g., conduct problems, aggression, and delinquency) in children and adolescents with elevated CU traits, although future research needs to continue to refine the critical components for these successful interventions and to test the most cost-effective methods for implementing such treatments. Furthermore, very little research has focused on whether CU traits themselves respond to treatment, although a few studies provide promising results (Butler, Baruch, Hickey, & Fonagy, 2011; Hawes & Dadds, 2007; McDonald, Dodson, Rosenfield, & Jouriles, 2011; Somech  & Elizur, 2012). The strongest effects on CU traits appear to be for intensive interventions implemented early in development. For example, Somech and Elizur (2012) demonstrated significant reductions in CU traits in a sample of young children (ages 3–5 years) who received an intensive parent-training program, which consisted of 14, 2-hour treatment sessions and included components focused on both parent and child self-regulation. Relative to a minimal intervention control group, there was a significant decline in level of CU traits from pre- to post-treatment (d  =  0.85) and these gains were maintained at a 1-year follow-up. In summary, there is a critical need for more treatment research for children with severe conduct problems and CU traits. The available, albeit limited, research to date suggests that interventions that are (a)  implemented early in development, (b)  intensive, and (c)  tailored to the unique characteristics of these children (e.g., reward dominant, lack of empathy) can reduce the level and severity of their behavior problems—and possibly the CU traits themselves. As research advances our understanding of the causal mechanisms underlying the problems in social and behavioral adjustment displayed by youth with severe conduct problems and elevated CU traits, this progress could inform treatments that directly target these mechanisms (Frick, 2012). Furthermore, separating those children and adolescents with severe conduct problems and elevated CU traits from other youth with severe 370 Callous-Unemotional Traits

conduct problems has helped to refine our understanding of the causal factors related to the severe conduct problems in those without CU traits as well. This refined understanding could also lead to enhanced, tailored treatments for all children with CD (Frick, 2012).

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CH A PT E R

22

Low Intelligence and Poor Executive Function as Vulnerabilities to Externalizing Behavior

Michelle Pinsonneault, Sophie Parent, Natalie Castellanos-Ryan, and Jean R. Séguin

Abstract Relations between cognition and behavior have been at the center of many discussions/debates among researchers interested in externalizing psychopathology. This chapter focuses on links among low intelligence, executive function deficits, and externalizing behavior. The authors first provide a historical overview, followed by a review of studies on traditional approaches to externalizing behavior problems, including attention-deficit/hyperactivity disorder, conduct disorder, delinquency, antisocial personality disorder, and substance use disorders. Next, current research trends are presented, with a focus on recent research that explores how comorbidity among disorders, specific symptoms, and specific traits associated with externalizing spectrum disorders are related to cognitive impairments. Developmental considerations are then examined. The chapter closes with some proposed avenues to guide future study of cognition and externalizing behaviors. Key Words:  cognition, IQ, executive function, externalizing, developmental

Introduction

Relations between cognition and behavior are complex. During the past century, many studies focused on low intelligence (IQ) and executive function (EF) deficits as correlates of externalizing behaviors and disorders. In this chapter, we first provide a historical overview of earlier studies on relations between cognitive profiles and externalizing behavior. Second, we review links to traditional externalizing disorders, highlighting studies on relations between low IQ, EF deficits, and attention-deficit/hyperactivity disorder (ADHD), conduct disorder (CD), delinquency, antisocial personality disorder (ASPD), and substance use disorders (SUDs). Third, we cover current research approaches. The first approach reflects a categorical conceptualization of comorbidity among traditional externalizing disorders and how these may relate to cognitive function. Other approaches emphasize a dimensional approach to the study of externalizing

disorders. They examine how specific symptoms (e.g., disruptive and antisocial behaviors) or specific traits characteristic of the externalizing spectrum (e.g., impulsivity) are related to cognitive function. Next, we outline elements of a developmental model of the association among low IQ, poor EF, and externalizing behaviors and disorders from infancy to adulthood. We then provide an overview of certain controversies in the field. Finally, we propose avenues to guide further study.

Historical Context

The first studies linking externalizing spectrum disorders to cognitive function were published in the early 20th century and focused on intellectual profiles of juvenile delinquents and criminals. The first empirical study on offender’s IQs linked delinquency with mental disability (Goddard, 1914; Goring, 1913). Two main conclusions emerged from these early studies. First, although within the 375

limits of normality, the mean IQ of offenders was lower than in the general population. Research conducted over the years has since revealed that offenders’ IQs range from 8 to 10 points lower than the general population (Hirschi  & Hindelang, 1977; Parent, Larivée, Giguère,  & Séguin, 2011; Quay, 1987; Wilson  & Herrnstein, 1985). Furthermore, the percentage of variance in externalizing conduct explained by IQ is sometimes higher than that explained by social class (Herrnstein  & Murray, 1994; Moffitt, Gabrielli, Mednick, & Schulsinger, 1981; West & Farrington, 1973). This is an interesting observation considering that criminology had traditionally favored social forces as opposed to individual difference accounts of crime and delinquency (Herrnstein, 1995). Second, intellectual profiles of most offenders are characterized by lower verbal IQ (VIQ) than performance IQ (PIQ), which is commonly referred to as “the P>V sign.” Wechsler (1949, 1958) was the first to suggest P>V as a characteristic of offender’s intellectual functioning, a well-replicated finding (Isen, 2010; Parent et  al., 2011). This research was later expanded to encompass other externalizing disorders, including ADHD (Faraone et al., 1993; Korkman & Pesonen, 1994); CD (Goodman, Simonoff,  & Stevenson, 1995; Moffitt, 1993); ASPD (Simonoff et al., 2004); and SUDs (Kubicka, Matejcek, Dytrych, & Roth, 2001; Mortensen, Sorensen, Jensen, Reinish,  & Mednick, 2005). However, in the past 25  years, the relevance of general intelligence tests in clinical research, as opposed to more neuropsychologically based measures, has been questioned (Lezak, 1988; Rispens et al., 1997). Research began to focus on a subset of self-regulation and problem-solving skills that are related more specifically to externalizing behavior problems, with the goal of refining our understanding of cognitive deficits that are only approximated by IQ composite scores (Barkley, 1997; Moffitt, Lynam,  & Sylva, 1994; Pennington  & Ozonoff, 1996; Zelazo, Carter, Reznick,  & Frye, 1997). Some of these skills, now collectively known as EF, are examined in association with externalizing behaviors in children, adolescents, and adults. EF is typically defined by its outcome, which allows for deliberate control/regulation of thoughts, emotions, and behaviors to achieve motivated goals (Jurado  & Rosselli, 2007; Miyake et  al., 2000). These specific yet interrelated higher level cognitive processes are all involved to various degrees in subphases of problem solving:  representing the problem, planning a solution, executing the plan, 376

and monitoring and evaluating the adequacy of an attempted solution (Zelazo, Müller, Frye,  & Marcovitch, 2003). We can therefore understand EF as a set of processes involved in specific phases of problem solving. However, a lack of consensus remains regarding the nature of EF and specific processes involved. This is exemplified by the large number of different tasks and indicators used to assess EF. Nonetheless, many researchers consider selective attention, inhibitory control, working memory, and cognitive flexibility as core types of processes necessary for EF (Garon, Bryson, & Smith, 2008; Miyake et al., 2000; Stuss, 2011; Zelazo & Müller, 2010). Selective attention (often referred to as sustained attention, interference or attentional control, or concentration; and as opposed to the involuntary attention triggered by an orienting response) refers to dynamic allocation of attentional resources in a goal-directed task. Inhibitory control (sometimes referred to as self-control, cognitive control, or response inhibition) involves inhibition of dominant, prepotent, or automatized responses when engaged in goal-directed task completion. Working memory refers to holding and updating information in memory while dynamically manipulating this content. Cognitive flexibility (or shifting) is defined as the ability to shift fluently among multiple cognitive operations or representations, within or across tasks. These dimensions are typically examined during novel problem-solving tasks that are emotionally neutral or “cool” (Blair, Zelazo, & Greenberg, 2005; Hongwanishkul, Happaney, Lee,  & Zelazo, 2005; Metcalfe & Mischel, 1999; Zelazo & Müller, 2002). In contrast, “hot” EF represents the regulatory skills used in emotionally arousing tasks. A number of EF tasks have been developed and used to examine EF performance in hot problem-solving situations (e.g., tasks of delay aversion, affective decision making, or tasks involving punishment or reward processing). EF emerges early in the first year of life and develops gradually until early adulthood (Best  & Miller, 2010; Diamond  & Aspinwall, 2003; Zelazo & Müller, 2010). Its development correlates with prefrontal cortex (PFC) maturation (Bunge & Zelazo, 2006). For example, the dorsolateral circuit is associated with specific executive processes such as monitoring of thoughts and action and task setting (Stuss, 2011). Similarly, the cingulate cortex is implicated in response inhibition and flexibility (Ordaz, Foran, Velanova,  & Luna, 2013). Under “hot” conditions, ventromedial regions of the PFC (or lateral/medial orbitofrontal circuit) become

Low Intelligence and Executive Function

engaged in behavioral/emotional regulation (Blair et al., 2005; Stuss, 2011).

Links to Traditional Externalizing Disorders

Before we address dimensional approaches further in the upcoming sections, we note that externalizing disorders have traditionally been operationalized in psychopathology research in terms of clinical syndromes such as ADHD, CD, ASPD, and SUDs, and as related constructs including delinquency and psychopathy (e.g., Achenbach  & Edelbrock, 1984; Krueger, Markon, Patrick, Benning,  & Kramer, 2007; Patrick, Hicks, Krueger,  & Lang, 2005; Tackett, 2010). Traditionally, research on links between externalizing disorders and cognitive abilities have examined these syndromes separately, often using a categorical approach, and, until recently, with little consideration of comorbidity, but also sometimes grouping ADHD or CD along with oppositional defiant disorder (ODD).

Attention-Deficit/Hyperactivity Disorder

According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5, American Psychiatric Association [APA], 2013), ADHD is defined broadly as a “persistent pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development” (p.  59). A  meta-analysis of 123 studies showed that children with ADHD display mean overall IQ scores 9 points lower than controls (Frazier, Demaree, & Youngstrom, 2004). Furthermore, children with ADHD are more likely to have IQ scores at least 15 points lower than expected based on parental IQ (Biederman, Fried, Petty, & Faraone, 2012). EF is at the heart of some prominent etiological models of ADHD (e.g., Barkley, 1997), especially inhibitory control (sometimes referred to as behavioral inhibition, in line with Barkley’s model) and working memory. An impressive number of studies have thus examined associations between EF and ADHD. Overall, meta-analyses confirm a substantial association between EF and ADHD. For example, among school-aged children with ADHD, meta-analyses report mean effect sizes (Cohen’s d) of d = 0.46–0.69—medium to large by Cohen’s (1988) standards, for both impairments in inhibitory control (Pennington & Ozonoff, 1996; Willcutt, Doyle, Nigg, Faraone, & Pennington, 2005) and working memory (Martinussen, Hayden, Hogg-Johnson, & Tannock, 2005; Willcutt et  al., 2005), in addition to medium effect sizes for cognitive flexibility

problems (Pennington  & Ozonoff, 1996). Some of these associations are observed early in development: A recent meta-analysis of 25 studies, including 3,005 preschoolers at risk of ADHD, found effect sizes ranging from medium to large for selective attention (d  =  0.55), inhibitory control (d = 0.64), cognitive flexibility (d = 0.63), and hot EF (delay aversion) (d  =  0.80), and a small effect size for working memory (d  =  0.32; Pauli-Pott  & Becker, 2011). Numerous studies, summarized in several meta-analyses, provide empirical support for an association between cognitive impairments and ADHD in children and adolescents. Nevertheless, knowledge about the specific association between ADHD and IQ or EF skills remains limited due to the frequent grouping of ADHD with ODD and CD as “disruptive behavior problems.” Furthermore, because of high comorbidity with learning disabilities (LD), it is unclear whether IQ or EF deficits are more typical of children with both ADHD and LD, or if they are specifically related to ADHD and might play a role in the observed comorbidity between these two disorders (Bental & Tirosh, 2007). In any case, within-group variability is observed, such that not all children or adolescents with ADHD show cognitive impairments. Some of these issues are addressed further in the upcoming sections.

Conduct Disorder

CD consists of a “repetitive and persistent pattern of behavior in which the basic rights of others or major age-appropriate societal norms or rules are violated” (APA, 2013, p.  469). In contrast to ADHD or delinquency, relatively few studies have focused on the association between CD and IQ, and most are cross-sectional. Nevertheless, children and adolescents with CD (10- to 16-year-olds) show low overall mean IQ (between 72 and 99; Golden  & Golden, 2001; Hodges  & Plow, 1990; Pajer et  al., 2008; Rogeness, 1994; Rutter, Tizard,  & Whitmore, 1970; van der Meer  & van der Meer, 2004; Zimet, Zimet, Farley,  & Adler, 1994) compared to normative peers. VIQ and PIQ scores are also reported in three studies, with mean VIQ scores (between 93 and 96)  slightly lower than mean PIQ scores (between 99 and 103; Hodges & Plow, 1990; Rogeness, 1994; Zimet et  al., 1994). Low overall IQ, VIQ, and PIQ have also been found in clinically referred preschoolers with symptoms of conduct problems (Speltz, DeKlyen,

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Calderon, Greenberg,  & Fisher, 1999). Another cross-sectional study reported a small effect size association between low IQ and CD symptoms in a community sample of 13-year-old twins, controlling for other risk factors (socioeconomic status [SES], parental IQ, child school attainment, and co-twin characteristics; Goodman et  al., 1995). Finally, results from two longitudinal studies are available. One indicated that IQ assessed at age 7 years was associated prospectively with CD at age 17  years, controlling for mild neurological signs, aggressive or hostile behaviors during testing, and parents’ mental health (Schonfeld, Shaffer, O’Connor,  & Portnoy, 1988). The second demonstrated that spatial deficits (but not verbal) at age 3 years predicted high levels of CD symptoms at both 8 and 17 years among Indian and Creole youth from Mauritius, controlling for social adversity and other possible confounds (Raine, Yaralian, Reynolds, Venables, & Mednick, 2002). In contrast, both verbal and spatial deficits were found at age 11. There are thus some indications that children with CD have lower mean IQs than their normal peers, although this impairment becomes much less clear when children with CD are compared to peers with other psychiatric diagnostic (see, e.g., Clark, Prior, & Kinsella, 2000; Zimet et al., 1994), or when PIQ and VIQ are distinguished. Neuropsychological accounts of CD also abound (Séguin, Sylvers, & Lilienfeld, 2007). Results from a recent meta-analysis on EF and externalizing disorders indicate a significant association between EF and CD, with a medium mean effect size of d = 0.54 (Ogilvie, Stewart, Chan, & Shum, 2011). Although this association has been studied primarily among males, it has also been observed among female adolescents (Giancola  & Mezzich, 2000; Giancola, Mezzich,  & Tarter, 1998, 1999). Furthermore, a recent review of studies that examined neurobiological processes involved in decision-making impairments among children and adolescents with CD identified impairments in hot EF and inhibitory control (Matthys, Vanderschuren, & Schutter, 2013). For example, individuals with CD, particularly the early-onset form, are especially sensitive to reward and relatively insensitive to punishment (Fairchild et al., 2009). In short, CD has been associated repeatedly with low overall IQ and EF deficits, with more evidence for hot EF (punishment and reward processing) and inhibitory control. However, traditional studies of IQ, EF, and CD present overall limitations that 378

warrant caution; for example, most of them lack control for comorbid ADHD symptoms (Morgan  & Lilienfeld, 2000; Séguin & Pilon, 2013), and they do not take into account the heterogeneity of CD symptoms (Séguin  & Pilon, 2013). These limitations are discussed further in the next section.

Delinquency

In contrast to DSM-derived externalizing problems just reviewed, delinquency is a legal term, with a definition that varies according to the field of study (e.g., psychology, criminology, sociology). Delinquency implies behaviors that do not conform to legal or moral standards of society. Developmental research suggests a distinction between two groups of juvenile delinquents, one that is transitional and often limited to adolescence, and another that starts very early in life, is more severe, and typically persists into adulthood (Moffitt, 1993). Researchers have long tried to associate a specific intellectual profile with delinquency. Two conclusions emerge from recent reviews on IQ and delinquency. First, low overall IQ and the P>V sign characterize the intellectual profile of many delinquents (Isen, 2010; Manninen et al., 2013; Parent et al., 2011). Second, the P>V sign mostly characterizes a subtype of offenders who commit violent crimes against people and show significant interpersonal difficulties (low interpersonal maturity, self-serving bias and hypersensitivity to frustration, family history of abuse or neglect). In the late 1980s, Moffitt and colleagues were among the first to explore which cognitive dimensions—beyond IQ—were associated with delinquency (Moffitt, 1990; Moffitt  & Henry, 1991; Moffitt et al., 1994; Moffitt & Silva, 1988). The first of these studies showed delinquency-related deficits in neuropsychological functions (e.g., verbal skills, planning, inhibiting inappropriate responses, attention, concentration; Moffitt, 1990; Moffitt & Henry, 1991). In an impressive prospective study, Moffitt and colleagues (1994) later demonstrated a predictive link between these neuropsychological functions at age 13 and delinquent outcomes at age 18. In addition, low neuropsychological functioning was associated more strongly with the early-onset type of delinquency than with the adolescent-onset type. Two meta-analyses of EF deficits in relation to various categories of antisocial behavior provide further support for an association between delinquency and poor EF (Morgan & Lilienfeld, 2000; Ogilvie et  al., 2011). The largest effect sizes (Cohen’s d ) were observed for criminality (d’s = 1.09 and 0.61)

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and delinquency (d’s  =  0.86 and 0.41), compared with d = 0.54 for ADHD (Willcutt et al., 2005) and CD (Ogilvie et al., 2011). In conclusion, several cognitive impairments are observed among those who are delinquent, including low overall IQ, low VIQ (or P>V), and deficits in all dimensions of cool EF, with the largest effect sizes observed in working memory tasks (d = 0.54–0.83). However, the delinquents are far from homogeneous, and there are some indications that cognitive impairments, especially verbal deficits, might be more typical of a subtype who commit violent crimes and show significant interpersonal difficulties. Much more work is needed to clarify what specific dimensions of EF are associated with delinquency and to disentangle how these associations might vary depending on delinquents’ characteristics. Co-occurrence of delinquency with other externalizing disorders (ADHD, CD, ASPD, SUDs) also needs closer examination.

Substance Use Disorders

SUDs are defined as pathological patterns of behaviors associated with use of mood-altering substances (APA, 2013, p.  483). Few studies have examined links between cognitive functioning and SUDs, and results are contradictory and difficult to interpret. On the one hand, low IQ is associated with alcohol abuse and dependence among adolescents (Moss, Kirisci, Gordon, & Tarter, 1994) and with SUDs among adults (Mortensen et al., 2005). On the other hand, studies indicate that substance use may be associated with higher IQ in adolescence (Castellanos-Ryan, Séguin, Vitaro, Parent, & Tremblay, 2013; Johnson, Hicks, McGue,  & Iacono, 2009; White & Batty, 2012). Interestingly, studies reporting an association with low IQ focused on SUDs (i.e., pathological patterns of substance use), whereas studies reporting an association with high IQ focused on substance use (i.e., frequency of substance use). Furthermore, low IQ could be a consequence of prolonged SUDs. Indeed, a recent prospective study on persistent cannabis use and neuropsychological functioning showed that individuals with adolescent-onset persistent cannabis dependence presented greater IQ decline over the course of 20 years (Meier et al., 2012). With regard to EF, adults with SUDs, particularly alcohol use disorder, often demonstrate wide-ranging deficits in inhibitory control, cognitive flexibility, and working memory (d  =  0.53–0.93; Giancola & Moss, 1998; Giancola & Tarter, 1999). Although adolescents with SUDs show EF deficits

in the same domains (Giancola & Mezzich, 2003; Giancola, Shoal,  & Mezzich, 2001; Moss et  al., 1994), they do not share the same degree of EF impairment as adults (d = 0.49–0.70; Giancola & Tarter, 1999). Interpretation of such results is somewhat tricky, however, because EF deficits may occur as a consequence of chronic drug or alcohol use (Fontes et al., 2011; Giancola & Tarter, 1999; Gruber, Sagar, Dahlgren, Racine,  & Lukas, 2012; Meier et al., 2012). For example, early-onset cannabis use (before age 16 years) may have lasting effects on cognitive performance (Fontes et  al., 2011; Gruber et al., 2012). Nonetheless, EF deficits among children and adolescents may still be a risk factor to later maladaptive substance use. Children of parents with SUDs exhibit poorer EF than do children with no family history of SUDs (Giancola, Martin, Tarter, Pelham,  & Moss, 1996; Giancola et  al., 1998; Giancola, Moss, Martin, Kirisci,  & Tarter, 1996; Pihl, Peterson, & Finn, 1990). Furthermore, EF deficits are associated prospectively with later increased alcohol consumption among children of alcoholic parents (Deckel  & Hesselbrock, 1996). The processes involved still remain to be clarified, but recent studies have started to explore brain characteristics and EF in adolescents and young adults with a family history of alcoholism (DeVito et al., 2013; Herting, Schwartz, Mitchell, & Nagel, 2010; Seghete, Cservenka, Herting,  & Nagel, 2013), as well as the joint contribution of parental discipline and EF in the intergenerational transmission of substance use (Pears, Capaldi, & Owen, 2007). Overall, low IQ and EF deficits are found among adults with SUDs (d  =  0.53–0.93). Although similar patterns appear among adolescents with SUDs, effect sizes are slightly smaller (d  =  0.49–0.70). Positive associations between frequency of substance use and IQ have also been found, particularly when IQ was measured early, a possible sign that it may take good cognitive abilities for early access to substances, which can be seen as a valuable resource (Hyman, Malenka, & Nestler, 2006). Children from families with histories of SUDs also show EF impairments, and these impairments prospectively predict alcohol consumption among children of alcoholic parents. However, it remains unclear to what extent low IQ or poor EF might be vulnerabilities versus consequences of SUDs. To date, too few prospective longitudinal studies have been conducted to disentangle such questions. Furthermore, because SUDs occur more frequently among adolescents with other externalizing disorders that are also

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characterized by cognitive impairments (CD, ADHD, delinquency), our developmental understanding of any independent associations among IQ, EF, and SUDs is far from complete.

Antisocial Personality Disorder and Psychopathy

ASPD is characterized by a “pervasive pattern of disregard for and violation of the rights of others” (APA, 2013, p.  659). In comparison, psychopathy is a distinct but overlapping clinical syndrome characterized by poor capacity for remorse and behavioral control (Blair, 2003). Although most psychopaths who have been through the justice system meet criteria for ASPD, only a subset of those with ASPD are psychopathic. Literature on cognitive profiles of individuals with ASPD is still very limited in scope and inconsistent due to variations in inclusion criteria. To our knowledge, the literature on ASPD and IQ is almost nonexistent, with only one longitudinal study demonstrating no association between adolescent IQ scores and later adult ASPD outcomes (Simonoff et al., 2004). However, psychopathy and intelligence have been the focus of several studies. Whereas findings are mixed when dichotomous psychopathy variables are used (Harpur, Hare,  & Hakstian, 1989; Hart  & Hare, 1996; Johansson & Kerr, 2005; Walsh, Swogger, & Kosson, 2004), a consensus emerges from recent studies in which psychopathy is construed dimensionally. Results suggest a consistent pattern of differential relations among four dimensions of psychopathy (interpersonal, affective, lifestyle, and antisocial) and measures of intelligence across various samples. Interpersonal (Salekin, Neumann, Leistico,  & Zalot, 2004; Vitacco, Neumann,  & Wodushek, 2008) and antisocial dimensions (Vitacco et al., 2008) are associated positively with intelligence, whereas affective and lifestyle dimensions are associated negatively with intelligence (Neumann, Hare,  & Newman, 2007; Vitacco, Neumann, & Jackson, 2005; Vitacco et al., 2008). In contrast to IQ, many studies have examined links between ASPD and EF, but findings are inconsistent in magnitude. Although statistically significant, a large meta-analysis yielded a very small association (d = 0.10) between ASPD and EF, the lowest magnitude among studies of externalizing disorders (Morgan  & Lilienfeld, 2000). More recent studies, however, found stronger associations between ASPD and specific dimensions of EF among adult males (d’s ranging from 0.47 to 1.01), including planning (Dolan  & Park, 2002), 380

verbal working memory (De Brito, Viding, Kumari, Blackwood,  & Hodgins, 2013; Dolan  & Park, 2002), cognitive flexibility and inhibitory control (Dolan, 2012; Dolan & Park, 2002), and hot decision making (De Brito et al., 2013). So far, research on intellectual profiles of those with ASPD show few indications of any strong association with IQ. In contrast, there are indications of associations between several dimensions of EF impairments and ASPD, but effect sizes are highly variable and observed only among male adults. Once again, lack of consideration of comorbid disorders and heterogeneity of symptoms included in ASPD tends to blur conclusions.

Section Summary

In sum, traditional research on the associations among IQ, EF, and externalizing disorders supports the relevance of considering cognitive impairments in etiological studies of these disorders. Delinquency and criminality (d’s between 0.41 and 1.09), ADHD (d of 0.54), and CD (d of 0.54) show the strongest links with EF impairments. Given these effect sizes and within-group variability however, one must keep in mind that not all children or adolescents with externalizing disorders are characterized by cognitive impairments. Furthermore, a number of limitations leave many questions unanswered and prevent us from formulating clear conclusions with regard to specific disorders that are associated with cognitive impairments, the specific cognitive dimensions that are concerned, and developmental pattern of their association(s). Current work is addressing some of these questions.

Current State of the Science

Although the traditional approach to studying associations between single externalizing disorders and cognition remains highly prevalent to this day, it is based on clinical categories that include heterogeneous sets of symptoms, some of which might be characterized by very different relations with cognitive characteristics. Researchers are now examining links between cognition and externalizing behavior using conceptualizations that extend beyond traditional diagnostic categories. Three current research trends can be identified, including (1) how comorbidity among disorders might be related to cognitive profiles, either using traditional categorical or dimensional approaches; (2) how specific symptoms of externalizing disorders (e.g., disruptive/antisocial behavior, aggressive behavior, and reactive, proactive or physical aggression) relate to cognitive

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profiles; and (3) how specific externalizing spectrum disorders traits (e.g., impulsivity) relate to cognitive profiles.

Comorbidity Among Externalizing Disorders

Externalizing behaviors and disorders, including ADHD, ODD, CD, SUDs, and ASPD, are highly comorbid conditions (Beauchaine  & McNulty, 2013). Consequently, studies on shared cognitive impairments among these disorders are in order. Cognitive impairments typical of individuals with comorbid ADHD and disruptive behavior disorders (including ODD and CD) have been the most studied. Indeed, one of the difficulties in clarifying the contribution of cognitive deficits to ADHD and CD is the high comorbidity between these disorders (Costello, Mustillo, Erkanli, Keeler, & Angold, 2003; Nigg, Hinshaw, Carte,  & Treuting, 1998; Oosterlaan, Scheres, & Sergeant, 2005). Current literature reveals inconsistencies with regard to the magnitude of cognitive impairments associated with comorbidity between ADHD and ODD/CD. Some studies show that comorbidity is linked to greater verbal IQ impairments (Nigg et al., 1998) and EF deficits (Di Trani et al., 2011) than in ADHD alone. For example, a recent study of school-aged children and adolescents showed lower IQ and poorer EF when ADHD was associated with other externalizing disorders (ODD or CD; Di Trani et  al., 2011). The authors suggested that neuropsychological characteristics of children and adolescents with ADHD are more closely related to comorbidity patterns than to ADHD subtypes (i.e., inattentive, hyperactive/impulsive, combined). Similarly, other studies show that comorbidity between ADHD and ODD/CD is associated with greater EF impairments than ODD/CD alone (Clark et  al., 2000; Hummer et  al., 2011). However, several other studies do not report more severe EF impairments for children with comorbid ADHD and ODD/CD (Barkley, Edwards, Laneri, Fletcher,  & Metevia, 2001; Barnett, Maruff,  & Vance, 2009; Nigg et  al., 1998; Oosterlaan et  al., 2005; Sarkis, Sarkis, Marshall,  & Archer, 2005; van Goozen et al., 2004). For example, results of a study of school-aged children with ADHD found no difference in EF between ADHD children with and without ODD or CD (Barnett et  al., 2009). Another study found different cognitive impairments were associated with CD symptoms versus ADHD symptoms in preschoolers, controlling for their co-occurrence:  physical aggression was

associated with low VIQ, whereas hyperactivity was associated with low PIQ (Séguin, Parent, Tremblay,  & Zelazo, 2009). These inconsistencies may be due to factors such as age, cognitive dimensions measured, and the criteria by which children or adolescents with ODD/CD or ADHD were selected for inclusion. Work has yet to be conducted for other patterns of comorbidity, such as SUDs and ADHD or SUDs and ODD/CD (but see Finn et al. [2009] or Giancola [2007] for some relevant examples).

A Dimensional Approach to Externalizing Behaviors

Using dimensional approaches, current studies are also examining cognitive impairments associated with specific symptoms or groups of symptoms of externalizing spectrum disorders. Disruptive/ antisocial behaviors, aggressive behaviors, and reactive, proactive, or physical aggression have been the focus of much attention. Many preschool and early childhood studies examined relations between cognitive impairment and overall levels of disruptive behavior, including inattention, hyperactivity, opposition, and aggression. In several studies (e.g., Dunn  & Hughes, 2001; Hughes, Dunn,  & White, 1998; Hughes, White, Sharpen,  & Dunn, 2000), global scales of disruptive behaviors were associated with specific measures of EF. Results of a recent meta-analysis show EF deficits to be related to externalizing behavior problems in preschool (Schoemaker, Mulder, Dekovic, & Matthys, 2013). A medium effect size was found for overall EF and externalizing behavior problems (d = 0.45). When dimensions of EF were analyzed separately, a medium effect size was found for inhibitory control (d = 0.49) and working memory (d = 0.45), whereas a small effect size was found for cognitive flexibility (d  =  0.26). Furthermore, the relationship was stronger among externalizing behavior problems, overall EF, and inhibitory control for older preschool children (4–1/2 to 6 years; d  =  0.58 and 0.65, respectively) compared to younger children (3 to 4–1/2  years; d  =  0.24 and 0.28, respectively). Finally, there was a stronger relationship between EF and externalizing behavior problems in studies featuring a higher percentage of boys. Overall, results suggest that inhibitory control and working memory are associated with behavioral disruptiveness in preschoolers (aged 4–5 years) even when controlling for verbal skills. Heterogeneity of symptoms included in the diagnostic criteria of externalizing spectrum

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disorders, from childhood to adulthood, prompted researchers to introduce further distinctions among externalizing behaviors. Notably, different patterns of cognitive impairments are observed in relation to aggressive versus nonaggressive antisocial behaviors and offences (Barker et  al., 2007, 2011; Giancola et al., 1998; Giancola & Mezzich, 2000; Hancock, Tapscott,  & Hoaken, 2010; Larivée et  al., 1994). Overall, results indicate that, among antisocial individuals, cognitive impairments are specific to those who are aggressive, with some studies also indicating that theft is associated with normal to higher than average cognitive abilities, both in community and clinical samples (Barker et al., 2007, 2011; Larivée et  al., 1994). For example, Barker and colleagues (2007) found EF (mostly selective attention and working memory during problem representation and planning) and VIQ to be associated negatively with physical violence, but associated positively with theft (although not always significantly). Consistent with studies of delinquency (e.g., Walsh, 1987), criminals with good cognitive skills were more likely to be involved in long-term planned crimes that require coordinated actions among several individuals and are usually related to material gain (Barker et al., 2007). Thus, antisocial behaviors are more likely to include physical violence when men have low VIQ and poor EF, whereas theft may be associated positively with some cognitive skills. Unfortunately, almost no data are available about the cognitive characteristics of women with aggressive versus nonaggressive antisocial behavior problems. As an exception, Giancola and colleagues (1998) examined EF and temperament among adolescent females with CD. A  composite EF score made a unique significant contribution to both aggressive and nonaggressive conduct problems, controlling for SES, ADHD symptoms, VIQ, and difficult temperament. However, when controlling for co-occurrence of aggressive and nonaggressive conduct problems, EF contributed negatively to aggressive antisocial behaviors. Just like externalizing behaviors more broadly, aggression is not a unitary construct. Several dimensions of aggression have been identified, some of which have been linked to specific cognitive impairments. For example, reactive aggression is associated with poor self-regulation, whereas proactive aggression is not, although both reactive and proactive aggressions are related to externalizing problems (White, Jarrett, & Ollendick, 2013). Others have focused on physical aggression. Development of physically aggressive behaviors is 382

associated with atypical EF development (Séguin, Boulerice, Harden, Tremblay,  & Pihl, 1999; Séguin  & Zelazo, 2005). Séguin, Pihl, Harden, Tremblay and Boulerice (1995) examined cognitive and neuropsychological deficits in connection with physical aggression among school-aged boys. In that study, a factor score reflecting selective attention, working memory, inhibitory control, and cognitive flexibility was associated with histories of physical aggression, over and above verbal ability. Séguin and colleagues (1999) further examined relations between poor EF (assessed by two working memory tasks) and physical aggression among male adolescents and young adults, controlling statistically for IQ and symptoms of ADHD. Their results indicated that different dimensions of EF characterize adolescents with stable physical aggression problems compared to adolescents whose physical aggression problems fluctuate over time. The former group was characterized by working memory difficulties associated with deductive reasoning (mostly executing a plan; e.g., applying rules to a given problem), whereas the latter group was characterized by working memory difficulties associated with inductive reasoning (mostly monitoring plan execution; e.g., figuring out and applying underlying rules in a given problem, with the use of experimenter’s feedback). Finally, other studies have focused on combinations of cognitive impairments. One such study examined how hot EF, assessed via delay of gratification, and VIQ might interact in their association with aggressive behavior in a sample of school-aged boys (Ayduk, Rodriguez, Mischel, Shoda, & Wright, 2007). Better verbal skills were associated with lower aggressive behaviors to a greater extent among boys who had effective hot EF skills than among those who had ineffective hot EF skills. These results, replicated in two samples, suggest a moderating role of hot EF skills in the relationship between VIQ and aggressive behaviors. Similarly, a longitudinal study on delinquency among boys sampled from the Pittsburgh Youth Study showed that early adolescent measures of inhibitory control and intelligence predicted the age-crime curve of participants between ages 11 and 28 years (Loeber et al., 2012). Low IQ was associated with the highest peak in criminal behavior during adolescence (a rapid acceleration of criminal behavior in middle adolescence and rapid decline of delinquency in late adolescence and early adulthood). A similar although less severe pattern was observed for boys with high IQ but low inhibitory control. Thus, the lowest age-crime curve

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was observed among boys who had both normal to high intellectual skills and good inhibitory control. Once again, these results suggest a moderating role of EF skills in the relation between IQ and externalizing behaviors (criminality). One last study of the joint contribution of different cognitive impairments found a significant relation between planning ability and reactive aggression among school-aged boys that was mediated by inhibitory control (Ellis, Weiss, & Lochman, 2009). Together, this set of innovative studies shows that associations among IQ, EF, and externalizing behaviors are complex, and several behavioral and cognitive dimensions interact to produce different patterns of vulnerabilities. Hopefully, future studies will replicate some of these findings and help clarify their meaning and developmental sequence.

Impulsivity

One possible explanation for high rates of comorbidity among externalizing spectrum disorders is through trait impulsivity, a shared, highly heritable vulnerability (Beauchaine  & McNulty, 2013). Trait or behavioral impulsivity is defined by personality traits such as poor self-control, poor self-regulation, and low behavior control, which are all related to a tendency to act without thinking or planning. Researchers have started to examine how impulsivity might help account for the relation between cognitive impairment and externalizing spectrum disorders, especially in the areas of SUDs. Studies have linked behavioral impulsivity to specific EF deficits and SUDs (Castellanos-Ryan, Rubia,  & Conrod, 2011; Finn  & Hall, 2004; Gunn  & Finn, 2013; Khurana et  al., 2013). One such study demonstrated that working memory capacity moderated risk for alcoholism associated with disinhibited traits (Finn & Hall, 2004). Other recent studies suggest a mediating role of trait impulsivity in the association between working memory and alcohol use in adolescence (Khunara et al., 2013) and adulthood (Gunn & Finn, 2013). The authors suggest that poor working memory predisposes to behavioral impulsivity, which in turn confers vulnerability to alcohol problems. Although examination of impulsivity and cognitive impairments is still in its infancy, these studies open the door to further examination of trait impulsivity in relation to cognitive profiles and externalizing disorders other than SUDs. In short, the studies just reviewed confirm the importance of considering externalizing disorders as heterogeneous phenomena. Overall,

reviewed studies suggest that there are indeed links among IQ, EF, and externalizing disorders beginning in preschool and continuing into adulthood. However, many questions remain. Despite enthusiasm for the study of links between EF and externalizing behaviors in the past two decades, knowledge remains fragmented. Studies are few, and they are rarely longitudinal. Furthermore, heterogeneity of measures of IQ, EF, and externalizing behaviors remains high across studies. If it becomes increasingly clear that global composite measures of cognitive function or behavior are unsatisfactory, narrow-band dimensions that deserve the attention of researchers will need to be determined. From a behavioral point of view, it appears that violent and aggressive behaviors (most likely reactive and physical aggression) are the dimensions of externalizing behaviors associated most reliably with cognitive deficits (IQ, VIQ, and EF). ADHD symptoms are also likely to show association with cognitive impairments, but these impairments might be different from those associated with aggressive/violent behaviors. Further examination of comorbidity patterns and within-group heterogeneity are also needed. From an EF perspective, dimensions considered are so variable from one study to another and from one developmental period to another that it becomes extremely tenuous to draw conclusions to guide future studies. Across all ages, working memory and inhibitory control show the strongest and most consistent association with externalizing spectrum disorders. In the preschool and school-aged periods, study of the capacity for delay of gratification, a hot EF skill, seems promising. However, this dimension has been less frequently examined in adolescence or adulthood. Finally, results regarding cognitive flexibility are mixed, and selective attention has been less frequently studied.

Developmental Considerations

Despite an accumulation of empirical studies supporting associations between externalizing behaviors and cognitive deficits, longitudinal studies examining these associations and corresponding developmental theories are lacking. Although there are a few exceptions, etiological studies need to better address the timing, nature, and severity of externalizing behaviors as they relate with low IQ and poor EF across development. This section lays some of the foundation for a model linking the externalizing spectrum trajectory (see Beauchaine  & McNulty, 2013) to cognitive deficits.

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There is a long history of debates about the meaning of the cognitive deficits observed in children, adolescents, or adults with externalizing problems, especially delinquency/criminality (Ayduk et  al., 2007; Bornstein, Hahn, & Suwalsky, 2013; Brownlie et  al., 2004; Dionne, 2005; Dionne, Tremblay, Boivin, Laplante, & Pérusse, 2003; Harry & Minor, 1986; Hirschi  & Hindelang, 1977; Huesmann, 1993; Loeber et  al., 2012; Lynam, Moffitt,  & Stouthamer-Loeber, 1993; Moffitt, Caspi, Silva, & Stouthamer-Loeber, 1995; Poehlmann et al., 2012; Stattin  & Klackenberg-Larsson, 1993; Utendale, Hubert, Saint-Pierre, & Hastings, 2011; White & Batty, 2012). Explanations that have been provided over the years can be grouped into three broad models: (1) externalizing problems as (direct or indirect) causes for cognitive impairments, (2)  cognitive impairments as (direct or indirect) causes for externalizing problems, and (3) the association between externalizing problems and cognitive impairments as the result of their association to a third variable. With regard to the first model, externalizing behaviors might interfere with development of cognitive skills through different mechanisms including hindered school participation, low motivation, or high impulsivity. With regard to the second model, several mechanisms have been suggested to explain how low cognitive skills might lead to increased externalizing problems. Among these, low cognitive skills might increase the likelihood of academic failure, which in turn would encourage affiliation with deviant peers, thus providing increased opportunities for antisocial behaviors. Finally, with regard to the third model, several “third variables” have been proposed to be responsible for the association between externalizing problems and cognitive impairments. Only a small number of these “third variables” have been tested empirically (SES, family adversity, ethnicity, reference source, parental IQ, parental mental health, and impulsivity). Interestingly, all three models have received some degree of empirical support. However, firm conclusions in favor of one or the other are not yet possible, mostly because rigorous prospective longitudinal studies are scarce and the choice of participants’ age for the assessment of target constructs seems more convenient than theory-driven and has been extremely variable from one study to another, thus producing results that are often contradictory (for a detailed analysis, see Parent et al., 2011). Furthermore, the conceptual framework that has emerged during the past decades for the study of developmental psychopathology emphasizes the 384

organizational and transactional nature of developmental processes (see, e.g., Achenbach, 1990; Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013; Carlson & Sroufe, 1995; Cicchetti, 1984; Cicchetti  & Lynch, 1993; Sameroff, 2000; Sameroff & MacKenzie, 2003). Within this framework, and as a consequence of the organizational nature of developmental processes, patterns of associations between different variables might change as a function of the developmental period under study. Thus, conclusions derived from adolescent studies might not apply to preschoolers, and vice versa. As a consequence of the transactional nature of developmental processes, externalizing problems and cognitive impairments might be contributing to each other dynamically, and their associations might be moderated by other developmental processes. Accordingly, it is necessary to examine longitudinally and dynamically the associations between externalizing problems and cognitive impairments within and across developmental periods and test how these associations might be moderated or explained by genetic, physiologic, behavioral, psychological, relational, environmental, and social processes. The model proposed in this chapter (see Figure 22.1) is designed to target some of the most promising developmental processes as a function of four major developmental periods: infancy (first 2 years, including, prenatal and perinatal factors), preschool (2–5 years), childhood years (6–11 years), and adolescence/early adulthood (12–25  years). It is not meant to be exhaustive. Consistent with studies conducted so far, the model is likely to be mainly relevant to boys who, more often than girls, may engage early in a developmental trajectory of impulsive, physically aggressive and violent behaviors.

Infancy

Some prenatal and perinatal factors predict both cognitive skills and externalizing behaviors prior to school entry. Notably, a number of pre- and perinatal risk factors are likely contributors to low IQ, poor EF, and externalizing behaviors because of their contribution to the development of neurological problems. These factors include exposure to teratogens during pregnancy (see Chapter 24; Huijbregts et  al., 2006; Huijbregts, Séguin, Zoccolillo, Boivin, & Tremblay, 2007; Huijbregts, Warren, de Sonneville,  & Swaab-Barneveld, 2008; Mattson, Crocker, & Nguyen, 2011) or exposure in the postnatal environment (Bouchard, Laforest, Vandelac, Bellinger, & Mergler, 2007; Bouchard et al., 2011),

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Genetic factors Cognitive development → EF development → Language acquisition

IQ/EF

Brain development Puberty Difficult temperament

Pre/perinatal environment

Prenatal

Disruptive behavior

Externalizing spectrum disorders

Daycare and school environment, peers Family environment

Infancy

Preschool

Childhood

Adolescence/early adulthood

Figure 22.1  Developmental model linking the externalizing spectrum trajectory to cognitive deficits.

maternal stress during pregnancy, and certain types of perinatal complications (Gatzke-Kopp, 2011), as well as prenatal and postnatal malnutrition (Séguin et al., 2007). Abuse, neglect, and chronic stress in infancy may also be added to this list of factors that interfere with normal brain development or cause brain damage (De Bellis, 2005; Lupien et al., 2011; McDermott et  al., 2013; Séguin et  al., 2007). Early manifestations of these neurological problems result, in particular, in a difficult temperament, including irritability, excitability, impulsivity, attention problems, and self-regulation deficits (e.g., Ishikawa & Raine, 2003; Rothbart & Bates, 2006). These behavioral manifestations convey particular challenges in the establishment of effective parent–child relationships, parenting, cognitive development, and learning of self-control and social skills (Bridgett et al., 2009; Choe, Olson,  & Sameroff, 2013; Guerin, Gottfried,  & Thomas, 1997; Lawson,  & Ruff, 2004; Stams, Juffer, & van Ijzendoorn, 2002). Some studies suggest that negative developmental sequelae of a difficult temperament are particularly likely in a family context characterized by parents’ mental health problems, social isolation, marital conflict, single parenthood, lack of resources, and poor parenting skills (e.g., Crockenberg, 1981; Dionne, 2005; Poehlmann et al., 2012; Tremblay et al., 2005; Tschann, Kaiser, Chesney, Alkon,  & Boyce, 1996). Unfortunately, the presence of both these family characteristics and neurological disorders in children is common

because such family characteristics also increase the likelihood of prenatal and perinatal complications. Several mechanisms could be involved, including lack of access to adequate medical care, exposure to maternal stress and unhealthy habits during pregnancy, and gene–environment correlations (Dick, 2005). Whereas warm parenting that is tailored to specific child needs might alleviate certain manifestations of difficult temperament (Kim & Kochanska, 2012; Poehlmann et  al., 2012; Vaughn, Bost,  & van IJzendoorn, 2008) and provide an optimal environment for EF development (Bernier, Carlson, & Whipple, 2010; Bibok, Carpendale, & Müller, 2009; Diamond  & Aspinwal, 2003; Kopp, 1982; Landry, Smith,  & Swank, 2006; Poehlmann et al., 2012), poor parenting promotes continuity of and increasing behavioral difficulties (Crockenberg, Leerkes, & Jo, 2008; Tremblay et al., 2005). Moreover, even in the absence of an extremely difficult temperament, poor parenting may lead to the development of similar difficulties among children who did not show regulatory difficulties at birth (Belsky, Rosenberger,  & Crnic, 1995; Cicchetti  & Lynch, 1993; Collins, Maccoby, Steinberg, Hetherington,  & Bornstein, 2000; De Bellis, 2005; Kochanska & Kim, 2013). Thus, several mechanisms in infancy, both biological and social, lay the foundation for the coexistence of cognitive and behavioral problems, mostly through interference with healthy brain development.

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Preschool Years

During the preschool period, research suggests that associations between cognitive impairments and externalizing behaviors become more firmly established (Schoemaker et al., 2013; Séguin et al., 2009). At least three complementary mechanisms warrant further attention from researchers in order to enhance our understanding of these developmental relations between cognitive impairments in the preschool years and externalizing spectrum disorders. First, lack of a supportive, stimulating social environment may predict part of the shared variance between cognitive deficits and behavior problems. For example, in the early years, parents play an important role in stimulation, guidance, and socialization of their children. Culturally based knowledge, such as language or knowledge about numbers and writing systems (which are heavily represented in VIQ assessments), depend on social mechanisms for their acquisition. For example, experimental studies show that enhancing parent–child interactions during shared book reading increases vocabulary acquisition in preschool children (Mol, Bus, de Jong, & Smeets, 2008). A difficult parent–child relationship interferes with the quality and amount of cognitive stimulation (Landry  & Smith, 2010) and with the development of children’s social skills, emotion regulation, and self-concept through modeling and reinforcement of social behaviors (Keller, Spieker, & Gilchrist, 2005; Landry & Smith, 2010; Perez & Gauvain, 2010; Thompson & Meyer, 2007). In parallel, high-quality nonmaternal care services (e.g., day-care centers) enhance outcomes among children in terms of both cognitive and behavioral development (Love et al., 2002; Schweinhart et al., 2005). They could play a protective role, moderating negative effects of a difficult parent–child relationship by providing cognitive stimulation and alternative modeling of social behaviors (Côté et al., 2007; Geoffroy et al., 2010). A second mechanism, difficult temperament in early childhood, is likely to alter cognitive development through interference with social participation. For example, Gauvain and Perez (2008) observed that mothers of noncompliant preschoolers expressed more disapproval and were more directive during joint cognitive activity than were mothers of compliant children, and noncompliant children were less involved in the task than were more compliant children. Gene–environment correlational studies demonstrate that parental negativity and harsh discipline are moderately heritable (Jaffee et  al., 2004; O’Connor, Deater-Deckard, 386

Fulker, Rutter, & Plomin, 1998). Both twin (Jaffee et al., 2004) and adoption (O’Connor et al., 1998) studies reveal that much of this heritability reflects genetic influences on externalizing behavior that prompt negative responses from adults. There is little literature on the relationship between child temperament and language development. Nevertheless, researchers have found that difficult temperament (negative emotionality) is related to poorer receptive vocabulary skills among preschoolers, both directly and indirectly, through mediating influences of parental stress (Noel, Peterson, & Jesso, 2008). This highlights the transactional interplay among temperament, parenting, and development of cognitive abilities. A third possible mechanism has received less attention during preschool. Although social interactions predict cognitive development, cognitive deficits are also hypothesized to interfere with emerging social understanding (Hala, Pexman, Climie, Rostad, & Glenwright, 2010) and with the acquisition of more sophisticated social skills such as applying appropriate social rules, anticipating consequences of one’s actions, and understanding the emotions and intentions of others (Caspi & Moffitt, 1995), thus resulting in higher levels of externalizing behaviors. For example, in a twin study, a 0.20 correlation between physical aggression and expressive vocabulary was accounted for entirely by a significant phenotypic path from expressive vocabulary to physical aggression (Dionne et al., 2003). Thus, cognitive difficulties may interfere with the normative decrease in externalizing behaviors during the preschool period, as well as negatively affect parent–child interactions. During the preschool years, it is thus expected that social mechanisms within the family or the community (e.g. day-care services) continue to be partly responsible for the coexistence of cognitive impairments and externalizing behaviors. In addition, transactional mechanisms of influence from the behavioral to the cognitive domain—and vice versa—should strengthen their co-occurrence. Furthermore, given children’s rapid development during this period, the relative contributions of these three mechanisms could vary within the preschool years.

