NEUROANATOMICAL CORRELATES OF CHILD BEHAVIOR CHECKLIST AGGRESSIVE BEHAVIOR SCORES IN TYPICALLY DEVELOPING CHILDREN
Simon Ducharme MD (260287392) Department of Psychiatry McGill University, Montréal
Manuscript‐Based Thesis submitted on September 2011 “A thesis submitted to McGill University in partial fulfillment of the requirements of the degree of M.Sc. in Psychiatry”
©Simon Ducharme, 2011
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TABLE OF CONTENTS
1. English Abstract……………………………………………………………..3 2. Résumé en français…………………………………………………………5 3. Acknowledgements………………………………………………………...7 4. Preface……………………………………………………………………….....10 4.1 Definitions………………………………………………………..10 4.2 Neurobiology of Aggression……………………………….13 4.3 Anterior Cingulate Cortex………………………………….16 4.4 Orbito‐Frontal Cortex………………………………………..16 5. Rationale of the Research………………………………………………18 5. Contributions of Authors………………………………………………..22 6. Manuscript…………………………………………………………………….23 6.1 Introduction………………………………………………………25 6.2 Methods…………………………………………………………….28 6.3 Results…………………………………………………………...….33 6.4 Discussion………………………………………………………….36 6.5 Acknowledgements and Disclosures……………………42 6.6 Tables and Figures……………………………………………...43 6.7 Supplemental Material………………………………………..48 7. Postface………………………………………………………………………….55 7.1 Integrative Models……………………………………………...60 8. Final Conclusions…………………………………………………………….65 9. Elements of Originality.……………………………………………………66 10. References……………………………………………………………………67
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ENGLISH ABSTRACT
Background: The anterior cingulate cortex (ACC), orbito‐frontal cortex (OFC) and basal ganglia have been implicated in the neurobiology of pathological aggression.
They are thought to be the structures involved in top‐down regulation of impulses from the limbic system. This study aimed at identifying neuroanatomical correlates of impulsive aggression in healthy children.
Methods: Data from 193 representative 6‐18 year‐old healthy children were obtained from the NIH MRI Study of Normal Brain Development after a blinded quality control (1). Cortical thickness and subcortical volumes were obtained with automated software. Aggression levels were measured with the Aggressive Behavior scale (AGG) of the Child Behavior Checklist (CBCL). AGG scores were regressed against cortical thickness and basal ganglia volumes using first and second‐order linear models while controlling for age, gender, scanner site and total brain volume.
‘Gender by AGG’ interactions were analyzed. Whole brain random field theory corrections for multiple comparisons were implemented.
Results: There were positive associations between bilateral striatal volumes and
AGG scores (right: r=0.238, p=0.001; left: r=0.188, p=0.01). A significant association was found with right ACC and subgenual ACC cortical thickness in a second‐order linear model (p<0.05, corrected). High AGG scores were associated with a relatively
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thin right ACC cortex. An ‘AGG by gender’ interaction trend was found in bilateral
OFC and ACC associations with AGG scores.
Conclusion: This study shows the existence of relationships between impulsive aggression in healthy children and the structure of the striatum and right ACC. It also suggests the existence of gender specific patterns of association in OFC/ACC grey matter. These results may guide research on oppositional‐defiant and conduct disorders.
KEYWORDS: Aggression, Cortical Thickness, Anterior Cingulate Cortex, Orbito‐
Frontal Cortex, Striatum, Magnetic Resonance Imaging (MRI)
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RÉSUMÉ EN FRANÇAIS
Contexte: Le cortex cingulaire antérieur (CCA), le cortex orbito‐frontal (COF) et les noyaux gris centraux ont été identifiés dans les recherches sur la neurobiologie de l’agressivité pathologique. Ces structures seraient impliquées dans la régulation
‘topdown’ des impulsions produites dans le système limbique. Le but de cette étude
était d’identifier les corrélations neuroanatomiques de l’agressivité impulsive chez les enfants en santé ayant un développement normal.
Méthodologie: Les données de 193 sujets de 6 à 18 ans ont été obtenues de l’étude
NIH MRI Study of Normal Brain Development après un contrôle visuel de qualité des données (1). L’épaisseur corticale et les volumes sous‐corticaux ont été obtenus avec des programmes automatisés. Le niveau d’agressivité a été mesuré avec l’échelle de comportements agressifs (AGG) obtenue du questionnaire Child Behavior Checklist.
Les scores de AGG ont été analysés en régression linéaire avec l’épaisseur corticale et le volume des noyaux gris centraux en utilisant des modèles de premier et de deuxième ordre, et en contrôlant pour les effets de l’âge, du sexe, du numéro de scanner et du volume cérébral total. L’interaction ‘AGG X sexe’ a aussi été analysée.
Une correction statistique de type random field theory pour comparaisons multiples a été appliquée aux résultats.
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Résultats: Une association positive a été trouvée entre les scores de AGG et le volume du striatum bilatéralement (droite: r=0.238, p=0.001; gauche: r=0.188, p=0.01). Une association significative était aussi présente entre AGG et l’épaisseur corticale du CCA droit et du CCA sous‐géniculé droit dans un modèle linéaire de deuxième ordre (p<0.05, corrigé). Les scores AGG élevés étaient associés à un CCA droit relativement mince. Une tendance d’association entre l’épaisseur corticale et l’interaction ‘AGG X sexe’ a aussi été trouvée dans le COF et le CCA bilatéralement.