Childhood

The transition to school is a major developmental challenge for children. A  transactional model of three possible sets of mechanisms is proposed to account for the developing association

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between cognition and externalizing behaviors during the early school years. In the first set, mechanisms through which poor cognitive skills (verbal skills, EF) might contribute to worsening problem behaviors are proposed. Conversely, the second set involves worsening cognitive abilities for children with behavioral problems in the early school years. Finally, this bidirectional relationship between cognitive abilities and behavioral problems could be moderated through social or genetic mechanisms. Mechanisms through which cognitive deficits lead to increased risk of externalizing behaviors may be twofold. First, EF development provides an important foundation for cognitive and social-emotional skills associated with school readiness (Blair, 2002; Blair  & Razza, 2007). Thus, children who enter elementary school with a combination of verbal and EF deficits, whether or not combined with externalizing behaviors and unstable family relationships, are at high risk of academic failure and school disengagement (Duncan et  al., 2007; Martel et  al., 2007; Pagani, Fitzpatrick,  & Parent, 2012), which in turn reduces the possibility of positive socialization experiences (Catalano  & Hawkins, 1996; Lösel  & Farrington, 2012). Academic failures or underachievement have indeed been linked to later externalizing behaviors in several studies (Morgan, Farkas, Tufis,  & Sperling, 2008; Sabol  & Pianta, 2012; Tremblay et al., 1992; Vaillancourt, Brittain, McDougall,  & Duku, 2013), suggesting that one possible mechanism through which low cognitive skills might lead to externalizing problems, or to increases in externalizing problems, is through academic failures during the elementary school years. Although academic failures might also lead to other types of problems (e.g., internalizing problems; see van Lier et al., 2012), antisocial pathways might be the most likely option for children already struggling with impulsivity, hyperactivity, and/or aggression at school entry (e.g., Diamantopoulou, Rydell, Thorell, & Bohlin, 2007). The second pathway through which cognitive skills might contribute to increasing externalizing problems involves social relationships. Children with poor verbal skills and EF deficits may be less likely to deal skillfully with complex social situations (Séguin et al., 1995) and may have trouble seeking help from peers and adults (Dionne, 2005; Nigg & Huang-Pollock, 2003). Again, for children who already exhibit high levels of externalizing problems, limitations in communication skills, inhibitory control, and problem solving may encourage

use of externalizing behaviors such as physical aggression to resolve interpersonal conflicts, thereby further limiting opportunities for positive social interactions and learning more complex social behaviors. Such children may be somewhat trapped; their poor problem-solving abilities, disinhibition, and limited communication skills encourage the use of antisocial means to achieve their goals, and the consequent short-term gains obtained reinforce these behaviors and prevent the development of more socially acceptable alternatives. In addition, rejection by socially competent peers often results in an association with other deviant peers, which increases exposure to deviant activities (Caspi  & Moffitt, 1995; Patterson, Reid,  & Dishion, 1992; Vitaro, Pederson, & Brendgen, 2007). The school years could also be a time when verbal and EF deficits worsen for children who already exhibit behavior problems on school entry. These behaviors, especially aggression, predict lower intellectual skills (Goodman et al., 1995; Huesmann & Eron, 1984) and interfere with school participation (Brennan, Shaw, Dishion, & Wilson, 2012; Lanza et  al., 2010; Scanlon  & Mellard, 2002; van Lier et al., 2012), which in turn limits opportunities for learning. These bidirectional relations between cognitive skills and behavior problems might be accentuated or attenuated through social or genetic processes. The quality of the family and school environments and their ability to enhance or alleviate these mechanisms represents a possible moderator of this transactional process, although with limited available empirical support (but see Curby, LoCasale-Crouch, et  al., 2009; Curby, Rimm-Kaufman, & Ponitz, 2009; Fallu & Janosz, 2003; Moffitt, 1990; Pianta, Belsky, Vandergrift, Houts, & Morrison, 2008; Tsai & Cheney, 2012). For example, lack of parental involvement in child school experiences (supervision of school activities, use of professional resources, and communication/ cooperation with school) could amplify behavioral difficulties (El Nokali, Bachman, & Votruba-Drzal, 2010). Genetic factors might also be at play. For example, several candidate genes have been identified in recent literature to be associated with both externalizing behaviors and poor EF (Langley, Heron, O’Donovan, Owen,  & Thapar, 2010). Furthermore, recent neuroimaging genetic studies on externalizing behaviors have focused on the dopamine transporter genes and inhibitory control as risk pathways (see Durston, de Zeeuw, & Staal, 2009, for review).

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Taken together, these mechanisms suggest that relations between cognitive deficits and externalizing behaviors persist throughout the school years and may best be described as transactional:  worsening externalizing behaviors as a consequence of low cognitive abilities at school entry, combined with worsening cognitive deficits as a consequence of externalizing behavior problems. The family and school environments, as well as genetic factors, are possible moderators of these transactional processes.

Adolescence and Early Adulthood

Adolescence is known as a period of increased peer influence, whether positive or negative (Dishion, Nelson,  & Bullock, 2004; Marschall-Lévesque, Castellanos-Ryan, Vitaro,  & Séguin, 2014). The adolescent’s social world expands, and opportunities for new experiences abound. Pressure for autonomous decision making, in both hot and cool contexts, increases (Albert, Chein, & Steinberg, 2013). Adolescence is also a period of rapid biological transformations, with puberty triggering increased gonadotropin and sex steroid secretion. The developing brain is sensitive to these gonadal hormone increases, which contribute to many physiological (synaptic reorganization) and morphological changes, particularly at the neuronal (gray matter) level. These changes culminate in peak thickness in the frontal lobes (at around 12  years in boys and 11  years in girls; Giedd et  al., 1999; Thompson et  al., 2000) and corresponding self-regulatory function changes (Cameron, 2004). There is also a relatively rapid development of the limbic system (e.g., the nucleus accumbens and the amygdala; Casey, Jones, & Hare, 2008). The accumbens is particularly sensitive to reward cues, and adolescence is a period of enhanced sensitivity to rewards relative to costs (Ernst & Mueller, 2008). All of these changes contribute to emergence of new developmental challenges and new forms of externalizing disorders. For example, substance use is partly driven by reward systems (Koob & Kreek, 2007; Koob  & Le Moal, 2008). Furthermore, Canadian statistics show that by grade 11, a majority of teens (86 %) use alcohol, at least occasionally (Dubé et  al., 2009), and around 25% report having tried an illicit substance (Johnston, O’Malley, & Bachman, 2007; Office of National Statistics, 2008). Both puberty timing (age at which a certain stage of pubertal development is reached or pubertal status) and tempo (rate of growth through pubertal stages) are associated with externalizing behaviors (mostly SUDs). For example, one longitudinal 388

study showed that pubertal status at age 12 and tempo were independent predictors of substance use and problems in mid to late adolescence (Castellanos-Ryan, Parent, Vitaro, Tremblay,  & Séguin, 2013). EF skills, especially hot EF skills, are hypothesized to play an important role in adolescents’ ability to resist negative peer pressure and to focus on positive school and social involvement (Riggs, Jahromi, Razza, Dillworth-Bart, & Mueller, 2006; Steinberg, 2009). Support of a significant adult (in the family, in school, from the sport club) might play a moderating role in the relationship between EF skills and involvement in externalizing behaviors. In a developmental period when personal regulatory mechanisms may lag behind regulatory demands expected in social settings, adolescents who can count on the positive external regulatory support of meaningful adults may become less vulnerable (Anderson, Christenson, Sinclair,  & Lehr, 2004; Graber, Brooks-Gunn,  & Warren, 2006; Yang et  al., 2007). Genetic factors might also moderate the relation between cognitive impairments and externalizing behaviors, as was found in one study in which the link was moderated by the DRD4 genotype (DeYoung et al., 2006). Externalizing behaviors might also contribute to prediction of later cognitive skills. For example, involvement in antisocial activities is a well-known predictor of school disengagement and drop out (Hoffmann, Erickson,  & Spence, 2013; Janosz, Le Blanc, Boulerice, & Tremblay, 2000), reducing opportunities to practice higher level abstract reasoning skills typical of higher education and cognitive testing (Cole, 1990). In addition, as discussed in previous sections, early cannabis and other substance use is negatively associated with later EF skills and IQ (see Gruber et al., 2012; Meier et al., 2012). As for the other developmental periods, both social and biological mechanisms are at play during adolescence, which may enhance or attenuate the bidirectional relations between cognitive impairments and externalizing problems. New developmental challenges emerge and add to the possibility of an increasing association between externalizing problems and EF or IQ.

Controversies

Although convincing evidence supports an association between cognitive impairments and externalizing disorders, debate remains over the meaning of this association, including its generalizability and the specific nature of cognitive impairments.

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Among such debates, we discuss sex differences and hot/cool executive processes.

Are Links Between Cognition and Externalizing Behaviors Unique to Males?

As seen earlier, the majority of samples of the reviewed studies included males only and it is unknown whether findings generalize to females. Nonetheless, despite significant prevalence differences, externalizing behavior trajectories across males and females share notable similarities (Broidy et al., 2003; Moffitt & Caspi, 2006). So, is the relation between cognition and externalizing behaviors also important for females? Available data are mixed with regard to this question. For example, one meta-analysis showed that the relation between EF and externalizing behavior problems was moderated by sex (Schoemaker et  al., 2013): the association was stronger in studies that used a higher percentage of boys. On the other hand, Raine and colleagues (2002) found an association between IQ and CD that was not moderated by sex. To our knowledge, only Giancola and colleagues (1998, 1999, 2000, 2001, 2003) explored the role of EF on externalizing behaviors specifically among females. Their results suggest a link between EF deficits and externalizing behaviors in adolescent females with CD and SUDs. To what extent the effect sizes are similar in males and females remains to be clarified. These findings call for further studies on cognition and externalizing behaviors in males versus females.

How Useful Is the Distinction Between Hot and Cool EF?

Following Metcalfe and Mitchell (1999), researchers have distinguished those general regulatory skills used during problem-solving tasks that are emotionally neutral from the regulatory skills used in emotionally arousing tasks (e.g., Brock, Rimm-Kaufman, Nathanson,  & Grimm, 2009; Carlson; 2009; Hongwanishkul et al., 2005; Willoughby, Kupersmidt, Voegler-Lee,  & Bryant, 2011; Zelazo  & Carlson, 2012; Zelazo  & Müller, 2010). EF is said to be cool when problem-solving strategies are activated in a context with low emotional or motivational arousal. Performance in cool EF tasks is associated with general intellectual abilities. In contrast, hot EF is invoked when an emotional or motivational appetitive or aversive component is involved. For example, one of the most frequently used neuropsychological tasks to assess hot EF is the Iowa Gambling Task (IGT;

Bechara, Damasio, Damasio,  & Anderson, 1994), a decision-making task involving risk taking to achieve gains and avoid losses. Research supports the key role of emotional processes in the IGT (see Dunn, Dalgleish,  & Lawrence, 2006, for a review). Although interconnected, performance in cool versus hot EF tasks can also be distinguished on a neurophysiological level because they are associated with different PFC areas (Bechara, 2004; Happaney, Zelazo, & Stuss, 2004; Hongwanishkul et al., 2005). Cool EF is based on neural networks crossing the lateral PFC and the anterior cingulate cortex, whereas hot EF also activates the orbitofrontal regions of the PFC and posterior cingulate cortex and is highly connected to the limbic system. There is accumulating support for this distinction in behavioral research with adolescents and adults (Bechara, Damasio, Tranel,  & Anderson, 1998; Séguin, Arseneault, & Tremblay, 2007), supported by neuroimaging research (Happaney et al., 2004; Krain, Wilson, Arbuckle, Castellanos,  & Milham, 2006) and lesion studies (Bechara, 2004; Bechara et al., 1994). In contrast, only some studies found a distinction between hot and cool EF in children (Brock et al., 2009; Carlson, Moses, & Breton, 2002; Davis-Unger  & Carlson, 2008; Hongwanishkul et al., 2005; Huijbregts et al., 2008; Wahlstedt, Thorell,  & Bohlin, 2009; Willoughby et  al., 2011), whereas other studies failed to find evidence of a distinction (Allan & Lonigan, 2011; Sulik et al., 2010). Notwithstanding these conflicting results, when studies distinguish between these two sets of processes, emerging evidence suggests that hot EF processes are associated with externalizing behavior (e.g., Huijbregts et al., 2008; Matthys et al., 2013; Pauli-Pott & Becker, 2011; Willoughby et al., 2011). For example, a study of preschool children used latent factors representing hot and cool regulation in relation to disruptive behaviors and academic achievement. The results showed that, when considered together, cool EF was associated with academic achievement, whereas hot EF was associated with inattentive-overactive behaviors (Willoughby et al., 2011). These results suggest that hot EF tasks might be more strongly associated with externalizing behaviors, compared to cool EF tasks. However, a recent neuroimaging literature review comparing brain structure, function, and connectivity in children and adolescents with ADHD and CD concludes that ADHD is mostly characterized by abnormalities in “cool” brain regions and network, whereas CD is mostly associated with abnormalities in brain regions linked to “hot” EF (Rubia,

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2011). It thus seems that the cool versus hot EF distinction could be useful in the study of externalizing disorders as long as comorbidities among disorders, especially among ADHD versus CD/ODD, are taken into account. Whether the actual “hot” versus “cool” terminology is used is less important than the idea of pursuing, in an integrated fashion, the research tradition into the processes that these labels denote.

Research Agenda and Future Directions

We now address four limitations of the current literature that interfere with our understanding of the associations between cognitive impairments and externalizing problems: lack of a unified definition of EF, heterogeneity of symptoms or groupings of symptoms (e.g., ADHD with or without comorbid CD, aggressive vs. nonaggressive CD), lack of longitudinal studies with repeated measures of cognitive impairments and externalizing problems within and across developmental periods, and lack of experimental or genetically informative studies. First, there is a lack of consensus regarding the definition of EF. Because current tasks are not always clear about the conceptual approach taken to measure EF, the conclusions are unclear and incomplete. The proposed research agenda includes elaboration of a unifying conceptualization of EF and development of a battery of EF tasks that would provide assessment for all facets of this conceptualization. Based on the current literature, we suggest the elaboration of a three-dimensional conceptualization of EF that would include subphases of problem solving, as originally articulated by Luria (1966; representing the problem, planning a solution, executing the plan, monitoring and evaluating the adequacy of an attempted solution); types of processes necessary for EF, consistent with Miyake’s work (2000; selective attention, inhibitory control, working memory, and cognitive flexibility); and the emotional context of EF performance, as suggested by Metcalfe and Mitchell (1999; cool vs. hot). Second, as discussed earlier, findings suggest inconsistencies in the magnitude of cognitive impairments associated with comorbidity between ADHD and disruptive behaviors (ODD/CD). Additionally, recent research reveals the possibility of distinct cognitive impairments between ADHD and CD. One study found that physical aggression problems among preschoolers were associated with verbal deficits, whereas hyperactivity problems were related to nonverbal deficit (Séguin et al., 2009). Another study points to disorder-specific 390

brain abnormalities associated with ADHD and CD in the absence of comorbidity (Rubia, 2011). Inconsistencies were also found with respect to aggressive versus nonaggressive symptoms of CD or antisocial behaviors, in which aggressive symptoms are associated uniquely with cognitive impairments (e.g., Barker et  al., 2007, 2011; Hancock et  al., 2010). Thus, future research into specific facets of externalizing problems should investigate cognitive impairments while considering joint developmental trajectories of ADHD and CD symptoms, distinguishing between inattentive and impulsive/ hyperactive forms of ADHD, aggressive and nonaggressive forms of CD, and subfactors of ODD. This should be conducted in the context of also trying to understand what is common across externalizing behaviors (Beauchaine & McNulty, 2013; Krueger et al., 2007). Third, a developmental approach of the cognition–behavior association is in order. For example, longitudinal studies are needed to better understand the role of cognitive impairments in the stability of externalizing spectrum disorders in development. Are specific deficits linked to stable patterns of externalizing problems? Are cognitive deficits stable across developmental periods, and, if so, are they associated in a homotypic fashion with the same behavioral problems across time? Another direction for future research is to examine low IQ and EF deficits and their association with externalizing behaviors during sensitive environmental transitions throughout development (e.g., preschool to school, elementary to high school, high school to workplace). Because these transition periods are characterized by changing adjustment pressures on children’s and adolescents’ behaviors, they could be associated with rapid changes in the strength of association or pattern of transactional association between cognitive impairments and externalizing behaviors. Finally, experimental and genetically informative longitudinal studies of the mechanisms through which externalizing behaviors and cognitive skills development contribute to each other over time and are modulated through social and genetic mechanisms is highly needed to enhance our understanding of the interplay between these mechanisms. Experimental research should be designed both for validation of theoretical assumptions about causal pathways and for guiding practitioners. Research on the development of preventive interventions that focus on improving EF could indeed be useful (see Diamond [2012] and Diamond & Lee [2011]

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for full reviews). For example, computerized training via specially designed games improves working memory and reasoning in preschool and school-aged children (Holmes, Gathercole, & Dunning, 2009; Holmes et al., 2010; Klingberg et al., 2005; Thorell, Lindqvist, Nutley, Bohlin,  & Klingberg, 2009). Testing the effects of these interventions on externalizing behaviors is a necessary next step. Other promising avenues include examination of genetic moderators of the cognition–behavior association (e.g., DeYoung et  al., 2006; Durston et  al., 2009; Langley et al., 2010) and examination of epigenetic processes. Recent research on epigenetic alterations to DNA structure brought about through adverse life events suggests intergenerational transmission of environmental effects (Champagne & Curley, 2009; Diamond, 2009; Essex et al., 2013). For example, maternal lifestyle (nutrition, substance use, stress, mental health) during pregnancy has epigenetic effects on child brain development and function. Studies also demonstrate differential DNA methylation signatures associated with chronic histories of aggression (Guillemin et  al., 2014; Provençale et al., 2013).

Conclusion

Since the first empirical studies in which offender’s IQs were linked to delinquency, research advances during the past century have progressed toward a more complex and nuanced view of associations between cognition and externalizing behaviors. Our aim in writing this chapter was to highlight current research trends and developmental considerations on relations between cognitive impairment and externalizing behaviors. Although the traditional diagnosis-based approach described herein remains prevalent, researchers are now examining links between cognition and externalizing behaviors using conceptualizations that extend beyond these traditional diagnostic categories. Three current research trends include (1) exploring comorbidity patterns among externalizing disorders in relation to cognitive profiles, (2)  examining associations between cognitive impairments and specific symptoms of externalizing disorders assessed along continua, and (3) examining associations between specific traits related to externalizing spectrum disorders (impulsivity) and cognitive impairment. An organizational and transactional model of potential developmental mechanisms was proposed, one in which cognitive deficits and the externalizing spectrum disorder trajectories contribute to each other over time, with specific

individual-level vulnerabilities and environmental risk factors operating at different development stages. Many questions remain unanswered, some of which have been highlighted. The adverse social and economic consequences of externalizing disorders on society are tremendous. Prevention will be best informed by this developmental evidence base.

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Head Injury and Externalizing Behavior

Joan P. Gerring and Roma A. Vasa

Abstract This chapter deals with traumatic brain injury (TBI) and its link to externalizing disorders in children, with emphasis on advances in the field that have been driven by new medical and technologic discoveries. It begins with a historical overview of TBI diagnosis and treatment, citing the important role played by neuroimaging. It then looks at the features and correlates of new and persistent externalizing disorders that arise in pediatric patients after TBI, focusing on closed head injury. It also examines attention-deficit/hyperactivity disorders and disruptive behavior disorders, the latter of which include oppositional defiant disorder and conduct disorder. In addition, personality change (aggression, antisocial personality disorder) following TBI is discussed. The article concludes by evaluating pharmacological and psychological treatments available to children with externalizing disorders. Key Words:  traumatic brain injury, children, treatment, neuroimaging, externalizing disorders, closed head injury, attention-deficit/hyperactivity disorders, disruptive behavior disorders, personality change, antisocial personality disorder

Traumatic brain injury (TBI) is an important public health problem among children and adolescents and can result in life-long emotional and behavioral impairments (Rutland-Brown et  al., 2006). In the United States, an average of 700,000 TBIs occurred each year between 2002 and 2006 among individuals between the ages of 0 and 19 years, which led to 60,000 hospitalizations and 6,000 deaths (Faul et al., 2010). Psychiatric sequelae of pediatric TBI include both externalizing and internalizing disorders. These disorders vary according to intensity of injury, age at injury, location(s) of injury, and time elapsed since injury. Importantly, sequelae of pediatric injuries differ from adult brain injury sequelae because the injury and recovery occur within the context of a developing brain. In this chapter, we present an overview of advances in the field of pediatric TBI that have been driven by new medical and technologic discoveries. Historical information is followed

by descriptions and correlates of new and persistent externalizing disorders that occur after pediatric TBI. Our focus is on pediatric closed head injury, in contrast to penetrating head injury, because this is the most common type of TBI among children and adolescents. Head injury affects behavioral, cognitive, and affective functional domains. Deficits in these domains often overlap and influence one another. Therefore, TBI research investigations include both behavioral and cognitive components, in varying proportions, depending on the primary focus of study. Because psychological and cognitive domains often overlap and may be difficult to distinguish, it is important to study both aspects together in order to reach well-informed conclusions.

Historical Context

In 1974, Teasdale and Jennett used neurologic and physiologic observations to create an intensive 403

care scale that measures severity of brain injury in a simple fashion that is easily administered by medical personnel worldwide. The Glasgow Coma Scale (GCS) consists of three components:  eye opening, best motor response, and best verbal response. GCS scores indicate severe (3–8), moderate (9–12), or mild (13–15) injury. This scale provides a common language for diagnosis and treatment of coma and is a primary indicator of long-term prognosis. Subsequent scales, including the Galveston Orientation and Amnesia Test (GOAT; Levin, O’Donnell, & Grossman, 1979), the Children’s Orientation and Amnesia Test (COAT; Ewing-Cobbs et  al., 1990), the Rancho Los Amigos Scale (Hagen, Malkmus, & Durham, 1979), the Disability Rating Scale (Gouvier, Blanton, LaPorte,  & Nepomuceno, 1987), and the Functional Independence Measure (FIM; Hamilton, Laughlin, Fiedler,  & Granger, 1994) have been developed to monitor course of recovery and to indicate prognosis. In the 1980s, the specialties of neuropsychiatry and neuropsychology developed in response to the increased survival rates among children and adolescents with head injuries. Methodologies emphasized attention to understanding causal processes, appreciation of the role of developmental mechanisms, and consideration of continuities between normality and psychopathology (e.g., Beauchaine  & McNulty, 2013). Specialists now collaborate in multidisciplinary rehabilitation clinics to treat and monitor behavioral and cognitive sequelae during recovery, as well as during continuing development. Academic centers possess advanced neuroimaging facilities that allow performance of static and functional correlation analyses in research projects. Many studies in the United States occur across institutions because improved TBI diagnosis and treatment have resulted in diminished numbers of patients and also because researchers in different locations can communicate rapidly using technologically advanced methods. As interest has focused on behavioral and emotional sequelae of TBI, existing psychiatric and behavioral criteria have been adapted for use in patients with clinical brain disorders. Biomedical technologies have been developed to enable rapid diagnosis and improved treatment of brain injuries. Physiologic therapies have been developed to treat the metabolic cascade of events that occurs rapidly after the initial mechanical injury and results in handicap or death if not addressed rapidly. These treatments include respirators, peripheral and cerebral arterial 404 Head Injury

monitoring devices, and medications to control cerebral pressure. The concurrent development of neuroimaging has revolutionized diagnosis and treatment of TBI. Neuroimaging methods have developed from the 1970s to the present with consequent increases in precision of measurement with respect to both structural and functional brain mapping. Structural neuroimaging procedures, including computed axial tomography (CT) and magnetic resonance imaging (MRI), compute locations and volumes of anatomic structures. Beginning with the invention of CT in 1972 by Hounsfield, the presence of hemorrhage or skull fracture could be assessed and rapidly treated (Hounsfield, 1973). Invention of MRI in 1973 (Lauterbur, 1973) and development of scanning sequences of increasing sensitivity in detecting hemorrhagic lesions (Beauchamp et  al., 2011) led to increased diagnostic accuracy and elimination of the radiation risk that occurs with CT. Proton magnetic resonance spectroscopy (MRS) identifies and quantifies metabolites of intracellular brain compounds and can detect abnormalities in brain areas that appear normal via conventional imaging even after mild TBI (Vagnozzi et  al., 2010). Diffusion tensor imaging (DTI) is an MRI method developed in 1985 in which water molecule diffusion patterns can reveal details about neural networks and longitudinal changes in white matter tracts after TBI (Le Bihan & Breton, 1985; Wilde, Hunter, & Bigler, 2012). CT is the most commonly used imaging technique in the acute phase of head injury because it can quickly detect hemorrhagic contusions, skull fractures, and other lesions for which surgical intervention can be applied. MRI is the imaging procedure of choice in the subacute phase of head injury and during the follow-up period. It is more sensitive than CT in identifying diffuse axonal injury, nonhemorrhagic contusions, small subdural hematomas, and brainstem injuries. Functional neuroimaging, including positron emission tomography (PET) and functional MRI (fMRI), is used widely to examine relations between specific psychological and cognitive processes and brain activity. PET is a nuclear medicine functional imaging technique invented in 1973 in which three-dimensional images of a radioactive tracer introduced into the blood are constructed by computer analysis (Grubb, Phelps, Raichle,  & Ter-Pogossian, 1973). High cost and radiation exposure limit use of PET scanning. fMRI, developed in 1990, is the most widely used current technique

to measure brain activity (Ogawa, Lee, Nayak,  & Glynn, 1990). Neuropsychological and psychiatric testing paradigms are adapted for administration in the scanner. Changes in blood oxygenation and flow that occur in response to neural activity are used to produce activation maps demonstrating brain locations involved in a particular mental process.

Closed Head Injury

Closed head injury is the most common type of TBI in children and adolescents. Falls are the leading cause of TBI, with rates being highest among 0- to 4-year-olds. Motor vehicle accident is the second leading cause of TBI and results in the largest percentage of TBI-related deaths (Faul et al., 2010). Closed head injury is in contrast to open head injury (or penetrating head injury), in which the dura of the skull is penetrated. Closed head injury is a diffuse process, with the force of impact radiating out through brain substance. The most common primary injury is diffuse axonal injury, a consistent finding that occurs in mild, moderate, and severe injuries (Metting, Rodiger, DeKeyser,  & van der Naalt, 2007). Focal primary lesions include contusions, hemorrhages, and hematomas. Secondary brain injury develops within hours after impact and consists primarily of ischemia (for extended discussion, see Shannon Bowen, & Gatzke-Kopp, 2013). Trauma-induced movement of the frontal and temporal lobes creates a mechanical vulnerability for damage to fronto-temporo-limbic brain regions because of the location of the cranial fossa and dura mater (Bigler, 2013). The most prominent clinical effect of white matter disruption is loss of overall brain connectivity, which can result in diminished psychological and cognitive control among persons with brain injury. Increased injury severity is associated with decreased white matter volume, decreased total brain volume, and increased cerebral atrophy. No two brain injuries produce identical effects because of different mechanisms of injury, different pathologies, and different alterations of brain morphology (Wilde et al., 2012). Furthermore, pediatric brain injury occurs in the context of a developing brain. Pediatric neuroimaging has moved beyond identification of normal and abnormal structures, big and small lesions and their locations, and identification of regions that subsume specific functions. A current research focus is to map the trajectory of normal development of neurocognitive abilities along with the trajectory of recovery in order to determine the extent to which a return to baseline functioning occurs (see e.g., Wilde et  al., 2012).

The magnitude of difference between the trajectory of normal development and that of anatomic change due to injury may be variable depending on the time between injury and assessment and may also vary by tissue type and brain region. Also, the regulatory process of pruning or reduction of brain cells and synapses begins around age 3, with a wave of synapse formation in the frontal cortex occurring around ages 11 and 12, followed by a second pruning in adolescence (Giedd & Rapoport, 2010). The brain consolidates learning by pruning away synapses and wrapping myelin around axons to stabilize and strengthen them. Thus, given the child’s current brain development and subsequent neurodevelopmental processes, injuries of similar severity may have very different sequelae at age 3 than, for example, at ages 7 and 10. The ability to functionally compensate for early injury varies across brain injuries and cognitive-behavioral domains (Anderson, Damasio, Tranel,  & Damasio, 2000). TBI is most likely to damage the frontal and temporal lobes—later maturing regions that underlie executive function and behavioral control. Diffuse axonal injury affects multiple, widely distributed neuronal connections, with the possibility of multiple deficits as an outcome. In contrast, focal brain injuries often result in a small number of discrete deficits. Failure to undergo changes identified in normally developing control populations may reflect altered development following TBI.

Assessment and Description of Externalizing Disorders Post-TBI

When child psychiatrists and psychologists describe externalizing behaviors in a diagnostic or categorical manner, they use terminology contained in the “Disorders Usually First Diagnosed in Infancy, Childhood, or Adolescence” section of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV; American Psychiatric Association [APA], 2000) or in the “Neurodevelopmental Disorders” or “Disruptive, Impulse-Control, and Conduct Disorders” sections of the DSM-5 (APA, 2013). Most researchers incorporate a DSM-derived structured interview into their assessment method and use diagnostic terms including attention-deficit/ hyperactivity disorders (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD). They may also use dimensional measures. Adult head injury researchers are more likely to use dimensional measures and place greater focus on symptoms than on diagnosis. Gerring, Vasa

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Two National Institutes of Health interagency TBI Outcomes Workgroups were established to select measures to maximize the ability of clinical researchers to (1)  document course of recovery, (2) enhance prediction of later outcomes, (3) quantify treatment effects, and (4) facilitate comparisons among centers. The first workgroup focused on adult TBI (Wilde et al., 2010) and the second on child TBI (McCauley et  al., 2012). Common outcome measures chosen for pediatric studies included the Schedule for Affective Disorders and Schizophrenia (K-SADS-PL; Kaufman et  al., 1997), which is a categorical diagnostic measure. Dimensional measures include the Child Behavior Checklist for ages 6–18 (CBCL/6–18; Achenbach & Rescorla, 2001), an instrument with multicultural norms that is a used widely for parent-report assessments of youth symptoms. Eight factor analytically derived syndromes can be obtained from the CBCL/6–18, including anxious/depressed, withdrawn/depressed, somatic complaints, social problems, thought problems, attention problems, rule-breaking behavior, and aggressive behavior. A  second popular measure is the Strengths and Difficulties Questionnaire (SDQ; Goodman, 1997), a brief (25-item) dimensional screening questionnaire for children aged 4–16  years, with acceptable psychometric properties and translations in multiple languages. The assessment can be completed by parents or teachers. Initial TBI research focused on severe head injuries, for which sequelae are more obvious and thus easy to distinguish from normal behaviors and more likely to be observed by scanning procedures. However, neuropsychological testing has become more precise and neuroimaging sequences have been developed that can detect much smaller lesions. Accordingly, attention has turned to examining correlates of mild TBI. Mild head injury is commonplace in childhood, and research using sophisticated new neurocognitive and neuroimaging tools is expanding into this area. Persistent sequelae of mild injuries and sequelae of repetitive mild TBI are public health problems now receiving research scrutiny (MTBI Guidelines Development Team, 2010).

Attention-Deficit/Hyperactivity Disorder Prevalence

ADHD and secondary ADHD (S-ADHD) are the externalizing disorders with highest prevalence after TBI. Children who meet no criteria for, or fail to meet full criteria for ADHD prior to injury and then attain full criteria after injury, except for 406 Head Injury

the age-of-onset criterion, are described as having S-ADHD. In contrast, primary ADHD is referred to as “developmental ADHD,” has a strong genetic liability, and shows phenotypic variation as development proceeds. Both disorders have behavioral and cognitive components in domains of attention, executive function, behavioral inhibition, and motor control. In one study of 99 moderate and severely injured children aged 4–19  years when referred for rehabilitation, the premorbid prevalence rate of developmental ADHD was 20%— significantly higher than expected in a reference population (Gerring et al., 1998). The incidence of new-onset S-ADHD in the same study was 19%, significantly higher than the incidence of primary developmental ADHD in a reference population. The high premorbid prevalence of ADHD and the high incidence of new-onset ADHD related to TBI have public health implications because identification and successful treatment of ADHD could become a means of reducing the rate of moderate and severe TBI in the childhood population.

Behavioral Studies

Studies of behavioral correlates of ADHD and S-ADHD use both categorical and dimensional methods of assessment. Impairment is a necessary criterion for a DSM categorical diagnosis. In one prospective study of 141 children including orthopedic controls, the 75 children with complicated mild to severe TBI were aged 7–17 years at the time of injury (Max et  al., 2012). Complicated-mild TBI is defined by GCS scores of 13–15, with brain lesions (contusions, hematomas, diffuse cerebral swelling), as indicated by CT scans. Six of 65 children at the 3-month followup and one of the 53 orthopedic controls developed S-ADHD. Max and others also studied 143 children who were 5–14  years of age with mild to severe TBI with no premorbid history of ADHD (Max et al., 2005). Occurrence of S-ADHD was 15% from 6 to 12  months post-injury, and 21% from 12 to 24  months post-injury Although Max used the categorical measure of diagnosis in both studies, the samples are different in age range, severity, and duration since injury, making the results difficult to compare. Yeates et  al. (2005) used the ADHD Rating Scale (DuPaul, Power, Anastopoulos,  & Reid, 1998) and other rating scales in a study of 132 children, including 41 with severe TBI, 41 with moderate TBI, and 50 with orthopedic injuries. Dimensional rating scales quantify severity of ADHD symptoms but often do not inquire

about impairment. At 4  years post-injury, 20% of the severe TBI group displayed symptoms consistent with a diagnosis of the combined subtype of ADHD, compared with 4% of the orthopedic injury. Higher levels of premorbid attention problems increased vulnerability to long-term attention problems among children with severe TBI. Linear regressions predicting post-injury from pre-injury attention problems indicted that higher levels of premorbid attention problems increased risk for long-term attention problems in children with severe TBI compared with children with orthopedic injuries. Increased inattention/hyperactivity has been described after childhood mild TBI. Behavioral sequelae of mild head injury were studied in a longitudinal prospective study using dimensional measures among a birth cohort of 1,265 children who were all born within a 3-month period (McKinlay, Dalrymple-Alford, Horwood, & Fergusson, 2002). Mild head injury was defined as a concussion or a suspected concussion that received medical attention. In addition, loss of consciousness needed to be less than 20 minutes, and there could be no evidence of skull fracture. Confirmed cases of mild head injury between ages 0 and 10 years were divided on the basis of outpatient medical attention (n  =  84) or inpatient hospital overnight (n = 28) and compared with the noninjured remainder of the cohort (n  =  807). Members of the cohort with nonspecific head injury, with more severe head injuries, or severe developmental delay were excluded from the study. Twelve child and family measures were used as covariates to control for possible confounds. The inpatient, but not the outpatient group, displayed increased inattention/hyperactivity and conduct symptoms between ages 10 and 13 years, especially if the injury occurred before age 5.

Risk Factors for ADHD/S-ADHD Post-TBI

Significant predictors of new psychiatric disorders after TBI vary depending on the time elapsed since injury. In the Max et al. (2012) 3-month followup study, psychosocial variables were not significant predictors of new psychiatric disorder. The authors postulated that injury-related variables have a greater influence on outcome relative to psychosocial variables, the shorter the time elapsed since injury. In the Max et  al. (2005) 6- to 24-month followup study, family psychosocial adversity and preinjury adaptive function, assessed via Vineland testing (Sparrow, Balla, & Cicchetti, 1984, predicted S-ADHD 18 months after injury. Neither severity

of injury nor lesion location predicted S-ADHD from 6 to 24 months post-injury. Children in the Gerring study who developed S-ADHD by 1 year post-injury had significantly greater premorbid psychosocial adversity, posttraumatic affective lability and aggression, posttraumatic psychiatric comorbidity, and overall disability than children who did not develop S-ADHD (Gerring et  al., 1998). Psychosocial adversity becomes a dominant predictor of S-ADHD in the chronic recovery phase of TBI. If the central feature of ADHD is a deficit in behavioral inhibition, then life in a disorganized chaotic environment encourages emergence and proliferation of ADHD features in vulnerable children.

Cognitive Studies

Studies of cognition in ADHD and S-ADHD have focused on several deficits, in the domains of attention, executive functioning, memory, and inhibitory control (Slomine et al., 2005; Sinopoli, Schachar,  & Dennis, 2011; Yeates et  al., 2005). Although ADHD and S-ADHD share commonalities in cognitive, behavioral, and functional outcomes, some differences exist. For example, Sinopoli studied the effect of reward on four groups of children: those with TBI who did not exhibit S-ADHD, those with TBI who did exhibit S-ADHD, developmental ADHD, and healthy controls (Sinopoli et  al., 2011). Unlike children with developmental ADHD, children with S-ADHD did not show significant difficulties with restraint inhibition, similar to children with TBI without attention difficulties. The authors concluded that despite similarities in clinical manifestations, S-ADHD and developmental ADHD appear to have different cognitive-behavioral phenotypes. Such findings may result from the fact that much of reward processing is mediated by deep subcortical structures that are impaired among those with ADHD (see Beauchaine  & McNulty, 2013; Rubia, 2011) but that may be more likely to be spared by TBIs to cortical regions.

Neurobiology

There is substantial evidence implicating frontal-striatal and cerebellar dysfunction in developmental ADHD (see, e.g., Rubia, 2011), with global maturation delay in cortical thickness and reduced gray matter and white matter volumes in the frontal lobes (Cortese & Castellanos, 2012; De La Fuente, Xia, Branch, & Li, 2013). The anatomic pattern of TBI, with major involvement of the frontal lobes Gerring, Vasa

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and subcortical gray matter, overlaps with portions of the frontal lobe-striatal-cerebellar pathway that is implicated in developmental ADHD. A  structural MRI study was performed on 76 children who did not have a prior history of ADHD (Herskovits et al., 1999). Of the 76 moderately and severely TBI injured children, 15 developed S-ADHD. Children who developed S-ADHD had more lesions in the right putamen than did children who did not develop S-ADHD. Another study examined correlates of sustained attention using a continuous performance test adapted for fMRI. Five children (mean age 9.4 years) with a history of moderate/severe TBI 1–3  years previously and eight with a history of orthopedic injuries were studied. Results indicated overactivation of parietal and frontal regions among children with TBI relative to orthopedic controls. This finding contrasts with that obtained in imaging studies of developmental ADHD in which underactivation of these regions has been documented (e.g., Kramer et  al., 2008). A  number of reasons have been offered for these discrepant findings. The first is that observed attention deficits in pediatric TBI and ADHD may not share a common underlying neuropathology because inattention may be a nonspecific cognitive symptom that can result from a number of neuropathologies. A  second reason is that children with TBI may require overactivation to achieve comparable performance to children without neurological insults. There is also the possibility that TBI results in a generalized pattern of overactivation in the brain, other than overactivation specific to attention processing.

Summary

Two kinds of ADHD symptoms occur after TBI:  (1)  S-ADHD, without prior diagnosis of ADHD, and (2) developmental ADHD, with full DSM criteria present both before and after injury. Premorbid presence of ADHD often leads to amplification of symptoms post-injury. S-ADHD is defined by the same symptoms as developmental ADHD, except for age of onset. Frontostriatal and cerebellar brain regions are implicated in the etiology of both disorders. Both kinds of ADHD post TBI have high prevalence and impairment, mandating development and implementation of effective treatments.

Disruptive Behavior Disorders

Disruptive behavior disorders (DBD) are characterized by deficits in self-control of behavior and emotion (APA, 2013). Diagnoses include ODD 408 Head Injury

and CD, with criteria ranging in severity from noncompliance to physical aggression. Using a dimensional approach for analysis is more likely to capture the full range of disruptive symptoms, given that most children with ODD or CD have one or more symptoms of the other disorder (indeed, most children with CD meet full criteria for ODD). However, meaningful cutoff points for inclusion into ODD and CD that reflect true discontinuities have not been identified, so, in practice, categorical and dimensional views of these clinical phenomena are both used (Hinshaw, 1994).

Oppositional Defiant Disorder

ODD criteria comprise both behavioral and affective symptoms that include negative and defiant behaviors coupled with enhanced affective reactivity. For example, defiant behaviors include “often actively refuses to comply with requests from authority figures and often deliberately annoys others,” and affective criteria include “often loses temper, often touchy or easily annoyed, and often angry and resentful” (APA, 2013). Max et al. (1998) studied 50 consecutive admissions of children and adolescents for 2  years after closed head injury. Thirty percent were severely injured, 18% were moderately injured, and 52% were mildly injured. ODD symptoms increased from baseline to 12  months, then declined in the second year but remained elevated compared to preinjury levels. Ten patients met criteria for new-onset ODD at some point during the 2-year followup. ODD symptoms in the first year after TBI were related to preinjury family function, social class, and preinjury ODD symptoms, and severity of injury predicted ODD symptoms 2 years after injury. Gerring et al. (2009) studied 94 children, aged 4–19  years, for 1  year after severe closed head TBI. Pre-injury prevalence of ODD in the TBI sample was 6%, and incidence of new-onset ODD at 1  year was 9%. These figures do not include a diagnosis-specific impairment criterion. It is calculated that up to two-thirds of participants in this study would meet this criterion. Significant pre-injury predictors of post-injury new and persistent ODD were placement in special education and increased psychosocial adversity. Trend-significant predictors included increased CBCL delinquency scores and increased affective lability scale scores.

Conduct Disorder

CD is a diagnosis characterized by serious levels of overt (e.g., aggression) and covert (e.g., lying,

rule violation) antisocial behavior. Pre-injury prevalence of CD in the Gerring et  al. (2009) severe head injury study was 8%, and the incidence of new-onset CD at 1  year was 8%. Again, it is calculated that up to two-thirds of participants would meet the diagnosis-specific impairment criterion. Psychosocial adversity was a significant pre-injury predictor of post-injury new and persistent CD. Lower socioeconomic status (SES) was a marginally significant predictor. There was also a significant increase in the mean post-injury dimensional disruptive score compared with the mean pre-injury dimensional disruptive score. However, there was no significant increase in the mean frequency of CD symptoms from pre- to post-injury. Pre-injury significant predictors of post-injury disruptive symptoms of ODD and CD included increased CBCL aggression scores, increased CBCL delinquency scores, increased affective lability scores, lower SES, and increased psychosocial adversity.

Neurobiology

Fahim et  al. (2011) assessed symptoms among 47 8-year-old boys (22 with disruptive behavior disorders, 25 healthy controls) using cortical thickness analysis and voxel-based morphometry. Among DBD participants compared to controls, the authors found (1) decreased overall mean cortical thickness; (2) thinning of the cingulate, prefrontal, and insular cortices; and (3)  decreased gray matter density in these brain regions. The authors suggested that the thinning and decreased gray matter density of the insula disorganized prefrontal circuits, thus diminishing the influence of the prefrontal cortex (PFC) on aggression, anger, and impulsivity. In another study, structural MRIs were analyzed in adolescents aged 10–18 years, 22 with CD and 27 healthy controls (Wallace et  al., 2014). Adolescents with CD showed reduced cortical thickness within superior temporal regions and reduced amygdala and striatal volumes.

Summary

In summary, DBDs, including ODD and CD, and disruptive behavior symptoms often increase significantly after injury. Disruptive disorders may have been present before the injury or may appear in the early months or several years after the injury. Furthermore, new-onset DBDs and new-onset ADHD are frequently comorbid. Pre-injury predictors of new and persistent DBDs and symptoms include increased psychosocial adversity, low SES, family history of substance abuse, and increased

affective lability. Risk factors for these post-injury disturbances are similar to risk factors in non-TBI populations.

Personality Change Following Traumatic Brain Injury

Personality change due to TBI is a common and often persistent sequela. Symptoms may include disinhibition, aggression, affective lability, apathy, and poor social judgment. These characteristics are often attributable to frontal lobe dysfunction. The lateral orbital-frontal cortex (which controls inhibition, modulates behavior, and processes reward) and anterior cingulate cortex (which is involved in extinction learning, motivation, and error monitoring) comprise frontal-subcortical circuits that subserve many of these personality functions. Personality change is frequently comorbid with ADHD, S-ADHD, ODD, and CD. There is symptom overlap between ADHD and personality change due to TBI, with disordered inhibition considered to be a primary feature of both. Subtypes of personality change, including disinhibited, labile, aggressive, and apathetic, are considered to be direct physiological consequences of TBI. It is likely that brain injury pathology results in outward manifestations that may be subsumed under either ADHD or personality change due to TBI. This comorbidity probably contributes to qualitative differences between ADHD and S-ADHD (Gerring et al., 1998). In the Gerring et al. (1998) study, children with TBI also developed symptoms suggestive of personality change due to TBI, including affective lability and aggression. Children with S-ADHD developed significantly greater affective lability and aggression by the first year after injury than those children who did not develop S-ADHD. Max et al. (2000) assessed 94 participants aged 5–14 years at the time of hospitalization after a TBI. A standardized interview, the Neuropsychiatric Rating Schedule (Max, Castillo, Lindgren,  & Arndt, 1998), was used to assess symptoms of personality change due to TBI. Personality change occurred in 59% of severe and 5% of mild/moderate TBI participants. About 40% of severe TBI participants had persistent personality change an average of 2 years post-injury. Among mild/moderate participants, personality change was always transient. Among 37 severe TBI participants, the labile subtype was most common (49%), followed by the aggressive and disinhibited subtypes (38% each) (Max, Robertson, and Lansing, 2001). Among severe TBI participants, persistent Gerring, Vasa

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personality change was associated with injury severity, adaptive and intellectual functioning decrements, and comorbid S-ADHD but was not related to psychosocial adversity variables.

Aggression

Aggression is a construct associated with many psychiatric symptoms, from disruptive behaviors, to anxiety, mood, and psychosis. Aggression is common after pediatric TBI, where it is often considered to be a criterion of ODD, CD, or personality change due to TBI. Research studies of adult TBI are more likely to focus on the construct of post-TBI aggression as “difficult to manage in any setting, and rated as highly distressing by families and caregivers” (Baguley, Cooper,  & Felmingham, 2006).

Prevalence

Prevalence estimates in adult studies of post-TBI aggression range from 11% to 34%. The prevalence of aggression in one study of 67 adult participants who were seen within 3  months of injury was 28.4%, and the aggression was predominantly verbal (Rao et  al., 2009). The mean GCS score was 11 in the aggressive group of 19 participants and 12.7 in the nonaggressive group of 48 participants. Post-TBI aggression was associated with new-onset major depression, poorer social functioning, and increased dependency for activities of daily living.

Behavioral Studies

In a second study of adults, 319 TBI patients who were admitted to inpatient rehabilitation were followed for 6, 24, and 60  months post-discharge (Baguley et  al., 2006). Median GCS was 6; 15% of participants had mild injury, 17% had moderate injury, and 68% had severe injury. The Overt Aggression Scale (Yudofsky, Silver, Jackson, Endicott,  & Williams, 1986) was the main outcome measure. Twenty-five percent of participants were classified as aggressive at each followup. There was an overall tendency for within-subject aggression to improve over time, with 1 in 6 nonaggressive patients becoming aggressive and 1 in 3 aggressive patients becoming nonaggressive. Depression was the main predictor of aggressive behavior at 6, 24, and 60 months post-TBI. Younger age was the only other significant association. No associations were found between aggression and psychiatric history, substance abuse, injury severity, or cognitive function. In another study of 134 brain-injured adults 410 Head Injury

who exhibited aggression compared to 153 participants who sustained comparable injuries but did not exhibit aggression, the aggressive group demonstrated deficits in verbal memory and visuoperceptual skills and impairment in executive function (Wood & Liossi, 2006). There is one prospective study of aggression after severe pediatric TBI (Cole et  al., 2008). Ninety-seven children consecutively referred to a neurorehabilitation unit were followed for 1  year. Their mean GCS score was 5.42. Pre-injury psychiatric status was obtained retrospectively at enrollment, and post-injury psychiatric status was assessed at 1 year. Results revealed increased levels of verbal and physical aggression from pre- to post-injury. Using regression analyses, pre-injury ratings of aggression accounted for the largest amount of variance. Pre-injury attention problems and pre-injury anxiety predicted post-injury physical aggression, above and beyond pre-injury aggression ratings.

Neurobiology

One model implicates an imbalance between prefrontal regulation and hyperresponsivity of the amygdala and other limbic regions in the etiology of impulsive aggression (i.e., aggression that is triggered by anger-provoking stimuli; Siever, 2008). Neuroimaging studies of penetrating brain injuries among adults support the association of increased aggression with focal orbitofrontal or ventromedial frontal injury (e.g., Brower  & Price, 2001). The ventrolateral prefrontal and cingulate cortices are other regions that play important roles in regulation of aggressive behavior. Pardini et al. (2011) evaluated 155 male veterans with penetrating injuries (36–39 years post-injury) and 42 non–head-injured controls using CT imaging. The sample included 58 brain-injured veterans who were classified as aggressive and 60 brain-injured veterans who were classified as nonaggressive. Prefrontal cortex (PFC) lesions were overrepresented in the aggressive group compared to the nonaggressive group. The goals of the Pardini study were to examine effects of the interaction between lesion location and monoamine oxidase-A (MAO-A) gene polymorphism on aggressive behaviors in veterans and to evaluate effects of brain lesions and MAO-A activity on the relationship between early and current psychological trauma and brain injury–related aggression. Study results showed an interaction between lesion location and MAO-A polymorphic activity on brain-related aggressive activity. There were increased levels of aggression among participants with non-PFC

lesions and MAO-A high-activity alleles. There was a significant correlation between aggression and childhood and adult psychological trauma among participants without PFC lesions and controls. The authors concluded that PFC integrity was necessary for modulation of aggressive behaviors by gene susceptibility and trauma experiences. Posttraumatic aggression directed against others is the ultimate behavioral escalation of TBI and is implicated in multiple homicides. Lewis et  al. (1988) described characteristics of 14 juvenile males who were sentenced to death in the United States. Nine had suffered TBI with loss of consciousness, and at least six of these injuries were severe. In addition, 6 of the 14 had suffered severe head and facial insult. In a separate study of 41 adults who were charged with murder or manslaughter and referred for a PET scan in conjunction with a psychiatric evaluation, the murderer group had significantly lower overall prefrontal metabolic rates compared with matched controls. When participants were separated into a predatory group with controlled aggression from an affective group with impulsivity, the affective group had significantly lower prefrontal metabolic activity compared to controls. In contrast, frontal metabolism in the predatory group resembled that observed among controls (Brower & Price, 2001).

Antisocial Personality Disorder

When conduct-disordered youth reach age 18 years following a history of conduct symptoms before age 15 and fulfill DSM-5 diagnostic criteria, they receive a diagnosis of antisocial personality disorder (ASPD; APA, 2013). Twenty-five to forty percent of conduct-disordered youth go on to fulfill ASPD criteria. Age of onset and number of antisocial behaviors exhibited at age of onset are the best predictors of ASPD (Hinshaw, 1994). Youth with ASPD demonstrate a pervasive pattern of disregard for and violation of the rights of others, with deceit and manipulation as central features. There are few studies of ASPD and other personality disorders that have used standard diagnostic assessments among those with TBI. Hibbard et. al (2000) studied 100 participants at least 1  year post-TBI who were between 18 and 65  years of age. The Structured Clinical Interview for DSM-IV Personality Disorders (SCID-II; First et  al., 1997) was administered to participants to diagnose 12 personality disorders pre- and post-TBI. Pre-TBI personality disorders were significantly greater than the 3% found in community-based samples. ASPD

was the most frequent pre-TBI personality disorder (15%), significantly more among men than among women. It is not surprising that individuals with symptoms including impulsivity, irritability, and reckless disregard for safety would be at high risk for TBI. Neither severity of injury nor duration since injury was related to the type or number of post-TBI personality disorders. No additional ASPDs were diagnosed post-TBI.

Treatment of Externalizing Disorders

Interventions for externalizing disorders post-TBI lag behind the rapidly increasing knowledge of the neurobehavioral sequelae of TBI. It has been common practice to adapt evidence-based and other established treatments in the non–head-injured population to the childhood TBI population. Most of these studies, both psychopharmacological and behavioral, have small numbers of participants, which makes statistical significance for a specific treatment difficult to achieve. Many treatments are promising but have limited empirical support and need replication (Warschausky, Kewman, & Kay, 1999).