Conclusion: Cette étude démontre l’existence d’une relation entre l’agressivité impulsive chez les enfants en santé et la structure anatomique du striatum, ainsi que du CCA droit. Elle suggère également l’existence de patterns d’associations spécifiques au sexe dans la matière grise du COF et du CCA. Ces résultats pourraient guider la recherche clinique sur le trouble oppositionnel avec provocation et le trouble des conduites.
MOTS CLÉS: Agressivité, Épaisseur corticale, Cortex cingulaire antérieur, Cortex orbito‐frontal, Striatum, Imagerie par résonance magnétique (IRM)
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ACKNOWLEDGEMENTS
The design of the NIH MRI Study of Normal Brain Development and data collection were performed by a multisite network called the Brain Development
Cooperative Group*, which includes the Montreal Neurological Institute as the Data
Coordinating Centre. The design of the current study on impulsive aggression and statistical analyses described in the manuscript were performed by the author of this thesis under the supervision of Dr. Sherif Karama and Dr. Alan C. Evans. The author acknowledges the help and support of other members of the McConnell Brain
Imaging Centre, including Dr. Claude Lepage (member of staff), Dr. D. Louis Collins
(member of staff), Samir Das (member of staff), Dr. Hooman Ganjavi (fellow student) and Dr. Tuong‐Vi Nguyen (fellow student). There were no research assistants involved in this study. The author also wants to thank US collaborators including Dr.
James J. Hudziak and Matthew D. Albaugh at the University of Vermont, in addition to Dr. Kelly N. Botteron at Washington University for their intellectual contribution to the design of the study and editorial review of the submitted manuscript. Finally, the author wants to recognize Dr. Sherif Karama and Dr. Alan C. Evans for their close supervision and precious advices at every steps of this masters’ project.
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*Brain Development Cooperative Group
Key personnel from the six pediatric study centers are as follows: Children’s
Hospital Medical Center of Cincinnati, Principal Investigator William S. Ball, M.D.,
Investigators Anna Weber Byars, Ph.D., Mark Schapiro, M.D., Wendy Bommer, R.N.,
April Carr, B.S., April German, B.A., Scott Dunn, R.T.; Children’s Hospital Boston,
Principal Investigator Michael J. Rivkin, M.D., Investigators Deborah Waber, Ph.D.,
Robert Mulkern, Ph.D., Sridhar Vajapeyam, Ph.D., Abigail Chiverton, B.A., Peter Davis,
B.S., Julie Koo, B.S., Jacki Marmor, M.A., Christine Mrakotsky, Ph.D., M.A., Richard
Robertson, M.D., Gloria McAnulty, Ph.D; University of Texas Health Science Center at Houston, Principal Investigators Michael E. Brandt, Ph.D., Jack M. Fletcher, Ph.D.,
Larry A. Kramer, M.D., Investigators Grace Yang, M.Ed., Cara McCormack, B.S.,
Kathleen M. Hebert, M.A., Hilda Volero, M.D.; Washington University in St. Louis,
Principal Investigators Kelly Botteron, M.D., Robert C. McKinstry, M.D., Ph.D.,
Investigators William Warren, Tomoyuki Nishino, M.S., C. Robert Almli, Ph.D.,
Richard Todd, Ph.D., M.D., John Constantino, M.D.; University of California Los
Angeles, Principal Investigator James T. McCracken, M.D., Investigators Jennifer
Levitt, M.D., Jeffrey Alger, Ph.D., Joseph O’Neil, Ph.D., Arthur Toga, Ph.D., Robert
Asarnow, Ph.D., David Fadale, B.A., Laura Heinichen, B.A., Cedric Ireland B.A.;
Children’s Hospital of Philadelphia, Principal Investigators Dah‐Jyuu Wang, Ph.D. and Edward Moss, Ph.D., Investigators Robert A. Zimmerman, M.D., and Research
Staff Brooke Bintliff, B.S., Ruth Bradford, Janice Newman, M.B.A. The Principal
Investigator of the data coordinating center at McGill University is Alan C. Evans,
Ph.D., Investigators Rozalia Arnaoutelis, B.S., G. Bruce Pike, Ph.D., D. Louis Collins,
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Ph.D., Gabriel Leonard, Ph.D., Tomas Paus, M.D., Alex Zijdenbos, Ph.D., and Research
Staff Samir Das, B.S., Vladimir Fonov, Ph.D., Luke Fu, B.S., Jonathan Harlap, Ilana
Leppert, B.E., Denise Milovan, M.A., Dario Vins, B.C.,, and at Georgetown University,
Thomas Zeffiro, M.D., Ph.D. and John Van Meter, Ph.D. Ph.D. Investigators at the
Neurostatistics Laboratory, Harvard University/McLean Hospital, Nicholas Lange,
Sc.D., and Michael P. Froimowitz, M.S., work with data coordinating center staff and all other team members on biostatistical study design and data analyses. The
Principal Investigator of the Clinical Coordinating Center at Washington University is Kelly Botteron, M.D., Investigators C. Robert Almli Ph.D., Cheryl Rainey, B.S., Stan
Henderson M.S., Tomoyuki Nishino, M.S., William Warren, Jennifer L. Edwards
M.SW., Diane Dubois R.N., Karla Smith, Tish Singer and Aaron A. Wilber, M.S. The
Principal Investigator of the Diffusion Tensor Processing Center at the National
Institutes of Health is Carlo Pierpaoli, MD, Ph.D., Investigators Peter J. Basser, Ph.D.,
Lin‐Ching Chang, Sc.D., Chen Guan Koay, Ph.D. and Lindsay Walker, M.S. The
Principal Collaborators at the National Institutes of Health are Lisa Freund, Ph.D.
(NICHD), Judith Rumsey, Ph.D. (NIMH), Lauren Baskir, Ph.D. (NIMH), Laurence
Stanford, PhD. (NIDA), Karen Sirocco, Ph.D. (NIDA) and from NINDS, Katrina Gwinn‐
Hardy, M.D., and Giovanna Spinella, M.D. The Principal Investigator of the
Spectroscopy Processing Center at the University of California Los Angeles is
James T. McCracken, M.D., Investigators Jeffry R. Alger, Ph.D., Jennifer Levitt, M.D.,
Joseph O'Neill, Ph.D.
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PREFACE
All humans experience aggressive thoughts, and aggressive actions are frequent in normal childhood development. Higher levels of aggression are often present in externalizing pediatric psychiatric disorders, which include oppositional‐ defiant disorder (ODD), conduct disorder (CD) and, to a lesser degree, attention‐ deficit/hyperactivity disorder (ADHD) (2, 3). Aggressive behaviors in teenagers and adults have major social consequences for both the legal and health systems (4, 5).
Therefore, aggression has been an important topic of research for many years, with increasing attempts recently to identify the neurobiological underpinnings (6).
Definitions
Aggression is operationally divided into impulsive/reactive and instrumental/proactive subtypes (6‐8). This study has chosen to focus on impulsive aggression, which can be defined as a lower threshold for activation of motoric aggressive responses to external stimuli, without adequate reflection or regard for the aversive consequences of the behavior (6). Instrumental aggression rather refers to premeditated, non‐emotional actions that are carried out in order to obtain specific goals. In that sense, instrumental aggression is often a feature of conduct and antisocial personality disorders (9). On the opposite, impulsive aggression acts usually occur in the context of intense negative affects, and are exhibited in response to perceived environmental threats. This type of impulsivity is present in externalizing disorders, but it has also been reported to be associated with
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internalizing symptoms such as anxiety and depressed mood (10‐14). Another nosological distinction found in the literature is between direct and indirect/relational aggression (15). Direct aggression refers to straightforward actions such as yelling, hitting, biting, etc. Indirect forms of aggressiveness include behaviors such as social rejection, talking behind someone’s back, badmouthing, etc.
Some studies have found direct aggression to be more prevalent in boys, and the reverse for indirect forms (15, 16).
Occasional aggression is frequently observed in normal childhood development, but it tends to be more frequent and/or severe in children with psychiatric disruptive disorders (also referred to as externalizing disorders) that include oppositional‐defiant disorder, conduct disorder and ADHD. ODD is characterized by a recurrent pattern of negativistic, defiant, disobedient, and hostile behavior toward authority figures that occurs more frequently than what is expected for the developmental age of a child (2). The behaviors usually start before age 8 and the prevalence has been estimated between 2 and 16% depending on studies (2, 17). Children with this disorder frequently lose their temper, argue with adults, blame others, etc. They are often angry and can be resentful and vindictive.
As such, they are prone to impulsive/reactive type of aggressive behaviors (18, 19).
However, they do not typically exhibit instrumental aggressions such as planned harmful actions against people/animal and destruction of properties, which are rather features of conduct disorder.
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The diagnosis of conduct disorder is based on repetitive and persistent patterns of behaviors in which basic rights of others and major age‐appropriate societal norms or rules are violated (2). DSM‐IV TR diagnostic criteria are divided into four categories: aggressive conduct that causes or threatens physical harm to other people or animals, nonaggressive conduct that causes property loss or damage, deceitfulness or theft, and finally serious violations of rules. Behaviors are pervasive in more than one environmental setting. There is a childhood‐onset subtype (prior to age 10) that tends to be persistent, includes callous‐unemotional traits and often evolves into adult antisocial personality disorder (20, 21). The adolescent‐onset (after age 10) subtype is usually accompanied by less aggressive behaviors, more normative peer relationships, and has a better long‐term prognosis
(21). By definition, children with CD often engage in instrumental aggressive actions to obtain various personal gains with little guilt or remorse. However, they also tend to be impulsive and prone to react with impulsive aggressions when feeling threatened (7).
Attention‐deficit/hyperactivity disorder is a clinical diagnosis based on the presence of either multiple symptoms of age‐inappropriate inattention (e.g. failure to pay attention to details, difficulty sustaining attention on tasks, loosing things, being forgetful or easily distracted, etc.) and/or multiple symptoms of hyperactivity‐ impulsivity (e.g. fidgeting, running/climbing excessively, blurting out answers, interrupting/intruding on others, etc.) (2, 22). Symptoms start before 7 years of age, and the estimated prevalence in school age children is between 3 and 7% (22, 23).
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As opposed to ODD and CD, aggressive behavior is not an intrinsic component of
ADHD. However, these children manifest more frequently impulsive aggressive behaviors than the average due to their impulsivity and hyperactivity (7, 22). In addition, ADHD is a highly significant factor in the neurobiological studies of
ODD/CD due to its high comorbidity with these two disorders (24).
Neurobiology of Aggression
In neurobiological terms, impulsive aggression is conceived as an inadequate
“top‐down” control of “bottom‐up” drives from the limbic system (6). Animal and human studies have demonstrated the role of deep structures, such as the hypothalamus, and limbic structures, including the amygdala, in the generation of aggressive impulses prior to cognitive appraisal of the threatening stimuli (25‐28).