Biomarkers

Biomarkers and biospecimens may provide opportunities to clarify injury severity and injury mechanisms, improve individual patient management, and predict clinical outcomes (Berger, Beers, Papa,  & Bell, 2012). Biomarkers released from injured or dying cells represent three major cellular components in the brain—neurons, axons (the single and usually long process of a neuron that conducts impulses away from the cell body), and astrocytes—and provide additional diagnostic or prognostic information. Current lack of pathophysiologic information about TBI has hindered development of pharmacologic interventions, and no proven pharmacologic treatment exists at present (Papa et  al., 2008). The ability to detect and quantify diffuse axonal injury through a blood sample would help to assess the extent of dysfunction in a mild head injury. Axonal injury marker myelin basic protein (MBP) shows marked and sustained increases in the cerebrospinal fluid of children with TBI. A drug targeting diffuse axonal injury could be beneficial. Five biomarkers that have been used in most pediatric studies include the neuronal markers neuron-specific enolase (NSE) and ubiquitin C-terminal hydrolase-L1 (UCH-L1), the astrocyte markers S100β and glial fibrillary protein (GFAP), and the axonal injury marker MBP (Kochanek et al., 2013). Gerring, Vasa

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Pharmacological Treatments

Those who have sought to develop medications for TBI have focused on S-ADHD and symptoms of inattention and aggression. There is a lack of consensus regarding components within the domain of attention that makes study comparisons difficult (Chew & Zafonte, 2009). But because of similarities in anatomy and symptoms between developmental ADHD and S-ADHD, it makes sense to use stimulants as a first-line treatment option for S-ADHD and inattention post-TBI. To date, however, there is a paucity of well-designed studies on the efficacy of stimulant therapy in reducing symptoms of attention, concentration, and impulsivity. Inconsistent findings have appeared in the literature so far, from no significant change in any measure to significant treatment effects on attention and concentration (Senior et  al., 2013). In a review of seven studies of 118 participants, including 41 children and adolescents, methylphenidate effects on hyperactivity and impulsivity were evident but not as robust as those usually observed with methylphenidate in developmental ADHD (Jin  & Schachar, 2004). The Journal of Neurotrauma Neurobehavioral Guidelines Working Group reviewed 61 studies and offered use of methylphenidate and dextroamphetamine as treatments for attentional dysfunction (Warden et al., 2006). Aggressive behavior occurs on a spectrum from mild irritability to explosive and violent outbursts. Because severe forms may constitute a danger to head-injured patients and their caretakers, treatment frequently includes medication trials. There is currently no medication that presents sufficient evidence to be considered a standard treatment for aggressive behavior secondary to TBI (Warden et al., 2006). First- and second-generation antipsychotic agents are commonly used, but there is little evidence to support their long-term use (Chew & Zafonte, 2009). The β-blockers propranolol and pindolol provide fair evidence to support their use in treatment of post-TBI aggression. These medicines are clinically beneficial in some studies, but statistical significance is lacking or methodology is flawed in some studies. Other medicines that have been used to treat post-TBI aggression include methylphenidate, serotonin reuptake inhibitors, valproate, lithium, tricyclic antidepressants, and buspirone.

Psychological Treatments

Psychosocial treatments for externalizing disorders focus on deficits most prevalent after TBI. 412 Head Injury

These deficits include aggressive and disinhibited behaviors and impaired social skills. Because treatment often occurs in multidisciplinary clinics and in special education classrooms, professional providers may include behavioral psychologists, speech and language therapists, special education teachers, and occupational therapists. The overarching goal of programming is usually school and social reintegration. The current standard of care for children and adolescents with behavioral problems after TBI requires a positive patient-focused approach that emphasizes (1)  modification of the environment to minimize occurrence of undesirable behaviors, (2)  functional assessment, and (3)  use of positive operant contingencies to teach behavioral coping and self-regulation skills. Once developed, these strategies can be taught to the family, hospital, or school staff (Slifer & Amari, 2009). Recovery from TBI, mild to severe, is a lengthy, chronic process and frequently results in increased stress for everyone whose life is touched by the patient. This increased stress manifests in anxiety, depression, physical complaints, disruptive behavior, and aggression in the patient and in those around him or her. Medications alleviate some symptoms, but for recovery to progress, the family needs to reduce stress so they can work together and with community supports for the benefit of the injured person. Different psychological therapies have been developed to address family distress. These include stress management interventions such as self-monitoring and cognitive coping (Warschausky et  al., 1999), Parent-Child Interaction Therapy (Cohen, Heaton, Ginn,  & Eyberg, 2012), and a web-based family intervention (Wade, Wolfe, Brown, & Pestian, 2005). In the web-based study, six children with moderate to severe TBI and eight parents participated in 12 separate sessions. The children were between 5 and 16 years and had been injured more than 15  months previously. Weekly videoconferences with a therapist were conducted after the family completed self-guided sessions on problem solving, communication, and behavior management strategies. Analysis revealed significant improvements in injury-related burden, parental psychiatric problems, parenting stress, and the child’s antisocial behaviors, but not in the child’s depressive symptoms.

Future Directions

Externalizing disorders are common after childhood TBI. These disorders may be severe and persistent. Fortunately, many specialties share interest

in this area, and their willingness to collaborate is leading to an increase in information about normal and abnormal brain functioning and, beyond, to possibilities for treatments. Neurosurgeons and intensive care doctors collaborate on improved methods to control intracranial pressure and the metabolic cascade that follows initial head injury. Neuroscientists develop structural MRI sequences that reveal evidence of injury at all severity levels of TBI. Functional MRI sequences are used during recovery to elaborate brain mechanisms involved in injury and repair of specific functions. Intensive care doctors investigate the usefulness of biomarkers to predict clinical course and outcome. The specialty of physical medicine and rehabilitation (PM&R) developed as an offshoot of surgery to facilitate acute and chronic recovery. With respect to TBI, physical medicine doctors and physiatrists are leaders of the comprehensive treatment team during the subacute and chronic stages of recovery. These specialists work with neurologists and physical therapists on problems of seizures, headaches, and motor movements. Neuropsychologists create testing paradigms to elucidate brain mechanisms and define deficits for remediation. They monitor neurocognitive recovery with speech-language pathologists, educators, and occupational therapists. Neuropsychiatrists, psychologists, and recreational therapists focus on behavioral and emotional sequelae that often constitute the primary obstacles to a good functional recovery. Their current challenge is to devise evidence-based treatments, effective medications, and leisure activities that promote reintegration back into the community. In comprehensive TBI rehabilitation programs, medical, cognitive, and psychological specialists obtain and share information with one another. New technologies have created vast amounts of data. The challenge for TBI mental health professionals is to use available common data elements in the domains of psychological functioning, communication, and family environment to adjust existing treatments and devise new treatments for TBI externalizing disorders.

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24

Teratogen Exposure and Externalizing Behavior

Diana M. Graham, Leila Glass, and Sarah N. Mattson

Abstract Behavioral teratology is the study of the effects of exposure to teratogenic agents (e.g., drugs, toxins) on subsequent behavior among offspring. Several decades of study link teratogenic exposure to cognitive and behavioral effects, including psychopathology. This chapter summarizes the literature on selected teratogens and externalizing behaviors and disorders.  Although other risk factors also exist, teratogenic exposures are associated with the development of attention-deficit/hyperactivity disorder, oppositional defiant disorder, conduct disorder, antisocial personality disorder, and substance use disorders. Compared to other behavioral and cognitive deficits, the relation of teratogenic exposure to externalizing disorders has not been extensively studied.  Additional research is needed to determine the contribution of dose and timing of exposure, as well as interactions among exposure and sociodemographic, environmental, and genetic factors, on externalizing behaviors. Key Words:  behavioral teratology, externalizing disorders, prenatal exposure, attention-deficit/ hyperactivity disorder, oppositional defiant disorder, conduct disorder, antisocial personality disorder, substance use disorder, alcohol

Teratogens are contaminant agents that can lead to anatomical birth defects and functional damage to the fetus (Stein, Kline, & Kharrazi, 1984). Human teratogens can be classified as harmful prenatal exposure to drugs of abuse (e.g., alcohol, cigarettes, cocaine), prescription drugs (e.g., antidepressants, anticonvulsants), environmental toxins (e.g., lead, mercury, manganese), and maternal disease (e.g., rubella, toxoplasmosis, insulindependent diabetes mellitus). Behavioral teratology, a term coined in 1963 by Werboff and Gottlieb, is the study of the postnatal functional consequences (e.g., cognitive, social, motor) of teratogenic exposure (Hutchings, 1978; Riley & Vorhees, 1986). Fetal exposure to teratogens results in a spectrum of negative outcomes that differ in severity, with or without physical abnormalities, and may not always manifest during infancy. Mechanisms through which fetuses become exposed to teratogens vary greatly. As just noted, 416

exposures can include licit and illicit drugs, ongoing or acute medication usage, illness, or environmental agents. Pregnant women may suffer from medical conditions, such as epilepsy or major depressive disorder, that require ongoing pharmacological treatment. Exposures to environmental toxins may occur unwittingly, and disease exposure may occur throughout gestation (e.g., due to viral infection or gestational diabetes). Approximately half of all pregnancies are unplanned, thus increasing chances of exposure prior to the recognition of pregnancy (Finer  & Zolna, 2011). Regardless of pregnancy awareness, addictive behaviors can be difficult to manage, and women may struggle to abstain from drugs of abuse during this period. Cigarettes were the most frequently used drug among pregnant women between 2011 and 2012 (16%; Substance Abuse and Mental Health Services Administration [SAMHSA], 2012) despite decades of Surgeon General warnings about possible fetal injury (Sims

and the Committee on Substance Abuse, 2009). Similarly, alcohol use often continues in pregnancy, with 8.5% of women reporting continued use after pregnancy recognition (SAMHSA, 2012). This statistic supports a recent review that suggests that alcohol-warning labels have limited efficacy in changing drinking behaviors (Thomas, Gonneau, Poole, & Cook, 2014). Research in behavioral teratology has proliferated over the past several decades, bolstering awareness and understanding of the dangers associated with teratogenic exposure. Prenatal exposure renders the fetus vulnerable to structural abnormalities, cognitive deficiencies, and socioemotional difficulties throughout life. Exposure to teratogens (such as alcohol) remains the most preventable cause of cognitive and behavioral problems among children (e.g., Gahagan et al., 2006). Therefore, it is imperative to clarify direct and indirect effects of prenatal exposure and to engage in community outreach to more effectively convey risk. In this chapter, we focus on the relation between prenatal teratogen exposure and externalizing behaviors. Externalizing conditions vary in frequency and clinical expression by age and include a diverse set of behaviors associated with poor attention, opposition, aggression, impulsivity, and delinquency, the legal interpretation or conceptualization of antisocial behavior that is subject to adjudication and involvement with the criminal justice system (Bongers, Koot, van der Ende, & Verhulst, 2004; Wakschlag, Pickett, Cook, Benowitz,  & Leventhal, 2002). Specific externalizing disorders include attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), conduct disorder (CD), antisocial personality disorder (ASPD), and substance use disorders (SUDs; American Psychiatric Association [APA], 2013). Research on the etiology of externalizing behaviors includes studies on genetic and environmental influences among affected individuals. Although causes of externalizing psychopathology are diverse (see Beauchaine  & McNulty, 2013) prenatal care and exposures have attracted significant attention over the years (e.g., Bada et al., 2007; Oberlander et al., 2007; Wakschlag, Leventhal, Pine, Pickett, & Carter, 2006). Prenatal exposure by means of maternal substance use during pregnancy is the focus of this chapter, despite alternative pathways by which the fetus can be exposed (e.g., second-hand smoke). Because alcohol and cigarettes are the most commonly used and widely studied teratogens, we discuss them most extensively.

Historical Context

Awareness that infant and child behavior is affected by maternal exposure to substances has existed for hundreds of years. As early as the 18th century, warnings of infant death and mental deficiency associated with maternal alcohol use during pregnancy were given (Hutchings, 1978). For years, birth defects were attributed to consequences of such factors as supernatural causes, developmental arrest, hospital trauma, and genetic factors, with research in teratology concentrated primarily on the appearance of gross structural malformations. Despite early recognition of teratogen exposure, physical and cognitive abnormalities in the fetus were often attributed to genetic factors; the uterus and placenta were considered effective barriers to toxins in the mother’s bloodstream (Hutchings, 1985). Seminal research in behavioral teratology began in the late 1940s with reports of intrauterine drug exposure increasing risk of impairment on behavioral tasks in human offspring (Riley  & Vorhees, 1986). It was not until the early 1970s that teratology began to focus on behavioral manifestations of prenatal exposure to toxic agents (Hutchings, 1983). Animal studies became more extensive because they could control for confounds. Several toxic exposures leading to behavioral disruption in rats, even in the absence of physical defects, were identified. Yet investigators struggled to extend these findings to humans because many scientists were hesitant to include the study of functional abnormalities in the field of teratology (Fried, 2002). Retrospective reports among clinical human populations began in the 1970s, focusing on behavioral assessments of the offspring of mothers who abused heroin and alcohol during pregnancy (Hutchings, 1978). Most notable were structural and functional abnormalities linked to prenatal alcohol exposure. This led to clinical delineation of fetal alcohol syndrome (FAS) in 1973 (Jones & Smith, 1973), which increased public awareness of the dangers of alcohol consumption during pregnancy (Thomas et al., 2014). Today, the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) includes neurobehavioral disorder associated with prenatal alcohol exposure (ND-PAE) as a condition requiring further study and explicitly mentions prenatal alcohol exposure as a specifier for other specified neurodevelopmental disorder (APA, 2013). Ongoing research continues to address developmental effects of teratogenicity, with the hopeful result of improving public awareness of various classes of teratogens. Graham, Gl ass, Mat tson

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Links Between Teratogen Exposure and Externalizing Disorders Attention-Deficit/Hyperactivity Disorder

ADHD is the most commonly diagnosed disorder of childhood, with prevalence rates as high as 8–11% among children and adolescents in the United States (Bloom, Cohen, & Freeman, 2011; Visser et  al., 2014) and a worldwide prevalence of more than 5% (Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007). Primary symptoms include inattention, hyperactivity, and impulsivity, which significantly impair functioning (APA, 2013). Children with ADHD often also present with clinical levels of delinquency, aggression, and other socially inappropriate behaviors (Gaub  & Carlson, 1997). ADHD symptomatology stems from various etiological sources, one of which is prenatal exposure to drugs of abuse. Indeed, children with ADHD are significantly more likely than their peers to have been exposed prenatally to drugs of abuse, cigarettes, and/or alcohol (Mick, Biederman, Faraone, Sayer,  & Kleinman, 2002). Although most children diagnosed with ADHD have not been prenatally exposed, multiple teratogens are associated with an increased risk of ADHD and related behaviors (see Tables 24.2 and 24.3).

Alcohol

Alcohol is one of the most commonly investigated behavioral teratogens, with decades of studies indicating that prenatal exposure can result in a broad array of cognitive and behavioral deficits (Mattson, Crocker, & Nguyen, 2011) known as fetal alcohol spectrum disorders (FASD). Research by SAMHSA found that 8.5% of pregnant women (15–44 years of age) in the United States report current alcohol use, with 2.7% participating in binge drinking (Figure 24.1A), which declines in subsequent trimesters (Figure 24.2; SAMHSA, 2012). Women considered to be high-risk (i.e., who already have a child with prenatal alcohol exposure) have an estimated average of five alcoholic drinks per week throughout pregnancy (May et al., 2013). Whether a child manifests neurobehavioral effects of intrauterine alcohol exposure depends on a multitude of factors, including genetics and environment. However, investigations of prenatal alcohol exposure document increased behaviors consistent with the core features of ADHD (Mattson et al., 2011). As noted earlier, core 418

Teratogen Exposure

features of FASD include both inattention and impulsivity (Mattson et al., 2011), and upwards of 60% of children with an FASD also have a diagnosis of ADHD (Burd, Klug, Martsolf, & Kerbeshian, 2003; Fryer, McGee, Matt, Riley, & Mattson, 2007). Prenatal alcohol exposure results in neuroanatomical and neurochemical changes that are similar to those seen in idiopathic ADHD, including an increase in dopamine (DA) transporter binding, which can result in a deficiency in availability of synaptic DA (Riikonen et al., 2005). Reduced DA function is well replicated in idiopathic ADHD (Vles et al., 2003; Chapter 3) and is linked to more severe displays of impulsivity and hyperactivity (Waldman et al., 1998; Chapter 8). Executive dysfunction and inattention, which are core impairments of both alcohol-exposed children and children with ADHD, are associated with reduced caudate nucleus volumes (Castellanos et al., 1994). Indeed, both alcohol-exposed and nonexposed children with ADHD demonstrate reduced caudate volumes (Filipek et al., 1997; Mattson et al., 1996), suggesting that the caudate is an overlapping structural abnormality contributing to ADHD behaviors. Although ADHD is common in children with FASD, research has increasingly addressed the specificity of behavioral and cognitive deficits observed in alcohol-exposed children with ADHD versus children with idiopathic ADHD. Both groups perform more poorly than typically developing children on continuous performance tasks (CPTs; Kooistra, Crawford, Gibbard, Kaplan, & Fan, 2011; Kooistra, Crawford, Gibbard, Ramage, & Kaplan, 2010). Upon direct comparison, group differences between alcohol-exposed children with ADHD and those with idiopathic ADHD may depend on ADHD subtype (Kooistra et al., 2010; 2011). Although additional study is needed, response inhibition may be less compromised in alcohol-exposed children (Kodituwakku, Handmaker, Cutler, Weathersby, & Handmaker, 1995) than in children with ADHD alone (Burden et al., 2010). These findings suggest that teratogenic effects of alcohol result in similar presentations of ADHD behaviors, but with distinct differences between exposed and nonexposed children with ADHD. Whereas the majority of recent studies of ADHD-related behaviors in FASD include participants with heavy prenatal alcohol exposure, epidemiologic, prospective research indicates a

(A)

Cigarettes

187,000

Smokeless tobacco Cigars 352,000

6,000

Pipe tobacco Alcohol

33,000

11,000

(B)

4,000 4,000 6,000

Marijuana and hashish Cocaine Heroin Methamphetamine

115,000

Figure  24.1  A.  Alcohol and tobacco use in the past month among pregnant females ages 15 to 44:  Annual averages based on 2011–2012. B. Illicit drug use in the past month among pregnant females ages 15 to 44: Annual averages based on 2011–2012. Note: Total sample size of pregnant women aged 15–44 = 2,206,000; data from 2012 SAMHSA National Survey on Drug Use and Health (http://www. samhsa.gov/data/NSDUH/2012SummNatFindDetTables/)

Pregnant women (in thousands)

dose–response relationship (≥1 drink/day, on average) with attention deficits (O’Callaghan, O’Callaghan, Najman, Williams,  & Bor, 2007; Sood et  al., 2001). Delayed response times,

suggesting inattention, are particularly sensitive to alcohol exposure, even at relatively low levels (Jacobson, Jacobson, & Sokol, 1994), compared to other behaviors (Sood et al., 2001).

180 160 140 120

Cigarettes

100

Alcohol

80

Marijuana

60

Illict drug use

40 20 0

1st trimester

2nd trimester

3rd trimester

Figure 24.2  Drug and alcohol use among pregnant women by trimester Note: Total sample size of pregnant women aged 15–44 = 2,206,000; 1st trimester = 707,000; 2nd trimester = 795,000; 3rd trimester = 697,000. All data from the 2012 National Survey on Drug Use and Health: Summary of National Findings and Detailed Tables (http://www.samhsa.gov/data/NSDU H/2012SummNatFindDetTables/)

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Studies of retrospectively identified heavily exposed participants and prospective studies of low levels of alcohol exposure highlight the need to address important covariates and contributory factors. Age and sex may be particularly relevant. For instance, even though low levels of exposure are associated with inattention, prenatally exposed males display exacerbated attentional difficulties, over-and-above those seen in females (O’Callaghan, Williams, Andersen, Bor, & Najman, 1997; Sood et al., 2001). Furthermore, prenatal alcohol exposure may alter the impact of age on particular deficits in children with ADHD in as much as deficits may worsen with age in alcohol-exposed individuals, but may lessen with age in nonexposure individuals (Bresnahan, Anderson, & Barry, 1999; Hart, Lahey, Loeber, Applegate, & Frick, 1995; Spohr & Steinhausen, 2008). Additionally, when controlling for (a) familial risk of alcoholism, (b) prenatal exposure to other substances, (c) parent psychopathology, and/or (d) genetic factors, the significant association between prenatal alcohol exposure and ADHD may attenuate or disappear (D’Onofrio et al., 2007; Hill, Lowers, Locke-Wellman, & Shen, 2000).

Tobacco and Cigarette Byproducts

Links between maternal smoking and ADHD have been researched extensively. Prenatal exposure to tobacco and cigarette byproducts (e.g., nicotine, formaldehyde) may result in low birthweight, prenatal hypoxia, neurobiological changes, and multiple neuropsychological impairments such as attention deficits (Bruin, Gerstein, & Holloway, 2010; Cornelius & Day, 2009; Thompson, Levitt, & Stanwood, 2009). Daily smoking during pregnancy (≥10 cigarettes/day) is associated with increased rates of ADHD (Button, Thapar, & McGuffin, 2005; Langley, Holmans, van den Bree, & Thapar, 2007) and deficits in attention and response inhibition (Fried, O’Connell, & Watkinson, 1992; Fried & Watkinson, 2001). In a case–control study, mothers who smoked during pregnancy were twice as likely to have children with attention deficits compared to nonexposed peers, although parental psychopathology, a strong predictor of child psychopathology (Befera & Barkley, 1985), was not controlled (Landgren, Kjellman, & Gillberg, 1998). Furthermore, a more recent study separating the effects of prenatal exposure and genetic contribution on ADHD symptoms found that an increase in ADHD behaviors might be inherited, rather than a true environmental effect of prenatal cigarette exposure (Thapar et al., 2009). 420

Teratogen Exposure

Nicotine, which appears to have its strongest effect on offspring behavior during the second and third trimesters (see Table 24.1), may be the primary contributor to abnormal fetal development and long-term effects of prenatal cigarette smoke exposure (Gatzke-Kopp  & Beauchaine, 2007b; Leech, Richardson, Goldschmidt,  & Day, 1999). Smoking during pregnancy can prematurely activate nicotinic acetylcholine receptors (nAChRs) as well as increase their density (Ernst, Moolchan, & Robinson, 2001), which in animals increases risk for hyperactivity, specifically in males (Tizabi, Popke, Rahman, Nespor, & Grunberg, 1997). As nAChRs stimulate release of DA (McGehee & Role, 1995), alteration in nAChRs can produce dysregulated DA neurotransmission in prefrontal and striatal regions (Cao, Surowy,  & Puttfarcken, 2005), influencing desensitization to the reward system and increased impulsive behavior (Gatzke-Kopp  & Beauchaine, 2007a). As with alcohol, many environmental and genetic confounds may contribute to or moderate effects of prenatal exposure. For example, maternal smoking is related strongly to lower birthweight (Lieberman, Gremy, Lang,  & Cohen, 1994), which may heighten the risk for ADHD (Nigg & Breslau, 2007). Nevertheless, maternal cigarette use relates to increased ADHD among offspring even after controlling for parent psychopathology, birthweight, other substance use, gestational age, and income (Gatzke-Kopp  & Beauchaine, 2007b; Mick et al., 2002). In twin studies, which control for genetic effects, even though prenatal exposure effects on overall externalizing behaviors are reduced (Maughan, Taylor, Caspi,  & Moffitt, 2004), cigarette use during pregnancy remains an independent contributor to ADHD symptoms (Milberger, Biederman, Faraone, & Jones, 1998; Rodriguez & Bohlin, 2005; Thapar et al., 2003; 2009).

Pharmaceutical Agents

Many women suffer from conditions for which pharmacologic treatment is necessary throughout pregnancy, such as seizure disorders or depression. Antiepileptic medications, including valproic acid, phenytoin, and carbamazepine, are linked to both structural and functional congenital abnormalities (Ornoy, 2006). Exposure to valproic acid, in particular, may lead to fetal valproate syndrome, associated with developmental delay (Shepard et  al., 2002). Newborns exposed to phenobarbitone, phenytoin, and valproic acid exhibit signs of hyperactivity and withdrawal, resulting in considerable

Table  24.1 Description of  Exposure and Critical Periods for  Teratogen Use During Pregnancy as  Reported in Published Studies Teratogen

Criteria

Critical period

Description

References

Gestational Timing

References

Nicotine

Smoking ≥10 cigarettes or more per day, on average, throughout gestation

(Wakschlag et al., 1997) (Weissman et al., 1999)

2nd Trimester 3rd Trimester

(Gatzke-Kopp & Beauchaine, 2007b) (Leech et al., 1999) (Lieberman et al., 1994)

Alcohol

≥2 drinks per day (on average) or ≥5 drinks per occasion ≥5 drinks per occasion at least once every 2 weeks throughout gestation ≥0.3 fluid ounces of absolute alcohol per day

(Streissguth, Barr, & Sampson, 1990)

1st Trimester

(Larkby et al., 2011) (Streissguth et al., 1990)

Marijuana

≥6 joints per week

(Fried & Watkinson, 2001)

1st Trimester (Inattention) 2nd Trimester (Impulsivity)

(Goldschmidt et al., 2000) (Leech et al., 1999) (Richardson et al., 2002)

Cocaine

≥1 line per day of cocaine or gram equivalent of crack

(Richardson et al., 2008)

1st Trimester 3rd Trimester

(Chasnoff, Griffith, MacGregor, Dirkes, & Burns, 1989) (Leech et al., 1999) (Richardson et al., 2011)

Methamphetamine

Use ≥3 days per week throughout gestation

(LaGasse et al., 2012) (Smith et al., 2008)

1st Trimester 3rd Trimester

(Smith et al., 2008)

Lead (Pb)

≥2 μg/dL of lead

(Braun, Kahn, Froehlich, 1st Trimester Auinger, & Lanphear, 2006) 2nd Trimester (Froehlich et al., 2009) 3rd Trimester

(Hu et al., 2006) (Wright et al., 2008) (Schnaas et al., 2006)

>1.3–5 μg/dL of lead

(Bailey et al., 2004) (Sood et al., 2001)

Mercury (Hg)

Maternal hair levels 4.3–8.3 ppm Hg ≥1.2 ppm of Hg maternal hair levels

(Banerjee, Middleton, & Faraone, 2007) (Oken et al., 2005)

3rd Trimester

(Grandjean, Budtz-Jorgensen, Jorgensen, & Weihe, 2005)

Anticonvulsants

600–1,500 mg/day valproic acid

(Tomson & Battino, 2012)

1st Trimester

(Moore et al., 2000)

Antidepressants

Bupropion

2nd trimester

(Figueroa, 2010)

μg/mg, milligrams; dL, deciliter; ppm, parts per million

irritability and tremors (Koch et al., 1996). Among school-aged children with prenatal exposure to valproic acid, phenytoin, or carbamazepine, 39% displayed hyperactivity or impaired attention (Moore et al., 2000). Higher rates of ADHD diagnoses were reported as well, although this study lacked a control group for comparison. In 2004–2005, nearly 8% of pregnant women were prescribed an antidepressant (Andrade et al., 2008). Common antidepressants include bupropion and selective serotonin reuptake inhibitors (SSRIs). Bupropion, a selective norepinephrine-dopamine reuptake inhibitor, doubled the risk for ADHD, particularly when exposure occurred in the second trimester (Table 24.1; Figueroa, 2010). In contrast, prenatal exposure to SSRIs (or to nonmedicated maternal depression) was not associated with ADHD (Figueroa, 2010). This pattern of findings suggests that effects on DA neurotransmission contribute to ADHD symptoms.

Marijuana and Illicit Drugs

Research on use of marijuana and illicit drugs during pregnancy is limited. However, available literature suggests that use of these substances may lead to congenital malformations, behavior problems, and adolescent substance use among offspring (Eyler, Behnke, Conlon, Woods, & Wobie, 1998; Zuckerman et al., 1989). Approximately 5.8% of a nationwide sample of women between the ages of 15 and 44 years used marijuana and/ or illicit drugs during pregnancy in 2011–2012 (Figure 24.1B; SAMHSA, 2012). Marijuana is the most frequently used, federally illegal, substance among pregnant women (Figure 24.1B; SAMHSA, 2012). Marijuana use during pregnancy results in an increase in parent-reported ADHD-related behaviors, including inattention, hyperactivity, and impulsivity (Fried, O’Connell et al., 1992; O’Connell & Fried, 1991; Richardson, Ryan, Willford, Day, & Goldschmidt, 2002). Marijuana use (≥2 joints/day) during the first and second trimesters (Table 24.1) is linked to ADHD-like behaviors (Goldschmidt, Day, & Richardson, 2000; Leech et al., 1999; Richardson et al., 2002), including inability to sustain attention, and such use increases impulsivity on CPTs (Fried & Watkinson, 2001; Fried, Watkinson, & Gray, 1992). However, not all studies show inhibition deficits in relation to prenatal marijuana exposure (e.g., Fried, O’Connell et al., 1992). This discrepancy may be due to the age at examination because impulse control and attention deficits in exposed samples may become more 422

Teratogen Exposure

evident with development (Fried & Smith, 2001; Fried, Watkinson, & Gray, 1998). Effects of prenatal cocaine exposure on behavior are variable and often discrepant in the literature, as a function of factors controlled for, measures used, and rate/frequency of exposure (Accornero,  Anthony, Morrow, Xue, & Bandstra, 2006; Accornero, Morrow, Bandstra, Johnson, & Anthony, 2002). For instance, when accounting for lower maternal warmth, which is associated with prenatal cocaine use and predictive of child behavior problems, no significant relation between cocaine exposure and externalizing behaviors was found (Eiden, Granger, Schuetze, & Veira, 2011). In addition, cocaine use often occurs simultaneously with use of other drugs of abuse (e.g., alcohol, cigarettes, and marijuana), thus confounding the effects of any one substance. Differences in results may be due to study design, such as controlling for polydrug use, measurement of a dose–response effect, informant, and trimester of exposure. Studies showing detrimental effects of prenatal cocaine include dose-dependent increases in excitability and dysregulated arousal as soon as 3 weeks after birth (Tronick, Frank, Cabral, Mirochnick, & Zuckerman, 1996), and cocaine-specific impairments in attention, impulsivity, and parent-reported hyperactivity in childhood (Bada et al., 2007; Richardson, Goldschmidt, Leech, & Willford, 2011). These behaviors are associated specifically with use during the first and third trimesters (Table 24.1; Leech et al., 1999; Richardson et al., 2011). However, other studies have failed to support these findings (Accornero et al., 2002; Richardson, Goldschmidt, & Willford, 2008). Moreover, teacher reports often do not agree with parent reports, and typically indicate no relation between attention problems and prenatal exposure (Delaney-Black et al., 2000; Richardson, Conroy, & Day, 1996). Other, more objective reports support cocaine-related differences in inattention, but not impulsivity (Bandstra, Morrow, Anthony, Accornero, & Fried, 2001; Richardson et al., 1996; Savage, Brodsky, Malmud, Giannetta, & Hurt, 2005). Although research is sparse, other highly addictive drugs, including methamphetamine and heroin, have been investigated in regard to offspring ADHD. Methamphetamine use during pregnancy has been studied prospectively and is related to symptoms of hyperactivity (Smith et al., 2008), attention deficits, and clinical levels of ADHD by age 5 years (LaGasse et  al., 2012). One study of children who were exposed prenatally to methamphetamine reported

deficits in sustained attention (Chang et al., 2004). However, this study did not control for common confounds (e.g., other teratogenic exposures). A subsequent investigation reported no association between methamphetamine exposure and attention when accounting for birthweight, socioeconomic status (SES), and other substance use (Smith et al., 2008). These studies examined children at different ages, suggesting the need for further investigation into the effects of methamphetamine exposure on behavior across development. Most research on prenatal opiate exposure has been small in scale. However, maternal heroin use is associated with parent reports of inattention and hyperactivity in school-aged children (Ornoy, Segal, Bar-Hamburger, & Greenbaum, 2001). In another study, the same research group compared exposed children to nonexposed children with environmental deprivation and to typically developing children. Among children whose mothers used heroin during pregnancy, 53% displayed increased hyperactivity, inattention, and impulsivity. However, 42% of children born to heroin-dependent fathers and 37% of environmentally deprived children also exhibited clinical levels of ADHD behaviors (Ornoy, Michailevskaya, Lukashov, Bar-Hamburger,  & Harel, 1996). Furthermore, children who are both heroin-exposed and remain under the care of their biological mothers are at higher risk for exacerbated symptoms related to risk for environmental deprivation (Ornoy et al., 1996; 2001). Methadone is the most generally accepted opiate replacement. Methadone treatment may facilitate stabilization of maternal opiate use and reduce potential for detrimental effects of acute opiate withdrawal on the fetus. Importantly, however, methadone is itself an opiate and may affect the developing fetus. Although there have not been many well-controlled studies, use of methadone during pregnancy is associated with poorer attention and hyperactivity during infancy (Hans, 1989).

Other Toxins and Teratogens

Environmental toxins associated with high rates of ADHD symptomatology include lead, polychlorinated biphenyls (PCBs), and methylmercury. Lead is one of the most common heavy metal of concern because it continues to be found in house paint and toys despite health warnings and environmental regulations. As a result, effects of prenatal exposure to lead (measured through umbilical cord blood and dental enamel) are often confounded by continued postnatal exposure (Ostrea et al., 2002). Effects of

postnatal lead exposure are described elsewhere in this volume (Chapter 24). Research results specific to prenatal, rather than postnatal, lead exposure and its relationship with ADHD-consistent behaviors have been mixed and require further investigation. Prenatal exposure is related to poorer attention based on the Mental Development Index from the Bayley Scales of Infant Development–Second Edition (BSID-II) but not when using the Behavior Rating Scale from the BSID-II (Plusquellec et al., 2007) or the Teacher Report Form of the Child Behavior Profile (Bellinger, Leviton, Allred, & Rabinowitz, 1994). Production of PCBs occurred primarily for industrial use, and these substances have unique chemical properties ultimately determined as toxic. Although production was banned in 1979 (Boucher, Muckle, & Bastien, 2009), PCBs are still a concern through contamination of fish. Prenatal exposure to PCBs, measured through umbilical cord blood, is associated with increased impulsivity and poorer attention and response inhibition as indexed through a high rate of commission errors (Jacobson & Jacobson, 2003; Stewart et al., 2003; 2005). In general, PCB exposure does not affect sustained attention in particular, although one study did indicate such an effect (Vreugdenhil, Mulder, Emmen, & Weisglas-Kuperus, 2004). Methylmercury (MeHg) is consumed by women of childbearing age at an average of about 0.3 milligrams per gram in the United States (Myers et al., 2003). Research describing effects of MeHg on child behavior has been mixed. Among 7-year-old children with prenatal exposure, deficits were pronounced in specific neuropsychological functions, including attention (Grandjean et al., 1997). However, a separate study found no effect on attention, and improved parent ratings of hyperactivity at age 9 in relation to prenatal exposure, as indexed by maternal hair samples during pregnancy (Myers et al., 2003). This suggests MeHg exposure may increase risk for some ADHD-related behaviors while sparing deficits in others. Further study is needed. Although outcomes of prenatal exposure to other environmental toxins on ADHD-like behaviors are not documented prominently, a relationship between increased ADHD symptoms and umbilical cord blood levels of the pest control substance chlorpyrifos was found in a prospective study of inner-city children (Rauh et  al., 2006). Prenatal exposure to polybrominated diphenyl ether flame retardants, commonly used in furniture Graham, Gl ass, Mat tson

423

manufacturing, infant products, and electronics, is associated with maternally reported DSM-IV Inattention scale scores and ADHD index scores at 7  years of age (Eskenazi et  al., 2013). Conversely, an earlier study documented that dialkylphosphate metabolites were not associated with Child Behavior Check List (CBCL) attention problems but were linked to poorer mental development in general, including pervasive developmental problems in toddlers (Eskenazi et al., 2007). Retinoid, an ingredient in skin creams, has also been examined as a potential risk factor for externalizing behaviors. To date, however, no association has been documented (Adams, 2010).

Oppositional Defiant Disorder and Conduct Disorder

Externalizing behaviors such as delinquency, disobedience, and antisocial behavior often co-occur and may lead to a clinical diagnosis within the disruptive behavior disorder section of the DSM-5 (APA, 2013). ODD and CD can co-occur or occur in a sequential fashion; if untreated, between 25% and 50% of children diagnosed with ODD will later meet criteria for CD according to studies conducted with previous DSM criteria (Lahey, Loeber, Quay, Frick, & Grimm, 1992; Moffitt, 1993). Biopsychosocial developmental models have been proposed (Beauchaine & McNulty, 2013; Dodge & Pettit, 2003) that consider prenatal risk factors that

contribute to the etiology of aggression and violence (LaPrarie, 2011) and various teratogenic exposures that have been linked to increased rates of antisocial behavior, delinquency, ODD, and CD (Tables 24.2 and 24.3).

Alcohol

Children with prenatal alcohol exposure have higher rates of ODD and CD compared to typically developing children (Bailey et al., 2004; Disney, Iacono, McGue, Tully, & Legrand, 2008; Famy, Streissguth, & Unis, 1998; Fryer et al., 2007; Ware et al., 2013). High rates of delinquency, oppositional behavior, and aggressiveness are well documented and often bring alcohol-exposed children to clinical attention (Coles et al., 2000; Mattson & Riley, 2000; Olson et al., 1997). Prenatal alcohol exposure is associated significantly with increased risk for CD, even after controlling for maternal externalizing disorders, alcoholism, and drug abuse/ dependence (Disney et al., 2008). This association is robust for young adults (age 16), even at low levels of prenatal exposure (≥1 drink/day) during the first trimester (Table 24.1; Larkby, Goldschmidt, Hanusa, & Day, 2011). A longitudinal epidemiological study of more than 8,000 children (D’Onofrio et  al., 2007) revealed a dose-dependent association between prenatal alcohol exposure and maternal reports of conduct problems after controlling for various

Table 24.2 Associated Disruptive Behaviors, and Teratogenic Exposures Linked to Increased Rates of Externalizing Behavior Externalizing Disorder Associated Behaviors

Teratogenic Exposure

Attention-deficit/ hyperactivity disorder

• Hyperactivity • Impulsivity • Inattention

Alcohol, cigarette byproducts, anticonvulsant and antidepressant medications, marijuana, cocaine, methamphetamine, heroin, lead, methylmercury, chlorpyrifos

Oppositional defiant disorder/Conduct disorder/Delinquency

• Disobedient • Vindictive • Aggressive toward people and animals • Thievery • Property destruction

Alcohol, cigarette byproducts, antidepressant medications (selective-serotonin reuptake inhibitors), marijuana, cocaine, opiates, methamphetamine, lead, phthalates

Antisocial personality disorder

• Unlawful behavior • Deceitfulness • Lack of remorse • Disregard for safety • Impulsivity

Alcohol, cigarette byproducts, marijuana, cocaine, lead

Substance use disorders • Substance abuse • Substance dependence

424

Teratogen Exposure

Cigarette byproducts, alcohol, marijuana

Table 24.3 Teratogens Associated with Increased Externalizing Behaviors by Behavior Category. Teratogen

Delinquency

Impulsivity

Nicotine

+

+

Alcohol

+

+

+

Cocaine

+

+

+

+

+

+

Opiates

Aggression

Methamphetamine

Substance Use

Criminal Involvement

+

+

+

+

+

+

Marijuana

+

Prescription medications

+

Lead Mercury

Hyperactivity

+

+ +

+ +

+ +

+ +

Marked cells indicate relations between exposure and behavior. Empty cells indicate inadequate data to support a relationship.

confounds (offspring sex, maternal use of other substances during pregnancy, race, and ethnicity). Children with higher levels of exposure displayed more conduct problems than siblings with less exposure or unrelated children. As in the nonexposed population, externalizing disorders are often comorbid (Fryer et  al., 2007; Streissguth, Barr, Kogan,  & Bookstein, 1996). Alcohol-exposed children with ADHD have higher rates of ODD and CD compared to alcohol-exposed children without ADHD, and they have higher rates of CD than children with idiopathic ADHD (Ware et al., 2013). However, not all studies reveal differences between controls and alcohol-exposed individuals on CD symptoms or aggression (Coles et al., 1997). This may be due to differences in exposure levels across samples and/or other uncontrolled moderating factors. Prenatal exposure to alcohol is often also associated with poor self-regulation, juvenile justice involvement, and trouble with the law (Fast, Conry, & Loock, 1999; Streissguth et al., 1996; Ware et al., 2013). Children and adolescents with histories of heavy prenatal alcohol exposure have high rates of delinquency, although the relation may be moderated by home placement, as well as by amount and pattern of exposure (Schonfeld, Mattson, & Riley, 2005; Streissguth et al., 1996). For example, maternal binge drinking, but not volume of exposure, was related to delinquent behavior after controlling for quality of home

environment and exposure to violence (Bailey et al., 2004). Furthermore, a study of low-income, predominately black youth found no difference in variety or frequency of delinquent acts between alcohol-exposed and nonexposed males or females (Lynch, Coles, Corley, & Falek, 2003). However, a comparison of alcohol-exposed and typically developing children matched on race and SES did report increased delinquency, CD-related behaviors, and deficits in moral judgment (Schonfeld et al., 2005). Of particular concern is the overrepresentation of adolescents with prenatal alcohol exposure in the criminal justice system (Fast & Conry, 2009). In a 1-year period, an estimated 23% of juveniles who were detained in a forensic psychiatric inpatient unit in Canada had histories of prenatal alcohol exposure (Fast et  al., 1999). A  study in the United States found that 60% of 415 patients with exposure had trouble with the law, and 50% had confinement in jail, prison, or a psychiatric inpatient setting (Streissguth et  al., 2004). Thirty-three percent of adolescents with FAS commit their first delinquent crime (most commonly theft, burglary, assault) between the ages of 9 and 14 years (Streissguth et al., 1996). Alcohol exposure remains associated with increased legal problems and criminal justice involvement throughout adulthood (Barr et  al., 2006; Fast et  al., 1999; Streissguth et al., 1996), thus prompting the need for early intervention methods and greater public health awareness. Graham, Gl ass, Mat tson

425

Tobacco and Cigarette Byproducts

Children with prenatal exposure to tobacco and cigarette byproducts are more likely to be diagnosed with ODD than are nonexposed children, and they have a higher rate of subsequent diagnoses of CD (Day, Richardson, Goldschmidt,  & Cornelius, 2000; Wakschlag et  al., 2006; Wakschlag, Pickett, Kasza, & Loeber, 2006). Exposed children also have an earlier age of onset of delinquent behavior and first police contact (Gibson, Piquero,  & Tibbetts, 2000; Weissman, Warner, Wickramaratne,  & Kandel, 1999). Prospective epidemiological studies in the United States indicate that smoking during pregnancy (≥20 cigarettes per day) contributes to increased rates of CD (Braun et al., 2008; Fergusson, Woodward, & Horwood, 1998) independent of risk associated with levels of prenatal alcohol exposure (Olson et al., 1997) and concomitant ADHD (Ellis, Berg-Nielsen, Lydersen,  & Wichstrom, 2012). Additionally, in the National Health and Nutrition Examination Survey, increased blood levels and serum cotinine levels (cigarette metabolites) were associated with a greater than eightfold increase in the odds of meeting DSM-IV criteria (APA, 1994) for CD (Braun et  al., 2008). Exposure at lower rates (>10 cigarettes per day) was associated with a two- to fourfold increase in prepubertal-onset CD (Wakschlag et al., 1997; Weissman et al., 1999). Other factors also affect the relation between exposure and CD-related behaviors. Data from a twin study found that light (1–9/day), moderate (10–14/day), and heavy (≥15/day) smoking during pregnancy predicted child conduct problems. However, after controlling for genetic risk, parental antisocial behavior, and family adversities, effects were reduced, and only heavy smoking was significantly associated (Maughan et al., 2004). In a longitudinal study of the relation between maternal smoking and conduct behaviors among low- and high-SES samples, overt (e.g., starting fights, robbery, and violence), but not covert (e.g., burglary, theft, and lying) conduct problems were associated with prenatal smoking exposure in the low-SES sample, with no apparent association for participants in the high-SES sample (Monuteaux, Blacker, Biederman, Fitzmaurice, & Buka, 2006). Another longitudinal study indicated that maternal cigarette smoking during pregnancy and low 1-minute Apgar scores at birth predicted later criminal offending behavior, but the effects were not independent (Gibson & Tibbetts, 1998). Importantly, if maternal smoking ceases post birth, risk of conduct problem behavior was no different from nonexposed 426

Teratogen Exposure

children (Maughan, Taylor, Taylor, Butler, & Bynner, 2001). Thus, persistent maternal smoking, low Apgar scores, and low SES may be greater risk factors for conduct problems than prenatal exposure alone. The relation between prenatal cigarette exposure and CD may vary based on sex. Although some findings indicate similarities in the effects of exposure between males and females after controlling for social background and maternal factors (Maughan et al., 2001), others find a more pronounced effect for males (Fergusson et al., 1998; Weissman et al., 1999). Sex differences may vary based on exposure because heavy exposure was associated with increased CD behaviors in both sexes after controlling for psychosocial factors, whereas light exposure was associated with increased risk only in females, suggesting unique sensitivity (Hutchinson, Pickett, Green,  & Wakschlag, 2010). Similarly, tobacco use throughout pregnancy was associated with increased aggressive behaviors in girls but not boys at 18 months, whereas exposure during early pregnancy alone was not associated with increased problem behaviors for either sex (El Marroun et al., 2011). These differences may be due to variability in use, application of diagnostic criteria across sexes, or sex differences in the effects of teratogenicity. The association and potential causation between maternal smoking and delinquent behaviors remains debated as additional confounds are examined. Some studies fail to find a dose–response curve (Ellis, Widmayer, & Das, 2012; Rantakallio, Läärä, Isohanni, & Moilanen, 1992), whereas others find effects related to criminal arrests across sexes when controlling for demographics as well as for parental and perinatal risk factors (Brennan, Grekin, & Mednick, 1999). In fact, one investigation revealed that smoking exposure is a risk factor for CD only after adjusting for covariates. Thus, risk may be conditional on other factors (e.g., maternal age, social class, sex, parental lifetime history of CD, prenatal exposure to other drugs or alcohol; Biederman, Monuteaux, Faraone, & Mick, 2009). Smoking during pregnancy is a robust predictor of violent and persistent offenses in males (Räsänen et  al., 1999), but additional genetic and environmental background variables continue to confound the association (D’Onofrio et al., 2010).

Pharmaceutical Agents

Findings relating prenatal exposure to SSRIs to disruptive behaviors have been mixed. Indeed, genetic variations can influence serotonin

metabolism, and dosage, as well as maternal mental illness must be considered (Hanley & Oberlander, 2012). Parent and teacher ratings of externalizing behaviors on the CBCL (i.e., aggression, oppositional, delinquent behaviors) show no significant differences between children with prenatal SSRI exposure and nonexposed children at ages 4–5. Regardless, a greater proportion of children with SSRI exposure had parent-reported externalizing scores beyond the clinical cutoff, with oppositional and aggressive behaviors rated as the highest (Oberlander et  al., 2007). This association has shown to be particularly strong for children who experience neonatal withdrawal (Oberlander et al., 2007). However, findings are discrepant, and a systematic review showed no conclusive association between use of SSRIs during pregnancy and conduct or delinquent behaviors (Gentile & Galbally, 2011). Thus, further investigation is required.

Marijuana/Illicit Drugs

Prospective studies on effects of intrauterine exposure to marijuana on delinquency have found direct and indirect effects mediated by inattention and depressive symptoms, which is consistent with the notion that many factors contribute to the rise of delinquency during adolescence (Day, Leech, & Goldschmidt, 2011). Even after a decrease in use during pregnancy, marijuana exposure results in increased caregiver-reported delinquency in offspring at age 10 (Day et  al., 2000). The effect of exposure may differ by sex because a prospectively recruited sample found intrauterine cannabis exposure was associated with increased aggressive behavior and attention problems only for girls at 18 months of age. Most cannabis exposure occurred in the context of co-use of tobacco during pregnancy (84.5%) so it is difficult to separate the precise etiology of the relations (El Marroun et al., 2011). According to parent reports, prenatal cocaine exposure also increases delinquent behavior, over-and-above effects of polydrug exposure, environmental influences, and caregiver factors (Minnes et al., 2010). When analyzed by sex, the relation remains significant for females only. With each increased unit of cocaine exposure, 9-year-old children are 1.3 times more likely to be rated as being aggressive, with females twice as likely to be rated as having delinquent behavior. Exposed females overall were seven times more likely than nonexposed females to be delinquent, with no difference for males. Thus, females may be particularly vulnerable

to an increase in delinquency following cocaine exposure (McLaughlin et al., 2011). Unlike delinquency, aggression was associated with prenatal exposure to cocaine independent of sex and environmental risk factors (Bendersky, Bennett,  & Lewis, 2006; Sood et  al., 2005). This relation is supported by a recent systematic review that found consistent associations between cocaine exposure and behavior problems across parent-report, teacher-report, adolescent self-report, and laboratory paradigms (Bada et  al., 2007; Buckingham-Howes, Berger, Scaletti,  & Black, 2013). Behavior problems associated with prenatal cocaine exposure are not limited to the home environment because teachers, blind to exposure history, report twice the rate of clinically significant externalizing and delinquent behaviors when assessing exposed males (22% for exposed) compared to nonexposed control males (Delaney-Black et al., 2000). The effects of prenatal cocaine exposure are mixed and may not be specific to aggression or delinquency problems. In a sample of African-American teens with and without heavy prenatal cocaine exposure, laboratory measures of aggression revealed significantly more responses related to escape and social withdrawal rather than aggressive behavior (Greenwald et  al., 2011). Despite a general trend suggesting that cocaine exposure appears to increase delinquent and aggressive behaviors (Accornero et  al., 2011), and a statistically robust association between cocaine exposure and increased risk of ADHD, there was no association with increased risk of ODD (Morrow et al., 2009). In a study assessing behavior among 6-year-old children, prenatal cocaine exposure was not related to parent-reported aggression or problem behaviors. However, exposed children were more likely to self-report symptoms in the probable clinical range for ODD (Linares et  al., 2006). In addition, fetal exposure to opiates, including methadone, increases risk of conduct problems 10–13  years later (de Cubas  & Field, 1993). However, at this point, there have not been enough well-controlled studies with pregnant women to suggest a clear association between opiate use and ODD or CD.

Other Toxins and Teratogens

Much of the literature on prenatal lead exposure is confounded by postnatal exposure (see earlier discussion), and not all studies find the same association (Minnes et al., 2010; Plusquellec et al., 2007). However, prospective investigations support Graham, Gl ass, Mat tson

427

an association between prenatal lead exposure and increased frequency of parent- and self-reported rates of antisocial and delinquent behaviors (Burns, Baghurst, Sawyer, McMichael,  & Tong, 1999; Dietrich, Ris, Succop, Berger, & Bornschein, 2001; Wright et  al., 2008). These results remain robust after accounting for covariates such as iron status, other drug exposures (e.g., cigarette, alcohol, marijuana), and socioeconomic factors (Dietrich et al., 2001). Effects may be dose-dependent because children of mothers with higher lead levels participated in an average of 2.3 more delinquent acts during a 12-month period compared to children with the lowest blood levels (Dietrich et  al., 2001). Other neurotoxins and heavy metals fail to demonstrate an association with delinquency (Davidson et  al., 1998; Myers & Davidson, 2000; Myers et al., 2009), although prenatal exposure to phthalates, a group of chemicals used to make plastics, may be associated with aggression and conduct problems, although no replication has occurred (Engel et al., 2010).