These impulses are modulated by neural networks providing emotional regulation and cognitive appraisal of potential consequences (25). The prefrontal cortex (PFC), including medial PFC regions such as the anterior cingulate cortex (ACC) and orbito‐ frontal cortex (OFC) in particular, has been most consistently associated with impulse regulation in neurobiological studies of aggression (6, 7, 26, 28). This disequilibrium between strong impulses and inadequate cortical regulation leading to increased behavioral aggressions is often summarized under the expression cortico‐limbic network deficits (25, 28). Although not as extensively studied in aggression specifically, basal ganglia are now known to be involved in complex behavior regulation and multiple evidence point to a potential role in aggression
(29). This includes the frequent presence of aggressive and impulsive behaviors in
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patients suffering from neuropsychiatric pathologies associated with basal ganglia neurodegeneration (30). Further review on the link between basal ganglia and aggression is provided in the manuscript.
Most neuroimaging data related to the underlying neurobiology of aggression have been derived from studies of psychopathologies associated with increased levels of aggressive behaviors and individuals having exhibited more extreme criminal actions, studies that were usually done with adult subjects (6, 7). Early studies have identified decreased bilateral prefrontal activity and increased subcortical activity on positive emission tomography in impulsive murderers (31,
32), in addition to reduced overall grey matter (32‐34). More recent neuroimaging investigations have usually yielded findings in frontal regions involved in behavioral inhibition, punishment/reward anticipation, social cognition and affective regulation such as the dorsolateral prefrontal cortex (DLPFC), medial and lateral
OFC, ACC and ventromedial prefrontal cortex (vmPFC) (7, 9, 35, 36). Overall, functional imaging studies of emotional processing in antisocial and aggressive adults have found abnormalities in the amygdala, OFC and ACC (6, 7).
In children and adolescents, most studies have focused on conduct disorder, with a paucity of studies on ODD. In terms of structural neuroimaging, an early study found a 6% reduction in grey matter in children with CD, particularly in the left OFC
(37). Reduced mesial temporal, OFC and bilateral insular grey matter have also been identified in CD (37, 38). Correlations between prefrontal volumes and disruptive
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disorders are usually negative, one exception being the De Brito et al. (2009) study which found increased OFC and ACC grey matter, although this was in a non‐clinical cohort of children with callous‐unemotional traits (39). The only structural imaging study of correlates of aggressive behaviors in healthy children found a negative association between right ACC cortical thickness and aggressive and defiant behavior in boys (measured with the Pediatric Behavior Scale) (40).
A functional imaging study in boys with CD revealed decreased activation of the left dorsal ACC when viewing negative affective pictures (41). This finding was however not replicated by a closely designed study of Herpetz et al. (2008) (42).
Importantly, ACC activity has been shown to modulate subcortical amygdala responses to emotional cues (43‐45). The level of amygdala activation in response to negative cues has sometimes be found to be higher in CD (42, 46), but most of the time it is found to be lower when there is presence of callous‐unemotional traits (47,
48). Increased activation is usually seen in association with anxiety and depressive symptoms, which are more often associated with impulsive/reactive aggression than instrumental aggression (7, 41, 42). Other significant results include increased vmPFC activity during punished reversal errors in neuropsychological tasks, a potential marker of increased sensitivity to frustration when a reward is not obtained, thereby decreasing the threshold for aggressive responses (49). In addition, there are some shared inhibitory deficits between subjects with CD and
ADHD, a possible window into the high comorbidity rate between the two disorders
(7, 50).
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Anterior Cingulate Cortex
The ACC is located in the medial wall of each cerebral hemisphere, above and adjacent to the corpus callosum (51). It classically includes Brodmann areas 24, 25,
32 and 33. It is divided into a posterior part (caudal/dorsal ACC, more recently referred to as middle cerebral cortex) and an anterior part (rostral/ventral ACC)
(51, 52). The rostral ACC is further divided into the pregenual and the subgenual
ACC (BA 25), which has unique properties (53, 54). The ACC plays a crucial role in the integration of neural circuitry for affect regulation through its major connections with both the limbic system and more cognitive prefrontal cortical areas (51, 53, 55,
56). In terms of cytoarchitecture, the ACC is marked by the presence of Von
Economo neurons, which are large cells that are much more numerous in humans than in any other primates (57, 58). ACC functions potentially related to aggression include affect regulation, behavioral monitoring, conflict resolution, and impulse regulation (45, 51, 58).
OrbitoFrontal Cortex
The orbito‐frontal cortex is located on the ventral surface of the anterior part of the frontal lobe, just above the orbits. It includes Brodmann areas 11 and 12 medially (medial OFC) and extends to area 47 in its lateral part (lateral OFC) (59).
Area 10, which is located in the inferior frontal pole is sometimes included as a part of the OFC (60). It is a polymodal brain area with connections to the limbic system, the ACC and the DLPFC, in addition to receiving inputs from all five sensory
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modalities (59). The OFC is involved in the integration of sensory, visceral, limbic and motor inputs. Through its connections with the ACC, it forms a network sometimes referred to as the orbital and medial prefrontal network (OMPFC) (61).
This network, which is closely related to the commonly used concept of ventromedial prefrontal cortex (vmPFC), is involved in emotional regulation and has been convincingly linked to mood disorders (55, 62). The medial OFC is also implicated in monitoring and learning/memory of potential rewards/values, while the lateral part is more implicated in response inhibition, evaluation of punishments and reversal of learning (59, 63). In terms of aggression related functions, the OFC is involved in value judgments, decision making, impulse control and social norms integration (59, 64). Neuronal activity of the OFC often co‐occurs with activity in the
ACC in tasks requiring emotional integration and modulation (65).