Antisocial Personality Disorder

ASPD consists of a constellation of behaviors in which individuals over the age of 18 express severe antisocial behavior, including severe, persistent, and pervasive disregard for social rules and norms and the rights of others (APA, 2013). Among children, severe antisocial behavior is diagnosed as CD, a diagnosis that may evolve into ASPD after age 18 (an ASPD diagnosis requires evidence of CD prior to age 15, and CD can only be diagnosed after age 18 if criteria are not met for ASPD). Therefore, there is requisite overlap among teratogenic exposures linked to ODD and CD that increase risk for ASPD (APA, 2013). Although limited, convergent evidence supports a relationship between prenatal teratogen exposure and higher rates of antisocial behavior (Tables 24.2 and 24.3).

Alcohol

Delinquent, oppositional, antisocial, and conduct-related behaviors associated with prenatal alcohol exposure throughout childhood and adolescence are generally not ameliorated with age because heavy exposure remains associated with adult antisocial personality traits (Barr et al., 2006). Children exposed to binge drinking in utero are more likely to have adolescent antisocial behavior (Olson et al., 1997) and are three times more likely to have a diagnosis of ASPD by the age of 25, compared to those who are not exposed to binge drinking (Barr et al., 2006). In a study of 25 adults with fetal alcohol 428

Teratogen Exposure

effects (IQ >70), four met criteria for ASPD (16%; Famy et al., 1998), indicating a much higher rate than the national average of 0.6% (Lenzenweger, Lane, Loranger, & Kessler, 2007).

Tobacco and Cigarette Byproducts

Smoking during pregnancy results in greater odds (1.5–4 times) of developing severe antisocial behavior (Wakschlag et al., 2002)—a relation that is specific to antisocial and externalizing behaviors (Fergusson, 1999; Weissman et al., 1999). Although research is limited regarding dose–response relations, there is suggestion of a linear effect between number of cigarettes and the likelihood of developing severe antisocial behavior (Brennan et al., 1999). In a prospective study, exposure was associated with higher risk of self-reported criminal conviction at ages 16–35, with no significant sex interactions (Murray, Irving, Farrington, Colman,  & Bloxsom, 2010). Other influential factors should also be investigated because relations between exposure and psychopathy are observed in two-parent but not single-parent households, suggesting other confounding factors at play (Beaver, DeLisi, & Vaughn, 2010).

Other Toxins and Teratogens

Research on prenatal effects of environmental toxins is limited. Generally, higher lead levels are associated with antisocial behaviors (Chiodo et al., 2007; Dietrich et  al., 2001). Maternal blood lead concentrations, measured during the first or early second trimester, are associated with adult criminal behavior, including increased rates of total arrests and later arrests for violent crimes (Wright et al., 2008). Furthermore, prenatal exposure to severe maternal nutritional deficiency in the first and second trimesters is associated with increased rates of ASPD in males (Neugebauer, Hoek, & Susser, 1999). Effects of other toxins are not well researched, although, as mentioned, there is unavoidable overlap with exposures associated with CD, suggesting the need for understanding the developmental course of exposed children with antisocial behaviors.

Substance Use Disorders

In the DSM-5, SUDs are diagnosed on a continuum from mild to severe, specific for each substance (APA, 2013), and associated with a general increase in addictive behaviors (Shaffer, Fisher, Lucas, Dulcan, & Schwab-Stone, 2000). Risks associated with prenatal exposure for other externalizing disorders subsequently increase risk for SUDs. Approximately 40% of children with ADHD have

comorbid CD, a combination predictive of higher rates of SUDs later in life (Molina & Pelham, 2003). Adolescent initiation of illicit drug use among individuals exposed prenatally to teratogens clearly increases risk for SUDs and is a strong correlate of later drug use problems (Anthony  & Petronis, 1995). This trajectory is often attributed to increased vulnerability of comorbid disruptive disorders during childhood and associated with a high level of addictive characteristics (Wilson  & Levin, 2001). Although there are few examinations of the direct effects of prenatal teratogenicity and SUDs, there is a link between exposure and SUD-related behaviors (Tables 24.2 and 24.3) including increased drug use, which correlates with subsequent drug abuse (Anthony & Petronis, 1995; Brook, Brook, Zhang, Cohen, & Whiteman, 2002).

This relation may differ by sex, although results are inconsistent. The probability of continuing cigarette use is higher in females (Kandel, Wu,  & Davies, 1994), suggesting an increased likelihood of dependency compared to males. However, in a 30-year longitudinal study, nicotine-exposed offspring were more likely to become nicotine dependent in adulthood, with males showing a more pronounced manifestation of nicotine dependence than females (Buka, Shenassa, & Niaura, 2003). It is also unclear how the association between cigarette exposure and subsequent increased substance use is affected by postnatal exposure to cigarettes, family views toward smoking during childhood and adolescence, and other sociodemographic factors (Piko & Kovacs, 2010).

Alcohol

Although the literature is sparse on the relationship between exposure to illicit drugs and subsequent SUDs, prenatal exposure to marijuana increases risk of initiation and use of marijuana in adolescence (Porath & Fried, 2005). This relation may be dependent on dose and sex because greater exposure was associated with increased risk of initiation of smoking by male offspring (Porath & Fried, 2005). A  recent study further reported that 15-year-olds, primarily low-income African Americans, who were exposed prenatally to cocaine were 2.8 times more likely to have substance use-related problems compared to nonexposed children (Min et  al., 2014). An indirect pathway between illicit drug exposure during fetal development and later substance use problems also involves exacerbation of the risk for both ADHD and CD, which further increases the likelihood of developing drug abuse in adolescence and adulthood (Disney, Elkins, McGue, & Iacono, 1999). Therefore, the precise etiology of illicit substance use may be mediated by other psychopathology in addition to teratogenic exposure.

Retrospective data on adopted children reveal increased likelihood for alcohol-exposed children to exhibit abuse of alcohol, cigarettes, and illicit substances (Yates, Cadoret, Troughton, Stewart, & Giunta, 1998). Among adults with prenatal alcohol exposure, alcohol and/or drug dependence is the most commonly reported DSM-IV Axis I diagnosis (60% of sample; Famy et al., 1998). Prospective studies have confirmed the increased presence of substance abuse and found that infants exposed prenatally to as little as two alcoholic drinks per occasion, on average, show a higher prevalence of substance abuse at 14-year follow-up (Carmichael Olson et  al., 1997). Furthermore, with maternal consumption of three or more alcoholic drinks/ occasion, offspring have a 2.47 times greater chance of having an alcohol disorder later in life compared to nonexposed children (Alati et  al., 2006). Similarly, drinking habits in a cohort of 21-year-old adults found prenatal alcohol exposure to be associated with increased clinical rates of alcohol-related problems after accounting for family alcohol history and exposure to other teratogens (Baer, Sampson, Barr, Connor, & Streissguth, 2003).

Tobacco and Cigarette Byproducts

Risk of cigarette experimentation among adolescents is increased by prenatal exposure to cigarette byproducts (Porath & Fried, 2005) but is unrelated to whether the mother smoked post-pregnancy (Al Mamun et al., 2006). Furthermore, chances of cigarette experimentation are increased by a factor of 5 by prenatal exposure to 10 or more cigarettes/day (Cornelius, Leech, Goldschmidt,  & Day, 2000).

Marijuana/Illicit Drugs

Current State of the Science

Prospective epidemiological studies continue to improve understanding of possible causal relations between prenatal exposure and externalizing behaviors. These efforts have resulted in increased awareness of the complex relationships between interacting risk factors and precise causal mechanisms, many of which have yet to be elucidated fully. Although findings of ongoing teratologic research have been incorporated into the DSM-5 (APA, 2013) and the Research Domain Criteria (RDoC; Insel et al., 2010) to better assess, diagnose, and treat disorders, Graham, Gl ass, Mat tson

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an accurate understanding of dose-dependency, safe cutoffs, and interactions with pertinent sociodemographic factors is still unknown. In most studies of children who were exposed prenatally to teratogens, postnatal environment and parenting characteristics are considered confounds. However, these factors may not be appropriately categorized or assessed. Treating parenting as a confounding variable assumes that the behavior of the parent is independent from the child’s behavior, yet research consistently indicates bidirectional relations (Lengua & Kovacs, 2005), suggesting overlap in these effects (Hans, 2002). The interplay between an individual’s behavior and environment may modify expression of teratogenic effects; as discussed, environmental deprivation is reportedly associated with poorer behavioral outcomes (Ornoy et al., 1996). However, such effects are often ignored in behavioral teratology. Furthermore, lack of standardization regarding confounds, precise dosage information, and inclusion criteria across studies hinders advancement of knowledge. There are robust associations between teratogenic substances and disruptive/externalizing behaviors across diverse samples and developmental periods. Regardless, correlation alone does not help us understand causal pathways, precise etiology, treatment efficacy, or unique behavioral phenotypes. Currently, effects of prenatal exposure on externalizing disorders support a relation between alcohol and nicotine; however, the amount of exposure required for effects and the influence of other co-occurring exposures and genetic factors is not yet clear. Prenatal exposure is one of many causes of externalizing disorders, and diversities in etiology suggest a need to examine the need for potential targeted interventions. Interventions for externalizing disorders that consider/target etiology are beginning to be applied among some populations (e.g., guidelines for treating conduct disorder in children with FASD; Brown, Connor, & Adler, 2012), but additional study is necessary to help children with externalizing behavioral problems resulting from teratogenic exposure.

exacerbate risk for further negative outcomes (Wilens, 2004). Externalizing behaviors may be additionally aggravated by independent effects of prenatal exposure to teratogens (e.g., Milberger et  al., 1998). Conversely, the developmental course from ODD to CD to ASPD may be influenced by accumulation of post-birth environmental and biological risk factors (e.g., sex, SES) that may be more harmful than teratogen exposure itself. SUDs, for example, are linked to indirect effects such as poorer social interactions as a result of other specific impairments associated with teratogenicity (e.g., Schonfeld, Paley, Frankel,  & O’Connor, 2006). The number of contributing factors associated with externalizing disorders illustrates the importance of addressing the primary role of prenatal teratogen exposure, how effects of exposure may be altered by environment, and how behavioral expression changes over time. Understanding the complexity of how exposure and environment interact can aid in the development of targeted interventions. For example, age is an important factor in affecting developmental pathways of behavioral deficits in certain exposed populations (e.g., Spohr, Willms, & Steinhausen, 2007). The age at which an exposed child is introduced to drugs can differ by home environment and community, which may subsequently interact with existing externalizing diagnoses and increase vulnerability to initial substance use. Therefore, family and community interventions may consider enhanced monitoring of exposed children throughout developmental stages deemed more sensitive to externalizing behaviors (e.g., school age and the onset of peer contagion; Dishion & Tipsord, 2011). Although not all instances of prenatal teratogenicity increase externalizing behaviors, the developmental trajectory of exposed individuals can be associated with lifelong supportive care (Spohr & Steinhausen, 2008), prompting the need to examine interacting developmental characteristics to help intervene and re-route the course of behavioral deviation.

Developmental Considerations

Appropriate nationwide public policy responses regarding prevention of substance use during pregnancy are unclear (Ondersma, Simpson, Brestan, & Ward, 2000) because recent findings indicate that methods such as the Alcohol Beverage Labeling Act are only minimally effective (Thomas et  al., 2014). In the case of maternal health, weighing the risks of pharmacological treatment for medical

Understanding critical periods of fetal development in relation to effects of teratogen exposure on postnatal behavior is essential for determining the etiology of, and effective prevention and intervention programs for, psychopathology. Externalizing disorders can result in poorer prognoses throughout development, are highly comorbid, and can 430

Teratogen Exposure

Controversies

or mental health issues during pregnancy becomes an ethical conundrum and a risk–benefit analysis must be conducted. Even in cases of necessary medical treatment during pregnancy, screening for legal and illicit exposure is controversial. Universal screening raises the debate of cost-effectiveness due to lack of distinction between high- and low-risk populations, whereas targeted screening may result in negative stereotyping and overidentification of marginalized populations (Zizzo et  al., 2013). The issue of consent is also raised because a positive screen may implicate the biological mother in terms of negative legal outcomes, increased social stigma, confidentiality issues, potential false-positive results, and criminal prosecution (Yan, Bell, & Racine, 2014). There is ongoing discourse as to how much of any particular substance is safe during pregnancy, and determining cut-points is limited by ethical bounds preventing case–control studies and confounded by individual differences and genetic vulnerabilities (Brown, 2001). For example, current studies have both supported and refuted the safety of light to moderate alcohol consumption during pregnancy (Jacobson et al., 1994; Robinson et al., 2010). Because the current literature is not conclusive, the National Institute of Alcohol Abuse and Alcoholism and the US Surgeon General recommend that alcohol abstinence during pregnancy is the safest option, contrary to some reports in current popular media. Often, children with prenatal teratogen exposure are not raised by biological parents, which may be due to adoption or placement by child protective services. Placement of exposed children into foster or adoptive care raises additional ethical concerns. One of the greatest challenges remains in the ability to obtain accurate and complete exposure information. Lack of knowledge or disclosure of exposure for foster or adoptive placements can lead to concerns of caretakers not being fully informed and thus unable to handle externalizing behaviors (Davies & Bledsoe, 2005). This question has been prominent in the media because there are several news reports of adoptive parents giving up exposed children due to externalizing disorders that they were not able to handle emotionally, physically, or financially (James, 2010; Twohey, 2013; Swarns & Herszenhorn, 2013). Additional legal and ethical questions arise regarding maternal exposure to toxins and second-hand smoke because some exposures may unfairly affect certain demographic groups.

Research Agenda and Future Directions

The relationship between prenatal exposure to teratogens and externalizing behavior remains complex and disputed, requiring additional research. Although there is a general consensus that males have an increased likelihood of externalizing disorders (e.g., Alegria et al., 2013), the onset of disruptive and antisocial behavior in females has increased in the past two decades, with little known about early precursors that may help identify risk (Hipwell et al., 2002). As discussed, prenatal teratogen exposure can have different effects depending on sex, suggesting that a focus of future research should be to better understand why prenatal teratogens affect males and females differently. Cross-generational studies will be an imperative move forward to differentiate maternal inherited effects (e.g., maternal psychopathology and vulnerability to stress) from the causal effects of teratogen exposure. Understanding different pathways between teratogenicity and externalizing behaviors based on biological characteristics may improve identification accuracy and intervention efficacy. Disparities in treatment for externalizing disorders are important to consider because efficacy may be distinct based on etiology (e.g., Doig, McLennan, & Gibbard, 2008). Other than effects of sex, other sociodemographic factors may impact substance use during pregnancy, which is poorly understood across teratogens and may have a larger role in the expression of externalizing disorders than is understood currently. Studies have attempted to control for differences in SES among participants. However, specific effects of SES on quality of preand postnatal care (e.g., education and surveillance of fetal health) and postnatal environment should be addressed more consistently. Untangling relations between teratogen exposure and disruptive behavior can improve prevention efforts based on community need. For example, specific to methamphetamine, lower SES is associated with use during pregnancy (Wu et  al., 2013), suggesting that prevention attempts in low-income communities may be of highest importance. In the years ahead, it will be important to extend findings on the effects of prenatal teratogen exposure to revisions made in the DSM-5. Disruptive mood dysregulation disorder (DMDD) is new to the DSM-5 and is characterized by severe temper outbursts and persistent anger (APA, 2013). DMDD has some parallels with ODD symptoms, suggesting possible overlap in how teratogen exposure may affect its development. However, it Graham, Gl ass, Mat tson

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is important to recognize the distinctiveness of the two externalizing disorders. Overall, the field of behavioral teratology has begun to understand the relationship between teratogenic exposure and externalizing behaviors. Future standardized and multimethod studies across teratogens, populations, and behaviors can begin to resolve the various pathways resulting in externalizing behavior.

Acknowledgments

Support for this chapter is provided by NIAAA grants U01 AA014834 and R01 AA019605. Additional support was provided by NIAAA grant F31 AA022261. The authors thank the families who graciously participate in our studies and the members of the Center for Behavioral Teratology for ongoing assistance and support.

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Externalizing and Internalizing Comorbidity

Florence Levy, David J. Hawes, and Adam Johns

Abstract Comorbidity between externalizing and internalizing disorders, which occurs at much higher rates than expected by chance, has long been a focus of empirical research. In this chapter, we provide an overview of issues related to heterotypic comorbidity, including studies of sex differences in externalizing and internalizing liabilities, evidence demonstrating moderating effects of comorbid anxiety on aspects of externalizing disorders, and perspectives on comorbidity that have emerged from research on family and peer socialization risk factors, based on both attachment and social learning models. Implications of comorbid externalizing and internalizing conditions for psychosocial and pharmacological treatments are also examined, along with potential directions for future research. Key Words:  internalizing, externalizing, comorbidity, DSM-5, sex differences, socialization, Research Domain Criteria (RDoC)

Background

Questions and controversies concerning comorbidity have driven major shifts in the conceptualization and classification of psychopathology in recent years, and are sure to feature prominently in future research. The aim of addressing excessive comorbidity associated with previous diagnostic systems was explicit throughout development of the newly revised DSM-5 (APA, 2013). This same aim has likewise guided conceptualization and implementation of the Research Domain Criteria (RDoC) project by the National Institute of Mental Health, which advocates for classification of mental disorders based on (a) dimensions of observable behavior and (b) neurobiological markers (Cuthbert & Kozak, 2013; Insel, Cuthbert, Garvey, Heinssen, Pine, Quinn, et al., 2010). In this essay we provide an overview of the background to these developments, with respect to comorbid externalizing and internalizing disorders, and we consider implications of comorbidity for clinical models and practice.

Comorbidity among classes of child and adolescent psychiatric disorders has been described frequently by researchers and clinicians. Angold, Costello, and Erkanli (1999) reviewed literature on overlap among diagnostic conditions, and pointed out that the way in which comorbidity is treated has changed over time. For example, the International Classification of Diseases (ICD) version-10 allowed a mixed category of conduct and emotion problems, whereas proponents of the factor analytic approach to diagnosis developed the idea that patterns of psychopathology are a matter of continuous variation along a series of scales, suggesting a failure of categorical diagnosis to (a) appropriately describe separable syndromes and (b) recognize covariation among naturally occurring dimensional syndromes. For example, between 50% and 80% of children with attention-deficit/hyperactivity disorder (ADHD) also meet diagnostic criteria for other disruptive behavior disorders, including oppositional defiant disorder (ODD; see Waldman & Lilienfeld, 1991) and conduct disorder (CD; see Thapar, 2001). 443

Angold et al. (1999) defined heterotypic comorbidity as diagnostic co-occurrence of psychopathology across different domains (e.g., externalizing, internalizing, ADHD, and anxiety). Such comorbidity might occur concurrently or successively over time. Despite possible referral and overlapping symptom biases, their meta-analysis of representative general population studies eliminated methodological factors as a major cause of heterotypic comorbidity. Furthermore, estimates of strengths of association between CD and ODD was weaker than that between CD and depression. This argued against existence of a unitary association between internalizing and externalizing disorders. Angold et al. pointed out that associations between different types of symptoms occur at the extremes of psychopathology, not just at the level of mild factor scores. In addressing comorbidity between externalizing and internalizing disorders, Angold et  al. (1999) noted a number of perplexities and potential confounds. For example, severity of CD predicted comorbidity with non-antisocial disorders, whereas CD only predicted later adult depression among individuals who had persistent antisocial behavior (Zoccolillo, 1992). However, structural equation modeling of general population data suggest that much of the covariation between CD and depression can be explained by common or correlated risk factors (e.g., Fergusson, Lynskey,  & Horwood, 1996).

Categorical versus Dimensional Approaches

Waldman and Lilienfeld (1991) proposed that latent variable methods might characterize covariation among taxa or diagnoses such as ODD and CD. Lilienfeld, Waldman, and Israel (1994) argued that the term “comorbidity” was useful only in the context of well-validated disease entities in which pathology and etiology were well understood. Lilienfeld (2003) pointed out that the presence of comorbidity posed a serious challenge to existing classification systems and etiological models. He indicated that “classical models of classification” characterized by discrete or mutually exclusive categories with few or no intermediate cases applied to few, if any, domains of psychopathology. Lilienfeld suggested that in the presence of method covariance, controlling statistically for measures of one set of disorders might increase the relationship between measures of the other set of disorders and relevant outcome measures, because shared variance might decrease discriminant validity. 444

For example, in some studies of “neurotic delinquency,” anxiety exerts a protective effect (Walker et  al., 1991). A  further possibility, according to Lilienfeld (2003), is that externalizing disorders might in some cases give rise to anxiety, secondary to adverse life consequences such as legal difficulties, family conflict, and/or academic problems. Krueger et  al. (2005) compared latent class and latent trait modeling of comorbidity among externalizing symptoms and concluded that comorbidity among externalizing disorders was best modeled by an underlying, normally distributed continuum of risk for multiple disorders within the externalizing spectrum. Krueger and Markon (2006) applied quantitative genetic models to multiple disorders with latent nosological, structural, etiological, and etiological liability factors leading to a spectrum model of comorbidity. The above boundary issues are discussed in a conceptual and data-analytic model discussed by Weiss, Susser, and Catron (1998), who describe different levels of common and specific features of child psychopathology. They define common features as those that differentiate psychopathology from healthy functioning, broadband-specific features as those that differentiate internalizing problems from externalizing problems, and narrow-band–specific features as those that differentiate between different narrowband syndromes. The authors point out that although it may be heuristically important to determine which factors are causally related to psychopathology, it is important to specify precisely the boundaries of syndromes, and to determine which discriminate between syndromes at the narrow band as well as broadband level, for pragmatic reasons involving assessment and treatment. In the present context, ADHD subtypes might represent narrow-band syndromes, within the broadband ADHD syndrome, but the validity of categorical subtype boundaries and their overlaps has been questioned (see Rasmussen et al., 2002), as have relationships of subtypes to comorbid symptoms or syndromes. On the other hand, Jablensky (2012) points out that difficulties with dimensional models of psychopathology lie in (a)  absence of empirically grounded metrics, (b) disagreement on the number and nature of dimensions required to account adequately for clinical variation, and (c)  complexity, which is cumbersome in clinical practice. Zachar (2012) has argued that a “plurality of validation” should view validation as a process of developing constructs and theories that cohere with as many standards as possible.

Externalizing and Internalizing Comorbidit y

Sex Differences

Levy et al. (2004) used a DSM-based rating scale of data from the Australian Twin Study of ADHD, obtained from 1,959 families, to determine whether responses differed between males versus females. The questionnaire contained items about ADHD, CD, and separation anxiety disorder (SAD), as well as language and reading questions. Correlations were conducted to evaluate correspondences between comorbid conditions and age. Analyses of variance (ANOVAs) were conducted to evaluate sex differences in inattentive symptoms, hyperactive/impulsive symptoms, and symptoms of ODD, CD, and SAD. Finally, χ2 analyses were used to determine whether the prevalence of comorbid conditions differed by subtype of ADHD and sex. Children with the combined subtype showed consistently more comorbid internalizing symptoms. Across subtypes of ADHD (hyperactive/impulsive, inattentive, combined), both males and females showed higher levels of SAD than children with no ADHD symptoms. Although comorbidity profiles differed among ADHD subtypes, there were no significant sex differences in ADHD comorbidity with externalizing disorders, but there were significant sex differences for SAD among those with the inattentive ADHD subtype, and for SAD among those with the combined subtype. In both cases, girls reported significantly more symptoms than boys. Kramer, Krueger, and Hicks (2008) investigated the latent structure of 11 psychopathological syndromes in the community-based Minnesota Twin Registry, using the Psychiatric Diagnostic Screening Questionnaire and an adult antisocial behavior scale among 2,992 middle-aged twins. A two-factor invariance model differentiated internalizing from externalizing disorders, in which women demonstrated higher mean levels of internalizing disorders and men demonstrated higher mean levels of externalizing disorders. Eaton et al. (2012) used factor analysis and latent class analysis to investigate sex differences in mental disorder prevalence among 43,093 individuals from the first wave of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC). Major depressive disorder, dysthymic disorder, generalized anxiety disorder, panic disorder, social phobia, alcohol dependence, nicotine dependence, marijuana and other drug dependence and antisocial personality disorder diagnoses—all derived from structured interviews—were examined. The study demonstrated sex-invariance. Thus, observed sex differences in prevalence rates did not

result from distinct latent structures of psychopathology for men versus women. Rather, sex differences were accounted for by higher mean levels of internalizing symptoms in women, and higher mean levels of externalizing symptoms in men. This suggests that sex must be accounted for in studies of externalizing and internalizing comorbidity.

Neural Mechanisms of Comorbidity

First (2012) points out that although the DSM and ICD have been moderately successful in facilitating communication, and fairly successful in achieving reliability, at least for major psychiatric diagnoses (see Beauchaine, Klein, Erickson, & Norris, 2013), psychiatry’s goal of predicting clinical course and treatment response has been only modestly successful, and its goal of identifying underlying causal mechanisms has been “spectacularly unsuccessful” (Bolton, 2012). According to First, the RDoC project represents a true paradigm shift in classification of psychopathology, moving away from defining mental disorders based on descriptive phenomenology and instead focussing on disruptions in neural circuitry as the fundamental classificatory principal (Insel & Cuthbert 2009). The RDoC are intended to establish a framework for creating research classifications that reflect functional dimensions stemming from translational research on genes, neural circuits, and behavior. Implications of comorbidity for the RDoC project remain to be established, but the goal is to develop new ways of classifying mental disorders based on dimensions of observable behavior and neurobiological measures.

Neural Circuit Approaches

Levy (2010) suggested that the “paradox” of comorbid externalizing and internalizing behavioral phenotypes is better understood in terms of excess communication between subcortical brain circuits. According to this account, individual differences in functioning of reciprocal and nonreciprocal connections between modular neural circuits might provide a basis for understanding previously puzzling aspects of externalizing and internalizing comorbidity (Levy, 2010). Heimer (2003) outlined two major neuroanatomical systems that play a major role in control of behavior. The first are parallel basal ganglia-thalamocortical circuits, in particular the ventrostriatopallidal system. Heimer (2003) described cortical projections from the olfactory cortex and hippocampus (allocortex) and nonisocortical (mesocortical) areas Levy, Hawes, Johns

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such as the entorhinal, insular, and cingulate cortices, as well as the posterior orbital-medial cortex and the temporal pole to the striatum. The ventral striatum (nucleus accumbens, olfactory tubercle, ventral caudate-putamen) receive projections from the entire cortical mantle. According to Heimer (2003), “The realisation that the whole cortical mantle is related to the basal ganglia, has provided a new blueprint of forebrain organisation, and the ventral striatum and ventral pallidum are integral parts of a new theoretical framework for adaptive responding and neuropsychiatric disorders.” The second system is the extended amygdala. The concept that the limbic forebrain system projects to the hypothalamus was, according to Heimer, incorrect (Heimer, 1972). He showed that the nucleus accumbens and striatal areas of the olfactory tubercle receive cortical projections, not only from the olfactory cortex and hippocampus but also from other parts of the greater limbic lobe. This led to the description by Alexander (1986) of corticosubcortical reentrant circuits. Alexander (1990) described a series of five parallel segregated frontal/subcortical circuits linking specific regions of the frontal cortex to the striatum, globus pallidus, substantia nigra, and thalamus. Heimer (2003) characterized the ventrostriatopallidal system as a “motive” circuit, which is critical for initiation and mobilization of appropriate adaptive reward-guided behavior. As outlined in Essays 11 and 12 of this volume, aberrant tonic and phasic activity in this system are central to theories of behavioral impulsivity, ADHD, and vulnerability to a wide range of externalizing conduct (see also Sagvolden, Johansen, Aase,  & Russell, 2005). Levy (2004) noted that although empirical studies of ADHD show comorbidity with anxiety, studies of behavioral inhibition tend to suggest independent disruptive and anxiety traits. This paradox was potentially resolved through understanding of the functions of the amygdalar and mesolimbic dopamine systems. Grace (2001) suggested that the amygdala gates events based on their affective valence, and influences the nucleus accumbens by facilitating prefrontal (PFC) stimulation, but only within a very narrow single-event–related time frame. The PFC and hippocampus were thought to have overlapping tonic/ phasic influences on accumbens neurons, whose bi-stable state allowed appropriate gating of “fear” stimuli from the amygdala. Although the hippocampus was thought to generate long-duration 446

activity in accumbens neurons, when keeping participants focused on a current task, the amygdala provided a brief event-related gating of prefrontal throughput in the accumbens. Thus, emotionally charged events might predominate when PFC influences are shut down by excessively high levels of dopamine and noradrenaline (Arnsten, 2000). Both the apparent fearlessness of young ADHD children and the excessive anxiety of others might be attributed to immaturity or disruptions of synaptic gating at the accumbens level. This approach emphasizes the importance of key synaptic gating mechanisms in understanding both comorbidity and moderator effects of comorbidity on treatment, which may result from gating deficits. In addition to functional interactions between mesocorticolimbic and amygdalar circuits in producing heterotypic comorbidity, recent work also demonstrates interactions between externalizing and internalizing psychopathology in predicting volumes within these brain regions among adolescent boys with conduct problems (Sauder, Beauchaine, Gatzke-Kopp, & Shannon, 2012).

Common Neural Dysfunction Across Externalizing and Internalizing Disorders

In addition to Levy’s (2010) gating model of heterotypic comorbidity, others have focused on neural substrates of behaviors that cut across externalizing disorders and depression. Among such behaviors are anhedonia, irritability, and low motivation (APA, 2013). Neuroimaging studies (both PET and fMRI) link this constellation of symptoms to deficiencies in both tonic and phasic neural responding in mesolimbic and mesocortical brain regions, which are rich in dopamine neurons. In fact, an increasingly large body of research indicates blunted mesolimbic and/or mesocortical reactivity to incentives among those with ADHD (see Bush, Valera,  & Seidman, 2005; Carmona et  al., 2011; Dickstein, Bannon, Castellanos, & Milham, 2006; Durston, 2003), CD (e.g., Rubia, Smith et al., 2009), substance-related and addictive disorders (see, e.g., Martin-Soelch et al., 2001; Volkow, Fowler, & Wang, 2004), and antisocial traits (e.g., Oberlin et  al., 2012). Moreover, compromised functional connectivity between mesolimbic and mesocortical structures is observed among adolescents with ADHD and CD (e.g., Shannon, Sauder, Beauchaine,  & Gatzke-Kopp, 2009). Very similar findings are observed among adolescents with major depression (e.g., Forbes & Dahl, 2012; Forbes et al.,

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2006, 2009). Thus, consistent with the RDoC perspective, described here earlier, it is important to consider behavioral traits and biological vulnerabilities that cut across the externalizing and internalizing spectra if we wish to understand heterotypic comorbidity. Differentiation between externalizing and internalizing disorders occurs within other neural systems, which are not the focus of this essay (see, e.g., Beauchaine, Hinshaw, & Gatzke-Kopp, 2008). Forbes and Dahl (2012) reviewed the neural systems involved in affect dysregulation. They pointed out that depression might involve an elevated threshold, less intense response, or failure of activation or sustaining of appropriate positive affective responses. This conclusion is very similar to Sagvolden et al.’s (2005) highly influential theory of hyperactive-impulsive ADHD, in which tonic DA release is abnormally low, resulting in a heightened threshold for phasic DA responding to incentives, and less positive affect. Forbes and Dahl (2012) emphasized alterations to positive affect systems as a critical feature in depression, though increased negative affect such as crying, sadness, or irritability might be more obvious. As outlined earlier, animal models of reward responding implicate the midbrain dopamine system, the ventral striatum, and the orbitofrontal cortex (OFC; Robbins  & Everitt, 1996; Schulz, 2000). Neuroimaging studies conducted with humans generally support these conclusions and also suggest the amygdala as an important reward center. The OFC and striatum show enhanced activation to receipt of reward, whereas the amygdala and OFC exhibit sensitivity to predicted reward value (Gottfried et  al., 2003; Knutson et al., 2005, 2008). In their review of altered reward function in adolescent depression, Forbes and Dahl (2009) noted that adolescents with major depressive disorder exhibit less striatal responding than healthy comparison adolescents during both reward anticipation and reward outcome but greater responding in the dorsolateral and medial PFC (see also Forbes et  al., 2009). Dopamine is postulated to facilitate learning and goal-directed behavior by engaging the VTA and ventral striatum with projections to medial prefrontal cortex in PFC (e.g., Haber  & Knutson, 2010). Forbes et  al. (2009) explained dopamine effects in terms of tonic dopamine transmission providing a steady low baseline level of dopamine regardless of external stimuli, with phasic dopamine transmission occurring in response to external stimuli (tonic/phasic dopamine theory). Goal-directed behavior was thought to be associated

with reduced phasic dopamine transmission in response to nonreceipt of a reward, in patients experiencing depression. Once again, similar models of aberrant learning processing apply to ADHD and CD, where functional alterations in both tonic and phasic responding of midbrain and prefrontal DA systems are observed (see, e.g., Gatzke-Kopp  & Beauchaine, 2007; Gatzke-Kopp et al., 2009). In addition, Forbes et al. (2009) described findings from pharmacologic challenge studies that have been associated with low dopamine functions in depression, associated with greater sensitivity to stimulant drugs. For example, Tremblay et al. (2002, 2005) reported on adults with depression experiencing greater subjective rewarding effects but less striatal response than healthy adults, postulating effects related to the “dopamine reward system” in ventrolateral prefrontal cortex, caudate, and putamen. In this regard, a study by Levy, Wimalaweera, Moul, Brennan, and Dadds (2013) is of interest in demonstrating that the major allele of the D1 receptor predicted rigid motor effects in response to relatively high doses of methylphenidate in vulnerable children treated for ADHD. Levy and Dadds (2014) expanded on this finding in terms of the tonic/phasic dopamine theory (Bilder, Volavka, Lachman, & Grace, 2004), suggesting that less than optimal D1 transmission in the PFC and striatum might allow greater phasic dopamine responses to be transmitted at inhibitory striatal D2 receptors. Importantly, methylphenidate administration normalizes both frontocingulate underactivity (Rubia, Halari, Mohammad, Taylor, & Brammer, 2011) and frontostriatal functional connectivity deficits (Rubia, Halari, Cubillo, Mohammad, & Taylor, 2009) among children with ADHD. Whereas Forbes and Dahl (2012) review evidence of low striatal/medial PFC function in depression, and Levy et  al. (2013) noted lateral PFC/striatal executive circuit deficits in ADHD, it is also of interest to examine work by Rubia et al. (2009) in relation to possible overlaps between executive and affective cortical/subcortical circuits. Rubia and colleagues used event-related fMRI during a rewarded Continuous Performance Task to investigate potential disorder-specific abnormalities in the neurobiological correlates of motivation and sustained attention in children and adolescents with pure CD and those with pure ADHD. Those with noncomorbid conduct disorder and noncomorbid ADHD showed disorder-specific brain abnormalities during rewarded and nonrewarded tasks. During sustained attention, those with CD demonstrated Levy, Hawes, Johns

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dysfunction in paralimbic regions of the insula and hippocampus and in the postgenual anterior cingulate gyrus. In contrast, those with ADHD demonstrated disorder-specific underactivation in the right and left ventrolateral prefrontal cortex but increased activation in a cluster comprising the cerebellum, thalamus, and hippocampus. Rubia et al. also reported that during reward, those with CD showed dysfunction in the right orbitofrontal cortex, whereas those with ADHD showed reduced activation in the posterior cingulate and precuneus. Rubia et al. suggested that CD and ADHD represent two clinically overlapping disruptive behavior disorders but might be based on different physiological substrates. However, developmental models have been proposed that challenge this assertion. For example, according to Beauchaine’s ontological process model, different neural correlates are observed in CD versus ADHD not because they necessarily represent different disorders (although sometimes they may) but rather because, for many individuals, CD represents a more advanced stage of disease process (see Beauchaine, Hinshaw, & Pang, 2010; Beauchaine & McNulty, 2013; Essay 27; see also Sroufe, 2009).

Neuroendocrine Mechanisms of Comorbidity

There is also considerable interest in relations between hypothalamic-pituitary-adrenocortical (HPA) axis function and effects of maltreatment, stress, and adversity on development. Gunnar and Vazquez (2006) drew attention to hypocortisolism flowing exposure to trauma and prolonged stress (see also Essay 15). Gunnar and Donzella (2002) noted further that cortisol activity and reactivity are sensitive to social regulation and caregiver sensitivity. McCrory et al. (2010) described variability across studies of cortisol in maltreated children and suggested that exposure to early adversity might be associated with stress habituation over time but that genetic vulnerability and gene × environment interactions might also be important. A review by Hawes, Brennan, & Dadds (2009) suggested that patterns of diurnal and morning cortisol levels might vary depending on type of antisocial behavior, patterns of internalizing comorbidity, and early environmental adversity. They speculated that early adversity was important to development of chronic antisocial behavior in children with both low levels of callous-unemotional traits and HPA-axis hyperactivity but that high levels of callous-unemotional traits and HPA-axis hypoactivity characterized a particularly severe subgroup, for whom antisocial behavior developed somewhat independently of adversity. 448

McCrory et  al. (2010) pointed out that that while the human and animal literature supported a link between stress and HPA functions that predisposed to psychiatric vulnerability in later life, mechanisms of their interactions remain unclear. However, possibilities include structural brain differences, functional brain mechanisms, and social factors. The authors found relatively consistent evidence for reduced corpus callosum and cerebellar volumes, and functional studies showed hypoactivity in a number of brain regions, including parts of the PFC and limbic and paralimbic systems. At least one in four children between 1 and 3  years of age experiences a traumatic event, with higher rates observed among at-risk samples (Grasso, Ford,  & Briggs-Gowan, 2012). Early childhood exposure to trauma is associated with posttraumatic stress symptoms, such as hyperarousal, avoidance/numbing, and reexperiencing, and emotional difficulties, such as anxiety, withdrawal, and disruptive behavior (e.g., Bogat, DeJonghe, Levendosky, Davidson,  & von Eye, 2006; Briggs-Gowan, Carter, Clark, Augustyn, McCarthy,  & Ford, 2010a). This association between adverse events (e.g., family violence) and trauma-related symptoms is observed in children as young as 18 to 36  months (Briggs-Gowan, Ford, Fraleigh, McCarthy,  & Carter, 2010b). In a longitudinal investigation using a large representative sample of children, Briggs-Gowan, Carter, and Ford (2012) found that exposure to parent-reported neighborhood violence and family violence or conflict in early childhood was associated with more severe trauma symptoms at age 3, and predicted later externalizing and internalizing problems during primary school, even when controlling for sociodemographic risk factors. Moreover, trauma-related symptoms at age 3  years partially or fully mediated longitudinal pathways from early exposure to traumatic events to later externalizing and internalizing, suggesting that trauma impedes typical development. Consistent with this interpretation, the authors reported that children who exhibited early avoidance and arousal trauma symptoms were at greater risk for later internalizing problems, whereas those who experienced early arousal symptoms were at greater risk for later externalizing problems. Exposure itself was not related significantly to later psychopathology once trauma-related symptoms were controlled. In a cross-sectional study using a large community sample of 2- to 4-year-old children, Grasso, Ford,

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and Briggs-Gowan (2012) found that children with a history of potentially traumatic events and current life stress had significantly higher levels of internalizing and externalizing behavior problems than did (a) children with a history trauma exposure but no current stress and (b)  those without a history of traumatic exposure with or without current stress. The authors also reported preliminary evidence of a sex difference, with girls showing higher risk for internalizing problems when reporting a history of trauma exposure and current stress than boys. Taken together, this research suggests a stress sensitization model in which early trauma exposure produces posttraumatic stress symptoms that impair a young child’s stress response system, which can heighten his/her vulnerability to disruptions in emotion and behavior regulation when confronted with subsequent nontraumatic life stressors (Briggs-Gowan et  al., 2012; Grasso et  al., 2012). Between 60% and 90% of adolescents and adults have experienced a potentially traumatic event, with those experience adverse events at significantly higher risk for developing posttraumatic stress as well as other externalizing and internalizing disorders (e.g., Copeland, Keeler, Angold,  & Costello, 2007). Consequently, association between trauma and both externalizing and internalizing problems are evident across the life span.

Comorbidity and Socialization in Family and Peer Contexts

Processes through which family and peer environments contribute to development of comorbid externalizing and internalizing disorders have become a growing focus of research in recent years. Comorbidity of this kind is thought to arise in part from socialization risk factors that are shared across externalizing and internalizing disorders (Bubier  & Drabick, 2009; Klein  & Riso, 1993; Oland & Shaw, 2005). This common risk perspective has been emphasized in emerging transdiagnostic models of psychopathology (e.g., Beauchaine & McNulty, 2013; Fraire & Ollendick, 2013) and is supported by evidence that environmental risk factors for externalizing disorders overlap largely with those associated with internalizing disorders. Among the many family-based risk factors for externalizing and internalizing disorders, some of the most established include social adversity (e.g., family poverty, exposure to community violence), parental psychopathology and substance abuse, marital conflict, and negative parental attributions

concerning the meaning of a child’s behavior (Hudson & Rapee, 2005). It is now understood that many of these risk factors operate through mechanisms based in moment-to-moment dynamics of parent–child interactions. Research investigating the role of parenting on the emergence and maintenance of externalizing problems has been particularly extensive. Across early to mid-childhood, features of ODD and CD demonstrate proximal associations with high levels of harsh and inconsistent discipline, as well as low levels of parental warmth and involvement (Hawes  & Dadds, 2005a). Evidence of such parenting among families of children with conduct problems has provided much support for social learning–based conceptualizations, which include modeling of aggression, as well as escalating cycles of coercion based on escape/avoidance conditioning. These cycles function as “reinforcement traps” that reward both parents and children for use of aversive control tactics (e.g., whining, nagging, shouting, hitting) and squelch positive family interactions. Participation of siblings in coercive cycles also contributes to family-based risk for conduct problems. Children become increasingly skilled in use of coercion and are then more difficult to discipline, as the quality of parenting and family relationships is progressively eroded (e.g., Dishion  & Patterson, 2006). Translation of this social learning model into evidence-based parent training interventions for conduct problems (Eyberg et al., 2008) is among the most influential innovations in the mental health sciences. Compared with ODD/CD, considerably less research has investigated the contributions of socialization processes to ADHD. Ellis and Nigg (2009) examined the unique cross-sectional associations between ADHD and specific parenting practices commonly associated with externalizing problems more generally. High levels of inconsistent discipline and low levels of parental involvement were each associated uniquely with ADHD symptom severity, controlling for other child characteristics including comorbid features of conduct problems (Ellis  & Nigg). The potential importance of inconsistent discipline to development and maintenance of ADHD has also been demonstrated by cross-sectional research on gene × environment interactions. Martel et  al. (2011) found that the dopamine receptor D4 gene (DRD4) moderated family environment effects on ADHD, such that it was associated with increased risk for the disorder only among children exposed to highly inconsistent discipline. Levy, Hawes, Johns

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Dimensions of parenting have also been associated with risk for ADHD in emerging prospective research. Hawes et  al. (2013) examined specific parenting practices as predictors of hyperactivity/ inattention across a 12-month follow-up period in a large community cohort. High levels of parental involvement were associated with reduced levels of hyperactivity/inattention but only across early childhood. Conversely, increases in child age were associated with increased levels of hyperactivity/ inattention across mid-childhood but only among children exposed to high levels of inconsistent discipline. Inconsistent discipline and parental involvement appear to be uniquely associated with prospective hyperactivity/inattention across childhood, independent of associated conduct problems. The harsh/rejecting parenting associated with coercive cycles is also associated with risk for internalizing problems, including child and adolescent depression (McLeod, Weisz,  & Wood, 2007; Rapee, 1997), and self-injury (Crowell et al., 2013). In contrast, models of child anxiety have emphasized risk arising from overprotective/overcontrolling parenting, wherein parents excessively restrict children’s engagement with situations or behaviors based on anticipation of potential threat (Rapee, Schniering,  & Hudson, 2009). This may extend to psychological control expressed through intrusive or passive–aggressive parenting behaviors that inhibit autonomy. Such parents may withdraw affection or induce guilt as means of discipline, creating a family environment in which acceptance is contingent on children’s behavior (Barber, 1996). Meta-analytic research has not found the association between such parenting practices and child anxiety to be moderated by age, suggesting that risk may be invariant across development (McLeod, Wood,  & Weisz, 2007). Overcontrolling parenting may confer risk through a number of mechanisms, potentially functioning to (a) model anxious responding to innocuous events, (b) enhance children’s threat interpretations, (c) prevent habituation of anxious arousal by limiting children’s exposure to fear-provoking events, and (d) interfere with the adaptive development of emotion regulation skills (Ollendick, Costa, & Benoit, 2010). Importantly, overprotective/overcontrolling parenting is associated not only with child anxiety but also with externalizing problems (e.g., Gere et  al., 2012; Hudson & Rapee, 2001; Pettit et al., 2001). Through this parenting, children may learn dysfunctional ways of reacting to their environments, which may in turn place them at risk for both anxiety and/ 450

or disruptive behavior problems (Bubier & Drabick 2009). Such parenting dimensions contribute to transdiagnostic risk through processes that may present differentially depending on context (Harvey et  al., 2004). For example, psychological control may present as attempts to reduce distress in children who exhibiting anxiety but as attempts to increase compliance in children who exhibit oppositional behaviors (Fraire & Ollendick, 2013). Considerable evidence of shared family-based risk for externalizing and internalizing psychopathology has also been reported in research guided by attachment theory (Fearon et al., 2010; Groh, Roisman, van IJzendoorn, Bakermans-Kranenburg, & Fearon, 2012; van IJzendoorn et al., 1999). Research comparing secure versus insecure (combining ambivalent, avoidant and disorganized categories) attachment relationships indicates that secure attachment relationships predict—and are concurrently associated with—lower internalizing and externalizing problems across childhood and adolescence (Marchand, Schedler,  & Wagstaff, 2004; McCartney, Owen, Booth, Clarke-Stewart,  & Vandell, 2004; Roelofs et al., 2006; Wood, Emmerson, & Cowan, 2004). McCartney et al. (2004), for example, examined prediction by mother–child attachment relationships measured at 15, 18, 24, and 36 months of age of externalizing and internalizing behavior problems measured at age 36 months in a large community sample. Attachment security at 24 and 36 months was correlated negatively with both externalizing and internalizing problems at 36 months. Similarly, Roelofs et al. (2006) found that insecure attachment relationships among 9- to 12-year-old children and their primary attachment figure was associated with higher anxiety, depression, and aggression. The potential to integrate attachment principles into social learning–based accounts of child psychopathology has received increasing attention in recent years (Dadds  & Hawes, 2006; Greenberg et  al., 1993; Scaramella & Leve, 2004). Such integration is supported by evidence that disruptions to child attachment security may interact with coercive parenting to confer risk for child dysfunction (e.g., Kochanska et al., 2009). Peers play an increasingly important role in amplification and transformation of externalizing behaviors across development, through interactions characterized by rejection, coercion, and selective reinforcement of deviancy in friendships with antisocial children and adolescents (e.g., Snyder et  al, 2008). Similar peer processes are implicated in risk for internalizing problems. For example,

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longitudinal research demonstrates that increased deviant peer affiliations are associated with growth in depressive symptoms across adolescence (Fergusson et al., 2003). Furthermore, Galambos et al. (2003) found that involvement with deviant peers was associated with presence of both externalizing and internalizing problems. Accordingly, the potential for overlapping peer contagion processes in the development of conduct problems and depression has received increasing attention in recent years (see Dishion & Tipsord, 2011). Interestingly, some research has found that the extent to which family and peer processes operate as shared risk factors for externalizing and internalizing problems may vary as a function of sex. For example, Lee and Bukowswi (2012) found that although parental violence was a shared risk factor across early adolescence for boys, affiliation with deviant peers was a shared risk factor for girls.

Differentiating Children and Adolescents with Pure versus Comorbid Disorders

In addition to extensive evidence that family and peer contexts operate as shared risk factors for both externalizing and internalizing disorders, such factors also differentiate children and adolescents with comorbid externalizing and internalizing disorders from those with “pure” presentations of these disorders. This evidence may suggest (among other mechanisms) that comorbid trajectories implicate environmental risk factors that are broader in range, or that occur at higher intensity, than those implicated in the problem trajectories of children who develop externalizing or internalizing disorders in isolation. Alternatively, it is possible that such trajectories arise from biological liabilities that render some children particularly sensitive to environmental influences. Certain neurobiological characteristics may confer differential susceptibility to environments, which may in turn provide a central mechanism for regulation of alternative patterns of human development. In other words, some individuals may be influenced more by their experiences and environments than others. This susceptibility is assumed to involve enhanced sensitivity to both negative and positive environments; that is, to conditions associated with both risk and protection (Belsky & Pluess, 2009; Ellis et al., 2011). Compared with children with symptoms of either ODD or anxiety in isolation, those with comorbid features of both disorders experience higher levels of family conflict (Drabick et  al., 2008). Similarly, families of children with comorbid

anxiety and ADHD demonstrate higher levels of family dysfunction, including child maltreatment, lack of warmth, scapegoating, ineffective communication, family breakdown, and social isolation compared with children with ADHD alone (Freitag et  al., 2012). Similar findings have been reported in longitudinal research, where exposure to parental maltreatment in kindergarten predicts development of comorbid aggressive and anxious symptoms in adolescence (Lansford et al., 2002). Emerging research using “person-centered” methods also indicates that factors in the family environment differentiate children and adolescents with comorbid versus pure externalizing and internalizing disorders. Using latent class analysis, Basten et al. (2013) examined whether a subgroup of children with co-occurring externalizing and internalizing behaviors could be identified within a large community cohort (N  =  6131, ages 5–7  years). In addition to groups comprising problem-free children (85.6%), exclusively high internalizing (5.3%), and exclusively high externalizing problems (7.3%), a smaller subgroup with high levels of both externalizing and internalizing problems emerged (1.8%). This comorbid group was characterized by significantly highly levels of parental psychopathology than the other groups, in the form of maternal and paternal hostility and affective symptoms. These findings are consistent with studies in which groups of children with pure CD, pure depression, and comorbid CD and depression have been recruited (e.g., Kopp & Beauchaine, 2007). Although Basten et  al. (2013) did not measure parenting practices explicitly, parent characteristics (e.g., psychopathology) that were observed in the comorbid subgroup have been associated with dysfunction across a range of parenting practices in much previous research (Hudson & Rapee, 2005). Evidence from attachment-based research is also consistent with findings of increased levels of dysfunction in families of children with comorbid disorders. In a high-risk sample of male adolescents between ages 13 and 19  years, Zaremba and Keiley (2011) found that insecure attachment was related to externalizing problems, internalizing problems, and affect dysregulation. Buist, Dekovic, Meeus, and van Aken (2004) examined reciprocal relationships between parental attachment and adolescent externalizing and internalizing behaviors in a longitudinal study of 288 adolescents between ages 11 and 15  years. Lower closeness, trust, and communication in parent-child attachment relationships predicted, and were concurrently related Levy, Hawes, Johns

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to, externalizing and internalizing behaviors in adolescence.

Socialization and Comorbidity: Reciprocal Processes

Common vulnerability to both externalizing and internalizing disorders are assumed to play out through transactional dynamics between child-level vulnerabilities and environmental risk factors across development (see Essay 27; Beauchaine  & McNulty, 2013). Such transactions are implicated in processes through which features of one disorder may confer risk for development of heterotypic disorders. One of the most influential examples of this is the “dual failure” model proposed by Patterson and Stoolmiller (1991), which predicts that occurrence of academic failure associated with conduct disorder contributes to peer rejection, which in turn increases risk for internalizing problems and involvement with antisocial peers. Evidence in support of such processes has grown considerably in recent years and has contributed to models of child and adolescent psychopathology based on developmental cascades (e.g., Cleverley et al., 2012; Vaillancourt et al., 2013). Evidence that internalizing problems precede externalizing problems is also available. In recent longitudinal research reported by Bornstein et  al. (2013), externalizing behavior problems in early adolescence were predicted by internalizing symptoms in early childhood. This is consistent with the notion that for some children with conduct problems, aggressive behavior and antisocial acts are committed impulsively as a result of emotion dysregulation (e.g., Frick, 2012). Similarly, temperament characterized by emotional reactivity and negative affectivity may contribute to initiation of coercive family process early in childhood (Scaramella & Leve, 2004).

Implications of Comorbidity for Treatment of Externalizing Disorders

It is often assumed in clinical settings that comorbid disorders complicate treatment and reduce clinical gains. However, evidence indicates that this is not necessarily the case for comorbid externalizing and internalizing psychopathology. An emerging body of research has examined questions regarding the extent to which clinical outcomes following treatment of externalizing and internalizing disorders differ as a function of comorbid presentations. This includes research that has examined the effects of comorbid internalizing problems on treatment 452

of externalizing problems in childhood. Rather than indicating that comorbidity is a risk factor for poor treatment outcomes, children and adolescents with conduct problems and comorbid internalizing symptoms may benefit more from such interventions than those with externalizing problems alone. Beauchaine et al. (2005) examined treatment outcome data collected via multi-informant report, in a large sample of children aged 3 to 8  years (N  =  514) with conduct problems. Latent growth curve modeling of symptoms revealed that higher levels of anxiety/depression were associated with a better treatment response, independent of baseline symptoms. Likewise, Connell et  al. (2008) used latent transition analysis to examine symptom trajectories among children aged 2 to 4  years in disadvantaged families. Co-occurrence of externalizing and internalizing symptoms was associated positively with transitions to problem-free levels following family-based intervention. Similarly, in a study investigating psychophysiological markers for treatment response in inpatient adolescents with conduct disorder and ADHD, comorbid depression conferred improved short-term prognosis among participants with high levels of heart rate variability (Beauchaine et al., 2000). Recent research on child temperament and pathways to antisocial behavior may inform conceptualizations of the protective effects that comorbid anxiety affords children treated for conduct problems. Anxiety is correlated negatively with callous-unemotional (CU) traits, especially when controlling for either impulsivity or conduct problems (Frick et al., 2013). Given that CU traits are associated with risk for poor treatment outcomes following various interventions for conduct problems (e.g., Hawes & Dadds, 2005b; Manders et al., 2013), anxiety may therefore protect against this risk. Interestingly, recent evidence suggests that anxiety may also be associated with positive treatment outcomes independent of associations with CU traits. Hawes et al. (2013) found that increased levels of CU traits were associated with persistent ODD symptoms following parent training intervention, independent of ODD severity at pre-treatment. Children with higher levels of anxiety were also more likely to be free of ODD at follow-up than children with lower levels of anxiety. The finding that children with conduct problems and comorbid anxiety benefit more from family-based intervention than those without comorbid anxiety is consistent with broader evidence that the problems of such

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children are more likely to be associated with family dysfunction (Cunningham  & Ollendick, 2010). Comorbid internalizing problems are also associated with responses to psychosocial and pharmacological treatments for ADHD. Since Tannock et  al. (1995) demonstrated that methylphenidate improved working memory among non-anxious ADHD children but not among comorbid children, there has been considerable interest in implications of comorbid anxiety in ADHD. Vance et  al. (2003) investigated the association of anxiety disorders, defined by either parent or child, among 146 medication naïve 6to 12-year-old children with ADHD combined type (ADHD-CT) (defined via semi-structured clinical interview), with and without dysthymic disorder (DD), using the Child Behaviour Check List (CBCL; Achenbach  & Edelbrock, 1978), the Anxiety Disorders Interview Schedule for Children (A-DISC; Albano  & Silverman, 1996), the Revised Child Manifest Anxiety Scale (Reynolds  & Richmond, 1985), and Parental Account of Childhood Symptoms (Taylor, 1993). The primary finding was an association of SAD and social phobia among children with ADHD-CT and DD, compared with children with ADHD-CT without DD. The investigators did not report on sex differences. Jensen et al. (2001) reported on ADHD comorbidity findings from the NIMH Collaborative Multisite Multimodal Treatment Study of Children with ADHD (MTA study). The investigators suggested that the overlap of ADHD with internalizing and/or disruptive disorders would be of interest if comorbid groups could be considered diagnostically meaningful or separate subtypes, thus allowing for more optimal treatment planning. Some “moderator” studies from the MTA data suggested that ADHD children with comorbid parent-reported anxiety disorders showed enhanced treatment response on outcome measures of parent-reported ADHD and internalizing symptoms, relative to non-anxious children. Also among anxious children, combined medication and behavior therapy treatments conferred greater improvements than medication management alone (MTA Co-operative Group 1996). However, these results were thought to be inconclusive in indicating whether ADHD comorbid internalizing or externalizing groups should constitute separate clinical entities. Interestingly, children with ANX tended to be more treatment responsive than ADHD+ODD/CD and ADHD alone across all

treatment modalities, whereas ADHD only and ADHD+ODD/CD responded better to treatments that included medication. Finally, Garcia et al. (2009) addressed issues of comorbid anxiety disorders in methylphenidate treatment of ADHD (Tannock et al., 1995, negative moderator; Jensen et  al., 2001, positive moderator; Diamond et al., 1999, no difference; Abikoff et  al., 2005, no difference). Two hundred eighty children, aged 4 to 17  years, were assessed by the K-SADS-E (Orvaschel et  al., 1982) (modified for DSM-IV criteria) and clinical review by a child psychiatrist. Medication responses were recorded after 1  month of treatment, using the SNAP-IV rating scale (Swanson, 1992), which had been used in the MTA study. Groups with ADHD+NX and ADHD-ANX were compared on sex, age, SES, IQ, distribution of comorbid conditions, intensity of ADHD symptoms, and dose of MPH, using analysis of covariance, with baseline scores and potential confounders as covariates. No significant between-groups difference in response to treatment with MPH was found after one month, either when SNAP-IV scores were assessed dimensionally or categorically, suggesting that comorbid ANX did not interfere with response to MPH or core ADHD symptoms. Although variation in findings in relation to moderating effects of anxiety on MPH treatment of ADHD remains to be explained, ascertainment, severity, and variations in baseline anxiety might contribute to the discrepancies. Interestingly, participants with ADHD and comorbid anxiety/depression may benefit more from CBT interventions for ADHD than those with ADHD alone. This differential response among adolescents (aged 14–18 years) was seen in the context of a 13- to 16-week CBT intervention that targeted knowledge and cognitions regarding ADHD and skills for organization and planning, distractibility, procrastination, and communication (Antshel, Faraone,  & Gordon, 2012). Following speculation that CBT interventions for childhood anxiety may target regulatory mechanisms implicated in hyperactivity/impulsivity (e.g., Kendall et  al., 2001), research aimed at maximizing treatment gains of children with comorbid externalizing and internalizing disorders has begun to investigate theory-driven integration of evidence-based components from the ADHD and anxiety literatures. Some support for such approaches has been reported, with data emphasizing more limited gains in relation to ADHD than anxiety symptoms (Jarrett & Ollendick, 2012). Levy, Hawes, Johns

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The current evidence base for the treatment of comorbid externalizing and internalizing disorders has also been informed by studies examining the impact of co-occurring externalizing symptoms of the treatment of primary presentations of internalizing problems, most often anxiety. Results from these studies have been somewhat mixed. Most research suggests that comorbid conduct problems do not decrease the efficacy of evidence-based treatments for anxiety among children and adolescents, when anxiety is measured continuously (e.g., Kendall et  al., 2001; Rapee, 2003). In contrast, studies in which treatment response is operationalized in terms of categorical end points (e.g., diagnosis-free, treatment responder status) associate comorbid externalizing problems with reduced treatment response (e.g., Liber et al., 2010; Storch et al., 2008). A recent study by Rapee et al. (2013) served to clarify such findings, indicating that distinguishing between such indices of treatment outcome may be of particular value to understanding implications of comorbidity for treatment of anxiety. This large-scale study of children (N  =  750) aged 6 to 18 years with primary diagnoses of anxiety disorders indicated that comorbidity did not reduce the rate or extent of response to CBT. In fact, high levels of comorbid externalizing behaviors were associated with greater change over time. However, children with comorbid disorders entered treatment with the most severe symptoms of anxiety, and these symptoms remained more severe following treatment compared with children with anxiety alone. These findings suggest that CBT for child anxiety can produce significant gains regardless of the comorbid externalizing diagnoses, while emphasizing the clinical utility of comorbidity as a predictor of diagnostic status following intervention (Rapee et al., 2013).

Directions for Future Research

The extent to which the DSM5 will serve to overcome existing problems associated with formulation of comorbid diagnoses will only become apparent in the coming years, which in itself represents an important direction for future research. For example, the newly introduced diagnosis of disruptive mood dysregulation disorder in DSM5 is classified as a depressive disorder. At the same time, this diagnosis would appear to present clear implications for diagnosis of various externalizing disorders, such as ODD and intermittent explosive disorder, with which it may share significant phenotypic overlap. 454

Similarly, ODD and CD are now classified as disruptive impulse-control and conduct disorders, whereas ADHD is classified as a neurodevelopmental disorder. Research is needed to investigate how these changes affect diagnosis of comorbid internalizing and externalizing forms of psychopathology in clinical settings, as well as at the population level. Such research is likely to benefit from quantitative genetic approaches, rather than candidate gene investigations, that may help to disentangle overlapping etiologies. Additional directions for future research are suggested by the various assumptions that underlie recent diagnostic and conceptual developments. Broadly speaking, current perspectives on psychopathology reflect a shift away from the notion of specific mental disorders as discrete and separate entities. Models such as the liability-spectrum conceptualization of psychopathology (Krueger  & Markon, 2006) are based on the assumption that extensive comorbidity among mental disorders reflects the existence of a smaller number of “liability constructs” that underlie multiple disorders. Such a view emphasizes the assumption that psychopathology is organized hierarchically, with specific mental disorders seen to reflect alternative manifestations of common underlying liabilities. This perspective has drawn considerable attention to the issue of whether these liabilities may be best conceptualized in terms of continuous dimensions, with recent research providing much support for this conceptualization across various domains (e.g., Caspi et al., 2013; Cuthbert & Kozak, 2013). Following from this perspective, a second broad aim for future research will be to better characterize the liability constructs or trait dimensions that may underlie forms of psychopathology commonly associated with comorbid externalizing and internalizing disorders. There is already considerable evidence that among children and adults with antisocial behavior, those who demonstrate high levels of callous-unemotional traits exhibit lower levels of anxiety than those without such traits, especially when controlling for either impulsivity or conduct problems (Frick et al., 2013). Future research building on this work is needed to further inform conceptualizations of comorbid internalizing problems among individuals with antisocial behavior. Another priority for such research, which has received comparatively little attention, is borderline personality disorder (BPD). Emerging models suggest that BPD follows a developmental trajectory comprising childhood features of internalizing and

Externalizing and Internalizing Comorbidit y

externalizing psychopathology, along with trait dimensions related to impulsivity and emotional dysregulation that are potentiated across childhood and adolescence by adverse environments (Belsky et al., 2012; Burke et al., 2012; Crowell et al., 2009; Stepp et  al., 2012). Research into liability constructs that confer risk for BPD, and the dynamics through which such liabilities interact and transact with environmental risk factors, has much potential to inform clinical strategies for intervening in the earliest stages of this trajectory (Hawes, 2014). Third, data on clinical implications of comorbid externalizing and internalizing disorders remain particularly limited. There is a strong need for theory-driven treatment outcome research in which comorbid features of these disorders are examined as predictors and moderators of therapeutic response, in samples comprising the high levels of comorbid psychopathology seen in “real world” clinical settings. A recent example of such research is an RCT reported by Dadds et al. (2012), which investigated benefits of a novel intervention to target the emotion recognition deficits emphasized in theoretical accounts of children with conduct problems and callous-unemotional traits. Inclusion criteria for the study allowed for sampling of children with conduct problems and a range of comorbid disorders (autism, anxiety/depression, ADHD), with 42.3% of participants (N  =  195) meeting criteria for two diagnoses, and 14.2% meeting criteria for three. Participant traits/symptom dimensions were then analyzed as moderators of response to this novel intervention, in combination with standard parent training, compared with standard parent training alone. Children high on CU traits were those most likely to respond poorly to standard parent training, and the only ones for whom the novel intervention enhanced treatment outcomes. Information on mechanisms of therapeutic change in this putative subgroup was further provided by mediation analyses examining performance-based measures of emotion recognition collected across the intervention. Such a design has potential to address a range of other questions pertaining to the treatment of individuals with comorbid psychopathology, including those arising from recent DSM5 revisions.

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CH A PT E R

26

Comorbidity Among Externalizing Disorders

Molly A. Nikolas

Abstract This chapter examines the nature of comorbidity among externalizing disorders, with the goal of identifying mechanisms that contribute to overlap among various dimensions of externalizing behavior. The author first examines measurement models of externalizing spectrum comorbidity to address how these models answer historical questions about externalizing behavior and to pose challenges for disentangling the nature of comorbidity among externalizing psychopathologies. Next, the author evaluates the current state of the science on etiological mechanisms of externalizing comorbidity, including a synthesis of findings from behavioral genetics, neurobiology, dispositional trait models, and family and environmental process research. Developmental influences on the nature of overlap among externalizing spectrum disorders are also discussed. The chapter concludes with a brief discussion of ongoing questions and controversies regarding co-occurrence of externalizing spectrum disorders, with suggestions for future research. Key Words:  comorbidity, externalizing, etiology, neurobiology, development

Introduction

Comorbidity among psychological disorders is a significant challenge for understanding the nature of causal mechanisms of psychopathology. Comorbidity of medical diseases has long been recognized. Epidemiologists define comorbidity as “an additional, distinct entity or disease that has existed or that may occur during the clinical course of a patient with an index disease” (Feinstein, 1970, p. 467). In psychiatry and psychology, comorbidity is defined similarly as the “co-occurrence of different diseases in the same individual” (Blashfield, 1990, p. 61). Generally, comorbidity among externalizing spectrum disorders is defined as the expression of different disorders within an individual, which can occur concurrently and/or over time (Lilienfeld, Waldman, & Israel, 1994). Following removal of diagnostic exclusion hierarchies from the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R; American Psychiatric Association, 1987), which precluded assigning more than one

disorder to any individual (see Beauchaine, Klein, Erickson, & Norris, 2013), comorbidity research exploded. Early on, this research demonstrated that rates of individual disorders within the general population do not account for the frequency with which different disorders co-occur (Caron & Rutter, 1991). Such findings suggested ways in which overlap among externalizing conditions might have implications for etiology (Angold, Costello, & Erkanli, 1999). However, much of this work also garnered skepticism regarding the meaning of comorbidity and highlighted significant challenges that diagnostic overlap among externalizing constructs posed for constructing reliable and valid tools for assessment and diagnosis (Kendell & Jablensky, 2003; Lilienfeld et al., 1994). A primary aim of scientific inquiry into externalizing (and other) comorbidity is to understand whether overlap among disorders reflects actual co-occurrence of separate disorders/ disease entities with largely non-overlapping etiological influences 461

or if observed co-occurrence is instead artifactual and due to arbitrary subdivisions across single disease entities (Angold et  al., 1999; Beauchaine  & McNulty, 2013; Lahey, Waldman,  & McBurnett, 1999). There is now considerable evidence to suggest that some aspects of externalizing comorbidity are, in fact, artifactual (Beauchaine & McNulty, 2013; Krueger & Tackett, this volume; Krueger, Markon, Patrick, & Iacono, 2005; McNulty, Beauchaine, & Hinshaw, this volume). Caron and Rutter (1991) demonstrated numerous ways in which externalizing comorbidity could be produced falsely by unreliable detection and diagnostic practices. Furthermore, both twin and population-based factor analytic work suggest that much of the observed overlap among different externalizing disorders is attributable to a common externalizing factor (e.g., Krueger et  al., 2005; Lahey et  al., 2012; Tuvblad, Zheng, Raine, & Baker, 2009). Thus, it appears that at least some of the overlap reflects potentially arbitrary boundaries between subsyndromes—one of many potential problems associated with classification systems that impose categorical architecture on behavior dimensions (Markon, 2010, 2013). Because research on comorbidity among externalizing disorders is ongoing, my aim here is not to answer all questions about true versus artifactual comorbidity. It is likely that behavioral overlap among externalizing disorders involves common etiological factors (with condition-specific proximal risk factors) and diagnostic misclassification/arbitrary syndrome subdivision. Instead, my goal is to review potential mechanisms that may account for observed overlap among externalizing disorders and dimensions with the aim of highlighting plausible etiological mechanisms worthy of further exploration and investment in future research. Indeed, refinement of and greater sophistication in neurobiological and genetic methodologies, quantitative methods, and collaborative efforts will make it possible to address long-standing questions regarding the complex origins of externalizing psychopathology (see Beauchaine & Gatzke-Kopp, 2012).

Historical Context

Comorbidity among psychiatric disorders— including externalizing disorders—was rarely examined prior to the release of the DSM-III-R in 1987 (Anderson, Williams, McGee,  & Silva, 1987). As already noted, earlier editions of the DSM invoked diagnostic hierarchies that ruled out most possibilities of comorbidity (see Beauchaine et  al., 2013; First, 2005). Elimination of diagnostic hierarchies 462 Comorbidit y

resulted in significant increases in rates of comorbidity and considerable expansion of studies aimed at addressing the nature of these patterns of diagnostic overlap (Angold et al., 1999; Klein & Riso, 1993). Indeed, the number of article titles including the word comorbidity grew from 0 in 1985 to 243 in 1993 (Lilienfeld et al., 1994). This trend has continued to the present, with more than 1,500 articles published with “comorbidity” as a keyword in 2013 alone. Importantly, comorbidity was recognized before 1987; substantial work prior to the DSM-III-R demonstrated that (a)  co-occurrence of disorders was a rule and not an exception (e.g., Widiger & Frances, 1985) and that (b) linked psychological syndromes were associated with differences in family histories, correlates, and treatment outcomes (e.g., Hinshaw, Lahey, & Hart, 1993). Factor analytic work demonstrates that psychiatric symptoms can be measured dimensionally and thus combined in ways that describe continuous and often correlated liabilities for psychopathology (see Achenbach, 1974). Following from this tradition, one potential explanation for high rates of co-occurrence among disorders is a failure of categorical models to account adequately for underlying correlations among dimensions of behavior. Multiple reviews have addressed the question of whether externalizing comorbidity reflects overlap of independent disorders (true comorbidity) or whether externalizing behaviors and disorders reflect alternate forms of the same, underlying liability (artifactual comorbidity). Empirical investigations of both clinical and community samples generally find more support for the alternate forms hypothesis (see Beauchaine, Hinshaw,  & Pang, 2010; Lahey et  al., 1999). Thus, a common set of etiological mechanisms may give rise to a general externalizing liability factor, which is then modified by environmental influences to produce multiple dimensions of externalizing behavior (Beauchaine & McNulty, 2013). Similar findings have emerged for internalizing disorders (Eaton, Krueger, & Oltmanns, 2011). Accordingly, the nature of overlap among externalizing disorders has become an important area of investigation in its own right because researchers recognize that understanding the nature of comorbidity holds potential to illuminate the fundamental nature of psychopathology (Angold et al., 1999). In comorbidity research, a critical distinction is often made between homotypic comorbidity, which refers to the co-occurrence of multiple disorders of the same “type” or “form” (e.g., comorbidity between different externalizing disorders or different

internalizing disorders) and heterotypic comorbidity, which refers to either (a)  the co-occurrence of internalizing and externalizing disorders or (b) the sequential development of different disorders across the life span (e.g., externalizing disorders in childhood predicting internalizing problems in adulthood, Sauder, Beauchaine, Gatske-Kopp, Shannon,  & Aylward, 2012). Issues related to heterotypic comorbidity between externalizing and internalizing disorders are covered in excellent detail by Levy, Hawes, and Johns (this volume). In this chapter, I consider both homotypic and heterotypic comorbidity among externalizing disorders across development or trajectories through which externalizing spectrum disorders unfold over time. In sum, the study of externalizing comorbidity has undergone a somewhat complex evolution. Viewpoints regarding the nature of this phenomenon have both been informed by and influenced ongoing revisions of diagnostic systems—the categorical nature of which may have perpetuated an illusion that externalizing disorders are indeed primarily independent syndromes (Kendell  & Jablensky, 2003). Findings of differences in outcomes among subgroups or subtypes of externalizing disorders (e.g., attention-deficit/hyperactivity disorder [ADHD] vs. conduct disorder [CD]) have previously been interpreted as support for this notion (see, e.g., Jensen, Martin, & Cantwell, 1997; Waschbusch, 2002). However, there is mounting evidence that a considerable degree of what is observed as “overlap” among different conditions is probably artifactual and due to subdivisions of correlated, naturally linked dimensional syndromes. Thus, comorbidity research, particularly during the last decade, has largely moved beyond questions of identifying independent disorders and differential outcomes, instead turning toward exploring why externalizing behaviors group together in the ways that they do and seeking the potential etiological sources for the observed patterns of covariation.

Links to Traditional Externalizing Disorders

The externalizing spectrum, although studied most often among children and adolescents, also extends to disorders that were once thought to occur only in adulthood. Traditionally, disorders grouped under the umbrella of the externalizing spectrum include ADHD, oppositional defiant disorder (ODD), CD, antisocial personality disorder (ASPD), and substance use disorders (SUDs; Krueger et  al., 2002) Some authors also include

psychopathy or callous-unemotional traits along (see Golmaryami & Frick, this volume). In addition to these diagnoses, numerous behaviors are considered to be externalizing constructs (e.g., disinhibition, impulsivity, rule-breaking, physical aggression, verbal aggression, substance use) that show different degrees of overlap with one another (Krueger et al., 2002). By most accounts, externalizing comorbidity is substantial. For example, nationally representative epidemiological samples indicate that rates of overlap between ODD and CD range from 56% to 62% (Maughan, Rowe, Messer, Goodman, & Meltzer, 2004). Furthermore, clinic-referred youth with ADHD are 18 times more likely than youth without ADHD to meet criteria for ODD and 40 times more likely to meet criteria for CD (Gau et al., 2010). An estimated 50% of youth diagnosed with ADHD meet criteria for ODD and/or CD (Ollendick et al., 2008), which has prompted long-standing debate about whether ADHD accompanied by co-occurring externalizing disorders actually represents a fundamentally different syndrome than ADHD alone (Hinshaw, 1987; Jensen et al., 1997). Among adults, rates of co-occurring SUD among those with ASPD range from 30% to 75% (Goldstein & Grant, 2009). Developmentally, ADHD in childhood often portends later diagnoses of ODD, and comorbid ADHD+ODD in turn predicts development of CD (see Lahey  & Loeber, 1994; Loeber, Burke, Lahey, Winters, & Zera, 2000). Similarly, comorbid ADHD and CD predict substance-related problems in adulthood (Groenman et  al., 2013), although evidence exists regarding risk for adult substance abuse among those with childhood ADHD independent of CD (Lee, Humphreys, Flory, Liu, & Glass, 2011). Interestingly, however, prior diagnosis of CD is not associated with subsequent development of ADHD (Burke, Pardini, & Loeber, 2008). In contrast, developmental relationships between ADHD and ODD appear to be more reciprocal in nature, at least early in development (Harvey, Lugo-Candelas,  & Breaux, in press). Furthermore, prior history of CD is a diagnostic criterion for ASPD (Loeber, Burke, & Lahey, 2002), and indeed, early age of onset of CD clearly portends developmental continuity of externalizing behavior into adulthood (Lahey et  al., 1998; Moffitt, 1993; Moffitt et  al., 2008). Thus, past research has identified significant overlap among externalizing spectrum disorders both within and across development. Nikol as

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Although rates of comorbidity across different diagnostic syndromes and across development are useful for understanding risk trajectories (Reef, Diamontopoulou, van Meurs, Verhulst, & van der Ende, 2011), externalizing comorbidity is most frequently investigated using factor analytic methods. Rather than evaluating rates of overlap among categorical entities, factor analytic models instead specify the latent structure of symptoms of externalizing comorbidity (e.g., Carragher et  al., 2014; Krueger, Markon, Patrick, Benning,  & Kramer, 2007; Vrieze, Perlman, Krueger,  & Iacono, 2012; Witkiewitz et al., 2013). As seen in Figure 26.1A, such models often identify a single dimensional factor of externalizing liability (often construed as trait impulsivity; see Beauchaine & McNulty, 2013) that accounts for most of the vulnerability to different first-order dimensions of externalizing behavior (e.g., inattention, hyperactivity-impulsivity, rule-breaking, aggression, property destruction, substance abuse). Replication of such models with diverse samples (clinical vs. community, representative and psychopathology-enriched, youth and adults) and across assessment techniques (diagnostic interview, questionnaires, behavioral observations) provides converging support for a dimensional conceptualization of vulnerability to externalizing psychopathology both in childhood/adolescence (Burns, deMoura, Beauchaine,  & McBurnett, 2014; Tackett et  al., 2013b; Tuvblad et  al., 2009) and in adulthood (Krueger et al., 2002; Vrieze et al., 2012; Witkiewitz et al., 2013). Importantly, a general externalizing factor also emerges when using person-centered (as opposed to variable-centered) analyses. Vaidyanathan, Patrick, and Iacono (2011) conducted latent class analyses within two nationally representative datasets using diagnostic interview data for both internalizing and externalizing disorders. Results indicated a separate externalizing comorbidity class (out of five classes). Although the number of distinct patterns of comorbidity was limited, one such pattern includes overlap among disorders on the externalizing spectrum. Importantly, even though factor analytic models consistently support a hierarchical latent structure of psychopathology in general (Lahey et al., 2012) and of externalizing psychopathology specifically (Krueger et al., 2005), the type of best-fitting model may require further evaluation in future research. Typically, a second-order factor model is fitted to the externalizing spectrum (see Figure 26.1A). According to such models, variance among individual items (i.e., symptoms or responses from diagnostic 464 Comorbidit y

interviews or questionnaires) is accounted for by several first-order factors, which can be mapped roughly onto behavioral dimensions associated with DSM diagnoses (e.g., ADHD, ODD, SUD). In turn, common variance across these first-order factors is accounted for by a second-order factor, which represents liability for externalizing psychopathology. However, the reformulated second-order bifactor model may also be worthy of consideration. As seen in Figure 26.1B, bifactor models are conceptually different from traditional, second-order factor models because they allow individual items (i.e., symptoms or responses from diagnostic interviews or questionnaires) to load simultaneously on both a general factor of externalizing psychopathology (“g” factor), as well as on specific factors that may correspond to different behavioral dimensions. In recent years, such bifactor models have demonstrated better fit than second-order factor models for individual externalizing disorders and behavioral dimensions (Martel, von Eye, & Nigg, 2010), for externalizing comorbidity (Burns et al., 2014), and for broad dimensions of psychopathology overall (Caspi et al., 2014; Krueger et al., 2007; Lahey et al., 2012). Overall, there appears to be emerging support for the concept of a general “g” factor of externalizing psychopathology, which is important to consider in future work that examines underlying mechanisms of externalizing spectrum comorbidity. That is, bifactor models provide a somewhat different framework for conceptualizing liability to co-occurring externalizing behavior problems (i.e., those that underpin the general externalizing factor) while simultaneously accounting for relations with more specific factors (e.g., behavioral dimensions of impulsivity vs. aggression). Importantly, factor analytic models, by themselves, tell us very little about etiological mechanisms of psychopathology. Although they may be useful for understanding patterns of covariation at a behavioral level, they provide no information regarding mechanisms that underlie externalizing spectrum comorbidity or about the ways in which these mechanisms may be mutually influential in maintaining externalizing behaviors across development. Furthermore, behavioral symptoms of externalizing disorders may not be particularly useful when used as lone indicators of etiology because these are often descriptors of evolving behaviors (with sometimes problematic interrater agreement; van der Ende, Verlhulst,  & Tiemeier, 2012) that are likely to result from a set of cascading developmental causal processes. Thus, as I turn next to

(A) Second-order factor model

Externalizing Liability Factor

Inattention

Hyperactivity -Impulsivity

RuleBreaking

Aggression

Substance Abuse

(B) Hierarchical (bifactor) model

Inattention

HyperactivityImpulsivity

Externalizing Liability Factor

Rule-Breaking

Aggression

Substance Abuse

Figure  26.1  Structural models of externalizing comorbidity. Depicted here are different structural models of latent dimensions of externalizing liability. (A) The second-order factor model; (B) the bifactor model. The second-order factor model captures the variance common among first-order latent variables with a second-order latent variable. The conceptually distinct hierarchical/bifactor model specifies that indicators of externalizing comorbidity (represented by indicators on each factor) load on both a general externalizing liability factor (second-order) and on specific (first-order) factors. The first-order factors depicted here represent variability in behavioral dimensions and map roughly onto DSM-based externalizing spectrum disorders. These dimensions were selected to be illustrative and do not represent an exhaustive list of psychopathology relevant for describing externalizing spectrum comorbidity.

examine the current state of the science regarding externalizing comorbidity, I focus on potential etiological mechanisms of a general factor (which may predispose to externalizing psychopathology and underlie overlap among externalizing behaviors) and highlight where etiological mechanisms may operate across specific dimensions of behaviors on the externalizing spectrum.

Current State of the Science

In this section, I  focus on several research traditions in evaluating etiological mechanisms of externalizing comorbidity. I  begin by examining behavioral genetic investigations on the relative magnitude of contributions from heritable versus environmental influences on externalizing comorbidity. Because specific genetic variants hypothesized to contribute to externalizing spectrum disorders are covered in detail elsewhere in this volume (Gizer, Otto,  & Ellingson, this volume), I instead briefly discuss types of environmental influences that may be relevant to externalizing spectrum comorbidity, with an emphasis on gene-environment interplay. I then review some neurobiological markers relevant for understanding how heritable and nonheritable vulnerabilities/risk factors manifest in the development of externalizing comorbidity. This review includes indices of reward sensitivity and inhibitory control, plus measures of dispositional traits. In each section, I discuss evidence for the effect(s) of these contributors on general externalizing liability (e.g., the second-order or “g” factor identified across decades of factor analytic work) and point out where syndrome or dimension-specific mechanisms may be operating. Throughout, I emphasize developmental processes.

Quantifying the Magnitude of Heritable and Environmental Contributors

The methods of behavior genetics, including twin and adoption studies, are particularly useful for understanding sources of overlap among different externalizing disorders/dimensions. These studies make use of differences in genetic relatedness across twins/adoptive siblings within families (i.e., monozygotic or identical twins share, on average, 100% of their genes, whereas dizygotic or fraternal twins share approximately 50% of their genes, and adoptive siblings share 0%) in order to quantify the magnitude of heritable versus environmental influence on a specific behavioral dimension or trait (for further discussion, see Baker, this volume). Biometric (structural equation) models parse 466 Comorbidit y

variance in a behavioral trait into three primary components:  additive genetic (A2, which includes the percentage of variance in the trait that is attributable to heritable effects), shared environmental (C2, which includes the percentage of variance in the trait that is attributable to environment that is common to siblings and therefore serves to increase sibling similarity regardless of the proportion of genes shared), and nonshared, or unique environmental variance (E2, which includes environmental effects that are not common to siblings and therefore differentiate them from one another, plus measurement error). Notably, behavioral genetics does not—and cannot—specify which DNA variants or environmental factors contribute to psychopathology or comorbidity (see Gizer, Otto, & Ellingson, this volume). Rather, twin and adoption studies provide estimates of proportions of variance in behavioral traits and their overlap with one another that are accounted for by heritable and nonheritable factors. The majority of twin and adoption studies use quantitative (dimensional) rather than typological (categorical) measurement. Thus, most samples are community-based, although some have been enriched with clinical cases. Behavioral genetics work on externalizing spectrum disorders began with investigations of heritable and environmental influences on (1) individual dimensions of externalizing behavior and/or (2) covariation between two or more externalizing dimensions. At first glance, heritable influences on externalizing comorbidity appear somewhat muddled. First, heritable influences on individual externalizing dimensions are quite different in magnitude. For example, heritable influences on ADHD, including its behavioral dimensions of inattention and hyperactivity-impulsivity, are quite high, accounting for 70% or more the variance in these traits (e.g., Burt, 2009; Nikolas  & Burt, 2010), whereas the magnitude of heritable effects on other externalizing dimensions is somewhat lower, between 45% and 60% (e.g., Agrawal  & Lynskey, 2008; Burt, 2009; Burt & Klump, 2012). Additionally, heritable influences on ADHD tend to remain high across development, whereas the magnitude of heritable influences on other externalizing behaviors (like most forms of psychopathology; see Beauchaine & Gatzke-Kopp, 2013) is somewhat lower in early childhood and increases with age (see Bergen, Gardner, & Kendler, 2007). Furthermore, there is some inconsistency across studies regarding the degree to which heritable versus shared environmental factors contribute to covariance

among dimensions of externalizing psychopathology. Several reports indicate that heritable influences make the largest contribution (Dick, Viken, Kaprio, Pulkkinen,  & Rose, 2005; Nadder, Silberg, Eaves, Maes, & Meyer, 1998; Silberg et al., 1996; Young, Stallings, Corley, Krauter, & Hewitt, 2000), although the magnitude of common heritable influences may vary, for example, depending on whether inattention is examined separately from hyperactivity-impulsivity (McLoughlin, Ronald, Kuntsi, Asherson, & Plomin, 2007; Nikolas & Burt, 2010). Consistent with findings on individual externalizing dimensions, nearly all behavioral genetics studies of externalizing comorbidity find that unique heritable contributors emerge for ADHD dimensions specifically (Burt, McGue, Krueger, & Iacono, 2005; Dick et al., 2005). Thus, heritability contributes to ADHD specifically, apart from influences on covariation of externalizing psychopathology more generally. Moreover, age and development also affect the magnitude of heritable effects on externalizing comorbidity. For example, shared environmental risk factors provide the greatest contribution to covariation among externalizing symptoms in middle childhood (Burt, Krueger, McGue,  & Iacono, 2001; Burt, McGue, Krueger, & Iacono, 2005). It may be the case that, earlier in development, both heritable and shared environmental influences are relevant for co-occurrence of externalizing spectrum disorders. As already noted, heritability of externalizing disorders increases over time (Bergen et  al., 2007), which is undoubtedly due to the increasing effects of gene-environment interplay as individuals mature (i.e., gene-environment correlation [rGE] and gene × environment interaction [G×E]). Accrual of rGE and G×E effects across development inflates heritability estimates for externalizing psychopathology and their overlap (see later extended discussion regarding gene-environment interplay). For example, developmental trajectories characterized by a high degree of overlap among externalizing dimensions appear to be strongly heritable, and this heritability increases with age (Larsson, Dilshad, Lichtenstein, & Barker 2011; Wichers et al., 2013), thus underlining the potential importance of interplay between heritable influences and environmental experiences in shaping the maintenance of externalizing comorbidity over time. More recently, twin studies have been used to investigate the degree of heritable versus environmental influence on the single, latent externalizing liability factor described earlier. Results indicate heritability coefficients (h2) of between .50 and .85

(Cosgrove et al., 2011; Krueger et al., 2002; Taylor, Alan, Mikolajewski, & Hart, 2013; Tuvblad et al., 2009). Such findings extend to both males and females, indicating that although there may be differences in mean levels of and variance in externalizing comorbidity (Hartung & Widiger, 1998), heritable influences on the general externalizing liability factor are quite similar across sexes. Furthermore, recent work with both twin and adoptive families demonstrates that similarity between parents and children in externalizing behavior appears to be due primarily to heritable transmission of a general liability to externalizing psychopathology (rather than dimension-specific transmission), also regardless of sex (Hicks, Foster, Iacono, & McGue, 2013). Overall, it appears that heritable contributions to externalizing comorbidity are significant and are likely to increase across development, as evidenced by lower heritable influences on externalizing factors in childhood compared to adulthood (see Krueger et  al., 2002; Tuvblad et  al., 2009). It bears repeating that this increase is due, in part, to increasing effects of gene-environment interplay over time. However, few studies have examined heritable influences using hierarchical models of externalizing comorbidity, including a general externalizing factor (which is, notably, free of measurement error given its specification of common variance among lower order factors). In addition, the types of externalizing psychopathology included in such models require additional investigation. For example, studies of younger children and teens often do not include—for obvious reasons—symptoms of substance abuse and dependence when modeling externalizing syndromes and their overlap (Larsson et al., 2011; Tackett et  al., 2013b; Tuvblad et  al., 2009), whereas studies of adults rarely consider dimensions of oppositionality or rule-breaking behaviors apart from aggressive behavior or status violations (Krueger et al., 2005). However, work toward mapping developmentally sensitive measurement models of externalizing comorbidity is beginning. Furthermore, as more studies indicate the importance of including callous-unemotional traits in models of the stability of externalizing comorbidity across development (Frick, 2012; Kendler, Patrick, Larsson, Gardner, & Lichtenstein, 2013), inclusion of these traits in such behavior genetic models also appears to be warranted.

Environmental Contributors and Gene-Environment Interplay Effects

Although considerable research supports a substantially heritable externalizing liability factor, Nikol as

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environment is also clearly relevant for understanding the development of externalizing comorbidities (e.g., Beauchaine  & McNulty, 2013; Burt, 2009) both via main effects and through interplay with genetic mechanisms (Rutter, Moffitt,  & Caspi, 2006). Environmental adversity and prenatal teratogen exposure may exert effects on comorbidity even before birth through epigenetic mechanisms (Cornelius & Day, 2009; Weder et al., 2014). These risk factors can modify gene expression via a number of mechanisms including methylation of DNA (which modifies gene expression), thereby conferring vulnerability to externalizing outcomes (Youngson & Whitelaw, 2008; see Gizer, Otto, & Ellingson, this volume). Moreover, decades of research demonstrate the importance of family environment for development of strong self-regulation skills in youth (Cole  & Deater-Deckard, 2009; Cole, Michel,  & Teti, 1994; Nigg, Hinshaw,  & Huang-Pollock, 2006). Parenting stress and parenting strategies characterized by high levels of inconsistency and hostility are robust predictors of externalizing comorbidity, both concurrently and over time (Burke et al., 2008; Deault, 2010). Furthermore, the strength of these associations is moderated by parental psychopathology (Wong, Zucker, Puttler, & Fitzgerald, 1999). Longitudinal investigations reveal some differences in the degree of reciprocality between parenting and externalizing comorbidity. For example, longitudinal studies from childhood through adolescence suggest that ODD and parenting that is characterized by admixtures of timid plus harsh discipline show reciprocal associations (Burke et  al., 2008) similar to those observed in coercive processes that maintain externalizing behavior over time (see Patterson, 1986). Coercion theory describes a process of mutual negative reinforcement in which caregivers inadvertently reinforce children’s difficult behaviors, which, in turn, then provoke caregiver negativity in an escalating cycle. This theory has been crucial in understanding heterotypic comorbidity among externalizing disorders because coercive exchanges account for associations between ADHD and subsequent development of conduct problems (Patterson, DeGarmo, & Knutson, 2000). Similarly, early-onset conduct problems are predicted by negative parent–child interactions that begin in infancy (Lorber  & Egeland, 2011). Consistent with such findings, child-driven effects on parenting increase in importance over time, as both ODD and CD predict problematic parent–child communication and poor parental supervision across development 468 Comorbidit y

(although each is also predicted by earlier ADHD; see Burke et al., 2008). Despite strong evidence implicating coercive exchanges and parenting in the emergence of externalizing comorbidity, much work in this area has failed to account for or control effects of heritable contributors that are shared between parents and children within the same family. Such effects may emerge through a variety of mechanisms, including rGE and G×E. rGE occurs when heritable vulnerabilities and environmental risk factors are correlated, in three forms: (1) when genetic predispositions of children are correlated with aspects of their rearing environment (passive rGE), (2) when children evoke responses from their environments that reinforce their genetic predispositions (evocative rGE), and (3)  when youth seek out environments that accentuate their genetic predispositions (active rGE). Although evocative effects are mostly stable across development, passive rGE is more relevant early in development, and active rGE appears to have more robust effects later in development. In addition, G×E is also likely to affect externalizing comorbidity, such that the impact of environmental factors on behavior depends on heritable factors (see Plomin, DeFries, McClearn,  & McGuffin, 2008, for review). Importantly, interactions between heritable and shared environmental factors are subsumed within the heritability coefficient (i.e., A2) of a traditional behavior genetics model (Purcell, 2002), meaning that it would be mistaken to interpret heritability coefficients as purely the results of genetic influence. Recent work has begun to measure the relevance of gene-environment interplay in the development and maintenance of externalizing comorbidity. Adoption designs are quite useful in this regard, given that sources of genetic and environmental covariation can be desegregated in adoptive families. Findings from these studies implicate shared genes between parents and children (passive rGE) as underlying associations between familial environmental risk and externalizing comorbidity (Bornovalova et  al., 2014). However, evocative rGE, whereby genes influencing externalizing psychopathology in youth are correlated with the negative reaction from parents elicited by their behavior, has also been implicated (Marceau et al., 2013). These evocative effects have been linked to subsequent main effects of parenting on trajectories in child behavior problems over time (Harold et al., 2013). Furthermore, as youth age, they have more freedom and independence to seek and create

their own environments (e.g., via selection of peer groups, activities, romantic partners, occupations, etc.). Deviant peer group affiliations are associated with both antisocial behavior and substance abuse (Moss, Lynch, & Hardie, 2003; Van Ryzin & Dishion, 2014, see Dishion, this volume), and there is indication that such active selection processes are influenced by dispositional traits (e.g., novelty seeking; see Hampson, Andrews,  & Barckley, 2008; Quinn  & Harden, 2013). There is increasing evidence that active rGE may account in part for the association between deviant peer group affiliation and externalizing psychopathology (Hou et al., 2013). Furthermore, effects of peer deviance on subsequent externalizing behavior problems in adulthood, including drug abuse, may depend on genetic risk for externalizing psychopathology (Kendler, Ohlsson, Sundquist, & Sundquist, 2014). Twin studies and studies of the children of parent twins (i.e., children-of-twins designs) are also useful for uncovering G×E effects on externalizing psychopathology. These studies are particularly relevant for examining how environmental risk factors may change the magnitude of both heritable and environmental influences on externalizing behavior. Indeed, there is growing evidence from this literature that family processes, including positive parenting and conflictual family environments, moderate the extent to which heritable and environmental influences affect the development of externalizing spectrum disorders (Burt & Klump, 2014; Harden et al., 2007; Nikolas, Klump, & Burt, 2012; 2015). Moderation of etiological contributors to ADHD in particular is notable, given past findings of significant heritable influences on ADHD (that are distinguishable from heritable influences on a general externalizing factor). One possibility is that progression from ADHD to other externalizing disorders (i.e., heterotypic comorbidity) may be the result of genetically influenced susceptibility among ADHD youth to environmental risk factors (i.e., coercive family processes; see Larsson et al., 2011). However, this possibility requires further testing in future empirical work. Finally, larger sociocultural contexts (e.g., neighborhood factors such as poverty, crime, structural deficiencies, and cohesiveness) also affect externalizing comorbidity (e.g., Bradley  & Corwyn, 2002; Rijlaarsdam et  al., 2013). However, effects of neighborhood risk on deviant peer group affiliation appear to be mediated by parental discipline and supervision (Chung & Steinberg, 2006). Given

the potential importance of social relationships and neighborhood risk on externalizing comorbidity, it will certainly be advantageous for future research to consider gene-environment interplay pathways, including rGE and G×E. The challenge for future research investigating the role of family and environmental processes will be to examine such associations using a mix of genetically informed designs (twins, adoptees, twin families, children of twins, etc.) in attempts to disentangle mechanisms of externalizing comorbidity.

Neurobiological Correlates of Externalizing Comorbidity

Behavior genetics research has established that both heritable and environmental influences play important roles in the expression of externalizing comorbidity and that gene-environment interplay (both rGE and G×E) become increasingly important contributors to externalizing comorbidity across development. However, identification of etiological mechanisms requires that we specify neurobiological substrates and correlates of externalizing behavior, including changes in neurobiology that accrue across development among those on heterotypically continuous trajectories of increasingly severe externalizing conduct problems. Methods for detecting central nervous system (CNS) structure(s) and function(s) that are associated with externalizing comorbidity have become increasingly sophisticated, including peripheral autonomic markers (e.g., electroencephalography, cardiac psychophysiology, electrodermal responding) and both structural and functional neuroimaging (e.g., magnetic resonance imaging [MRI], positron emission tomography [PET]; see, e.g., Patrick, 2008). Through use of these and other methods, several potential mechanisms of externalizing comorbidity have emerged. These include patterns of under-responding across multiple biological systems including mesolimbic reward structures and mesocortical networks associated with inhibitory control. Both systems appear to be underpinned by dysfunction in dopamine neurotransmission. Additionally, consistent alterations in autonomic arousal have also been observed (e.g., Beauchaine, Katkin, Strassberg,  & Snarr, 2001), which may underlie problematic emotion reactivity and regulation and associated impairments in frontal-limbic circuitry. Such mechanisms are probably interrelated and mutually influenced by the highly heritable predisposition discussed earlier (Beauchaine & McNulty, 2013). This heritable predisposition is likely to be expressed as dispositional Nikol as

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impulsivity, including failures to plan ahead, tendencies to respond to immediate over delayed rewards, and general emotionality. Behavioral impulsivity involves individual differences in sensitivity to and strength of responding to various types of reward (see Beauchaine & Gatzke-Kopp, 2012; Beauchaine et al., 2010; Oades et al., 2008). With this integrative framework in mind, I turn next to describing recent empirical findings related to CNS and autonomic substrates of externalizing comorbidity.

Reward Insensitivity

Decades of research on neural and autonomic responding to incentives (particularly with regard to substance abuse but also with regard to other externalizing behaviors) implicate the mesolimbic reward system, which comprises the ventral striatum (i.e., the ventral tegmental area) and its connections to other subcortical structures, including the nucleus accumbens. Typically, neural activity within this pathway increases during both the anticipation of (Schott et  al., 2008) and following delivery of rewards (Bjork, Chen, Smith,  & Hommer, 2010). This neural activation reflects increased dopamine (DA) neurotransmission (e.g., Schott et al., 2008). Investigations of activity within the mesolimbic reward system (and associated DA turnover) indicate that individuals with externalizing behaviors exhibit reduced mesolimbic reward reactivity as measured by functional MRI (fMRI) studies (e.g., Bjork et al., 2010; Casey & Durston, 2006; Rubia et al., 2009) and reduced resting DA neurotransmission in these regions, as indexed by PET and single proton emission computed tomography [SPECT] (e.g., Heldmann et  al., 2012; Hommer, Bjork, & Gilman, 2011; Volker, Fowler, Wang, Baler, & Telang, 2009). As noted in recent reviews of externalizing comorbidity (Beauchaine & Gatzke-Kopp, 2012; Beauchaine  & McNulty, 2013; Beauchaine et  al., 2010), attenuated tonic mesolimbic DA is associated with an irritable mood state (Laakso et al., 2003), which is likely to prompt novelty/reward-seeking behavior among individuals with externalizing psychopathology (Hink et al., 2013; Neuhaus & Beauchaine, 2013). This process may be facilitated by coordinated activity between the dorsolateral prefrontal cortex (DLPFC) and the nucleus accumbens, which selectively allocate more attention to rewarding stimuli, events, and cues (e.g., Kelley & Berridge, 2002). However, any improvement in mood state associated with seeking and attainment of rewards is often experienced only briefly, which leads impulsive individuals to 470 Comorbidit y

seek larger and more extended rewards (see e.g., Sagvolden, Johansen, Aase, & Russell, 2005).

Weak Inhibitory Control

In addition, weak top-down inhibitory control is also implicated in externalizing comorbidity. That is, the capacity to inhibit a prepotent or dominant response in favor of a nondominant response is crucial for long-term planning and for adapting to dynamic environmental circumstances. Deficient inhibitory control appears to be associated with developmental trajectories of externalizing comorbidity (e.g., Nigg et al., 2006) and may be a central domain of impairment across externalizing disorders (Patrick, Durbin, & Moser, 2012). Inhibitory control is assessed via several methodologies, including event-related potential (ERP), as well as through behavioral tasks of executive functioning. ERP studies consistently identify an association between reduced P300 amplitude and externalizing behavior, including ADHD (Yoon, Iacono, Malone, Bernat, & McGue, 2008), ODD (Baving, Rellum, Laucht, & Schmidt, 2006), CD (Bauer & Hesselbrock, 1999), adult antisocial behavior (Iacono, Carlson, Malone,  & McGue, 2002), and SUDs (Euser et  al., 2012). However, findings are not uniform across all dimensions of the externalizing spectrum, with some findings indicating no P300 reduction among individuals with high levels of premeditated or instrumental aggression (Stanford, Houston, Villemarette-Pittman,  & Greve, 2003). Similarly, deficits in executive function, particularly response inhibition, have been frequently associated with externalizing disorders, especially ADHD, but with considerable heterogeneity in the magnitude and types of deficits, suggesting potential utility in exploring neuropsychologically impaired subgroups (Fair, Bathula, Nikolas, & Nigg, 2012; Nigg, Willcutt, Doyle, & Sonuga-Barke, 2005). Furthermore, the impact of various co-occurring externalizing dimensions (e.g., conduct problems, callous-unemotional traits) on executive functioning, including inhibitory control, within subgroups evidencing specific impairment in inhibitory control remains an open area of investigation (Molina & Pelham, 2014). It is important to note that both the mesolimbic reward system and mesocortical functions underpinning inhibitory control are dopaminergic and interact to affect behavior. With regard to comorbidity among externalizing spectrum disorders, it appears that a combination of deficits across these interdependent systems (e.g., reward

insensitivity underpinned by mesolimbic systems and reduced inhibition underpinned by mesocortical systems) may underlie co-occurrence of various forms of externalizing problems. Additionally, adequate functionality within one system may offset impairments in the other. For example, strength in top-down inhibitory control may counteract weakness in reward insensitivity systems, thus potentially reducing associated symptoms and behavioral impairments (see Beauchaine  & McNulty, 2013). From this perspective, trait impulsivity, expressed behaviorally in ADHD, is linked to mesolimbic reward dysfunction, which is unlikely to be accompanied by comorbid CD unless concurrent, top-down mesocortical dysfunction is observed (see also Rubia, 2011).