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RATIONALE OF THE RESEARCH
Over 100 years ago, the great neuroscientist Franz Nissl realized the importance of understanding normal brain development for the study of psychopathology when he declared: “It was a bad mistake not to realize that the findings of brain anatomy bore no relationship to psychiatric findings, unless the relationships between brain anatomy and brain function were first clarified, and they certainly have not been to the present” (66). Only recently with the improvement of magnetic resonance imaging technology and automated imaging analysis software has the scientific community gain some precise access to the in vivo neuroanatomical study of brain development.
In 1999, the NIH Pediatric Neuroimaging Project provided some valuable information on healthy brain development (67). It was determined that total brain volume tends to remain stable after age 5, the increase in head size being accounted by an increase in skull thickness. However, there is a significant intracerebral reorganization process with a gradual linear increase in white matter size, while grey matter development follows an ‘inverted U’ shape. Grey matter cortical thickness increases up to age 10 to 16 (depending on the brain area), and gradually decreases thereafter as neural connections become more efficient (67, 68). This decline in cortical thickness occurs earliest in sensorimotor areas, and last in the dorsal prefrontal cortex. This decrease in cortical thickness on MRI could represent
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a pruning process, or a greater invasion of white matter fibers in the grey matter layer (67, 69, 70).
The more recent NIH MRI Study of Normal Brain Development, from which data of this study were taken, is a multisite collaborative effort at six data collecting hospitals in the US, in addition to the Montreal Neurological Institute acting as the
Data Coordinating Centre (1). The two main objectives of the study were to provide the largest normative database to use as a comparative for past and future neuroanatomical studies of abnormal brain development and psychopathology. In addition, this project aimed to longitudinally characterize brain‐behavior relationships, meaning the structural variations associated with various psychological components (temperament, emotions, cognitive abilities, behaviors, etc.) of healthy development. Details of the study can be found in the Evans et al.
(2006) article and are summarized in the submitted manuscript (1). Structural imaging data analyses were all performed at the McConnell Brain Imaging Centre
(Data Coordinating Centre) using automated segmentation and cortical thickness measurement software. Both the CIVET (cortical surface) and ANIMAL (subcortical volumes) algorithms have been validated, including comparisons with manual segmentation on multiple subjects in previous studies (71‐74). In addition, a blinded visual quality control of cortical surface to eliminate subjects with gross aberrations was done by two of the authors with an inter‐rater reliability over 0.9 (75).
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Most neuroimaging studies of childhood psychopathology have used a categorical approach in which a group of affected children is compared to a group of normative controls. However, this approach belies empirical evidence suggesting that many childhood psychiatric pathologies are extreme phenotypes located on a continuum from healthy behavior to severe disorders (76‐78). In that context, our research group has worked with the underlying hypothesis that looking at the relationship between a continuous measure of impulsive aggression (CBCL
Aggressive Behavior scores) and cortical thickness and subcortical volume would provide valuable insight on the neurobiology of aggression. A linear regression model of analysis was chosen to identify brain areas that are anatomically proportionately related to a behavioral trait, as opposed to crudely comparing two states (i.e. illness vs. healthy). Standard control variables such as age, total brain volume, gender, scanner site, IQ and handedness were entered in the models to minimize their confounding effect. Gaining better understanding of structures involved in the regulation of aggression during typical development will allow much more valid comparisons with studies of psychopathologies associated with increased aggressive behaviors, such as oppositional‐defiant and conduct disorders.
The article included in this thesis describes the results of analyses looking at associations between cortical thickness/subcortical volumes and Child Behavior
Checklist Aggressive Behavior (AGG) scores in a cohort of 193 healthy children from
6 to 18 years of age taken from the public funded NIH MRI Study of Normal Brain
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Development. The range of AGG scores in this study was from 0 to 11, which corresponds to the lowest level up to aggression levels just below the clinical threshold. Given that the goal of the project was to understand the association between normative aggressive behavior and cerebral development, no children with
CD or ADHD were included in this cohort. Although ODD was not a formal exclusion criteria, no children with clinical range (t score over 70) of impulsive aggression as measured by the AGG scale were included in this NIH project. The following manuscript has been published in the August 1st 2011 edition of Biological
Psychiatry (impact factor 8.9) (79).
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CONTRIBUTIONS OF AUTHORS
1) Dr. Simon Ducharme: Main investigator, design of the study on CBCL
Aggressive Behavior, literature review, implementation of statistical
analyses, preparation of the manuscript.
2) Dr. James J. Hudziak: Provided input on the research use of the CBCL, editing
of the manuscript.
3) Dr. Kelly N. Botteron: Principal investigator at the Clinical Coordinating
Centre (St‐Louis, Missouri), editing of the manuscript.
4) Dr. Hooman Ganjavi: Support for the implementation of statistical analyses,
editing of the manuscript.
5) Dr. Claude Lepage: Programming of CIVET (automated MR images analysis
software for cortical thickness), editing of the manuscript.
6) Dr. D. Louis Collins: Programming of ANIMAL (automated MR images analysis
software for subcortical volumes).
7) Matthew D. Albaugh: Provided input on the research use of the CBCL, editing
of the manuscript.
8) Dr. Alan C. Evans: Principal investigator at the Data Coordinating Centre
(Montreal Neurological Institute), co‐research supervisor of S. Ducharme,
editing of the manuscript.
9) Dr. Sherif Karama: Main research supervisor of S. Ducharme, contributed to
the study design and statistical analyses, editing of the manuscript.