Autonomic Nervous System Activity and Reactivity

Similar to research that indicates reward insensitivity in mesolimbic circuitry and reduced inhibition, a substantial body of research demonstrates alterations in autonomic nervous system (ANS) activity and reactivity among those with externalizing psychopathology. Many of these investigations include measures of electrodermal responding and cardiac function. In general, individuals with externalizing psychopathology show lower levels of baseline ANS activity compared to controls (Lorber, 2004; Ortiz & Raine, 2004). However, findings of autonomic reactivity—heart rate or skin conductance responses when faced with provocation or stress—tend to show an opposite effect, although findings are mixed, particularly when examining individuals with comorbid externalizing profiles. Some have found that youth and adults with comorbid externalizing problems show enhanced heart rate variability and skin conductance in response to stressors (Hubbard et al., 2002; Lorber, 2004; Patrick  & Verona, 2007). However, others have failed to find group differences in electrodermal responding in response to stressors, as well as reduced cardiac reactivity (i.e., cardiac pre-ejection period) in response to incentives among youth with comorbid externalizing problems (Beauchaine et al., 2001). Such findings support some evidence of differentiation between autonomic indices of emotion dysregulation (i.e., heart rate variability) versus indices of reward sensitivity (cardiac pre-ejection period in response to rewards). Furthermore, these indices of autonomic activity may also serve as potential markers of liability for developing externalizing problems. For example, recent work indicates that

young children with low skin conductance develop more severe externalizing psychopathology when exposed to higher levels of parenting stress (Buodo, Moscardino, Scrimin, Altoè,  & Palomba, 2013). Twin studies also suggest that heritability contributes significantly to overlap between a general externalizing factor and electrodermal hyporeactivity (Isen, Iacono, Malone, & McGue, 2012). Several authors have also examined the role of limbic hypothalamic pituitary adrenal (LHPA) axis activity/reactivity and externalizing psychopathology. In general, findings indicate that individuals with externalizing disorders demonstrate blunted cortisol responses to stress. In contrast, internalizing comorbidity appears to be associated with enhanced cortisol response (Hartman, Hermanns, de Jong,  & Ormel, 2013). Additionally, anatomical connections between the amygdala and hippocampus appear to facilitate function of the LHPA axis (i.e., sensory information, particularly related to fear and other emotion cues, is processed in the amygdala and conveyed to the central nucleus, which then projects to other brain regions, including the hypothalamus and frontal regions). These connections between the amygdala and key frontal regions (including the orbitofrontal and prefrontal cortex involved in the processing of emotion-related information) may be impaired among individuals with externalizing comorbidity (Coccaro, Sripada, Yanowitch, & Phan, 2011), particularly those individuals demonstrating callous aggression and psychopathy (Blair, 2007).

Dispositional Traits

Overall, neurobiological investigations of externalizing comorbidity implicate under-responding across multiple biological systems, and severity of neural impairment appears to increase with severity of externalizing behaviors. In addition to measures of neurological functioning, dispositional traits also represent additional neurobiological correlates through which heritable and environmental risk factors coalesce in the development of comorbidity (Nigg, 2006). In this section, I discuss such models and their associations with externalizing comorbidity, with the assumption that their relation is at least, in part, etiological in nature (i.e., temperament/personality play causal roles in externalizing psychopathology) and that aspects of their association are also likely pathoplastic in nature (i.e., disposition and psychopathology are mutually influential; see Widiger, 2011). However, I  do not wish to apply a reductionist view of externalizing comorbidity in Nikol as

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which traits account fully for externalizing comorbidity (Markon, Krueger, & Watson, 2005). Rather, I review the latest views on the role of temperament and personality traits as both causal and maintaining factors of externalizing comorbidity. Trait impulsivity in particular has been argued to confer general vulnerability to externalizing spectrum disorders (e.g., Beauchaine  & McNulty, 2013; Beauchaine et  al., 2010). Impulsivity and novelty seeking in youth (as well as related traits among adults such as constraint), in addition to negative emotionality (or neuroticism), are critically important for understanding covariation among various dimensions of externalizing psychopathology (Iacono, Malone,  & McGue, 2008; Khan, Jacobson, Gardner, Prescott,  & Kendler, 2005). Evaluation of associations between personality traits and externalizing behaviors indicate that a hierarchical structure (the bifactor model depicted in Figure 26.1B), including a general externalizing factor, can be extended to include personality traits, particularly impulsivity or lack of constraint (Krueger et al., 2007; Krueger & Tackett, 2003). Behavioral genetics investigations of overlap between personality traits and a general externalizing liability factor reveal both heritable and nonshared environmental contributors to their covariation (Young et al., 2000). With regard to associations between child temperament and externalizing comorbidity, longitudinal investigations reveal that traits such as effortful control, impulsivity, negative emotionality, and anger predict development of externalizing comorbidity in children (Eisenberg et al., 2009). In fact, findings from a systematic review point toward the importance of both high levels of negative emotionality and low levels of effortful control in predicting emerging externalizing comorbidity across development (Muris  & Ollendick, 2005). In attempting to synthesize these findings (as well as those from the literature regarding adult personality), Lahey and colleagues (Lahey  & Waldman, 2003; Lahey et al., 2008) proposed a developmental propensity model in which a common set of dispositional traits underlie the manifestation of externalizing psychopathology (and their overlap) via interactions with the environment. Using the Child and Adolescent Disposition Scale, which encompasses three trait disposition dimensions (negative emotionality, prosociality, and daring), recent work tests phenotypic overlap with externalizing comorbidity. Results indicate a substantial correlation among all three traits and a general externalizing factor (Tackett 472 Comorbidit y

et  al., 2013b; Taylor et  al., 2013) and significant heritable overlap as well, particularly with negative emotionality. Notably, no unique heritable effects remain on the general externalizing liability factor apart from those shared with dispositional traits, thus further supporting the notion that dispositional traits are important for understanding overlap among externalizing disorders. Indeed, such models may also be important for understanding the trajectory of externalizing comorbidity. Recent work also indicates that trajectories of externalizing comorbidity from adolescence to young adulthood may be attributable to etiological commonalities between these behaviors and traits of callousness and grandiosity (Kendler et al., 2013). Given these links, dispositional trait models may also serve to link specific markers of genes, neural function, and environmental risk to the development of externalizing comorbidity. Consistent with developmental propensity models, negative emotionality mediates associations between specific markers of genetic risk (e.g., the promoter polymorphism of the serotonin transporter gene—a functional variant associated with several forms of psychopathology—especially following adversity) and child externalizing behaviors, including ADHD and ODD (Brammer  & Lee, 2013; Martel, Nikolas, Jernigan, Friderici,  & Nigg, 2010). Given their demonstrated utility as markers of externalizing comorbidity across early development (Eisenberg et al., 2001), and given empirical findings demonstrating intervening effects of dispositional traits between genetic risk and psychopathology, the inclusion of such traits in causal models of externalizing comorbidity will remain crucial for future research. In sum, heritable influences, particularly those involved in gene-environment interplay, as well as the manifestation of those risks via neurological functioning and dispositional traits, are all highly relevant for understanding the etiology of comorbidity across the externalizing spectrum. However, their interrelations are complex, and future research will require large-scale, multimethod approaches in order to better understand the specific ways in which these different mechanisms interact. A preliminary integrative model of these processes indicates that a set of common genetic risk factors is likely to confer general liability to externalizing psychopathology, that this liability underlies observed phenotypic associations among different dimensions (or categories) of externalizing psychopathology, and that this liability interacts with environmental

risk factors to promote the development of more severe externalizing conduct—including heterotypic comorbidity—as affected individuals mature (see Beauchaine  & McNulty, 2013; McNulty, Beauchaine,  & Hinshaw, this volume). These processes in all likelihood underlie alterations in neural circuitry and communication, specifically those involving hypoactivity within reward (mesolimbic) and inhibitory control (mesocortical) circuitry—systems that are both largely dopaminergic. Additionally, common genetic influences on externalizing psychopathology may exert their effects via alterations in ANS activity and through coordination of stress responses and emotion reactivity and regulation. Dispositional traits, particularly negative emotionality and impulsivity (lack of constraint), appear to be early behavioral manifestations of mechanisms that then continue to confer vulnerability for further development of externalizing comorbidity via person-environment interactions (and their reciprocal exchanges) across development. However, there are some specific issues related to effects of development on externalizing comorbidity deserving of special comment.

Developmental Considerations

As mentioned earlier, models of externalizing comorbidity often differ depending on whether child or adolescent samples are used (Krueger et al., 2007; Lahey et al., 2012), most notably due to developmental differences in the emergence of various dimensions of externalizing behavior (see Beauchaine  & McNulty, 2013, for an illustrative example). Substance use most frequently emerges during adolescence, whereas hyperactive behavior tends to follow a normative decline with age (Bramham et  al., 2012). However, more recent work has begun to examine whether structural models of externalizing psychopathology, particularly hierarchical models that specify a general externalizing factor, show invariance across development. Using longitudinal data collected from participants between ages 14 and 33, Farmer and colleagues (2009) examined multiple measurement models of externalizing spectrum disorders and found that a two-factor model fit best. One factor specified variability across ADHD- and ODD-related behaviors, whereas a second factor specified variability in social norm violation disorders (i.e., CD, antisocial behavior, and substance abuse and dependence). Importantly, however, analyses only compared fit among four different first-order models and did not examine the relative fit of second-order or

hierarchical (or bifactor) models (i.e., Krueger et al., 2007). In contrast, Vrieze et  al. (2012) examined the coherence of measurement models of externalizing psychopathology among both adolescents and middle-aged adults and found that a single latent externalizing trait appeared to explain covariation among disorders on the externalizing spectrum. Importantly, all parameters could be equated across both age groups (with the exception of alcohol dependence), suggesting developmental continuation of a general externalizing factor (Vrieze et al., 2012). Nevertheless, investigations of developmental coherence of structural models are few (see Tuvblad et  al., 2009), particularly those that span a wider age range of participants. Further evaluation of the potential equivalence of such structural models or changes in the relevance of different dimensions across the life span is clearly needed. Investigations with young children appear to support a developmental propensity model of externalizing comorbidity that also includes child temperament traits (Lahey et  al., 2008) and adult personality traits (Krueger et  al., 2007), indicating that linking such models with longitudinal data will likely continue to be a fruitful area for future research. Recent work has suggested remarkable coherence of hierarchical models of internalizing comorbidity among aging populations (Eaton et  al., 2011). Furthermore, there is some indication that overall severity of comorbid antisocial behavior and substance abuse declines somewhat during middle age for some (Remschmidt & Walter, 2010), although the association among externalizing spectrum disorders remains robust into later middle age (Agrawal, Narayanan, & Oltmanns, 2013). Additionally, specific developmental periods may be critical for understanding the developmental continuation of externalizing comorbidity. One such period worthy of future investigation may be adolescence, as new externalizing behaviors emerge (e.g., substance use and abuse, crime; Chen et  al., 2011) and youth begin to exert increasing influence on their environments through choices of activities, vocations, and social relationships (Harden, 2014). Furthermore, this critical developmental period also unfolds against the backdrop of important neurobiological changes, both activational (temporary) and organizational (permanent). For example, recent developmental behavioral and molecular genetics research indicates that additional genetic factors may come “online” for externalizing disorders during adolescence, which may reflect the influence of additional Nikol as

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genes (e.g., Chang, Lichtenstein, Asherson,  & Larsson, 2013) or may reflect an increase in effects due to active rGE (Knafo & Jaffee, 2013). Moreover, numerous physical and neurobiological changes associated with pubertal development, including organizing effects of circulating hormones on brain function (Arnold  & Breedlove, 1985) and evocative responses garnered through physical maturation (Mendle Turkheimer, & Emery, 2007), may be relevant for understanding persistence of externalizing comorbidity. For example, empirical work suggests that early pubertal timing may increase risk for development of externalizing psychopathology among females, whereas late pubertal timing may confer risk of externalizing problems for males (Graber, 2013). Furthermore, there is some indication that higher levels of testosterone may inhibit synaptic pruning in frontal regions, which may be important when considering sex differences in externalizing comorbidity, particularly during adulthood (Bramen et al., 2012). In all, although continued investigation of early markers of externalizing comorbidity will remain important (Eisenberg et al., 2009), adolescence may represent an important gating period for externalizing comorbidity. This warrants specific investigation of genetic and neurobiological processes that may contribute to its developmental continuation.

Controversies

There is no shortage of controversies when it comes to externalizing comorbidity. A long-standing debate involves the degree to which observed overlap among externalizing disorders reflects real differences among distinct categories or dimensions of behavior (Angold et  al., 1999) and whether the DSM model of externalizing psychopathology imposes artificial subdivisions across a larger externalizing spectrum (Krueger & South, 2009). Such issues are likely to remain at the forefront of discussions regarding externalizing comorbidity, especially when considering problems with psychiatric classification overall (see Markon, 2013). However, additional ongoing debates are highly relevant to understanding externalizing comorbidity. The Research Domain Criteria (RDoC), currently under development by the National Institutes of Mental Health (NIMH), is structured around the endophenotype/intermediate phenotype concept (Cuthbert  & Insel, 2013). In contrast to primarily clinical nosologies based exclusively on observable behaviors (which do not necessarily map onto underlying etiology), constructs within the RDoC are intended to reflect underlying etiology more 474 Comorbidit y

closely, in part through measurement of hypothesized neurobiological substrates. Such a framework should be useful for unifying mechanistic models of externalizing comorbidity, given the RDoC focus on exploring mechanisms of psychopathology across five broad domains:  negative valence systems (e.g., fear, anxiety, loss), positive valence systems (e.g., reward valuation), cognitive systems (e.g., attention, language), social processing systems (e.g., attachment, self-knowledge), and arousal and regulatory systems (e.g., arousal, sleep and wakefulness) while using primarily neurobiological units of analysis (genes, molecules, cells, circuits, physiology, behavior, self-report, paradigms; Cuthbert  & Insel, 2013). Some have gone so far as to argue that classification of psychopathology should be primarily based on biological considerations (e.g., Insel et  al., 2010), which has ignited controversy. Furthermore, the importance of development and change over time is notably absent from the RDoC, which will pose challenges for investigations of the trajectories of externalizing comorbidity—a clearly crucial avenue for future research. Additionally, some questions remain regarding what dimensions of behavior are included under the externalizing spectrum and how they might contribute to externalizing comorbidity. For example, there is evidence that associations between ADHD and other externalizing behavior problems appear to be largely driven by hyperactivity-impulsivity and not inattention (Waschbusch, 2002). As an example, first-order factor analysis of ADHD, ODD, and CD symptom dimensions indicate that inattention separates first from hyperactivity, impulsivity, ODD, and CD at the level of the two-factor model (Lahey et al., 2008). Furthermore, as noted earlier in this chapter, individuals who score high on premeditated aggression or psychopathy (i.e., callous-unemotional traits in youth) tend to show different patterns of cortical and autonomic arousal relative to individuals with impulsive aggression and other comorbid externalizing problems (see Patrick, 2008, for review). Therefore, it may be the case that general externalizing comorbidity may be driven, in part, by core dimensions (i.e., trait impulsivity, Beauchaine  & McNulty, 2013), whereas other dimensions (inattention, callousness) may show some specificity in terms of etiology and may be more peripherally related to externalizing comorbidity.

Research Agenda

First, the advent of the RDoC will undoubtedly encourage an increase in the multimethod, multitrait

studies of etiological mechanisms of externalizing comorbidity described in this chapter. A  focus on common etiological pathways—from genetics to neurobiology to temperament to environment—and their interactions will be of primary importance for explicating the complex web of mechanisms that give rise to heterotypic trajectories of psychopathology, particularly in light of numerous structural models that have identified a single, underlying externalizing liability factor. Additionally, future work would benefit from examination of potential sex differences in the etiological mechanisms that give rise to and maintain overlap among externalizing spectrum disorders (El-Sheikh, Keiley,  & Hinnant, 2010), particularly given the mean differences in these behaviors observed between the sexes throughout the life span. Similarly, attention must be paid to trajectories of heterotypic comorbidity, and, considering the multiple etiological inputs, particularly during transition periods from childhood to adolescence and from adolescence to adulthood, this is essential. Such work will require a continued emphasis on dimensional models of both behavior and dispositional traits that may transcend traditional DSM models of diagnosis. For example, recent work indicates that relational aggression may be a useful construct to include in models of the externalizing spectrum (Tackett et al., 2013a). Finally, continued investigation of the ways in which environmental factors both modify and shape these trajectories will be important. However, given the important role of heritable factors and how they are molded via interplay with environmental risk across the life span, it will be critically important for future research to investigate such pathways using a series of complementary genetically informative designs, including samples of twins, adoptees, and children of twins, as well as various extended family designs with appropriate controls (Burt, 2014). However, for such investigations to be of benefit, more precise measurements of environmental risk need to be incorporated to disentangle different types of gene-environment interplay and their relative impact across different stages of development. Although several controversies and challenges remain, investigators of externalizing comorbidity have made great strides toward specifying causal processes that give rise to overlap among diverse but closely related behavior problems. However, much work remains. Continued expansion of mechanistic research, particularly investigations that incorporate multiple levels of analysis (see Beauchaine  &

Gatzke-Kopp, 2012; Cicchetti, 2008), will remain of vital importance for clarifying the complex set of associations and processes that give rise to and maintain externalizing comorbidity across development. As methods for measuring genetic and neurobiological substrates improve, researchers interested in questions involving externalizing comorbidity must also incorporate measures and models that are equally sophisticated. Finally, once greater specification of such mechanisms has been achieved, subsequent work will be needed to translate these innovations into real, workable, and disseminable intervention and prevention strategies that can reduce the burden of externalizing comorbidity for individuals and families, as well as for society at large.

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Conclusions and Future Directions

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CH A PT E R

27

An Ontogenic Processes Model of Externalizing Psychopathology

Theodore P. Beauchaine, Tiffany M. Shader, and Stephen P. Hinshaw

Abstract This chapter describes an ontogenic process model that characterizes the progression of externalizing spectrum disorders and views comorbidities and continuities as a result of complex longitudinal transactions between interdependent individual-level vulnerabilities (e.g., genetic, epigenetic, neural, allostatic) and equally interdependent contextual risk factors such as coercive parenting and neighborhood criminality. It also examines the importance of taking into account the developmental psychopathology perspective in order to understand comorbidities, continuities, and discontinuities in externalizing behavior across the lifespan. More specifically, it discusses mechanisms that underlie homotypic comorbidity among externalizing syndromes, along with the latent structure of externalizing psychopathology and the role of mesolimbic dopamine dysfunction in the pathophysiology of externalizing behaviors. Finally, the chapter explores trait impulsivity as a predisposing vulnerability to all externalizing spectrum disorders, how operant reinforcement worsens externalizing behavior, and the role of mood, emotion, and behavior dysregulation in the etiology of externalizing behavior. Key Words:  ontogenic process, externalizing spectrum disorders, comorbidities, risk factors, psychopathology, dopamine, trait impulsivity, operant reinforcement, mood, emotion

Introduction

Chapters included in this volume summarize the latest research and present cutting-edge thinking on the pathophysiology of externalizing spectrum disorders. Among other insights, top scientists in the field have presented advantages of dimensional approaches to conceptualizing the externalizing spectrum; summarized mounting evidence for common genetic, temperamental, and neural vulnerabilities to externalizing behavior; described social and cultural mechanisms that promote externalizing conditions; and considered complexities of both homotypic and heterotypic comorbidity. In this closing chapter, we provide an integrated model of the development of externalizing spectrum disorders, in which we incorporate findings presented in preceding chapters with principles of the developmental psychopathology perspective.

Developmental Theory and Externalizing Continuities

As outlined by Hinshaw and Beauchaine (this volume), the developmental psychopathology perspective must be adopted if we wish to understand comorbidities, continuities, and discontinuities in externalizing behavior across the lifespan. Unlike related disciplines, developmental psychopathology focuses on transactions between individuals and their environments over time. Several important points about the developmental psychopathology perspective have been identified and discussed by Sroufe (1997) and others (Cicchetti, 2006; Hinshaw, 2013; Rutter  & Sroufe, 2000). Among these, certain biologically influenced behavioral proclivities and patterns render individuals vulnerable to developing psychopathology in the presence of exogenous risk. Alone, such vulnerabilities (most notably for purposes of this volume trait 485

impulsivity) are not disordered. However, among some individuals, temperamental vulnerabilities interact with environmental risk factors, such as coercive parenting, neighborhood criminality, and exposure to deviant peers, to potentiate the development of psychopathology (see Beauchaine  & McNulty, 2013; Beauchaine & Zalewski, in press). Thus, when we consider externalizing spectrum disorders as outcomes of longitudinal transactions between neurobiologically influenced behavioral predispositions and exogenous risk factors, underlying genetic and neural vulnerabilities are best conceptualized as individual differences rather than causes. For example, as noted by Zisner and Beauchaine (this volume) and described later, variation in mesolimbic dopamine (DA) function underlies heritable individual differences in trait impulsivity. Those with low tonic mesolimbic activity and low phasic mesolimbic reactivity are likely to exhibit attention-deficit/hyperactivity disorder (ADHD), but, in the absence of exogenous risk, this temperamental vulnerability is unlikely to advance to more severe forms of externalizing behavior (Beauchaine  & Gatzke-Kopp, 2012; Beauchaine, Hinshaw, & Pang, 2010; Beauchaine, Klein, Crowell, Derbidge,  & Gatzke-Kopp, 2009; Beauchaine  & McNulty, 2013). However, in the presence of such risk factors, escalation to pernicious externalizing behaviors becomes likely, as outlined herein. In accordance with this transactional perspective, we recently advanced an ontogenic process model of externalizing spectrum disorders in which comorbidities and continuities are viewed as products of complex longitudinal transactions between interdependent individual-level vulnerabilities (e.g., genetic, epigenetic, neural, allostatic) and equally interdependent contextual risk factors (e.g., coercive parenting, deviant peer group affiliations, neighborhood criminality; see Beauchaine & McNulty, 2013; Beauchaine et al., 2009, 2010). Through transactions across these and other levels of analysis, some individuals traverse the externalizing spectrum, beginning with heritable trait impulsivity in preschool, followed by oppositionality in elementary school, delinquency and conduct disorder (CD) in middle school, and substance use disorders (SUDs) and antisociality in adulthood (e.g., Robins, 1966; Moffitt, 1993). In the following sections, we summarize our ontogenetic process perspective, which links many of the externalizing behaviors and disorders discussed in this volume. 486

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Comorbidities and Continuities in Externalizing Behavior

In 2000, Rutter and Sroufe identified two interrelated phenomena that are especially important to understand for developmental psychopathologists. The first is homotypic comorbidity, or the co-occurrence of multiple externalizing or internalizing disorders within individuals (see Beauchaine & Gatzke-Kopp, 2012; Beauchaine et al., 2010). The second is heterotypic continuity, or the sequential development of apparently different disorders across the life span (see Beauchaine et  al., 2009, 2010). Our goals in writing this chapter are to (1) present a model of comorbidity and continuity of externalizing psychopathology that characterizes each as an ontogenic process in which trait impulsivity, a neurobiologically founded vulnerability, interacts with environmental risk factors (e.g., coercive parenting, trauma) to canalize maladaptive behavior over time; and (2) demonstrate how characterizing comorbidities and continuities as ontogenic processes integrates dimensional trait models (e.g., Research Domain Criteria [RDoC]; Insel et al., 2010; Sanislow et al., 2010) and traditional categorical approaches to the investigation of psychopathology. We begin with a brief discussion of comorbidity.

Homotypic Comorbidity: Common Vulnerability to Externalizing Spectrum Disorders

As outlined by Beauchaine, Klein, Erickson, and Norris (2013), early editions of the Diagnostic and Statistical Manual of Mental Disorders (DSM) specified diagnostic hierarchies (i.e., exclusion criteria) that in most cases precluded any possibility of comorbidity (see First, 2005). However, beginning with the DSM-III-R (American Psychiatric Association, 1987), diagnostic hierarchies were eliminated. This approach followed in part from research conducted in the early to mid-1980s, which indicated distinct family histories for several psychiatric disorders. Such differential heritability suggested loss of useful information when one disorder precluded diagnosis of another (e.g., Leckman, Weissman, Merikangas, Pauls, & Prusoff, 1983). Elimination of diagnostic hierarchies resulted in markedly increased rates of comorbidity (see Klein & Riso, 1993) and significant expansion of comorbidity research (e.g., Angold, Costello, & Erkanli, 1999; Caron  & Rutter, 1991; Hinshaw, Lahey, & Hart, 1993). Historically, it has been assumed in child psychiatry that different externalizing syndromes

(e.g., ADHD, oppositional defiant disorder [ODD], CD) reflect distinct forms of psychopathology. However, considerable comorbidity is observed, and the assumption that externalizing disorders are distinct has been challenged by modern transactional models (see Beauchaine  & Gatzke-Kopp, 2012; Beauchaine  & McNulty, 2013; Beauchaine et  al., 2009, 2010) and by both behavioral and molecular genetics research (e.g., Anney et al., 2008; Burt, Krueger, McGue, & Iacono, 2001; Krueger et al., 2002; Tuvblad, Zheng, Raine,  & Baker, 2009; Meier, Slutzke, Heath, & Martin, 2011). In the following sections, we discuss likely mechanisms of homotypic comorbidity among externalizing syndromes and present our ontogenic process model of externalizing psychopathology. According to this model, comorbidity—at least for many individuals—arises not from true co-occurrence of distinct disorders but from developmental changes in the behavioral expression of heritable vulnerability across the life span. This transactional model focuses on mesolimbic DA dysfunction, which confers vulnerability to increasingly more intractable externalizing behavior as affected individuals mature (see Beauchaine  & Gatzke-Kopp, 2012; Beauchaine et  al., 2009). Consistent with the RDoC perspective, we emphasize a transdiagnostic neurobiological substrate for disorders that have been traditionally considered distinct.

Latent Structure of Externalizing Psychopathology

The externalizing spectrum comprises DSM-5 (American Psychiatric Association, 2013)-defined syndromes including ADHD, ODD, and CD—as well as related constructs such as aggression and delinquency (see Achenbach  & Edelbrock, 1984; Tackett, 2010). It was first articulated by Achenbach (1966), who identified a single higher order factor (externalizing liability) that accounted for much of the covariation among first-order factors (e.g., attention problems, delinquent behavior, aggressive behavior). More recently, a similar latent structure has been upheld and replicated among adults. In these older samples, SUDs, antisocial personality disorder (ASPD), and sometimes psychopathy are included in the externalizing spectrum (e.g., Krueger et al., 2002; Krueger, Markon, Patrick, Benning, & Kramer, 2007; Patrick, Hicks, Krueger,  & Lang, 2005). This latent structure—with several covarying first-order factors loading on a single higher order externalizing factor—is observed in both population-based and twin studies, the latter of

which indicate extremely high heritability coefficients for the externalizing factor (e.g., Dick, Viken, Kapiro, Pulkkinen,  & Rose, 2005; Krueger et  al., 2002, 2007; Lahey, Van Hulle, Singh, Waldman, & Rathouz, 2011; Tuvblad et al., 2009).

Mesolimbic DA Dysfunction and Externalizing Vulnerability

As alluded to earlier, externalizing spectrum disorders evidence high rates of comorbidity in nationally representative, cross-cultural, and clinical samples of children, adolescents, and adults (see Beauchaine et  al., 2010; Hinshaw, 1987). These high rates of comorbidity, as well as the consistently replicated factor structure noted earlier, imply a mechanism or mechanisms through which a common latent trait confers vulnerability to comorbid externalizing syndromes. We have argued that much of this shared liability results from trait impulsivity, conferred through mesolimbic DA dysfunction and expressed as a preference for immediate rewards over larger, delayed rewards. Modern neurobiological theories of trait impulsivity all focus at least in part on the mesolimbic DA system and on other DA networks (Castellanos  & Tannock, 2002; Gatzke-Kopp, 2011; Gatzke-Kopp  & Beauchaine, 2007a, Gatzke-Kopp et  al., 2009; Kalivas  & Nakamura, 1999; Sagvolden, Johansen, Aase, & Russell, 2005). Mesolimbic theories of trait impulsivity follow from extensive animal, nonhuman primate, and human research on approach motivation, incentive salience, and substance abuse/dependence (see Knutson, Fong, Adams, Varner,  & Hommer, 2001; Milner, 1991; Schott et. al., 2008; Rolls et al., 1974). This research led to several theories in the mid-1980s in which impulsivity and related traits such as sensation-seeking, novelty-seeking, and extraversion were considered to arise from variation in activity/reactivity of mesolimbic DA structures (see Cloninger, 1987; Gray 1987). Psychopathologists borrowed DA theories of approach motivation to explain the excessive reward-seeking behaviors observed in ADHD, CD, and similar externalizing syndromes (e.g., Fowles, 1988; Quay, 1993). Although some parts of these theories were wrong—particularly those that emphasized excessive rather than deficient DA activity as related to externalizing behavior (see Beauchaine  & Gatzke-Kopp, 2012; Gatzke-Kopp  & Beauchaine, 2007a)—mesolimbic DA dysfunction is now recognized as an etiological agent in most forms of externalizing psychopathology (see Gatzke-Kopp, 2011). Beauchaine, Shader, Hinshaw

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Evidence for mesolimbic DA dysfunction in the pathophysiology of externalizing behaviors has emerged from single photon emission computed tomography (SPECT), positron emission tomography (PET), and functional magnetic resonance imaging (fMRI) studies of children and adults with ADHD. These studies demonstrate that DA agonists such as methylphenidate increase mesolimbic neural activity and thereby reduce symptoms (e.g., Vles et al., 2003; Volkow, Fowler, Wang, Ding, & Gatley, 2002). Methylphenidate also normalizes frontocingulate underactivity (Rubia, Halari, Mohammad, Taylor, & Brammer, 2011) and frontostriatal functional connectivity deficits (Rubia, Halari, Cubillo, Mohammad,  & Taylor, 2009) observed among those with ADHD. Thus, drugs that improve functional connectivity by inhibiting reuptake decrease hyperactivity, impulsivity, and related aggressive behaviors (e.g., Hinshaw, Henker, Whalen, Erhardt,  & Dunnington, 1989; MTA Cooperative Group, 1999). Importantly, research demonstrates that low levels of striatal DA correlate with trait irritability (Laakso et al., 2003) and that infusions of DA into mesolimbic structures induce pleasurable affective states (Ashby, Isen, & Turken, 1999; Berridge, 2003; Berridge & Robinson, 2003; Forbes & Dahl, 2005). It is not surprising that both children and adults with externalizing disorders score high on measures of trait irritability and negative affectivity (e.g., Asherson, 2005, Martel & Nigg, 2006). Given DA’s role in the manifestation of impulsivity, it is also not surprising that genetic association studies of ADHD, ODD, and CD have focused on genes that affect DA availability, turnover, and metabolism. As in most psychiatric genetics research, effects sizes for individual genes are small (see Beauchaine & Gatzke-Kopp, 2013). However, associations are observed among ADHD, ODD, and/or CD and the dopamine receptor D4 (DRD4) gene, the dopamine receptor D5 (DRD5) gene, the dopamine transporter (DAT1) gene, the monoamine oxidase A  (MAOA) gene, and the catechol-O-methyltransferase (COMT) gene (see DeYoung et  al., 2010; Faraone  & Mick, 2010; Gizer, Ficks,  & Waldman, 2009; see also Gizer, Otto, & Ellingson, this volume). Thus, multiple sources of evidence from research conducted with animals and from neuroimaging and genetics studies conducted with humans point toward reduced mesolimbic DA function as a neural substrate of trait impulsivity. This vulnerability predisposes affected individuals to externalizing 488

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spectrum disorders in contexts of environmental risk (see Beauchaine  & Gatzke-Kopp, 2012; Beauchaine et  al., 2009, 2010; Gatzke-Kopp, 2011). Reduced mesolimbic DA function is experienced as an irritable, aversive mood state (e.g., Laakso et  al., 2003) that affected individuals seek to escape. Associated reward-seeking behaviors temporarily elevate aversive mood through phasic activation of mesolimbic structures (Wassum, Ostlund, Loewinger,  & Maidment, 2013). Unfortunately, the obtained mood-elevating value is brief, leading to searches for additional rewards (see Beauchaine, Gatzke-Kopp, & Mead, 2007; Gatzke-Kopp, 2011; Gatzke-Kopp  & Beauchaine, 2007a; Sagvolden et  al., 2005). As a result, those with mesolimbic DA dysfunction are impulsive and hyperactive, making them vulnerable to developing more serious externalizing psychopathology in contexts of environmental risk.

Environmental Risk and Differences Among Externalizing Syndromes

Thus far, we have outlined how a single, largely heritable trait emerges as a higher order externalizing liability factor and confers vulnerability to all externalizing spectrum disorders (Krueger et  al., 2002; Tuvblad et al., 2009). As noted, however, various first-order externalizing factors (ADHD, ODD, CD, SUDs, ASPD) also emerge consistently across studies. In order to better understand this first-order factor structure, we must consider certain properties of factor analysis and specific sources of covariation among first-order externalizing syndromes. The aforementioned first-order factor structure of externalizing psychopathology is often taken as evidence for distinct disorders (i.e., ADHD, CD, ASPD; see Beauchaine et al., 2010). However, this construal is mistaken because factor analysis does not identify subtypes of disorders or people (see Waller  & Meehl, 1998). Instead, factor analysis specifies continuous dimensions on which individuals vary. Factor analyses of externalizing symptoms therefore do not reveal different types of disorder. In fact, those who score high on one dimension of externalizing behavior are likely to score high on all others (Hinshaw, 1987), especially if they are old enough to engage in criterion behaviors across all syndromes. The factor structure outlined in the preceding section has been observed repeatedly in population-based and twin studies of children, adolescents, and adults (e.g., Krueger et al., 2002; Lahey et al., 2011; Tuvblad et al., 2009). However,

several other findings are important to highlight. First, first-order externalizing syndromes correlate highly with one another (see, e.g., Kessler, Chiu, Demler,  & Walters, 2005). Given high rates of comorbidity among externalizing behaviors, such correlations are not surprising. Second, with the exception of ADHD, first-order externalizing syndromes are much less heritable than are higher order externalizing vulnerability (e.g., Krueger et  al., 2002). Third, first-order factors are affected much more by environmental influences than by higher order externalizing vulnerability (see Burt, 2009; Burt et al., 2001). Environmental influences, especially nonshared, account for considerable variance in all first-order externalizing syndromes (Krueger et al., 2002; Tuvblad et al., 2009). When combined, shared and nonshared environment effects often contribute more than heritability to the specific expression of externalizing liability (i.e., ODD, CD, SUDs, etc.). Thus, genetic vulnerability appears to be necessary but insufficient for early-life impulsivity—expressed as ADHD—to progress to more serious externalizing behaviors including CD, SUDs, and ASPD (see Beauchaine  & McNulty, 2013). We discuss this developmental progression next.

Heterotypic Continuity: An Ontogenic Process Perspective

vulnerability trait (RDoC focus) behavioral syndrome (DSM focus)

LEVEL OF ANALYSIS

Since Robins’s (1966) landmark text on the development of delinquency, we have known that antisocial adult males almost always follow a life-course trajectory that begins in preschool with ADHD, followed by ODD in elementary school;

deviant peer group affiliations in middle school; development of CD, substance abuse, and dependencies in high school; antisocial personality development in young adulthood; and incarceration and recidivism (see Beauchaine et al., 2010; Loeber & Hay, 1997; Loeber  & Keenan, 1994; Lynam, 1996, 1998). Although other routes to delinquency exist (see, e.g., Crocker, Fryer  & Mattson, 2013; Gatzke-Kopp, 2011; Lynam, 1996; Moffitt, 1993), we focus here on this heterotypically continuous pathway, which is likely to account for the majority of individuals who engage in lifelong delinquent behavior (see Beauchaine et al., 2009, 2010; Moffitt, 1993). This pathway is depicted in Figure 27.1, which dovetails this developmental progression of externalizing conduct with the first- and higher order factor structure described earlier. Importantly, although lifelong delinquency usually begins with ADHD, only about half of preschoolers who are hyperactive and oppositional develop more serious conduct problems in later childhood (Campbell, Shaw,  & Gilliom, 2000). Thus, ADHD does not determine later delinquency. Any ontogenic process model of externalizing conduct must account for desistance among some who show early-life ADHD and persistence to more serious externalizing conduct among others. If vulnerability to all externalizing syndromes is conferred by a single vulnerability (mesolimbic DA dysfunction), why do some individuals persist to more severe behaviors as they mature, whereas others suffer only from ADHD? Toward addressing such questions, developmental processes must be taken seriously (Sroufe, 2009),

Externalizing Liability (trait impulsivity)

temperament

birth

ADHD

preschool

ODD

CD

elementary school middle school

SUDs

ASPD

high school

adulthood

APPROXIMATE DEVELOPMENTAL PERIOD Figure  27.1  Model of heterotypic continuity in externalizing behavior across development. Trait impulsivity serves as a common, higher order vulnerability to sequential development of first-order syndromes as affected individuals mature. Adapted from Beauchaine and McNulty (2013).

Beauchaine, Shader, Hinshaw

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starting with temperamental precursors to ADHD and other externalizing behaviors. Temperamental constructs including inhibitory control, effortful control, and attentional focus overlap with impulsivity (e.g., Foley, McClowry, & Castellanos, 2008), and certain aspects of temperament share molecular genetic underpinnings with externalizing vulnerability (e.g., Schmidt, Fox, Perez-Edgar,  & Hamer, 2009). Furthermore, like externalizing vulnerability, temperament is highly heritable (e.g., Saudino, 2009). Our ontogenic process model expands on Figure 27.1 by including mechanisms of continuity, desistance, and progression in externalizing behavior across development. Understanding such multifinality requires that we consider processes at levels of analysis in addition to vulnerability traits and behavioral syndromes. Accordingly, in Figure 27.2, we plot heterotypic development of externalizing behaviors along the x-axis and levels of analysis including genetic vulnerability (e.g., MAO-A), neural/hormonal substrates (e.g., mesolimbic DA dysfunction), latent vulnerability traits (impulsivity), behavioral syndromes (e.g., ODD, CD), and environmental risk mediators (e.g., family coercion, neighborhood violence) down the y-axis. Importantly, all of the arrowed paths in Figure 27.2 have been identified empirically. Our model therefore demonstrates the unwieldy complexity of developmental pathways to antisocial behavior. A  primary objective of developmental psychopathology research is to disentangle such complexity by studying mechanisms and transactions across all relevant levels of analysis (see Beauchaine  & Gatzke-Kopp, 2012; Cicchetti, 2008; Hinshaw  & Beauchaine, this volume). Thus, we emphasize the importance of (1)  specifying etiological processes across levels of analysis (Cicchetti, 2008; Cicchetti  & Dawson, 2002); (2)  describing how mechanisms at one level of analyses (e.g., substance use) interact with, alter functioning of, and feed back to systems at other levels of analysis (e.g., mesocortical brain function), thereby magnifying risk (Beauchaine  & Gatzke-Kopp, 2012; Beauchaine, Neuhaus, Brenner,  & Gatzke-Kopp, 2008); and (3)  construing externalizing behaviors not as distinct syndromes with separate causes (an assumption that is often gleaned from assessing vulnerabilities and risk factors at static points in time) but as a set of ontogenic processes through epigenetic and allostatic mechanisms that alter neurobiological and behavioral functioning in some ways that are reversible and in other ways aren’t (Beauchaine 490

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et al., 2009; Mead, Beauchaine, & Shannon, 2010; Sroufe, 2009). From this perspective, two individuals who are on the same developmental trajectory but assessed at different ages may demonstrate very different neurobiological and behavioral signs of externalizing vulnerability not because they have different disorders, but because one has progressed further into the course of illness. With this point in mind, we now describe tenets of our ontogenic process model of heterotypic continuity.

Impulsivity Is a Continuously Distributed, Highly Heritable Trait

As outlined in preceding sections, mounting evidence suggests that trait impulsivity is a predisposing vulnerability to all externalizing spectrum disorders. In this section, we note that impulsivity is a continuously distributed, multifactorial individual difference that is influenced by many genetic loci, gene × gene interactions, interactions with other heritable traits (e.g., trait anxiety), and interactions with the environment (see Neuhaus  & Beauchaine, 2013). The importance of such complex interactions in the expression of multifactorial traits (e.g., intelligence) and diseases (e.g., type 2 diabetes) has been recognized for decades (see e.g., Bodmer & Bonilla, 2008). Yet, in psychopathology research, we often search for genes that are specific to particular disorders, such as ADHD or CD, rather than searching for sets of genes that confer additive (or multiplicative) vulnerability to traits such as impulsivity that are transdiagnostic. Fortunately, this state of affairs may finally be changing with the advent of RDoC, which focuses in part on traits that cut across traditional diagnostic boundaries. For multifactorial traits, no particular gene accounts for large effects on behavior, and interactions among genetic susceptibilities and environmental risk factors determine specific expression of vulnerability. This observation has major implications for research aimed at reifying traditional diagnostic syndromes (see Beauchaine et al., 2010). Specific genetic polymorphisms are unlikely to account for much variance in any complex psychiatric disorder, and genetic vulnerabilities are likely to be shared across disorders. Empirical findings support both of these assertions (see Beauchaine & McNulty, 2013; Plomin, 2013). Research conducted to date on externalizing spectrum disorders is consistent with multifactorial inheritance. For example, genetic differences among those with traditionally defined externalizing disorders account for very little variance in behavior

genetic

vulnerability alleles (e.g., DRD4)

epigenetic, allostatic processes

environmental risk mediator vulnerability

prefrontal (mesocortical) DA function

L-HPA axis function

vulnerability trait DSM focus)

midbrain (mesolimbic) DA function

amygdalar function

Mood Lability & Emotion Dysregulation

Trait Impulsivity (externalizing liability)

behavioral syndrome

LEVEL OF ANALYSIS

neural/hormonal substrate (RDoC focus)

sex-linked genetic factors (e.g., MAOA)

temperament

harsh, hostile, inconsistent parenting

prenatal environment

–9 months

ADHD

birth

preschool

CD

ODD

coercive family processes

deviant peer group affiliations

SUDs

availability, opportunity, exposure

middle school

ASPD

neighborhood violence, criminality

high school

adulthood

APPROXIMATE DEVELOPMENTAL PERIOD Figure 27.2  An ontogenic process model of externalizing spectrum behaviors in which multiple levels of analysis are plotted along the y-axis, and age is plotted along the x-axis. Trait impulsivity is the primary vulnerability to all externalizing spectrum disorders. However, specific syndromal manifestations (e.g., attention-deficit/hyperactivity disorder [ADHD], antisocial personality disorder [ASPD]) are influenced strongly by environmental risk, which accumulates across development. Trait impulsivity arises from vulnerabilities that are specified in the top two panels. These vulnerabilities are amplified through recursive feedback loops that span levels of analysis (dotted, bidirectional arrows). Through these mechanisms, high-risk behaviors (e.g., substance abuse) exacerbate heritable vulnerabilities. Emotion dysregulation, which develops after trait impulsivity, is influenced more by environmental influences than by heritability. Despite the overwhelming complexity of this model, many etiological influences (e.g., teratogen exposure, noradrenergic function, child maltreatment) are omitted for the sake of simplicity. Solid arrows represent directional processes, whereas dashed arrows represent bidirectional processes. Adapted from Beauchaine and McNulty (2013).

within and across syndromes. Caspi et  al. (2008) demonstrated that the COMT Val158Met polymorphism accounted for about 1% of the variance in antisocial/aggressive behavior in children, leaving 99% unaccounted for. Furthermore, studies that compare frequencies of candidate genetic polymorphisms (e.g., COMT) across externalizing disorder subtypes (e.g., ADHD vs. CD) often fail to find differences (e.g., Monuteaux, Biederman, Doyle, Mick,  & Faraone, 2009). Finally, genome-wide association studies indicate no added genetic burden among children with ADHD + CD compared to those with only ADHD (e.g., Anney et al., 2008). Also consistent with the multifactorial inheritance model, recent studies indicate the importance of evaluating gene × environment interactions in predicting externalizing behavior. For example, Caspi et  al. (2002) found that an interaction between the low monoamine oxidase A  (MAOA) activity genotype and child maltreatment predicted juvenile and adult antisocial behavior. Importantly, although the MAOA genotype explained only 1% of the variance in antisocial behavior, the gene × maltreatment interaction explained 65%. Thus, we must measure environment and its interactions with genetic vulnerability if we wish to understand the effects, both direct and indirect, of genes on behavior. Combined, these findings indicate that searches for genes that are specific to traditionally defined externalizing disorders may be misguided and that more effort should be expended toward determining how multiple vulnerability alleles interact with one another and the environment in predicting heterotypic progression of externalizing syndromes.

Prenatal Adversity Confers Vulnerability Through Allostatic and Epigenetic Processes

Allostasis refers to experience-dependent changes in the operating ranges of biological systems (Sterling & Eyer, 1988), whereas epigenesis refers to experience-dependent changes in DNA structure (Riggs, Russo, & Martienssen, 1996). Allostasis and epigenesis, which may co-occur, provide for biological adaptations to environmental adversity (see Mead et al., 2010). Such adaptations sometimes have untoward developmental effects. As outlined elsewhere (Beauchaine, Neuhaus, Zalewski, Crowell, & Potapova, 2011; Gatzke-Kopp, 2011; Neuhaus  & Beauchaine, 2013), a wide range of prenatal adversities confer vulnerability to later externalizing behaviors through mechanisms of allostasis and epigenesis. For example, prenatal nicotine exposure 492

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predicts later externalizing behaviors among offspring, over and above effects of parental antisociality (e.g., Brennan, Grekin,  & Mednick, 1999; Gatzke-Kopp  & Beauchaine, 2007b; Wakschlag et  al., 1997; but see Thapar et  al., 2009). This vulnerability appears to be effected through epigenetic alterations in mesolimbic DA function (see Gatzke-Kopp, 2011). Rats that are exposed to nicotine prenatally exhibit DA underresponding to external stimulation as adults (Slotkin, 1998). Importantly, children with high-risk DRD4 and DA transporter alleles are most vulnerable to developing later externalizing disorders following prenatal nicotine exposure (Becker, El-Faddagh, Schmidt, Esser,  & Laucht, 2008; Neuman et  al., 2007). Prenatal cocaine exposure also down-regulates mesolimbic DA function among rats, inducing life-long structural changes in the anterior cingulate cortex (ACC) (e.g., Minabe, Ashby, Heyser, Spear,  & Wang, 1992; Stanwood, Washington, Shumsky, & Levitt, 2001). Finally, both exposure to stress and maternal substance use during pregnancy sensitize children’s limbic-hypothalamic-adrenal (LHPA) axes, rendering them more reactive to stress and predicting development of externalizing behaviors (see Glover, 2011; Hunter, Minnis, & Wilson, 2011). In Figure 27.2, such effects are indicated by the indirect path from prenatal environment, through epigenetic/allostatic processes, to mesolimbic DA function.

In Early Life, Impulsivity Is Conferred Primarily Through Mesolimbic DA Dysfunction

In the preceding sections, we described how variation in midbrain (mesolimbic) DA function underlies individual differences in trait impulsivity. It is important to acknowledge, however, that compromises in the prefrontal (mesocortical) DA system are also associated with impulsivity and conduct problems (see, e.g., Kim  & Lee, 2011) and are observed among those with ADHD (e.g., Thorell  & Wȧhlstedt, 2006; Willcutt, Doyle, Nigg, Faraone,  & Pennington, 2005). In our ontogenic process model, we place midbrain DA function temporally ahead of prefrontal DA function in the developmental progression of externalizing behavior presented in Figures 27.1 and 27.2. Although development of prefrontal executive functions is already under way by preschool (Garon, Bryson, & Smith, 2008), we do not consider mesocortical mechanisms of impulsivity to be foundational for most children because these

brain regions do not mature until adolescence and early adulthood (see Welsh, Pennington,  & Groisser, 1991). We therefore view the midbrain DA system as a primary source of trait impulsivity very early in life (see also Halperin & Schulz 2006), with prefrontal contributions accumulating over time. From a transactional perspective, it is also important to note that neurodevelopment of frontal regions is influenced by early experiences that are themselves a product of impulsivity (see Beauchaine et al., 2008; Sagvolden et al., 2005). Thus, early-life impulsivity affects neurodevelopment of the later-maturing mesocortical DA system, especially in high-risk environments (see Beauchaine & McNulty, 2013).

Progression to Severe Externalizing Behavior Occurs Through Bidirectional Transactions Between Individual Vulnerabilities and Environments Across Time

Progression along the externalizing spectrum is promoted or inhibited by interactions between neurobiological vulnerabilities and high-risk or protective environments over time (for reviews, see Beauchaine et  al., 2010; Beauchaine  & Gatzke-Kopp, 2012; Beauchaine et  al., 2009; Gatzke-Kopp, 2011; Gatzke-Kopp & Beauchaine, 2007a). Overwhelming evidence suggests that children who are impulsive are more likely to become delinquent when exposed to maltreatment and neglect (e.g., De Sanctis et  al., 2012), hostile and inconsistent parenting (e.g., Drabick, Gadow,  & Sprafkin, 2006), neighborhood violence and criminality (e.g., Lynam et  al., 2000; Meier et  al., 2008), and other forms of adversity. Moreover, impulsive children evoke aversive reactions from their caregivers (O’Connor, Deater-Deckard, Fulker, Rutter,  & Plomin, 1998), which likely feed back to amplify their preexisting vulnerabilities (see later discussion). Such bidirectional effects between children and their environments are depicted in Figure 27.2 by dashed arrows that cross the boundary between behavioral syndromes and environmental risk mediators. For example, links from early-life ADHD to ODD and CD operate through a series of environmental risk mediators including coercive family processes and deviant peer group affiliations (see, e.g., Dishion  & Racer, 2013). These effects are well replicated in the empirical literature (e.g., Choe, Olson,  & Sameroff, 2013; Patterson, DeGarmo,  & Knutson, 2000;

Raudino, Fergusson, Woodward,  & Horwood, 2012; Snyder et  al., 2005, 2008). Although some authors have argued that such findings might be explained entirely by active or evocative gene-environment correlation (rGE), rGE does not account fully for externalizing spectrum progression (see Rutter, 2006). Nevertheless, it is clear that children affect their environments in ways that promote progression of delinquency (see Dishion  & Racer, 2013). For example, O’Connor et  al. (1998) reported an evocative rGE in a sample of children who were adopted away at birth and at high genetic risk for delinquency. Despite being raised by adoptive parents, these children were more likely than controls to evoke aversive reactions from caregivers (for more recent examples, see Harold, Leve, Barrett et  al., 2013; Harold, Leve, Elam et  al., 2013). Impulsive children and adolescents also expose themselves to high-risk environments through sensation-seeking behaviors and deviant peer group affiliations. Such mechanisms account for age of onset of alcohol and nicotine use, even though dependence is determined almost entirely by heritability (Boomsma, Koopsman, Van Doornen,  & Orlebeke, 1994; Koopsman, Slutzke, Heath, Neale,  & Boomsma, 1999; Koopsman, van Doornen,  & Boomsma, 1997; McGue, Iacono, Legrand, & Elkins, 2001; Viken, Kaprio, Koskenvuo, & Rose, 1999). Collectively, such findings suggest that transactions across levels of analysis are common in the developmental progression of externalizing behavior (see also Keijsers, Loeber, Branje,  & Meeus, 2011; Pardini, 2008). We wish to emphasize two additional points regarding transactions between vulnerability and environmental risk. First, environmental risk mediators are not conceptually or temporally distinct: they are depicted as such only for purposes of presentation. In reality, environmental risk factors correlate with one another and overlap developmentally. For example, inconsistent and coercive parenting, deviant peer group affiliations, and availability/exposure to substances of abuse are highly correlated phenomena (see, e.g., Dishion & Racer, 2013). Moreover, given the highly transactional nature of externalizing outcomes, prospective prediction of persistence, escalation, and desistance is exceedingly difficult. Precisely which environmental risk mediators an individual will encounter cannot be known in advance, and certain environmental risk factors may operate cumulatively (e.g., Gerard & Buehler, 2004). Beauchaine, Shader, Hinshaw

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Operant Reinforcement Amplifies Emotion Dysregulation, Worsening Externalizing Behavior

Overwhelming evidence indicates that progression of externalizing behavior is facilitated by operant reinforcement mechanisms that operate within families (see Beauchaine  & Zalewski, in press; Dishion, Kim,  & Tein, this volume). Over time, these reinforcement contingencies result in enduring patterns of emotional lability that amplify preexisting vulnerability. According to coercion theory (Patterson, 1982; Patterson, DeBaryshe,  & Ramsey, 1989), aversive dyadic interaction patterns that occur thousands of times across development between vulnerable parents and children reinforce aggression, emotion dysregulation, and antisocial behavior. Through escape conditioning mechanisms (i.e., aggressive behavior is reinforced because it promotes escape from an aversive partner), emotional lability, emotion dysregulation, and physiological reactivity escalate over time. Eventually, dysregulated and aggressive responses become primary means through which affected individuals cope with interpersonal distress. Over time, mood lability and emotion dysregulation take on trait-like qualities (see Snyder, this volume; Snyder, Edwards, McGraw, Kilgore,  & Holton, 1994; Snyder, Schrepferman, & St. Peter, 1997). According to our ontogenic process model, trait impulsivity will not progress to more serious externalizing syndromes in family environments where strong emotion regulation skills are socialized (see Beauchaine et al., 2007, 2009, 2010). Rather, consistent with the transactional nature of our model, trait impulsivity interacts with socialized deficiencies in emotion regulation to amplify risk for ODD, CD, SUDs, and ASPD across development. Thus, in Figure 27.2, ODD, CD, SUDs, and ASPD all include directional arrows from both trait impulsivity and trait emotion dysregulation. In contrast, temperament and ADHD are influenced primarily by trait impulsivity.

Mood, Emotion, and Behavior Dysregulation Co-develop with Compromises in Prefrontal Brain Function

As alluded to earlier, self-regulation is effected increasingly across development by frontal brain regions that mature into late adolescence and early adulthood (see, e.g., Gogtay et  al., 2004; Phillips, Walton,  & Jhou, 2007; Thayer, Hansen, Saus-Rose,  & Johnsen, 2009). Among most 494

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individuals, regulation of emotion, incentive motivation, and mood lability are subserved by the prefrontal cortex (PFC), which exerts top-down inhibitory effects that become increasingly important as children and adolescents transition into developmental stages that require internal control over behavior. Our ontogenic process model suggests complex interrelationships between exogenous influences, mesolimbic vulnerability, and prefrontal mechanisms of behavior regulation. High-stress environments, including those characterized by coercion, trauma, neighborhood violence, and criminality, may amplify heritable vulnerability among children who are impulsive due to compromises in mesolimbic DA function. Such environments may alter prefrontal cortical development in ways that facilitate progression of trait impulsivity to severe externalizing conduct (see Beauchaine et  al., 2008, 2011; Mead et  al., 2010). For example, abnormal patterns of age-related pruning of prefrontal gray matter among those with ADHD and CD, which may be adversely influenced by environmental risk but normalized via stimulant treatment (see Giedd  & Rapoport, 2010), predict substance use and abuse into adolescence (see Bava & Tapert, 2010). Such findings imply that differences in patterns of functional brain activity should be observed among those with ADHD versus those with CD, SUDs, and ASPD because the latter groups have progressed further into the neurodevelopmental course of illness. In a review of fMRI studies that compared children and adolescents with ADHD and CD, Rubia (2011) found that primary neural deficiencies among those with ADHD included reduced striatal (i.e., mesolimbic) activation, whereas children and adolescents with CD showed abnormal ventromedial PFC activation. Although Rubia concluded that these differences provide evidence for a distinction between ADHD and CD, findings of prefrontal deficits in CD are precisely what our ontogenic process perspective predicts:  those with CD exhibit deficiencies in prefrontal function that are not yet observed among ADHD-only individuals who have not developed CD. In other words, those who have progressed into a more advanced stage of externalizing behavior due to interactions between internal vulnerabilities and environmental risk exhibit more impairment, as reflected in different sets of symptoms and more pervasive brain dysfunction. Reduced functional connectivity between midbrain and prefrontal structures is also observed among

those with ADHD and CD (e.g., Shannon, Sauder, Beauchaine, & Gatzke-Kopp, 2009). These findings may be especially important given the wide range of self-regulatory functions served by interconnections between midbrain and prefrontal structures. As outlined earlier, prefrontal structures inhibit subcortical DA expression in the service of self-regulation. Pharmacological accentuation of prefrontal DA decreases DA activity in mesolimbic structures (Louilot, LeMoal,  & Simon, 1989). In contrast, reducing prefrontal DA levels increases mesolimbic DA activity. Disturbances in this feedback system, marked by reduced functional connectivity, may be one neural mechanism of trait impulsivity (Tisch, Silberstein, Limousin-Dowsey,  & Jahanshahi, 2004). Importantly, top-down regulation of mesolimbic DA activity/reactivity may be especially vulnerable to environmental adversity, given its well-characterized epigenetic and allostatic effects on mesolimbic and mesocortical DA systems (see Arnsten, 2009; Halperin  & Schulz, 2006; Spear, 2007; Sullivan & Brake, 2003). Amygdalar function also plays a prominent role in emotion regulation. The amygdala is involved in processing self-relevant information and generating both positive and negative emotional responses (see, e.g., Davis  & Whalen, 2001). Like the midbrain DA system, it is also developmentally sensitive to epigenetic and allostatic programming effects (see, e.g., Gillespie, Phifer, Bradley,  & Ressler, 2009). Moreover, it is interconnected with brain regions involved in self-regulation, including the mesolimbic structures and the PFC (Kim et al., 2011). Amygdalar reactivity has been associated repeatedly with inhibitory control among adults (e.g., Brown, Manuck, Flory,  & Harari, 2006). Both deficient top-down control of the amygdala by the PFC and reduced functional connectivity between the amygdala and the PFC are implicated in emotional lability and poor self-control (see, e.g., Churchwell, Morris, Heurtelou, & Kesner, 2009). Notably, few if any studies indicate functional deficiencies in the amygdala among children or adolescents with ADHD. However, children with CD often exhibit smaller amygdalar volumes and excessive amygdalar reactivity to emotionally evocative stimuli compared with controls (e.g., Decety, Michalska, Akitsuki,  & Lahey, 2009; Fairchild et  al., 2011; Sterzera, Stadlerb, Poustkab,  & Kleinschmidta, 2007; van Harmelen et al., 2013). According to our ontogenic process model (Beauchaine  & McNulty, 2013), amygdalar dysfunction and deficient top-down control of the

amygdala by the PFC develop from repeated and extensive longitudinal transactions between vulnerable individuals and high-risk environments. Indeed, amygdala sensitivity to fearful and angry faces is observed among those who were abused or maltreated as children (see, e.g., van Harmelen et al., 2013; Pollak, 2008). Although amygdalar hyperreactivity to fear and anger cues may be important in maltreatment contexts, it predicts poor social adjustment later in development (Hanson et  al., 2010). Importantly, social problems may in turn facilitate progression of ADHD to more severe externalizing conduct (see Mead et al., 2010). Collectively, these findings suggest that, among children who are already impulsive due to heritable compromises in mesolimbic DA expression, deficiencies in amygdalar function co-develop with deficiencies in prefrontal function, and environmental risk contributes significantly to the process. Deficiencies in top-down inhibition of the amygdala and mesolimbic DA system by the PFC result in poor self-control, mood lability, emotion dysregulation, and further loss of impulse control. Over time, emotion dysregulation and mood lability become trait-like, even though they are considerably less heritable than trait impulsivity (Goldsmith, Pollak, & Davidson, 2008).

High-Risk Behaviors Exacerbate Deficient Mesolimbic, Mesocortical, and Amygdalar Function

Many vulnerable individuals who exhibit functional compromises in mesolimbic, amygdalar, and mesocortical brain function engage in high-risk behaviors that compromise their brain function further. Perhaps the best example of this is substance use, abuse, and dependence. It is well known that those with preexisting midbrain and PFC dysfunction, expressed behaviorally as poor self-regulation, are at risk for addiction (see George  & Koob, 2010). Continued alcohol and drug use compromises prefrontal and orbitofrontal cortex structure and function further, resulting in increasingly impulsive decision making and vulnerability to relapse (see Schoenbauma  & Shahamd, 2008). Moreover, self-regulation is affected adversely by use-dependent compromises in top-down inhibition of mesolimbic structures by the PFC (see Goldstein & Volkow, 2011; Kalivas, 2008). Adolescents may be particularly susceptible to the incentive properties of alcohol and other substances of abuse and to use-dependent changes in neurodevelopment and behavior regulation, Beauchaine, Shader, Hinshaw

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given the immaturity of their PFCs in particular (e.g., Casey & Jones, 2010). Finally, substance-induced changes in functional interconnections between the amygdala and the PFC alter extinction of addictive behaviors (see Peters, Kalivas, & Quirk, 2009). Thus, abnormalities in mesolimbic, prefrontal, and amygdalar structure and function confer vulnerability to substance abuse/dependence, are aggravated by substance abuse/dependence, and intensify preexisting deficiencies in self-regulation to potentiate advancement along the externalizing spectrum to ASPD (see Beauchaine et  al., 2009, 2011). Figure 27.2 therefore includes bidirectional arrows between SUDs and mesolimbic, mesocortical, and amygdalar function.

Conclusion and Future Directions

Considerable advances have been made in our understanding of the etiology of externalizing spectrum disorders. In this chapter, we presented our ontogenic process model of progression along the externalizing spectrum. In doing so, we highlighted the importance of considering individual-level vulnerabilities and their interactions with risk and adversity across multiple levels of analysis over time as individuals advance from temperamental impulsivity early in life to increasingly severe externalizing conduct as they mature. Such progression is possible only through complex transactions that cannot be understood by studying traditionally defined externalizing syndromes at static time points. Rather, externalizing behaviors become self-reinforcing when embedded in high-risk environments that amplify and canalize preexisting impulsivity. When viewed from this perspective, the importance of intervening early—at all relevant levels of analysis—becomes clear (Beauchaine et al., 2008, 2014). When compared with contemporary transactional models, traditional approaches to diagnosing, subtyping, and studying externalizing disorders as discrete, developmental entities become woefully inadequate. As we have noted elsewhere (Beauchaine  & McNulty, 2013), traditional subtypes of externalizing behavior, as outlined in the DSM, obscure developmental pathways to antisociality and encourage continued molecular genetics and neuroimaging studies aimed at reifying existing diagnostic boundaries. Because these boundaries are artificial, such studies are not likely to identify meaningful subtypes of externalizing behavior. Instead, they may well lead 496

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the field down blind alleys. In our view, ontogenic trait approaches—in which common etiological substrates are identified—should advance the discipline in upcoming years.

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Beauchaine, Shader, Hinshaw

501

INDEX

Note: Tables, figures, and notes are indicated by t, f, and n. A A-DISC. see Anxiety Disorders Interview Schedule for Children (A-DISC) abuse sexual, 269 academic achievement intelligence and, 252 ACC. see anterior cingulate cortex (ACC) accentuation hypothesis, 211 acetylcholine in molecular genetic studies of externalizing spectrum, 155 Achenbach System for Empirically Based Assessment (ASEBA), 81–82 DSM–defined externalizing disorders and syndromes from, 81–82 Achenbach, T.M., 90, 487 acquired neuropsychological variation, 251–255 biological basis for, 253–255 described, 252 developmental immaturity and, 254 EFs in, 252 fetal programming vulnerability, 254 hemizygotic male and, 253 intelligence in, 252 mesolimbic DA system and, 254–255 placental vulnerability, 254 sex differences and, 251–255 vulnerability in, 252–254 X-linked gene mutation vulnerability, 253–254 activity defined, 171 activity level biological basis for, 249 defined, 172 sex differences effects on, 248–249 vulnerability in, 248 actor–partner independence model (APIM), 305–308, 306f, 307f in coercive joining, 307–308, 307f in deviancy training, 306, 306f, 307f Adams, E., 352 ADAPT. see Adolescent Depression Antidepressants and Psychotherapy Trial (ADAPT) “adaptational failures,” 94 ADD. see attention deficit disorder (ADD) Add Heath database, 317 addict(s)

“functional,” 54 addiction categorization of, 6–7 common liability for, 53 addiction cycle stages in, 46 ADHD. see attention-deficit/hyperactivity disorder (ADHD) ADHD Rating Scale, 406 “ADHD symptoms expression” PKU showing, 226 adolescence deviant peer clustering in, 304 low IQ and EF deficits in, 385f, 388 adolescent(s) BPD among, 68–69 externalizing psychopathology among, 68–69 SIJ among, 68–69 Adolescent Depression Antidepressants and Psychotherapy Trial (ADAPT), 63–64 adolescent-limited offenders, 334 adolescent problem behavior APIM in, 305–308, 306f, 307f friendship and, 303–312 implications of, 308–310 interventions for, 309–310 adoption studies ADHD–related, 110 behavioral genetic, 109 ADS. see alcohol dependence syndrome (ADS) affective decision-making, 205 in impulse control, 207–208 affective-motivational processing PFC in, 42 affectivity negative, 171 age–crime curve, 309 aggression attribution biases and, 351–353 causes of, 176 human, 241 MAOA gene and, 410 physical, 352–353 post-TBI, 410–411 reactive, 352 sex differences and, 241 subtypes of, 352 Agnew, R., 329

Akil, H., 50–51 alcohol abstinence during pregnancy, 431 Alcohol Beverage Labeling Act, 430 alcohol dependence syndrome (ADS), 7 alcohol exposure ADHD related to, 418–420, 419f, 421t, 424t, 425t ASPD related to, 424t, 425t, 428 ODD and CD related to, 424–428, 424t, 425t SUDs related to, 424t, 425t, 429 alcoholism. see also substance use disorders (SUDs) defined, 6 Alexander, G.E., 446 Alexander, M., 336 Alink, L.R., 275 Allen, J.L., 366, 367 allostasis, 274–276 defined, 492 g-aminobutyric acid (GABA) in genome-wide linkage studies of individual loci related to externalizing disorders, 128 in molecular genetic studies of externalizing spectrum, 154 amygdala high-risk behaviors effects on, 495–496 analytic strategies in molecular genetic studies, 136–137 Anderson, S.W., 43 Angold, A., 443, 444 ANS. see autonomic nervous system (ANS) activity/reactivity anterior cingulate cortex (ACC) anatomical location of, 203f in disinhibitory psychopathology, 47 in impulsivity, 201–219. see also impulsivity normative development of, 210 in regulating emotional behavior, 43 antipsychotic agents in externalizing disorders management post-TBI, 412 antisocial behavior (ASB) alternative pathways to, 366–367 behavioral genetic studies of, 113–115 coercive process in development of, 287–288 CU traits and, 362

503

antisocial behavior (Cont.) described, 106, 113–114 individual-level theories of, 313–314 LCP, 239 MAOA gene effects on, 68 neighborhood-level theories of, 314–318. see also neighborhood-level theories of ASB and criminal behavior neighborhood risk and development of, 313–322. see also neighborhood-level theories of ASB and criminal behavior; neighborhood risk neurobiological system processes as mediators in effects of SIL parenting interventions on, 293–294 public housing and, 315 with and without significant levels of CU traits, 363–366 antisocial development coercive family processes and, 290–291 antisocial personality disorder (ASPD) alcohol exposure and, 424t, 425t, 428 attribution biases and, 353 behavioral genetic studies of, 112–113 characteristics of, 112–113 comorbidity with, 14, 64, 463 criteria for, 8, 12 current state of the science, 12 developmental considerations, 14 DP related to, 91 DSM on, 7–8, 12 environmental toxins and, 424t, 425t, 428 future directions in, 16 historical context of, 7–8 low IQ and EF deficits and, 380 modification to diagnostic criteria for, 15 post-TBI, 411 research agenda, 16 SIJ, BPD, and externalizing disorders linked with, 14, 64 teratogen exposure and, 424t, 425t, 428 tobacco exposure and, 424t, 425t, 428 trait impulsivity in, 190–191 Antshel, K.M., 225–226 anxiety trait. see trait anxiety Anxiety Disorders Interview Schedule for Children (A-DISC), 453 APIM. see actor–partner independence model (APIM) approach defined, 172 approach–avoidance–conflict BIS in describing, 221–222, 222f personality and, 221–222, 222f Arsenio, W.F., 349, 352 artifactual comorbidity, 462 ASB. see antisocial behavior (ASB) ASEBA. see Achenbach System for Empirically Based Assessment (ASEBA)

504 Index

ASPD. see antisocial personality disorder (ASPD) assortative mating in behavioral genetics, 109 attention selective, 209–210, 376 sustained, 376 attention deficit disorder (ADD), 4 attention-deficit/hyperactivity disorder (ADHD) adoption studies of, 110 age as factor in, 21 age of onset of, 21 alcohol exposure and, 418–420, 419f, 421t, 424t, 425t attribution biases and, 349–350 Australian Twin Study of, 445 behavioral genetic studies of, 110–111 biological risk factors for, 27–28 BIS in, 225–227 “bottom-up” system of reward/motivation processing in, 23 CBT for, 453–454 CD with, 227–228 child maltreatment and, 272 cigarette byproducts exposure and, 419f, 420, 421t, 424t, 425t “combined” subtypes of, 20, 47, 225–227 combined-type, 227 comorbidity with, 26, 29–30, 112, 463. see also specific disorders, e.g., conduct disorder (CD) compared with other externalizing disorders, 19–37 criteria for, 4, 8–9 CU traits and, 362 current state of the science, 8–9, 30–31 deficits underlying, 22 defined, 8 described, 19, 110 developmental factors in, 13, 94–95 developmental progression of, 29–30 diagnosis of, 4, 8, 14, 20–21, 30 disinhibitory liability related to, 53 DP related to, 91 DSM-5 on, 377 DSM on, 4, 8–9 environmental toxins and, 423–424, 424t, 425t family studies of, 110 FFFS–BAS conflict in, 227 frontal brain in, 47 functional imaging in, 25–26 future directions in, 15, 31 genetic susceptibility to, 21–22, 26–28 genetics of, 110 GxE correlation in, 28–29 heritable factors for, 21–22, 26–27 historical context of, 4, 20 hyperactive-impulsive, 47, 225–227 illicit drug exposure and, 419f, 421t, 422–423, 424t, 425t

inattentive, 225–227 introduction, 19–22 lead exposure and, 421t, 423, 424t, 425t low effortful control and, 176–177 low IQ and EF deficits in, 377 marijuana exposure and, 419f, 421t, 422–423, 424t, 425t narrow-band syndromes with, 444 neurobiology of, 24–26 neurology of, 231–233, 232t pharmaceutical agents and, 419f, 420–422, 421t PKU and, 225–227 post-TBI, 406–408. see also attention-deficit/hyperactivity disorder (ADHD) post-TBI “predominantly hyperactive/ impulsive,” 20 “predominantly inattentive,” 20 presentation of, 20 prevalence of, 21, 418 psychosocial risk factors for, 28 research agenda, 15, 30–31 restless legs syndrome associated with, 49 retinoid and, 424 risk factors for, 21–22, 26–29 SIJ, BPD, and externalizing disorders linked with, 64 sleep disturbances related to, 49 socialization skills and, 29 structural imaging of, 24–25 subtypes of, 20, 47, 225–227 symptoms of, 4, 8–9, 20, 418 teratogen exposure and, 418–424, 419f, 421t, 424t, 425t tobacco exposure and, 419f, 420, 421t, 424t, 425t “top-down” system of executive functions and cognitive processes in, 22–23 trait impulsivity in, 189–190, 246–247 treatment of, 31 twin studies of, 110 types of, 225–227 underlying models of, 22–23 attention-deficit/hyperactivity disorder (ADHD) post-TBI, 406–408 behavioral studies, 406–407 cognitive studies, 407 neurobiology of, 407–408 prevalence of, 406 risk factors for, 407 attention/persistence defined, 172 attribution bias(es) ADHD and, 349–350 aggression and, 351–353 ASPD and, 353 CD and, 351–353 culture and, 356 current state of the science, 353–354 developmental considerations, 354–355 emotions and, 355–356 externalizing behaviors and, 347–359

future directions in, 355–356 historical context of, 347–349 introduction, 347 links to traditional externalizing disorders, 349–353 ODD and, 350–351 psychopathy and, 353 research agenda, 355–356 from SIP model, 347–349 Australian Twin Study of ADHD, 445 autonomic nervous system (ANS) activity/reactivity comorbidity related to, 471 B Bachorowski, J., 351 Bailey, C., 352 Bakermans-Kranenburg, M.J., 178, 296 Bargh, J.A., 348 Barglow, P., 63 Barker, E.D., 382 Barkley, R.A., 22, 46–47 Barnett, D., 268–269, 272 Baron-Cohen, S., 251, 255 Barratt Impulsiveness Scale, 48, 205 BAS. see behavioral approach system (BAS) Basten, M.M., 451 Bates, J.E., 171, 355 Bayley Scales of Infant Development–Second Edition (BSID-II) Behavior Rating Scale from, 423 Mental Development Index from, 423 Beach, S.R.H., 140, 274 Beauchaine, T.P., 21, 47, 53, 91, 210–211, 242, 297, 349, 445, 452, 485 Bechara, A., 43 behavior(s) adolescent problem. see adolescent problem behavior antisocial. see antisocial behavior (ASB) coercive, 287 externalizing. see externalizing behavior(s) high-risk, 495–496 Behavior Rating Scale from BSID-II, 423 behavioral approach system (BAS), 220, 222, 222f BIS and, 225 elements of, 222–224, 223f personality predisposition and, 224–225 behavioral genetic studies, 105–124 ADHD–related, 110–111 adoption studies, 109 ASB–related, 113–115 ASPD–related, 112–113 assortative mating in, 109 CD–related, 111–112 classical research designs in, 107–109 controversies related to, 117–118 current state of the science, 115–116 developmental considerations, 116–117

EEA in, 109–110 of externalizing problems and disorders, 105–124 family studies, 107–108, 108f future directions in, 119 generalizability in, 110 historical context of, 106–107 introduction, 105–106 links to traditional externalizing disorders, 110–115 methodological issues in, 109–110 ODD–related, 111 RDoC approach to, 119 research agenda, 118–119 SUDs–related, 113 twin studies, 108, 109 behavioral genetics classical research designs in, 107–109 of externalizing spectrum disorders, 149 studies of, 105–124. see also behavioral genetic studies of trait impulsivity, 186 behavioral inhibition/fear defined, 171 behavioral inhibition system (BIS), 220–238 in ADHD, 225–227 approach–avoidance conflict in, 221–222, 222f in CD, 227–228 components of, 223, 223f concept of, 220–221 described, 220 elements of, 222–224, 223f externalizing disorders and, 225 function of, 221 introduction, 220 neurobiology of, 222–224, 223f personality and clinical comorbidity, 224–225 in psychopathy, 228–231, 229f role of, 222, 222f behavioral inhibition system (BIS) theory, 221–222, 222f behavioral neuroscience coercion theory and, 289–290 behavioral teratology defined, 416 research in, 417 Bell, R.Q., 97 Berg, T., 276 Bernat, E.M., 54 Bernburg, J.G., 328 Berridge, K.C., 42, 44–46 Berthold, A., 240 Besíc, N., 366 Bevilacqua, L., 138, 139, 273–274 bias(es) attribution, 347–359. see also attribution bias(es) positive illusory, 350 self-serving, 350 Bijleveld, C.C.J.H., 326 binge/intoxication stage

in addiction cycle, 46 bioinformatics in molecular genetic studies, 137 biological vulnerabilities to externalizing spectrum disorders, 103–263 biomarkers in externalizing disorders management post-TBI, 411–412 biospecimens in externalizing disorders management post-TBI, 411–412 BIS. see behavioral inhibition system (BIS) Blair, J., 230 Blair, K., 230 Blair, R.J.R., 364, 367 Blakemore, J., 249 b-blockers in externalizing disorders management post-TBI, 412 blood oxygen level dependent (BOLD) response low, 207 Boden, J.M., 50 BOLD response. see blood oxygen level dependent (BOLD) response borderline defined, 62 borderline personality described, 62 borderline personality disorder (BPD), 454–455 ASPD with, 64 behavioral genetics and family studies in, 65–66 biological vulnerabilities to, 62, 65–66 comorbidity patterns of, 61–62 controversies related to, 69–70 current state of the science, 64–68 DA and, 66 defined, 62 developmental considerations for, 64, 68–69 developmental precursors to, 62–63 developmental trajectories for, 68 environmental risk factors for, 62, 66–68 externalizing spectrum of, 61–78 5-HT in, 66 fMRI studies in, 71n future directions in, 71 historical context of, 62–63 introduction, 61–62 links to traditional externalizing disorders, 63–64 presentation of, 62–63 prevalence of, 62 research agenda, 70–71 SIJ with, 61–78 stigmatizing nature of, 69 SUDs with, 64 trait impulsivity and, 64 underlying structure of, 69

Index

505

borderline personality disorder (BPD) labeling, 69 Bornstein, M.H., 452 Bosch, J.D., 352 bottom-up dysfunctions, 202–203 “bottom-up” system of reward/motivation processing in ADHD, 23 Boulerice, B., 382 BPD. see borderline personality disorder (BPD) brain environmental “programming” of, 93 frontal, 47 brain circuit(s) for reward and incentive salience, 44–46 brain circuitry for inhibitory control, 41–44 brain injury impulsivity related to, 203–204 traumatic, 403–415. see also traumatic brain injury (TBI) brain lesions impulsivity related to, 203–204 brain systems social experience related to, 291–292 Brennan, J., 448 Brewer-Smyth, K., 275 Briggs-Cowan, M.J., 448 Brislin, S.J., 364 Brooks-Gunn, J., 314, 317, 319 Brotman, L.M., 296 Brown, P.J., 98 Bruce, J., 295 BSID-II. see Bayley Scales of Infant Development–Second Edition (BSID-II) Buckholtz, J.W., 48, 191 Buist, K.L., 451 Bukowski, W.M., 451 Burgess, A.W., 275 Burke, J.D., 68 Burnette, M.L., 273 Burnette, S., 364 Burt, A., 179 Burt, S.A., 112, 115 Buss, A.H., 171, 175 Buss, D., 256n Butler, G., 368 Byrd, A.L., 140–141 C CAH. see congenital adrenal hyperplasia (CAH) Caldwell, M., 370 California Three Strikes and You’re Out Law, 325, 331 callous-unemotional (CU) traits, 50, 251 ADHD and, 362 ASB with and without, 362–366 assessment advances needed for, 369

506 Index

CD and, 360–374. see also conduct disorder (CD) cognitive and affective correlates of, 364 COMT polymorphisms and, 363 controversies related to, 367–369 current state of the science, 363–366 developmental considerations, 366–367 external disorders related to, 360–374 externalizing behaviors related to, 362 future directions in, 367–370 genetic and biological correlates with, 363–364 historical context of, 361–362 introduction, 360–361 research agenda, 367–370 social correlates of, 365–366 in those without conduct problems, 368–369 treatment advances needed for, 369–370 “calls for action,” 270 Cambridge Fetal Testosterone Project, 251 Cambridge Gamble and Risk Tasks, 205 Cambridge Study in Delinquent Development (CSDD), 318, 333–334, 334f cAMP response element-binding protein (CERB) gene child maltreatment and, 274 candidate gene studies of individual loci related to externalizing disorders, 128–129 career criminality, 242 Caron, C., 462 Carter, A.S., 448 Casey, B.J., 53, 54 Caspi, A., 68, 135, 174, 242, 245, 247, 273, 492 Castellanos, F.X., 24 Castellanos-Ryan, N., 207, 213 CAT-PD (Computerized Adaptive Test for Personality Disorder), 83 catechol-O-methyltransferase (COMT) gene, 153–154 catechol-O-methyltransferase (COMT) polymorphisms CU traits and, 363 categorical diagnosis defined, 79 Catton, T., 444 Cauffman, E., 335 CBC. see Child Behavior Checklist (CBC) CBT. see cognitive behavioral therapy (CBT) CD. see conduct disorder (CD) cell adhesion proteins GWA studies of, 157 CERB gene. see cAMP response element-binding protein (CERB) gene Cernkovich, S.A., 332

Chan, R.C.K., 46 chemical(s) ADHD related to, 27 Chess, S., 171, 175 Child and Adolescent Disposition Scale, 472 Child and Adolescent Psychiatric Assessment, 111 Child Behavior Checklist (CBC), 81, 424, 453 on CD, 111–112 externalizing spectrum defined by, 81–82 Child Development, 90 Child Development Project, 351–352, 355 child maltreatment. see also maltreatment ADHD and, 272 CERB gene and, 274 conceptualization of, 268–269 current state of the science, 273–276 defined, 268 developmental considerations, 276–278 experience of, 267 externalizing behaviors related to, 269–272 future research agenda, 278–279 gender moderated effects of, 276 GxE correlation in, 273–274 historical context of, 267–272 HPA axis and, 273 introduction, 267 links to traditional externalizing disorders, 272–273 medical-diagnostic approach to, 268 neuroendocrine functioning related to, 274–276 ODD and, 273 PFC in, 271 research on, 267–268 subtypes of, 269 SUDs and, 272 vulnerability to externalizing spectrum disorders related to, 267–285 Children’s Orientation and Amnesia Test (COAT), 404 chromosome(s) in sex differences, 243 Cicchetti, D., 268–269, 272, 273, 275 cigarette byproducts exposure ADHD related to, 419f, 420, 421t, 424t, 425t ASPD related to, 424t, 425t, 428 ODD and CD related to, 424t, 425t, 426 SUDs related to, 424t, 425t, 429 Clark, L., 207 classical strain theory, 329 Clear, T.R., 324 clinical authority in psychiatric diagnostic development, 79 Cloninger, C.R., 133, 134, 172–173 closed head injury, 405

CNVs. see copy number variants (CNVs) COAT. see Children’s Orientation and Amnesia Test (COAT) coercion construct of, 287 defined, 287 peer interaction dynamic, 303 as social process, 287 coercion theory, 286, 288–289, 468, 494 behavioral neuroscience and, 289–290 development and intervention, 288–289 SIL parenting interventions and neuroscience integrated with, 298–299 coercive behaviors described, 287 coercive family processes antisocial development and, 290–291 ASB development related to, 287–288 in externalizing behaviors, 286–302 integrating neuroscience into parenting and family intervention research, 292–293, 293f introduction, 286–287 LHPA stress regulatory systems in, 290, 296–297 mesocortical DA prefrontal systems in, 294–295 mesolimbic DA motivational system in, 290, 297–298 neurobiological systems and, 290–291 coercive joining described, 303, 307–308 as dynamic mediator, 307–308, 307f coercive social processes family intervention and, 288 COGA study. see Collaborative Study on the Genetics of Alcoholism (COGA) COGEND. see Collaborative Study of Nicotine Dependence (COGEND) cognition behavior related to, 375–400. see also executive function (EF) deficits; low intelligence (IQ) cognitive behavioral therapy (CBT) for ADHD, 453–454 cognitive coping in externalizing disorders management post-TBI, 412 cognitive deficits in adolescents, 385f, 388 in children, 385f, 386–388 controversies related to, 388–390 developmental considerations, 383–388, 385f in early adulthood, 385f, 388 future directions in, 390–391 infant manifestations of, 384–385, 385f in males, 389 in preschoolers, 385f, 386 research agenda, 390–391 cognitive flexibility

defined, 376 cognitive vulnerabilities to externalizing disorders, 345–400 Cohen, J.D., 42, 43, 352, 377 Collaborative Study of Nicotine Dependence (COGEND), 155 Collaborative Study on the Genetics of Alcoholism (COGA), 128, 155 Collective Clemency Bill, 331–332 commission errors, 205 comorbidity(ies) ADHD and, 463 aim of studies on, 461–462 among externalizing disorders, 461–481 among internalizing and externalizing disorders, 443–460 ANS anxiety/reactivity and, 471 artifactual, 462 ASPD and, 463 assessment of, 464, 465f background, 443–444 categorical vs. dimensional approaches to, 444 CD and, 463 controversies related to, 474 current state of the science, 466–467 defined, 461 developmental considerations, 473–474 dispositional traits and, 471–473 environmental effects on, 466–469 in externalizing behaviors, 486 future research directions in, 454–455 genetics and, 466–469 GxE correlates in, 467–469 heterotypic, 463, 486, 489–496, 489f, 491f historical context of, 462–463 homotypic, 462–463, 486–489 HPA axis in, 448 introduction, 461–462 LHPA axis activity/reactivity and, 471 low IQ and EF deficits related to, 381 neural circuit approaches to, 445–446 neural mechanisms of, 445–448 neurobiological correlates of, 469–470 neuroendocrine mechanisms of, 448–449 ODD and, 463 pure disorders vs., 451–452 research agenda, 474–475 reward insensitivity and, 470 sex differences and, 445 socialization and, 449–452 structural models of, 465f treatment-related, 452–454 true, 462 weak inhibitory control and, 470–471 compulsion(s), 224 computed tomography (CT) in head injury assessment, 404 Computerized Adaptive Test for Personality Disorder (CAT-PD), 83

COMT gene. see catechol-O-methyltransferase (COMT) gene COMT polymorphisms. see catechol-O-methyltransferase (COMT) polymorphisms conduct disorder (CD) ADHD with, 227–228 alcohol exposure and, 424–428, 424t, 425t attribution biases and, 351–353 behavioral genetic studies of, 111–112 BIS in, 227–228 causal model for, 360–374 characteristics of, 111 cigarette byproducts exposure and, 424t, 425t, 426 comorbidity with, 112, 463 CU traits and, 360–374. see also callous-unemotional (CU) traits current state of the science, 11 DA dysfunction in, 228 defined, 360 developmental considerations, 13–14 DP related to, 91 DSM-5 on, 360 DSM-II on, 361–362 DSM on, 5–6, 11 environmental toxins exposure and, 424t, 425t, 428 FFFS–BAS conflict in, 227 future directions in, 15–16 historical context of, 5–6 illicit drug exposure and, 424t, 425t, 427 introduction, 360–361 lead exposure and, 424t, 425t, 428 low effortful control and, 176–177 low IQ and EF deficits in, 377–378 marijuana exposure and, 424t, 425t, 427 modification to diagnostic criteria for, 14 neurobiology of, 409 neurology of, 231–233, 232t pharmaceutical agents exposure and, 424t, 425t, 426–427 post-TBI, 408–409 research agenda, 15–16 risk factors for, 360–361 SIJ, BPD, and externalizing disorders linked with, 64 symptoms of, 5–6, 11 temperament resulting in, 175 teratogen exposure and, 424–428, 424t, 425t tobacco exposure and, 424t, 425t, 426 trait impulsivity in, 190 congenital adrenal hyperplasia (CAH), 249 contagion described, 303 as peer interaction dynamic, 303

Index

507

contemporary post-DSM-5/RDoC landscape controversies in, 85–86 continuity(ies) in externalizing behaviors, 486 Continuous Performance Task, 447 control culture of, 325 effortful, 171, 176–177, 247–248. see also effortful control impulse, 207–213, 229. see also impulse control inference, 209–210 inhibitory, 41–44, 46, 376 low effortful, 176–177 reactive, 172 sex differences effects on, 246 weak inhibitory, 470–471 coping cognitive, 412 copy number variants (CNVs), 126 in molecular genetic studies, 137–138 Corr, P.J., 229 Costello, J., 444 Coy, K., 351 CpG sites. see cytosine-phosphate-guanine (CpG) sites Crick, N.R., 349 criminal and antisocial behavior (ASB) neighborhood risk and, 313–322. see also antisocial behavior (ASB); criminal behavior criminal behavior incarceration as learning environment for, 327 individual-level theories of, 313–314 neighborhood-level theories of, 314–318. see also neighborhood-level theories of ASB and criminal behavior neighborhood risk and, 313–322 public housing and, 315 criminality career, 242 criminological strain theory, 329 Crockenberg, S.C., 178 cross-disorder approaches in phenotyping strategies, 131–132 Cross-Disorders Working Group of PGC, 131–132 Crowell, S.E., 67 CSDD. see Cambridge Study in Delinquent Development (CSDD) CT. see computed tomography (CT) CU traits. see callous-unemotional (CU) traits Cullen, F.T., 328, 332 Cullerton-Sen, C., 276 culture in attribution biases, 356 culture of control, 325

508 Index

custodial vs. non-custodial sentences, 330 cytochrome P450 monooxygenases, 156 cytosine-phosphate-guanine (CpG) sites, 139 D DA. see dopamine (DA) Dadds, M.R., 366, 367, 369, 447, 448, 455 Dahl, E.E., 447 Dalley, J.W., 208 Damasio, H., 43 Darwin, C., 240 data reduction methods in phenotyping strategies, 134 Davidson, R.J., 43 Davies, P.T., 273 Daw, N.D., 45 DBDs. see disruptive behavior disorders (DBDs) de Moor, M.H.M., 133 de Werth, C., 309 Deater-Deckard, K., 97 Décary, A., 208 default mode network (DMN), 25 defensive distance, 221–222, 222f defiance theory incarceration and, 328 DeKlyen(s) M., 351 Dekovic, M., 451 delay discounting, 205, 208 delinquency defined, 378 incarceration and development of, 323–343. see also incarceration low IQ and EF deficits in, 378–379 DeMello, L., 355 Department of Housing and Urban Development (HUD) MTO program of, 318–319 deterrence general, 323 incarceration as, 326–327 specific, 323 development atypical, 94–95 causal processes in, 174 continuities in, 95–96 discontinuities in, 95–96 “greatest mismatch” in, 333 normal, 93–95 Development and Psychopathology, 90 developmental differentiation temperamental characteristics related to, 170 developmental immaturity acquired neuropsychological variation and, 254 developmental psychopathology (DP) atypical development and, 93–95 characterization of, 91 concepts of, 92–93 described, 90–91

equifinality and, 92 introduction, 90–92 multifinality and, 92 multiple levels of analysis in, 96–97 normal development and, 93–95 perspective on externalizing behavior dimensions and externalizing disorders, 90–102. see also specific disorders principles of, 93–99 protective factors against, 98–99 reciprocal, transactional models of, 97–98 resilience to, 98–99 risk factors for, 93, 98–99 deviancy training, 328 described, 303–304 as dynamic mediator, 306, 306f, 307f in prisons, 327 deviant peer clustering in early adolescence, 304 gang involvement related to, 304–305, 307–308 sexual promiscuity related to, 304 Diagnostic and Statistical Manual of Mental Disorders (DSM). see also specific editions on DBDs, 3–18. see also specific disorders and disruptive behavior disorders (DBDs) on diagnostic hierarchies, 486 Diagnostic and Statistical Manual of Mental Disorders, 2nd. ed. (DSM-II) on CD, 361–362 Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) DSM-5 vs., 85 Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5) on ADHD, 377 on CD, 360 DSM-IV vs., 85 on impulsivity, 201 on ND-PAE, 417 Personality Inventory for. see Personality Inventory for DSM-5 (PID-5) on teratogen exposure, 429–430 Diagnostic and Statistical Manual of Mental Disorders (DSM)–defined externalizing disorders and syndromes from ASEBA tradition in, 81–82 diagnostic hierarchies DSM on, 486 Diagnostic Interview Schedule for Children (DISC), 111 Dick, D.M., 134, 135 diet ADHD related to, 27–28 “difficult” temperament, 171, 175 diffusion tensor imaging (DTI) in head injury assessment, 404

Dinovitzer, R., 330 Disability Rating Scale, 404 DISC. see Diagnostic Interview Schedule for Children (DISC) Dishion, T.J., 304, 327 “disinhibition vs. constraint/ conscientiousness” sex differences effects on, 246 disinhibitory liability(ies) alternative addictive and aggressive expressions of, 49–52 P300 (P3b) amplitude reduction and, 48–49, 53–54 research agenda, 52–53 SUD–related, 46–54 unresolved questions related to, 52–53 disinhibitory psychopathology brain regions involved in, 47–52 dispositional liability(ies) for SUDs, 39–40 trait disinhibition vs., 51 dispositional traits comorbidity related to, 471–473 dispositional vulnerability to SUDs, 39–40 disruptive behavior disorders (DBDs). see also specific disorders, e.g., conduct disorder (CD) controversies related to, 14–15 criteria for, 9–10 current state of the science, 8–12 described, 112, 408 developmental considerations, 12–14 differential diagnosis of, 10 DSM on, 3–18 future directions in, 15–16 historical context of, 3–8 introduction, 3 post-TBI, 408–409 research agenda, 15–16 symptoms of, 10 types of, 3 disruptive mood dysregulation disorder (DMDD), 431–432 current state of the science, 9–10 developmental considerations, 13 future directions in, 15 modification to diagnostic criteria for, 14 research agenda, 15 DMDD. see disruptive mood dysregulation disorder (DMDD) Dmitrieva, J., 335 DMN. see default mode network (DMN) Dodge, K.A., 97, 349, 351, 354, 355 Donzella, B., 448 dopamine (DA) in molecular genetic studies of externalizing spectrum, 150–152 in SIJ, BPD, and externalizing psychopathology, 66 dopamine D2 receptor (DRD2) gene

in candidate gene studies of individual loci related to externalizing disorders, 128 dopaminergic (DA) dysfunction CD and, 228 dopaminergic (DA) projections in trait impulsivity, 186–187 Dosier, M., 297 Douglas, V.I., 22 DP. see developmental psychopathology (DP) Drabick, D., 105 Drago, F., 331–332 DRD2 gene. see dopamine D2 receptor (DRD2) gene drug(s) ADHD related to, 419f, 420–422, 421t classes of, 6 ODD and CD related to, 424t, 425t, 426–427 drug dependence defined, 6 drug metabolism genes in molecular genetic studies of externalizing spectrum, 156 DSM. see Diagnostic and Statistical Manual of Mental Disorders (DSM) DSM-IV. see Diagnostic and Statistical Manual of Mental Disorders (DSM), 4th ed. (DSM-IV) DSM-5. see Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5) DSM-II. see Diagnostic and Statistical Manual of Mental Disorders, 2nd. ed. (DSM-II) DTI. see diffusion tensor imaging (DTI) Duncan, G.J., 315 Dunedin Multidisciplinary Health and Development study, 242 E early adolescence deviant peer clustering in, 304 early adulthood low IQ and EF deficits during, 385f, 388 early brain lesions impulsivity related to, 203–204 EAS model. see Emotionality-Activity Sociability (EAS) model “easy” temperament, 171 Eaton, N.R., 445 Edmiston, E.E., 277 EEA. see Equal Environmental Assumption (EEA) EF deficits. see executive function (EF) deficits effortful control biological effects of, 248 defined, 171 low, 176–177 sex differences effects on, 247–248

vulnerability for, 247 EFs. see executive function(s) (EFs) Elia, J., 138 Elizur, Y., 370 Ellingson, J.M., 96 Ellis, B., 449 Else-Quest, N.M., 174, 248 EM. see emotional maltreatment (EM) emotion(s) SIP and, 349 emotion dysregulation future directions in, 355–356 operant reinforcement and, 494 emotional maltreatment (EM), 269 emotional vulnerabilities to externalizing disorders, 345–400 emotionality defined, 171 negative, 175–176 ODD resulting from, 175 positive, 172 Emotionality-Activity Sociability (EAS) model, 171 Empathizing Systemizing theory of sex differences, 251 empathy biological basis for, 251 sex differences effects on, 250–251 vulnerability for, 250–251 endophenotypes/intermediate phenotypes, 132–133 environment comorbidity related to, 466–469 criminal learning, 327 externalizing disorders related to, 488–489 as risk factor for psychopathology, 93 in SIJ, BPD, and externalizing psychopathology, 62, 66–68 temperament effects of, 173 environmental level chemical and toxins ADHD related to, 27 environmental “programming” of brain as risk factor for psychopathology, 93 environmental toxins ADHD related to, 423–424, 424t, 425t ASPD related to, 424t, 425t, 428 ODD and CD related to, 424t, 425t, 428 epigenesis defined, 492 epigenetic markers, 139 epigenetics defined, 139 in molecular genetic studies, 139–140 epigenome-wide association (EWA) studies, 140 epistasis defined, 107 Equal Environmental Assumption (EEA) in behavioral genetics, 109–110 equifinality DP and, 92

Index

509

Erickson, N.L., 445, 486 Erkanli, A., 444 ERN. see error-related negativity (ERN) error-related negativity (ERN) defined, 47 ESI. see Externalizing Spectrum Inventory (ESI) Evans, S.W., 349 Everitt, B.J., 208 EWA studies. see epigenome-wide association (EWA) studies executive function(s) (EF) acquired neuropsychological variation and, 252 hot vs. cool, 389–390 executive function (EF) deficits ADHD and, 377 in adolescents, 385f, 388 ASPD and, 380 CD and, 377–378 in children, 385f, 386–388 comorbidity among externalizing disorders related to, 381 controversies related to, 388–390 current state of the science, 380–383 delinquency and, 378–379 developmental considerations, 383–388, 385f dimensional approach to, 381–383 in early adulthood, 385f, 388 externalizing behaviors related to, 375–400 future directions in, 390–391 historical context of, 375–377 impulsivity and, 383 infant manifestations of, 384–385, 385f introduction, 375 links to traditional externalizing disorders, 377–380 in males, 389 in preschoolers, 385f, 386 psychopathy and, 380 research agenda, 390–391 SUDs and, 379–380 externalizing behavior(s). see also specific types and disorders attribution biases and, 347–359 biological vulnerabilities to, 242–244, 244f coercive family processes in development of, 286–302. see also coercive family processes comorbidities and continuities in, 486 CU traits and, 362 dimensional approach to, 381–383 DP perspective on dimensions of, 90–102. see also developmental psychopathology (DP) early findings relating maltreatment to, 269–272 EF deficits and, 375–400. see also executive function (EF) deficits expression of, 239–263

510 Index

low IQ and, 375–400. see also low intelligence (IQ) models of, 1–102 neural mechanisms of low trait anxiety and risk for, 220–238. see also behavioral inhibition system (BIS) post-TBI, 403–415. see also traumatic brain injury (TBI) prevalence of, 239–263 sex differences as factor in, 239–263. see also sex differences socialization mechanisms of, 265–343 temperament and, 170–183. see also temperament teratogen exposure and, 416–439. see also teratogen(s); teratogen exposure externalizing behavior dimensions developmental continuities and discontinuities and, 95–96 DP perspective on, 90–102. see also developmental psychopathology (DP) externalizing disorders. see also specific disorders, e.g., conduct disorder (CD) attribution biases links to, 349–353 BAS and, 225 behavioral genetic studies of, 105–124. see also behavioral genetic studies biological vulnerabilities to, 103–263, 242–244, 244f BIS and, 225 child maltreatment and, 267–285 cognitive vulnerabilities to, 345–400 comorbidity among, 443–481 CU traits and, 360–374. see also callous-unemotional (CU) traits DP perspective on, 90–102. see also developmental psychopathology (DP) emotional vulnerabilities to, 345–400 environmental effects on, 488–489 FFFS and, 225 genetic studies of, 105–124 impulsivity and, 205–207 low IQ and EF deficits in, 375–400. see also executive function (EF) deficits; low intelligence (IQ) molecular genetic studies of individual loci related to, 127–130 neural dysfunction in, 446–448 neurology of, 231–233, 232t ontogenic process perspective on, 185 ontogenic processes model of, 485–501. see also ontogenic processes model of externalizing psychopathology post-TBI, 403–415. see also externalizing disorders post-TBI; traumatic brain injury (TBI) sex differences in, 242

teratogen exposure and, 418–429. see also specific disorders treatment of, 411–412 externalizing disorders post-TBI, 405–409 ADHD, 406–408 aggression, 410–411 ASPD, 411 CD, 408–409 DBDs, 408–409 ODD, 408 personality change–related, 409–411 treatment of, 411–412 externalizing outcomes SUDs as, 38–60. see also substance use disorders (SUDs) externalizing phenomena direct comparison of discrete and dimensional models of, 82 externalizing psychopathology behavioral genetics and family studies in, 65–66 biological vulnerabilities to, 65–66 controversies related to, 69–70 DA and, 66 developmental considerations for, 68–69 environmental risk factors for, 66–68 5-HT in, 66 fMRI studies in, 71n future directions in, 71 intent structure of, 487 research agenda, 70–71 SIJ and BPD and, 61–78 externalizing spectrum behavioral genetic studies of, 105–124. see also behavioral genetic studies defined, 105 molecular genetic studies of, 125–169. see also molecular genetic studies externalizing spectrum disorders. see externalizing behavior(s); externalizing disorders; externalizing spectrum Externalizing Spectrum Inventory (ESI), 40, 41f development of, 82–83 externalizing spectrum model perspective on disinhibitory liability and its alternative phenotypic expressions, 46–52 of SUDs, 40–41, 40f externalizing spectrum of personality and psychopathology, 79–89 CAT-PD for, 83 controversies in contemporary post-DSM-5/RDoC landscape related to, 85–86 current state of the science, 82 developmental considerations for, 84–85 etiologic coherence of, 82–83 future directions in, 86–87 historical context of, 80–81

introduction, 79–80 research agenda, 86–87 F factor analytic methods in externalizing comorbidity assessment, 464, 465f Fahim, C., 409 failure to provide (FTP), 269 family intervention research integrating neuroscience into, 292–293, 293f family issues ADHD related to, 28 in SIJ, BPD, and externalizing psychopathology, 66–68 family processes coercive, 286–302. see also coercive family processes family relationships as factor in deviant peer clustering, 304 family socialization comorbidity and, 449–451 family studies ADHD–related, 110 behavioral genetic, 107–108, 108f Farmer, R.F., 473 Farrington, D.P., 315, 319, 330, 332–333, 335, 336 FASD. see fetal alcohol spectrum disorders (FASD) Fassbender, C., 209 FAST TRACK interventions, 279 FCAU. see foster care as usual (FCAU) fear/trait anxiety. see also trait anxiety biological basis for, 250 sex differences effects on, 249–250 vulnerability for, 250 fearfulness low, 176 Feldman, R.S., 277 Fergusson, D.M., 50 fetal alcohol spectrum disorders (FASD), 418 fetal programming acquired neuropsychological variation and, 254 fetus teratogen exposure in, 416–417 FFFS. see fight–flight–freeze system (FFFS) FFFS–BAS conflict in ADHD, 227 in CD, 227 in psychopathy, 229–231 fight–flight–freeze system (FFFS), 221–222, 222f BIS and, 225 elements of, 222–224, 223f personality predisposition and, 224–225 FIM. see Functional Independence Measure (FIM) Finger, E.C., 190