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MANUSCRIPT
Right Anterior Cingulate Cortical Thickness and Bilateral Striatal Volume Correlate with Child Behavior Checklist Aggressive Behavior Scores in Healthy Children
Authors:
1) Simon Ducharme, MD1
2) James J Hudziak, MD2
3) Kelly N Botteron, MD3
4) Hooman Ganjavi, MD PhD1
5) Claude Lepage, PhD1
6) D Louis Collins, PhD1
7) Matthew D. Albaugh, MSc2
8) Alan C Evans, PhD1
9) Sherif Karama, MD1,4 and the Brain Development Cooperative Group
Affiliations:
1 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University (Montreal, QC, Canada).
2 Vermont Center for Children, Youth, and Families, University of Vermont (Burlington, VT, USA)
3 Mallinckrodt Institute of Radiology, School of Medicine, Washington University, (St. Louis, MO, USA)
4Douglas Mental Health University Institute, McGill University (Montreal, QC, Canada)
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Abstract: 232 words
Text: 3,869 words
Figures: 4
Tables: 1
Supplemental Information: 7 (3 tables, 4 figures)
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INTRODUCTION
All humans tend to experience periods of worry, sadness, happiness, and aggression, among others. All of these are brain based and the study of what brain regions are implicated in specific human behaviors is one of the great frontiers of modern neuroscience. The past decade has seen great improvement in the knowledge of structural and functional imaging correlates of a wide variety of childhood psychiatric disorders (68, 80). A primary methodological approach has been to compare and contrast so‐called ‘normal controls’ to individuals with a specific disorder. This categorical approach to the study of human behavior and underlying brain biology belies empirical evidence suggesting that many human traits, including aggression, are continuously distributed (16, 81). Studying brain‐ behavior relations among healthy children exhibiting normal variation may provide critical insight on the neural substrate of human behavior and psychopathology.
This is the aim of this particular study on childhood impulsive aggression.
Human aggression is a ubiquitous phenomenon with important social consequences. Although the underpinnings are clearly multifactorial, the neurobiology of impulsive aggression is gradually being better understood.
Impulsive aggression can be defined as a lower threshold for activation of motoric aggressive responses to external stimuli without adequate reflection or regard for the aversive consequences of the behavior (6). In neurobiological terms, this is conceived as an inadequate “top‐down” control of “bottom‐up” drives from the
25
limbic system. The anterior cingulate cortex (ACC) and orbito‐frontal cortex (OFC) have been frequently implicated as important structures for the “top‐down” regulation of aggressive impulses (6, 7). There is a well‐documented clinical association between frontal lobe lesions and impulsive/aggressive behaviors (34,
82, 83). Functional PET studies have demonstrated reduced serotonin (a neurotransmitter associated with impulse control) transporter availability in the
ACC of individuals with pathological impulsive aggression (84). In addition, the imagination of aggressive scenarios by healthy adult subjects was associated with decreased activity of the OFC on PET (85).
Although not as extensively studied as the ACC and OFC, there is evidence indicating that basal ganglia are also implicated in the regulation of aggressive behaviors. It is known from past animal studies that these structures participate in the motor component of aggression (86, 87). It is now recognized that basal ganglia are also involved in more complex behaviors through their extensive connections with the frontal lobes and limbic system (88, 89). Various studies illustrate the involvement of basal ganglia in aggressive behaviors. Glenn et al. (2010) have found increased striatal volume in psychopathic individuals(90). In a SPECT study of adolescents and adult psychiatric patients with a recent history of aggressive behavior, aggression was associated with increased left basal ganglia activity (29). In addition, there is clinical evidence linking basal ganglia pathologies, such as
Huntington’s disease, Tourette’s disorder and Lesch‐Nyhan syndrome on the one hand, and irritability/aggressive behaviors on the other (30, 91‐93). Further,
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catastrophic aggressive outbursts with rages in a patient with bilateral caudate infarcts have been reported (94).
The Aggressive Behavior syndrome (AGG) of the Child Behavior Checklist
(CBCL) is a well‐validated measure of aggressive tendencies in healthy children and those suffering from externalizing disorders (19, 95, 96). AGG primarily measures components of impulsive aggression. It has a high test‐retest reliability (r =0.90,
Cronbach’s alpha =0.94) and the stability is high, with Pearson correlations of 0.82 and 0.81 reported for 12‐ and 24‐month intervals, respectively(97). It has been validated as an independent behavioral measurement in different cultures(98, 99).
It tends to co‐occur with the CBCL Rule Breaking scale that includes more instrumental forms of aggression; however factor analysis has revealed that these are separate syndromes with independent features (100‐102). The genetic contribution to impulsive aggressive behaviors has been estimated to be above 50% and the genetics of the AGG scale in particular is between 52 and 71% (16, 103‐106).
AGG scores significantly predicts DSM‐IV diagnosis of oppositional defiant disorder
(ODD) and conduct disorder (CD) (19). In terms of gender specificity, it was previously found that teenage females tend to use more “indirect” or relational forms of aggression, as opposed to males who tend to express it in more direct ways
(15, 16, 107).
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Most neurobiological studies have focused on pathological aggression/violence (i.e. criminals, personality disorders, conduct disorder, etc.) and few studies have looked at correlates of aggressiveness in healthy children (80).
Boes et al. (2008) identified decreased right anterior cingulate volume as a neuroanatomical correlate of aggression and defiance in boys from 7 to 17 years of age without psychopathology (40). In the current study, we assessed MRI neuroanatomical correlates of AGG scores in developmentally healthy children from
6 to 18 years of age using the NIH MRI Study of Normal Brain Development data (1).
We hypothesize that, even in non‐pathological subjects, there are anatomical variations in cortical thickness of the ACC(Brodmann Area (BA) 24, 25 and 32), the
OFC(BA 11‐12 and 47)(108, 109) and basal ganglia volume associated with CBCL
AGG scores. The literature was insufficient to clearly predict the direction of the associations, although reduced grey matter at the cortical level was more frequently associated with aggressive behaviors in previous studies (37, 40, 110). We also hypothesize that the gender differences in aggression expression are linked to sex specific neuroanatomical correlates.
METHODS AND MATERIAL
Sampling and Recruitment
The NIH MRI Study of Normal Brain Development is a multi‐site project providing a normative database to characterize healthy brain maturation in relationship to behavior (1). Subjects were recruited across the USA with a population‐based sampling method seeking to minimize biases of selection (111) .
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Based on available US Census 2000 data, a representative healthy sample of subjects was recruited at 6 pediatric study centers: Children's Hospital (Boston), Children's
Hospital Medical Center (Cincinnati), University of Texas Houston Medical School
(Houston), UCLA Neuropsychiatric Institute and Hospital (Los Angeles), Children's
Hospital of Philadelphia (Philadelphia) and Washington University (St. Louis).
Recruitment was monitored continuously in order to assure that the sample recruited across all pediatric centers was demographically representative on the basis of variables that included age, gender, race, ethnicity and socioeconomic status. Informed consent from parents and child assent were obtained for all subjects. The Objective 1 database (release 4.0) used for this study included 431 children from 4 years and 6 months to 18 years and 3 months who underwent extensive cognitive, neuropsychological and behavioral testing, along with MRI brain imaging. Data from the first visit were used for this study. Given that the goal of this project was to study developmentally normal children, there were extensive exclusion criteria including current or past treatment for an axis 1 disorder, evidence of most axis I disorder on structured parent or child interview (DICA), substance use other than nicotine, family history of major axis 1 disorder, family history of inherited neurological disorder or mental retardation due to non‐ traumatic events, abnormality on neurological examination, gestational age at birth
<37 weeks or >42 weeks, intra‐uterine exposure to substances known or highly suspected to alter brain structure or function, etc. Structural MRI and clinical/behavioral data were consolidated and analyzed within a purpose‐built database at the Data Coordinating Center of the Montreal Neurological Institute
29
(MNI), McGill University.
Child Behavior Checklist (CBCL)
The CBCL is an age appropriate standardized questionnaire filled by parents.
It is a validated assessment that is used as a screening tool by clinicians (96, 112).
The CBCL/4‐18 that was utilized in this study is divided into 8 subscales, including the AGG syndrome (113). The checklist provides raw values that can be converted to age adjusted t scores if needed. For AGG, t scores of less than 67 are considered normal, 67‐70 borderline and over 70 to be in the clinical range. All children in this study had t scores of less than 70. Raw scores (0‐11) were analyzed in this study because age was included as a control variable in the regression models.
MRI protocol
In order to collect data within the time limitations for this age range and allow automated morphometric analysis, 30‐45 minutes of data acquisition was allocated, with 1 mm in‐plane resolution, 1‐2 mm slice thickness, whole brain coverage and multiple contrasts (T1W, T2W and PDW) (1). A 3D T1‐weighted spoiled gradient recalled (SPGR) echo sequence was selected. Inter‐site reliability was monitored with both the American College of Radiology and living phantoms that were scanned at each site(1).
Automated Image Processing
Quality controlled (QC) native MR images were processed through the CIVET
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automated pipeline (version 1.1.9, 2006) (114). This pipeline includes the CLASP algorithm for generating cortical thickness measurements at 40,962 vertices per hemisphere (71, 72, 74, 114, 115). Cortical thickness is calculated as the distance between the 'outer CSF‐grey matter' and 'grey matter‐white matter' interfaces (71,
72, 115). Regional volumes of subcortical structures were obtained with the
ANIMAL algorithm (73, 74). Subjects with missing values (either anatomical measures or AGG scores) and subjects over 18 year‐old that had completed the
Young Adult Self Report (YASR) instead of the CBCL were eliminated. A stringent visual quality control (blinded as to the AGG score of the subjects) of the native cortical thickness images and subcortical structures of each subject was implemented to ensure that there were no aberrations in values for a given subject
(75). Only subjects that had successfully passed the QC for both cortical thickness
(CIVET) and subcortical structures (ANIMAL) were kept for analysis, meaning that selected subjects had imaging data of the highest quality. This stringent process left a final sample of 193 subjects (105 females, 88 males).
Statistical Analysis: Cortical Thickness
Statistical analyses were implemented using SurfStat, a statistical toolbox created for MATLAB 7 (The MathWorks, Inc.) by Dr. Keith Worsley
(http://wiki.bic.mni.mcgill.ca/index.php/SurfStat). Each subject's absolute native‐ space cortical thickness was linearly regressed against AGG score at each cortical point after accounting for the effects of age, total brain volume, gender and scanner
(since different scanners were used at the six sites). Handedness and IQ were also
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considered as potential confounding variables. It has been shown in healthy children and in childhood disorders that grey matter thickness can follow quadratic patterns
(gradual increase followed by a decrease)(67, 68). Therefore, a second‐order regression model was also implemented to identify the best fitting model:
Y = 1 + ƅ1AGG + ƅ2AGG^2 + ƅ3Age + ƅ4Gender + ƅ5TotalBrainVolume + ƅ6Scanner. ‘AGG by gender’ and ‘AGG by age’ interactions were analyzed (15, 40). To assess the specificity of findings to impulsive aggression, results were compared with other
CBCL syndromes (Rule Breaking, Attention Problems, Anxious/Depressed,
Internalizing).