First, M.B., 445 Fisher, P.A., 295–297 Five Factor Model of personality, 133 5-HT. see serotonin (5-HT) 5-HT receptor genes, 153 5-HT transporter (5HTT) gene, 152–153 5HTT gene. see 5-HT transporter (5HTT) gene Flagel, S.B., 50–51, 53 flight to deviant peers, 304 Flory, J.D., 66 fMRI. see functional magnetic resonance imaging (fMRI) Foley, M., 177 Forbes, R.E., 447 Ford, J.D., 448 Ford, T., 368 foster care as usual (FCAU), 295 Fox, N., 295 Frankenhuis, W.E., 309 Frick, P.J., 351, 362, 368, 369 friendship(s) adolescent problem behavior and, 303–312. see also adolescent problem behavior From Juvenile Delinquency to Adult Crime, 333 frontal brain in ADHD, 47 FTP. see failure to provide (FTP) “functional” addicts, 54 Functional Independence Measure (FIM), 404 functional magnetic resonance imaging (fMRI) in head injury assessment, 404 in impulse control, 211 in impulsivity studies, 211 in SIJ, BPD, and externalizing psychopathology, 71n G GABA. see g-aminobutyric acid (GABA) Gage, P., 43, 203–204, 213 Galambos, N., 451 Galbiati, R., 331–332 Galloway-Long, H.S., 368 Galveston Orientation and Amnesia Test (GOAT), 404 gang(s) deviant peer clustering related to, 304–305, 307–308 in prisons, 327 Gao, Y., 176 Garcia, S.P., 453 Garland, D., 325 Gatzke-Kopp, L.M., 190, 253–255 Gautreaux Program, 318 Gauvain, M., 386 GCS. see Glasgow Coma Scale (GCS) Geary, D., 251 Gelhorn, H., 112 gender differences. see also sex differences

child maltreatment–related, 276 sex differences vs., 256n gene- and pathway-based analysis in molecular genetic studies, 136–137 Gene Ontology Project, 136 gene–environment (GxE) correlation in ADHD, 28–29 in child maltreatment, 273–274 in comorbidity, 467–469 described, 245–246 MAOA gene and, 245, 247 in molecular genetic studies, 135–136 as risk factor for psychopathology, 93 in SIJ, BPD, and externalizing psychopathology, 66–67 temperament effects of, 173, 177–179, 245 general deterrence defined, 323 generalizability in behavioral genetics, 110 genetic studies of externalizing spectrum disorders, 105–124. see also behavioral genetic studies genetics behavioral, 105–124. see also behavioral genetic studies; behavioral genetics comorbidity related to, 466–469 in impulsivity, 490, 492 molecular. see molecular genetic studies genome-wide association (GWA) studies, 127 cell adhesion proteins in, 157 genes and gene systems identified by, 156–158 of individual loci related to externalizing disorders, 129–130 voltage-gated potassium channel genes in, 157 genome-wide linkage studies of individual loci related to externalizing disorders, 127–128 genomic interrogation technologies in molecular genetic studies, 137–140 genomic similarity methods, 130 Gerald, 240 Gerring, J.P., 408, 409 Giancola, P.R., 382, 389 Gibson, C.L., 317 Gilbert, F., 364 Giordano, P.C., 332 Gizer, I.R., 96 Gjone, H., 177–178 Glasgow Coma Scale (GCS), 404 Glueck study, 332 glutamate in molecular genetic studies of externalizing spectrum, 154–155 Go No-Go Task, 206, 208, 294 GOAT. see Galveston Orientation and Amnesia Test (GOAT)

Index

511

Godinet, M.T., 276 Goffman E., 325 Gold, J., 352 Goldsmith, H., 248 Golub, A., 354 Gomez, A., 355 Gomez, R., 355 Good Behavior Game, 309 Goodman, R., 368 Gordon, K., 297 Gottdiener, W.H., 99 Gottfredson, M., 314 Gottlieb, G., 98 on behavioral teratology, 416 Gottschalk, M., 324 Goy, 240 Grace, A.A., 446 Graham, S., 354 Granic, I., 294 Gray, J.A., 220–221 “greatest mismatch in development,” 333 Green, D.P., 331 Grogan-Kaylor, A., 273 Groves, W.B., 315 Guerra, N.G., 351, 353 Gunnar, M., 275, 448 GWA studies. see genome-wide association (GWA) studies GxE correlation. see gene–environment (GxE) correlation H Hagan, J., 330 Hallquist, M.N., 70 Hampton, A., 105 Hamsphere, M.L., 130 Haney, C., 324 Happe, F.G.E., 364 Harden, P.W., 382 Harlaar, N., 46 Harrington-Cleveland, H., 317 Hart, J., 275 Haslam, N., 99 Hawes, D.J., 369, 448, 450, 452, 463 head injury closed, 405 effects of, 403 externalizing behavior related to, 403–415. see also traumatic brain injury (TBI) future directions in, 412–413 historical context of, 403–405 imaging of, 404–405 introduction, 403 mild, 407 Heimer, L., 445–446 Helland, E., 331, 332 hemizygotic male acquired neuropsychological variation and, 253 Hemmens, C., 336 heritability

512 Index

of temperament, 173 heterotypic comorbidity, 463, 486, 489–496, 489f, 491f HEXACO model, 83 Hibbard, M.R., 411 Hicks, B.M., 48, 52–53, 65, 445 high-risk behaviors mesolimbic, mesocortical, and amygdalar dysfunctions related to, 495–496 Hines, M., 249 Hinshaw, S.P., 91, 96, 242, 485 hippocampus in regulating emotional behavior, 43 Hirschfeld, P., 315 Hirschi, T., 314 Hissel, S.C.E.M., 326 homotypic comorbidity, 462–463, 486–489 hormone(s) neurohypophysial, 155–156 Horwood, L.J., 50 Hounsfield, G.N., 404 HPA axis. see hypothalamic-pituitary-adrenal (HPA) axis HUD. see US Department of Housing and Urban Development (HUD) Hudley, C., 354 Hughes, J., 243 human aggression defined, 241 Hutchison, K.E., 46 Hyde, J., 248 Hyman, S.E., 42 hypothalamic-pituitary-adrenal (HPA) axis child maltreatment and, 273 comorbidity related to, 448 I Iacono, W.G., 52–53, 464 IBR. see Infant Behavior Record (IBR) ICAP theory. see integrated cognitive antisocial potential (ICAP) theory ICD-10. see International Classification of Diseases, version 10 (ICD-10) IGT. see Iowa Gambling Task (IGT) illicit drug exposure ADHD related to, 419f, 421t, 422–423, 424t, 425t ODD and CD related to, 424t, 425t, 427 SUDs related to, 424t, 425t, 429 illicit drug use dispositional liability for, 39 immaturity developmental, 254 imprisonment. see also incarceration aims of, 323 effects of, 325 impulse control affective decision-making in, 207–208 controversies related to, 211–212

developmental models of problems with, 210–211 fMRI in, 211 motor/response inhibition in, 208–209 in psychopathy, 229 selective attention/inference control and working memory in, 209–210 temporal/delay discounting in, 208 TMS studies in, 213 impulsivity ACC mechanisms of, 201–219 brain injury and, 203–204 brain neuroimaging technologies advances, 204 conceptualization changes related to, 204–205 as continuously distributed, highly heritable trait, 490, 492 control of, 207–212, 229. see also impulse control controversies related to, 211–212 current state of the science, 207–210 defined, 185, 201–202 described, 202 developmental considerations, 210–211 DSM-5 on, 201 early brain lesions and, 203–204 fMRI in, 211 future directions in, 212–213, 212f genetic studies of, 133–134 historical context of, 203–205 ICD-10-CM on, 201 imaging findings, 206–207 introduction, 201–203, 203f links to traditional externalizing disorders, 205–207 longitudinal studies on, 213 low IQ and EF deficits and, 383 measurement changes, 204–205 neuropsychological case studies, 203–204 “nonplanning,” 202 performance/behavioral findings, 206 PFC mechanisms of, 201–219 reasons for, 202 research agenda, 212–213, 212f self-report questionnaire assessing, 205 temporal/delay discounting and, 205 through mesolimbic DA dysfunction, 492–493 TMS studies on, 208, 213 trait. see trait impulsivity unified, integrative approach to, 212–213, 212f Impulsivity, Venturesomeness, and Empathy (IVE) scale, 205 inactivation defined, 253–254 “incapacitative effect,” 323 incarceration. see also imprisonment aims of, 323 controversies related to, 335–336

as criminal learning environment, 327 current state of the science, 330–333 custodial vs. non-custodial sentences, 330 defiance theory, 328 delinquency development related to, 323–343 as deterrent, 326–327 developmental considerations, 333–335, 334f deviancy training during, 327 effects of, 324 experience of, 325–326 future directions in, 336–337 gang life during, 327 historical context of, 324–325 increase in, 323 intergenerational transmission related to, 329–330 introduction, 323–324 labeling theory, 327–328 no effect of, 329–330 offending effects of, 323–343 parental, 328–330. see also parental incarceration as rehabilitation, 327 reintegrative shaming theory, 328 reoffending and, 330–333 research agenda, 336–337 theoretical framework, 326–330 incentive salience brain circuits in, 44–46 incentive sensitization model, 45 individual-level factors in neighborhood-level theories of ASB and criminal behavior, 315–316 individual-level theories of ASB and criminal behavior integrated with neighborhood-level theories, 317–318 infancy cognitive and EF deficit development in, 384–385, 385f Infant Behavior Record (IBR), 172 inference control in impulse control, 209–210 inhibition response, 205 inhibitory control brain circuitry for, 41–44 defined, 376 reward/incentive circuitry and, 46 Insel, T., 80, 239 insensitivity reward, 470 Institute of Medicine, 255–256 integrated cognitive antisocial potential (ICAP) theory, 319–320 intelligence (IQ). see also cognition academic achievement and, 252 acquired neuropsychological variation and, 252 defined, 252

described, 252 low. see low intelligence (IQ) performance, 376, 377 verbal, 376, 377 internalizing disorders comorbidity among, 443–460. see also comorbidity(ies) neural dysfunction in, 446–448 International Classification of Diseases, version 10 (ICD-10) on comorbidity, 443 on impulsivity, 201 Iowa Adoptee Study, 274 Iowa Gambling Task (IGT), 205, 389 IQ. see intelligence (IQ) irritability/frustration defined, 171 temperament effects of, 175–176 Irwin, J., 325 Israel, A.C., 444 IVE scale. see Impulsivity, Venturesomeness, and Empathy (IVE) scale J Jablensky, A., 444 Jaffee, S.R., 93 Jencks, C., 314 Jennett, B., 403–404 Jennings, W.G., 318 Jensen, P., 453 John Hopkins University, 309 Johns, A., 463 Jones, A.P., 364 Jones, K., 351 Jones, S., 317, 319 Jonson, C.L., 328, 332 Journal of Neurotrauma Neurobehavioral Guidelines Working Group, 412 Joyce, P.R., 68 K Kagan, J., 171 Kahn, R.E., 362 Karoly, H.C., 46 Katz, L.F., 319 KEGG. see Kyoto Encyclopedia of Genes and Genomes (KEGG) Keiley, M.K., 451 Kendler, K.S., 51, 136, 224–225 Kernberg, P.F., 62 Kert, M., 366 Khalife, N., 254 KIAA0010 SNPs, 137 Killias, M., 336 Klebanov, P.K., 314 Klein, D.N., 445, 486 Kling, J.R., 319 Kochanska, G., 367 Koops, W., 352 Kramer, M.D., 51, 445 Krohn, M.D., 328

Krueger, R.F., 40, 49–51, 65, 82, 186, 212, 444 Kruer, 445 Kruttschnitt, C., 326 Kupersmidt, J.B., 315 Kvaale, E.P., 99 Kyoto Encyclopedia of Genes and Genomes (KEGG), 136 L labeling of children with incarcerated parents, 329 labeling theory incarceration-related, 327–328 Lahey, B.B., 255, 472 Lanctôt, N-., 332 Lansford, J.E., 273, 354, 355 Larson, C.L., 43 Laub, J.H., 332 Laurenceau, J.P., 297 law-abidingness intelligence and, 252 LCP ASB. see life-course persistent (LCP) antisocial behavior (ASB) lead ADHD related to, 27 lead exposure ADHD related to, 421t, 423, 424t, 425t ODD and CD related to, 424t, 425t, 428 learning social interaction, 288–299. see also social interaction learning (SIL) LeDoux, J.E., 41 Lee, E.J., 451 Leerkes, E.M., 178 Lemerise, E.A., 349 Lenzenweger, M.F., 70 Leventhal, T., 317, 319 Levine, S., 297 Levy, F., 445–447, 463 Lewis, D.O., 411 Lewis, E., 297 Lewis, M.D., 294 LHPA system. see limbic hypothalamic-pituitary-adrenal (LHPA) system Lieberman, J., 80 Liebling, A., 325 life-course persistent (LCP) antisocial behavior (ASB), 239 sex differences and, 241–242 life-course persistent offenders, 334 “liking,” 44 Lilienfeld, S.O., 46, 444 limbic hypothalamic-pituitary-adrenal (LHPA) axis activity/reactivity comorbidity related to, 471 limbic hypothalamic-pituitary-adrenal (LHPA) system coercive family processes and, 290 stress regulatory system, 296–297

Index

513

Lindheim, O., 297 Lindstrom, P., 315 Linehan, M.M., 64, 67 Linehan’s biosocial theory, 64 Loeber, R., 316, 333, 335 Loeffler, C.E., 331 “looking for trouble,” 53 Lösel, F., 336 low intelligence (IQ) ADHD and, 377 in adolescents, 385f, 388 ASPD and, 380 CD and, 377–378 in children, 385f, 386–388 comorbidity among externalizing disorders related to, 381 controversies related to, 388–390 current state of the science, 380–383 delinquency and, 378–379 developmental considerations, 383–388, 385f dimensional approach to, 381–383 in early adulthood, 385f, 388 externalizing behaviors related to, 375–400 future directions in, 390–391 historical context of, 375–377 impulsivity and, 383 introduction, 375 links to traditional externalizing disorders, 377–380 in males, 389 in preschoolers, 385f, 386 psychopathy and, 380 research agenda, 390–391 SUDs and, 379–380 low trait anxiety neural mechanisms of, 220–238. see also behavioral inhibition system (BIS); trait anxiety Ludwig, J., 315 Luntz, B.K., 273 Luo, X., 156 Luthar, S.S., 98 Lykken, D.T., 229–230 Lynam, D.R., 185, 314, 316 M MAF. see minor allele frequency (MAF) magnetic resonance imaging (MRI) functional. see functional magnetic resonance imaging (fMRI) in head injury assessment, 404 resting state functional connectivity. see resting state functional connectivity MRI (rs_fcMRI) Maikovich-Fong, A.K., 93 maladaptive parenting ADHD related to, 28 Malone, P.S., 355 maltreatment. see also child maltreatment child, 267–285. see also child maltreatment

514 Index

conceptualization of, 268–269 described, 267 emotional, 269 experience of, 267 MLE, 269 PFC in, 271 as risk factor for psychopathology, 93 subtypes of, 269 Manly, J.T., 269 Manni, M., 297 Manuck, S.B., 140–141 MAOA gene. see monoamine oxidase A (MAOA) gene marijuana exposure ADHD related to, 419f, 421t, 422–423, 424t, 425t ODD and CD related to, 424t, 425t, 427 SUDs related to, 424t, 425t, 429 Markon, E., 444 Martel, M.M., 244, 247, 253–255, 449 Maruna, S., 325 matching law, 305 Matthys, W., 228 “maturational gap,” 211 Mauritius, 176 Max, J.E., 406–409 Mayer, S.E., 314 MBP. see myelin-basic protein (MBP) McCartney, K., 450 McClure, E.B., 272 McClure, S.M., 45 McCrory, E.J., 277, 448 McDermott, J.M., 295 McGue, M., 52–53, 130–131 McMahon, R.J., 362 McNulty, T., 21, 47, 53, 91, 349 Meehl, P., 82 Meffert, H.A., 364 MeHg. see methylmercury (MeHg) Meier, M., 247 memory working, 209–210, 376 Mental Development Index from BSID-II, 423 mercury ADHD related to, 27 Mersky, J.P., 276 mesocortical dopamine (DA) system(s) coercive family processes and, 290–291, 294–295 prefrontal, 294–295 in self-regulation, 185 vmPFC system, 290–291 mesocortical dopamine (DA) system dysfunction high-risk behaviors and, 495–496 mesolimbic dopamine (DA) system(s), 47–48 acquired neuropsychological variation and, 254–255 coercive family processes and, 290, 297–298

motivational system, 297–298 in reward processing and reward-related behavior, 44 in self-regulation, 185 mesolimbic dopamine (DA) system dysfunction externalizing vulnerability related to, 487–488 high-risk behaviors and, 495–496 impulsivity conferred through, 492–493 Metcalfe, J., 389, 390 methadone treatment, 423 methylmercury (MeHg) ADHD related to, 423 methylphenidate in externalizing disorders management post-TBI, 412 in normalizing effects on abnormalities in P3 response associated with ADHD, 49 midbrain neural mechanisms of trait impulsivity, 184–200. see also trait impulsivity mild head injury defined, 407 Miller, E.K., 42, 43 Milner, P., 188 Minnesota Twin Family Study (MTES), 65, 131 Minnesota Twin Registry, 445 minor allele frequency (MAF), 126 Mischel, W., 316 Mitchell, D., 230 Mitchell, W., 389, 390 Miyake, A., 390 MLE maltreatment. see moral/legal/educational (MLE) maltreatment Moffitt, T.E., 95, 114, 210, 240, 242, 244, 334, 366, 378 molecular genetic studies acetylcholine in, 155 of aggregate genetic variability for externalizing disorders, 130–131 analytic strategies, 136–137 approaches to studying externalizing spectrum, 125–148 background, 126–127 bioinformatics in, 137 candidate gene studies, 128–129 CNVs in, 137–138 current directions in, 131–140 DA in, 150–152 drug metabolism genes in, 156 epigenetics in, 139–140 of externalizing spectrum, 149–169 5-HT, 152–153 GABA in, 154 gene- and pathway-based analysis in, 136–137 genes and gene systems identified by GWA studies, 156–158 genome-wide linkage studies, 127–128

genomic interrogration technologies, 137–140 genomic regions identified in linkage studies, 158 glutamate in, 154–155 GWA studies, 129–130 GxE interaction in, 135–136 of individual loci related to externalizing disorders, 127–130 introduction, 125, 149–150 monoamine degradation in, 153–154 monoamine(s) in, 150–154 neurohypophysial hormones in, 155–156 next-generation sequencing and rate variants in, 138–139 phenotyping strategies, 131–135. see also phenotyping strategies SNPs in, 150 Monahan, K.C., 335 monoamine(s) in molecular genetic studies of externalizing spectrum, 150–154 monoamine degradation in molecular genetic studies of externalizing spectrum, 153–154 monoamine oxidase A (MAOA) gene aggression and, 410 ASB related to, 68 GxE correlation related to, 245, 247 in molecular genetic studies of externalizing spectrum, 153 VNTR polymorphism in, 273 Monshouwer, H.J., 352 Montague, P.R., 42, 45 mood, emotion, and behavior dysregulation prefrontal brain dysfunction and, 494–495 Moore, G.A., 365 moral/legal/educational (MLE) maltreatment, 269 moral socialization factors in, 176 Moran, P., 368 Morgan, A.B., 46 mosaicism defined, 253 motor/response inhibition in impulse control, 208–209 Moving to Opportunity for Fair Housing Demonstration (MTO) program, 318–319 MPQ. see Multidimensional Personality Questionnaire (MPQ) MRI. see magnetic resonance imaging (MRI) MTA study. see NIMH Collaborative Multisite Multimodal Treatment Study of Children with ADHD (MTA study) MTES. see Minnesota Twin Family Study (MTES)

MTFC. see Multi-dimensional Therapeutic Foster Care (MTFC) MTO program. see Moving to Opportunity for Fair Housing Demonstration (MTO) program Multi-dimensional Therapeutic Foster Care (MTFC) SIL, 295 Multidimensional Personality Questionnaire (MPQ) Tellegen’s, 39 multifinality DP and, 92 Munoz, L.C., 366 Murray-Close, D., 275 Murray, J., 330, 332–333 Musser, E.D., 368 myelin-basic protein (MBP) in externalizing disorders management post-TBI, 411 Myers, J., 51, 224–225 N Nagin, D.S., 331 narrow-band syndromes with ADHD, 444 National Center for Biotechnology Information database of genetic variation, 126 National Epidemiological Survey on Alcohol and Related Conditions (NESARC), 445 National Health and Nutrition Examination Survey, 426 National Institute of Alcohol Abuse and Alcoholism, 431 National Institute of Mental Health (NIMH), 80, 239 RDoC project of, 80, 119, 127, 133, 255, 443, 445, 447, 474 National Institutes of Health, 255 National Longitudinal Study of Adolescent Health, 317 National Longitudinal Study of Youth, 317 ND-PAE. see neurodevelopmental disorder due to prenatal alcohol exposure (ND-PAE) Neale, M.C., 224–225 need principle, 332 negative affectivity defined, 171 negative emotionality temperament effects of, 175–176 negative reactivity temperament effects of, 175 negative valence and arousal/regulation abnormalities, 119 negativity error-related, 47 neglect physical, 269 neighborhood-level theories of ASB and criminal behavior, 314–318

individual-level factors, 315–316 integrated with individual-level theories, 317–318 neighborhood risk ASB related to, 313–322. see also antisocial behavior (ASB); neighborhood-level theories of ASB and criminal behavior criminal behavior and, 313–322. see also criminal behavior; neighborhood-level theories of ASB and criminal behavior future research directions in, 319–320 in higher SES neighborhoods, 316 introduction, 313–318 in low-SES neighborhoods, 315–316 policy implications, 318–319 in public housing units, 315 NEO-PI-R, 83 NESARC. see National Epidemiological Survey on Alcohol and Related Conditions (NESARC) neural dysfunction externalizing and internalizing disorders and, 446–448 neural systems in self-regulation, 185 neurobiological correlates of comorbidity, 469–470 neurobiological systems coercive family processes and, 290–291 neurocognitive measures in phenotyping strategies, 134–135 neurodevelopmental disorder due to prenatal alcohol exposure (ND-PAE), 417 neuroendocrine functioning child maltreatment effects of, 274–276 neurohypophysial hormones in molecular genetic studies of externalizing spectrum, 155–156 Neuropsychiatric Rating Schedule, 409 neuropsychological variation acquired, 251–255. see also acquired neuropsychological variation Neville, H.J., 295 “new Jim Crow,” 336 Newman, J.P., 230, 351 next-generation sequencing in molecular genetic studies, 138–139 nicotine ADHD related to, 419f, 420, 421t, 424t, 425t Nicotine Addiction Genetics study, 155 Nigg, J.T., 53, 54, 176, 244, 245, 250, 368, 449 NIH Roadmap Epigenomics Mapping Consortium, 139 NIMH. see National Institute of Mental Health (NIMH)

Index

515

NIMH Collaborative Multisite Multimodal Treatment Study of Children with ADHD (MTA study), 453 “nonplanning” impulsivity, 202 nonsuicidal self-injury (NSSI), 63 age-related, 68 developmental considerations for, 68–69 studies of, 63 Norm Topic Code, 305 Norris, A.L., 445, 486 NSSI. see nonsuicidal self-injury (NSSI) O O’Connor, T.G., 493 ODD. see oppositional defiant disorder (ODD) Odgers, C., 242 OFC. see orbitofrontal cortex (OFC) offender(s) adolescent-limited, 334 life-course persistent, 334 types of, 334 offending incarceration and, 323–343. see also incarceration Offer, D., 63 Ogilvie, J.M., 46 Ohlin, L.E., 315 Olds, J., 188 ontogenic process perspective on externalizing spectrum disorders, 185 ontogenic processes model of externalizing psychopathology, 485–501 comorbidities and continuities in externalizing behavior, 486 developmental theory and externalizing continuities, 485–486 emotion dysregulation related to operant reinforcement, 494 future directions in, 496 heterotypic comorbidity and, 486, 489–496, 489f, 491f heterotypic continuity and, 489–496, 489f, 491f high-risk behaviors exacerbating mesolimbic, mesocortical, and amygdalar dysfunction, 495–496 homotypic comorbidity and, 486–489 impulsivity, 490–493 introduction, 485 mood, emotion, and behavior dysregulation, 494–495 prefrontal brain dysfunction effects, 494–495 prenatal adversity–related vulnerability, 492 severe externalizing behavior occurring through bidirectional transactions between individual vulnerabilities and environments across time, 493

516 Index

operant reinforcement emotion dysregulation related to, 494 oppositional defiant disorder (ODD) alcohol exposure and, 424–428, 424t, 425t attribution biases and, 350–351 behavioral genetic studies of, 111 characteristics of, 111 child maltreatment and, 273 cigarette byproducts exposure and, 424t, 425t, 426 comorbidity with, 64, 112, 463 criteria for, 4, 10–11 current state of the science, 10–11 developmental considerations, 13 DP related to, 91 DSM on, 4–5 emotionality and, 175 environmental toxins exposure and, 424t, 425t, 428 future directions in, 15 historical context of, 4–5 illicit drug exposure and, 424t, 425t, 427 lead exposure and, 424t, 425t, 428 low effortful control and, 176–177 marijuana exposure and, 424t, 425t, 427 modification to diagnostic criteria for, 14 pharmaceutical agents exposure and, 424t, 425t, 426–427 post-TBI, 408 research agenda, 15 SIJ, BPD, and externalizing disorders linked with, 64 symptoms of, 5, 10–11 temperament resulting in, 175 teratogen exposure and, 424–428, 424t, 425t tobacco exposure and, 424t, 425t, 426 Orban, L., 354 orbitofrontal cortex (OFC), 228 orbitomedial PFC function of, 43 O’Reilly, R.C., 43 Ormel, J., 82 Orobio de Castro, B., 352 Ostrov, J., 352 Otis, M.D., 273 Otto, J., 96 Ouellet-Morin, I., 275 Overt Aggression Scale, 410 Owen, B., 325 OXTR gene. see oxytocin receptor (OXTR) gene oxytocin receptor (OXTR) gene, 155 P P300 (P3b) amplitude reduction disinhibitory liability related to, 48–49 Pang, K., 242 Pardini, M., 410

Parent-Child Interaction Therapy, 412 Parental Account of Childhood Symptoms, 453 parental incarceration, 328–330 current state of the science, 332–333 outcomes for children during and after, 328–330 positive effects of, 330 undesirable effects of, 329 parent–child conflict in ADHD–ODD–CD comorbidity, 112 parenting style ADHD related to, 28 integrating neuroscience into, 292–293, 293f in SIJ, BPD, and externalizing psychopathology, 67–69, 71 SIL, 288–299 Patrick, C.J., 48, 50, 51, 54, 464 Patterson, G.R., 210, 366, 449–452 Patton, J.H., 185 PCBs. see polychlorinated biophenyls (PCBs) PCL-R. see Psychopathy Checklist-Revised (PCL-R) peer(s) developmental perspective on, 303–305 peer interaction dynamics, 303 peer socialization comorbidity and, 449–451 Peloso, E., 297 penitentiaries, 324 Perez, S.M., 386 performance IQ (PIQ), 376, 377 personality(ies) approach–avoidance–conflict in, 221–222, 222f borderline, 62 externalizing spectrum of, 79–89. see also externalizing spectrum of personality and psychopathology hierarchical structure of, 83 molecular genetic studies of, 125–148 RST of, 221 TBI effects on, 409–411 Personality Inventory for DSM-5 (PID-5), 83 personality measures in phenotyping strategies, 133–134 personality predisposition BIS and, 224–225 FFFS and, 224–225 pesticides ADHD related to, 27 PET. see positron emission tomography (PET) Petit, G.S., 355 PFC. see prefrontal cortex (PFC) PGC. see Psychiatric Genomics Consortium (PGC) pharmaceutical agents ADHD related to, 419f, 420–422, 421t

classes of, 6 ODD and CD related to, 424t, 425t, 426–427 PHDCN. see Project on Human Development in Chicago Neighborhoods (PHDCN) phenotype(s) endophenotypes/intermediate, 132–133 phenotyping strategies behavioral measures, 134–1351 cross-disorder approaches, 131–132 data reduction methods, 134 endophenotypes/intermediate phenotypes, 132–133 in molecular genetic studies, 131–135 neurocognitive and behavioral measures, 134–135 personality measures, 133–134 phenylketonuria (PKU) ADHD and, 225–227 externalizing behavior related to, 94 Phoenix, 240 physical aggression, 352–353 sex differences and, 241 physical medicine and rehabilitation (PM&R) in externalizing disorders management post-TBI, 413 physical neglect (PN), 269 PIB. see positive illusory bias (PIB) PID-5. see Personality Inventory for DSM-5 (PID-5) Pihl, R.O., 382 Pilkonis, P.A., 70 Pinker, S., 241 PIQ. see performance IQ (PIQ) Piquero, A.R., 317 Pittsburgh Youth Study, 316, 333, 382 Pittsburgh Youth Survey data, 316 PKU. see phenylketonuria (PKU) placental vulnerability acquired neuropsychological variation and, 254 play R&T, 248–249 sex differences effects on, 248–249 pleasure/hedonism pathway, 50 Plomin, R., 171, 175 PM&R. see physical medicine and rehabilitation (PM&R) PN. see physical neglect (PN) PNC. see Project Northland Chicago (PNC) polychlorinated biophenyls (PCBs) ADHD related to, 423 polymorphism(s) COMT. see catechol-O-methyltransferase (COMT) polymorphisms polythetic diagnosis defined, 79 Ponzi, C., 53 positive emotionality

defined, 172 positive illusory bias (PIB) in ADHD, 350 positron emission tomography (PET) in head injury assessment, 404 posttraumatic aggression directed against others, 411 Pratt, T.C., 317, 319 predation/antagonism pathway, 50 “prediction error,” 44–45 prefrontal brain dysfunction mood, emotion, and behavior dysregulation related to, 494–495 prefrontal cortex (PFC) in affective-motivational processing, 42 anatomical location of, 203f in child maltreatment, 271 cognitive control model of, 42–43 described, 42 in disinhibitory psychopathology, 47–48 5-HT associated with, 209–210 in impulsivity, 201–219. see also impulsivity normative development of, 210 orbitomedial, 43 in trait impulsivity, 187 pregnancy alcohol abstinence during, 431 teratogen exposure during, 416–439. see also teratogen exposure prenatal adversity vulnerability conferred through allostatic and epigenetic processes related to, 492 preoccupation/anticipation stage in addiction cycle, 46 preschooler(s) low IQ and EF deficits in, 385f, 386 Prescott, C.A., 51, 224–225 Price, J.M., 351 primary psychopathy described, 229 prison(s) aims of, 323 deviancy training in, 327 gangs in, 327 prisoner(s) incarceration effects on, 324 Project Northland Chicago (PNC), 318 Project on Human Development in Chicago Neighborhoods (PHDCN), 317 promiscuity deviant peer clustering and, 304 Propper, C.B., 365 protein(s) cell adhesion, 157 “PsV sign,” 376 Psychiatric Diagnostic Screening Questionnaire, 445 Psychiatric Genomics Consortium (PGC)

Cross-Disorders Working Group of, 131–132 psychological treatments in externalizing disorders management post-TBI, 412 psychopath(s) “successful,” 54 Psychopathic Personality Inventory impulsive-antisociality dimension of, 48 psychopathology developmental. see developmental psychopathology (DP) disinhibitory, 47–52 externalizing. see externalizing psychopathology externalizing spectrum of, 79–89. see also externalizing spectrum of personality and psychopathology hierarchical structure of, 83 intent structure of, 487 molecular genetic studies of, 125–148 temperament and, 174–175 temperament effects of, 177 psychopathy ASPD and, 112–113 attribution biases and, 353 BIS in, 228–231, 229f characteristics of, 228 described, 228 FFFS–BAS conflict in, 229–231 impulse control in, 229 low IQ and EF deficits and, 380 neurology of, 231–233, 232t primary, 229 secondary, 229 Psychopathy Checklist-Revised (PCL-R), 230 psychosocial treatment in externalizing disorders management post-TBI, 412 public housing units criminal behaviors and ASB risks associated with, 315 pure disorders comorbidity vs., 451–452 Putnam, K.M., 43Q “quasi-inmates,” 324 Quay, H.C., 227, 240 R Rancho Los Amigos Scale, 404 Rapee, R.M., 454 Raphael, S., 324 rare variants in molecular genetic studies, 138–139 Ray, J.V., 362 RDoC. see Research Domain Criteria (RDoC) reactive aggression, 352

Index

517

reactive control defined, 172 reactivity defined, 171–172 negative, 175 Reagan, R., Pres., 325 rehabilitation incarceration as, 327 reinforcement sensitivity theory (RST) of personality, 221 reintegrative shaming theory, 328 reoffending current state of the science, 330–333 incarceration and, 323–343. see also incarceration Research Domain Criteria (RDoC) in behavioral genetic studies, 119 of NIMH, 474 on teratogen exposure, 429–430 Research Domain Criteria (RDoC) project of NIMH, 80, 119, 127, 133, 255, 443, 445, 447 resilience defined, 98 to DP, 98–99 response inhibition, 205 responsivity principle, 332 resting state functional connectivity MRI (rs_fcMRI), 24, 25 restless legs syndrome ADHD and, 49 retinoid ADHD related to, 424 Revised Child Manifest Anxiety Scale, 453 reward brain circuits in, 44–46 reward/incentive circuitry inhibitory control and, 46 reward insensitivity comorbidity and, 470 Reynolds, A.J., 276 RFAB. see Twin Study of Risk Factors for Antisocial Behavior (RFAB) Rhee, S.H., 114 Richer, F., 208 Rieger, M., 208 Riggins-Caspers, K.M., 178 risk principle, 332 Robbins, T.W., 208 Robins, L.N., 480 Robinson, T.E., 42, 45, 50–51 Rochester Youth Development Study, 276 Rochester Youth Survey, 328 Roelofs, J., 450 Rogeness, G.A., 272 Rogosch, F.A., 275 Rosario, M., 277 Rosenbaum, J.E., 318 Rothbart, M.K., 171, 256n Rothbart model, 244 rough and tumble (R&T) play, 248–249

518 Index

Rowe, R., 362 Rozen, S., 243 rs_fcMRI. see resting state functional connectivity MRI (rs_fcMRI) RST. see reinforcement sensitivity theory (RST) R&T play. see rough and tumble (R&T) play Rubia, K., 193, 447–448, 494 Rutter, M., 91, 242–244, 256n, 462 S SA. see sexual abuse (SA) Sabley, M.H., 349 Sagvolden, T., 188 Salekin, R., 370 Salmon, K., 367 Salzinger, S., 277 SAMHSA. see Substance Abuse and Mental Health Services Administration (SAMHSA) Sampson, R.J., 315, 317, 319, 332 schema(s), 348 Schermerhorn, A.C., 178 Schneider, A.L., 330 Schultz, W., 45 SCID-II. see Structured Clinical Interview for DSM-IV Personality Disorders (SCID-II) Scott, E.S., 334 script(s), 348 secondary psychopathy described, 229 Sekol, I., 332–333 selective attention defined, 376 in impulse control, 209–210 self-control causes of problems in, 360 sex differences effects on, 246 self-inflicted injury (SIJ) age-related, 68 ASPD with, 64 behavioral genetics and family studies in, 65–66 biological vulnerabilities, 62, 65–66 BPD with, 61–78 comorbidity patterns of, 61–62 controversies related to, 69–70 current state of the science, 64–68 DA and, 66 described, 62–63 developmental considerations for, 68–69 diverse forms of, 63 environmental risk factors for, 62, 66–68 externalizing spectrum of, 61–78 5-HT in, 66 fMRI studies in, 71n future directions in, 71 historical context of, 63 introduction, 61–62

links to traditional externalizing disorders, 63–64 nonsuicidal. see nonsuicidal self-inflicted injury (NSSI) research agenda, 70–71 SUDs with, 64 self-monitoring in externalizing disorders management post-TBI, 412 self-regulation defined, 184 neural systems in, 185 temperament and, 245 self-serving bias in ADHD, 350 Semi-Parametric Group-Based Methodology (SPGM), 276–277 Sensation Seeking Scale, 205 sensitivity sensory, 172 sensory sensitivity defined, 172 septohippocampal system in self-regulation, 185 Séquin, J.R., 382 serotonergic anxiolytics personality effects of, 224 serotonin (5-HT) in molecular genetic studies of externalizing spectrum, 152–153 PFC and, 209–210 in SIJ, BPD, and externalizing psychopathology, 66 Serpell, Z.N., 349 SES. see socioeconomic status (SES) sex differences acquired neuropsychological variation related to, 251–255. see also acquired neuropsychological variation; attribution bias(es) activity level effects of, 248–249 in ADHD, 246–247 child maltreatment–related, 276 comorbidity and, 445 control effects of, 246 criteria for inferring that behavioral difference is linked to, 243–244, 244f in effortful control, 247–248 empathy effects of, 250–251 in externalizing behavior, 239–263 fear/trait anxiety effects of, 249–250 gender differences vs., 256n historical context of, 240 in human aggression, 241 introduction, 239 LCP ASB related to, 241–242 links to traditional external disorders, 242 low IQ and EF deficits related to, 389 neuropsychological variation related to, 244–246, 245f

in proximate effect, 244, 244f research agenda, 255–256 temperament-related effects of, 174, 244–246, 245f in trait impulsivity, 246–247 in violence, 241 X and Y chromosomes and, 243 sexual abuse (SA), 269 sexual promiscuity deviant peer clustering and, 304 Shannon, T., 325–326 Shaw, P., 192 Sheese, B.E., 178 Sherman, L.W., 328 Shiner, R., 174 Short, F., Jr., 307 Shults, J., 275 Shum, D.H.K., 46 SIJ. see self-inflicted injury (SIJ) SIL. see social interaction learning (SIL) Silva, P., 242 Simcha-Fagan, O., 314–315 Simonoff, E., 111 Sinclair, S., 364 single nucleotide polymorphisms (SNPs), 126 KIAA0010, 137 in molecular genetic studies, 150 single nucleotide variant (SNV), 126 Sinopoli, K.J., 407 SIP model. see social-information processing (SIP) model situational action theory, 320 Skeem, J., 370 Slaby, R.G., 351, 353 sleep disturbances ADHD and, 49 “slow-to-warm-up” temperament, 171 Smith, C.A., 276 SNAP-IV rating scale, 453 Snodgrass, G.M., 331 SNPs. see single nucleotide polymorphisms (SNPs) SNV. see single nucleotide variant (SNV) Snyder, J., 304 sociability defined, 171 social experience brain systems and, 291–292 social-information processing (SIP) model attribution biases from, 347–349 emotion and, 349 processes in, 348 social interaction learning (SIL), 288–299 Social Interaction Learning (SIL) Multi-dimensional Therapeutic Foster Care (MTFC), 295 social interaction learning (SIL) parenting, 288–299 effects on child ASB, 293–294 integrating neuroscience into, 292–293, 293f

social interaction learning (SIL) parenting interventions coercion theory and, 298–299 social interaction learning (SIL) theory, 288 social strain parental incarceration and, 329 socialization ADHD–related behavior related to, 29 moral, 176 peer, 449–451 socialization mechanisms of externalizing behaviors, 265–343 socialization skills ADHD related to, 29 Society of Criminology, 307 socioeconomic status (SES) as factor in CD post-TBI, 409 as factor in criminal and ASB, 313–322. see also antisocial behavior (ASB); criminal behavior as factor in deviant peer clustering, 304 sociopathy in ASPD, 7–8 Somech, L.Y., 370 Sonuga-Barke, E.J.S., 23, 231 specific deterrence defined, 323 “spectrum association,” 174 speech and language problems ADHD related to, 28 Speltz, M.L., 351 SPGM. see Semi-Parametric Group-Based Methodology (SPGM) Sroufe, L.A., 91, 485, 486 SSRT. see stop signal reaction time (SSRT) Stanford, M.S., 185 Stanislow, C.A., 70 Statrín, H., 366 Steinberg, E., 105 Steinberg, L., 334, 335 Stepp, S.D., 64, 67–69, 71 Stevenson, J., 177–178 Stewart, A.L., 46 stigmatization of children with incarcerated parents, 329 Stohr, M.K., 336 Stone, E.C., 354 Stoolmiller, M., 297, 452 stop signal reaction time (SSRT), 226 Stop Signal Tasks, 206, 208, 233 strain parental incarceration and, 329 STREGA studies. see STrengthening REporting of Genetic Association (STREGA) studies STrengthening REporting of Genetic Association (STREGA) studies, 141 stress parental incarceration–related, 329 Ströhle, A., 193

Structured Clinical Interview for DSM-IV Personality Disorders (SCID-II), 411 substance abuse and dependence. see substance use disorders (SUDs) Substance Abuse and Mental Health Services Administration (SAMHSA), 418 substance use disorders (SUDs) alcohol exposure and, 424t, 425t, 429 behavioral genetic studies of, 113 brain areas involved in, 46–52 brain circuitry for inhibitory control and, 41–44 child maltreatment and, 272 classifications of, 7 commonalities vs. distinctions among externalizing outcomes of, 38 comorbidity with ASPD, 14 criteria for, 7, 11–12 current state of the science, 11–12 developmental considerations, 14 diagnostic criteria modification for, 14–15 disinhibitory liabilities and, 46–54. see also disinhibitory liability(ies) dispositional liability for, 39–40 dispositional vulnerability for, 39–40 DP related to, 91 DSM on, 6–7, 11–12 ESI in, 40–41, 40f as externalizing outcomes, 38–60 externalizing spectrum model of, 40–41, 40f future directions in, 16 heritable factors for, 39 historical context of, 6–7 illicit drug exposure and, 424t, 425t, 429 interplay of inhibitory control and reward/incentive circuitry in, 46 low IQ and EF deficits in, 379–380 neurobiological systems relevant to train disinhibition and, 41–46. see also specific sites and systems personality correlates in, 39 research agenda, 16, 52–53 SIJ, BPD, and externalizing disorders linked with, 64 teratogen exposure and, 424t, 425t, 428–429 trait impulsivity in, 191–192 unresolved questions related to, 52–53 Substance Use Profile Scale (SURPS), 205 “successful” psychopaths, 54 SUDs. see substance use disorders (SUDs) suicide, 63 Sukhodolsky, D.G., 354 Sullivan, C.J., 317, 319 surgency defined, 171

Index

519

SURPS. see Substance Use Profile Scale (SURPS) Susser, K., 444 sustained attention defined, 376 Sykes, G.M., 325 T Tabarrok, A., 331, 332 Tallent, R., 355 Tannock, R., 453 TBI. see traumatic brain injury (TBI) Teasdale, G., 403–404 Tellegen, A., 39 temperament components of, 171–172 defined, 171 “difficult,” 171, 175 “easy,” 171 elements of, 170–171 environmental influences on, 173 externalizing behavior related to, 170–183 future directions in, 179 general concepts in, 171–173, 173f genetic influences on, 173–174 GxE influences on, 173, 177–179, 245 as how of behavior, 170 introduction, 170–171 low effortful control effects on, 176–177 low fearfulness effects on, 176 models of adult, 172–173 models of child, 171–172, 173f negative emotionality effects on, 175–176 negative reactivity effects on, 175 pathways to externalizing behaviors associated with, 175–177 psychopathology effects on, 177 self-regulatory aspects of, 245 sex differences in, 174, 244–246, 245f “slow-to-warm-up,” 171 structure of, 171–172, 173f vulnerability to psychopathology, 174–175 temperamental traits, 244–246, 245f defined, 170 temporal/delay discounting, 205 in impulse control, 208 teratogen(s) alcohol, 418–420, 419f, 421t, 424–429, 424t, 425t cigarette byproducts, 419f, 420, 421t, 424t, 425t, 426, 428, 429 defined, 416 described, 416 environmental toxins, 423–424, 424t, 425t fetal exposure to, 416–417 illicit drugs, 419f, 421t, 422–423, 424t, 425t, 427, 429 lead, 421t, 423, 424t, 425t, 428

520 Index

marijuana, 419f, 421t, 422–423, 424t, 425t, 427, 429 pharmaceutical agents, 419f, 420–422, 421t, 424t, 425t, 426–427 Surgeon General warnings about, 416–417 tobacco, 419f, 420, 421t, 424t, 425t, 426, 428, 429 teratogen exposure ADHD and, 418–424, 419f, 421t, 424t, 425t ASPD and, 424t, 425t, 428 CD and, 424–428, 424t, 425t controversies related to, 430–431 current state of the science, 429–430 developmental considerations, 430 externalizing behavior related to, 416–439 future directions in, 431–432 historical context of, 417 links between externalizing disorders and, 418–429. see also specific disorders ODD and, 424–428, 424t, 425t research agenda, 431–432 SUDs and, 424t, 425t, 428–429 Terracciano, A., 134 Teratology behavioral, 416, 417 Thomas, A., 171, 175 Thornton, L.C., 362 Tier Social Stress Test, 275 TMS studies. see transcranial magnetic stimulation (TMS) studies tobacco exposure ADHD related to, 419f, 420, 421t, 424t, 425t ASPD related to, 424t, 425t, 428 ODD and CD related to, 424t, 425t, 426 SUDs related to, 424t, 425t, 429 Tonry, M., 315 top-down dysfunctions, 203 top-down inhibitory control weak, 470–471 top-down processing PFC in, 42 top-down system of executive functions and cognitive processes in ADHD, 22–23 Topitzes, J., 276 toxin(s) ADHD related to, 27 environmental, 423–424, 424t, 425t. see also environmental toxins trait(s) CU, 50, 251, 360–374. see also callous-unemotional (CU) traits dispositional, 471–473 temperamental, 170, 244–246, 245f trait anxiety introduction, 220

low, 220–238. see also behavioral inhibition system (BIS) sex differences effects on, 249–250 trait disinhibition dispositional liability vs., 51 in SUDs, 41–46. see also specific sites and systems trait impulsivity, 487 in ADHD, 189–190, 246–247 animal studies and translational models, 188 in ASPD, 190–191 behavioral genetics of, 186 biological basis of, 246–247 BPD and, 64 in CD, 190 controversies related to, 193–194 current state of the science, 187–189 DA projections in, 186–187 defined, 184–186 developmental considerations, 192–193 in externalizing spectrum disorders, 64 introduction, 184–185 links to traditional externalizing disorders, 189–192 midbrain neural mechanisms of, 184–200 neuroimaging studies of, 189 research agenda, 193–194 sex differences effects on, 246–247 in SUDs, 191–192 vulnerability for, 246 Tranel, D., 43 transcranial magnetic stimulation (TMS) studies in impulse control, 213 in impulsivity, 208, 213 “trapped in a maturity gap,” 334 traumatic brain injury (TBI) ADHD after, 406–408. see also attention-deficit/hyperactivity disorder (ADHD) post-TBI aggression after, 410–411 ASPD after, 411 CD after, 408–409 DBDs after, 408–409 externalizing behavior related to, 403–415. see also head injury externalizing disorders after, 405–409. see also externalizing disorders post-TBI future directions in, 412–413 historical context of, 403–405 imaging of, 404–405 introduction, 403 ODD after, 408 personality change following, 409–411 prevalence of, 403 psychiatric sequelae of, 403 Tremblay, L.K., 447 Tremblay, R.E., 241, 382 Trickett, P.K., 275

Tridimensional Personality Questionnaire, 205 true comorbidity, 462 Trull, T.J., 64 twin studies. see also specific studies ADHD–related, 110 behavioral genetic, 108, 109 Twin Study of Risk Factors for Antisocial Behavior (RFAB) at USC, 111 U University of Southern California (USC) Twin Study of RFAB at, 111 UPPS Impulsive Behavior Scale, 205 U.S. Supreme Court, 334–335 US Department of Housing and Urban Development (HUD) MTO program of, 318–319 US Surgeon General on alcohol abstinence during pregnancy, 431 V Vaidyanathan, U., 464 van Aken, M.A., 451 Van Der Werff, C., 331 van Gelder, J.-L., 319 Van Hulle, C., 248 van IJzendoorn, M.H., 178 Van Rybrock, G., 370 van Zalk, M., 366 Vance, A., 453 Vanyukov, M.M., 52, 53 variable number tandem repeat (VNTR) polymorphisms, 126 in MAOA gene, 273 Vazquez, D., 448 Vazsonyi, A.T., 317 Veerman, J.W., 352 ventral striatum (VS) in trait impulsivity, 186–187 ventral tegmental area (VTA), 47 in trait impulsivity, 186–187 verbal IQ (VIQ), 376, 377 Vertova, P., 331–332 VET Registry. see Vietnam Era Twin (VET) Registry

Viding, E., 363, 364 Vietnam Era Twin (VET) Registry, 113 Vineland testing, 407 violence defined, 241 sex differences and, 241 VIQ. see verbal IQ (VIQ) Virginia Twin Registry (VTR), 113 Virginia Twin Study of Adolescent Behavioral Development (VTSABD), 111 vmPFC system. see mesocortical dopamine (DA) prefrontal cortex (vmPFC) system VNTR polymorphism. see variable number tandem repeat (VNTR) polymorphism Volkow, N.D., 94–95 voltage-gated potassium channel genes GWA studies of, 157 Vrieze, S.I., 473 VS. see ventral striatum (VS) VTA. see ventral tegmental area (VTA) VTR. see Virginia Twin Registry (VTR) VTSABD. see Virginia Twin Study of Adolescent Behavioral Development (VTSABD) vulnerability(ies) for activity level effects, 248 biological, 103–263 cognitive, 345–400 defined, 247 for effortful control, 247 emotional, 345–400 empathy and, 250–251 fear/trait anxiety and, 250 prenatal adversity effects on, 492 for trait impulsivity, 246 “vulnerability association,” 174–175 W Waisbren, S.E., 226 Waldman, I.D., 114, 255, 256, 444 Wallace, J.F., 230 Walton, K., 82

“wanting,” 44 Waschbusch, D.A., 364 Watson, S.J., 50–51 weak inhibitory control comorbidity and, 470–471 Wechsler, D., 376, 377 Weiss, B., 444 Welsh, B.C., 336 Werboff on behavioral teratology, 416 White, S.F., 364 Whiteside, S.P., 185 Widom, C.S., 273 Wiebe, R.P., 317 Wikström, P.-O., 316–319, 320 Wilcox, P., 319, 320 Willoughby, M.T., 98, 365 Winik, D., 331 withdrawal/negative affect stage in addiction cycle, 46 Witkiewitz, K., 83 working memory defined, 376 in impulse control, 209–210 Wright, A.G., 82 X X chromosome in sex differences, 243 X-linked gene mutations acquired neuropsychological variation and, 253–254 Y Y chromosome in sex differences, 243 Yang, L., 136 Yeates, K.O., 406 Young, S.E., 47, 240 Z Zachar, P., 444 Zahn-Waxler, C., 251 Zaremba, A., 451 Zenther, M., 171 Zisner, A., 210–211, 486 Zuo, L., 137

Index

521