A whole‐brain correction, using random field‐theory (RFT), was used to account for multiple comparisons (116). Based on the literature, the ACC (BA 24, 25 and 32) and OFC (BA 11‐12 and 47) were identified as regions of interest (ROI). In these a priori identified ROI, the threshold to identify trends was set to a p‐value of
α=0.005, uncorrected. Despite these a priori hypotheses, all figures included in this study are whole brain associations and are not masked in any way.
Statistical Analysis: Basal Ganglia Volume
Volumetric analysis was done with SPSS Statistics 17.0 (SPSS Inc., Chicago).
AGG scores were linearly regressed against the volume of the caudate, putamen and globus pallidus nuclei, controlling for age, gender, total brain volume and scanner.
Given that the caudate and putamen nucleus are often considered as a single unit
(striatum) due to their close histological and embryological relation, the analysis
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was repeated for both structures combined (117). A Bonferroni correction was applied to the usual α=0.05 to account for multiple comparisons. Taking into account the strong correlation between the size of basal ganglia among themselves
(r=0.578), the adjusted signification threshold was established at α=0.0235.
RESULTS
Demographics
There were no differences between the initial NIH cohort (n=431) and the QC sample (n=193) used in the statistical analysis for gender, socioeconomic status, race and handedness (table S1 available in supplemental information). Mean age was slightly older in the n=193 sample (11.8±0.25 vs. 10.4±0.20) because younger children tended to have lower image quality, resulting in a higher image processing failure rate. Mean AGG score (2.47±0.19) was also slightly lower, barely reaching significance. Mean age, race distribution, socioeconomic status, handedness and mean AGG scores were comparable between males and females (table S2 available in supplemental information). There were no significant differences in the distribution of AGG scores, age and gender across the 6 scanner sites of the study
(table S3 available in supplemental information)
Cortical Thickness
No first‐order linear association was found between AGG scores and cortical thickness when all subjects were pooled together. However, the second‐order model and quadratic term regressions revealed an association between AGG and the right
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ACC thickness (figure 1, p≤0.05 RFT corrected, n=193). The association was located in the ventral part of the pregenual ACC (BA 24) and the subgenual ACC (BA 25)
(scatterplots included in figure 2). This finding can be more concretely explained by a weak positive association in subjects with very low level of aggressiveness (AGG scores 0‐2), while subjects with higher scores (AGG 3‐11) had a strong linear negative association with the right ACC thickness (figure S4, available in supplemental information). This finding was independent of gender and there were no ‘AGG by age’ or ‘gender by age’ interactions. Adding handedness, IQ or CBCL
Anxious/Depressed score as control variables did not change the results. Other
CBCL scales, including the Rule Breaking, showed either no, or different anatomical associations with cortical thickness.
Figure 3 shows the map of the ‘AGG by gender’ interaction term against cortical thickness in a first‐order linear model (females‐males contrast, p≤0.005, n=193)( t map available in supplemental information, S5). Although there were no statistically significant associations, there were trends of differences in the right OFC
(p<0.001), the left OFC (p<0.005) and bilateral ACC (p<0.005). In males (n=88), there were negative trends in bilateral OFC and a small part of the right ACC (figure
S6, available in supplemental information). In females (n=105), there was a positive trend of association between AGG and the subgenual ACC (figure S7, available in supplemental information). In figure 3, associations outside of the ROIs in the left dorsolateral prefrontal cortex and the right anterior pole of the temporal lobe were not significant after RFT correction. There were no ‘AGG by gender’ interactions in a
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second‐order model.
Basal Ganglia Volumes
As shown in table 1 (n=193), AGG scores were positively associated with the volume of the right caudate (r=0.207, p=0.004), left caudate (r=0.191, p=0.008) and right putamen (r=0.193, p=0.008). There was a positive trend with the left putamen
(r=0.134, p=0.066). There was no association with bilateral globus pallidus. The positive correlation between the volume of the right striatum and AGG was slightly greater than the association for either the putamen or caudate nucleus alone (right: r=0.238, p=0.001; left: r=0.188, p=0.01). Scatterplots are shown in figure 4. There were no significant ‘gender by basal ganglia’ or ‘age by basal ganglia’ interactions.
Comparison between Cortical Thickness and Basal Ganglia
Multiple regressions revealed that the left striatum explained 3.3% of the variance in AGG score, that the right striatum explained 5.7% of variance in AGG score and that the AGG score explained 6.2% of the variance in right ACC mean thickness. The right striatum explained, by itself, a statistically significant amount of variance despite controlling for the left striatum and ACC mean thickness while the left striatum association became non significant once the right striatum was taken into account. The association between the ACC thickness and AGG was significant even when controlling for striatal volumes.
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DISCUSSION
As initially hypothesized, various neuroanatomical correlates of AGG were found in our large cohort of developmentally healthy children. At the subcortical level, the main findings were positive associations with striatal volume bilaterally.
At the cortical level, thickness of the right ACC (BA 24‐25) was observed to be associated with aggression in a second‐order (quadratic linear) model. In a first‐ order model, the ‘AGG by gender’ interaction suggested trends of gender differences in bilateral OFC and bilateral ACC associations with AGG.
The ACC has strong connections to both the limbic system and higher prefrontal areas, and it has repeatedly been found to be involved in the regulation of emotions, especially its ventral subpart (118‐123). Per example, multiple studies on borderline personality disorder, which is associated with emotional dysregulation and impulsivity, have found decreased volume in the ACC, both in adults and adolescents(124‐127). The quadratic association between AGG scores and right ACC thickness found in this study further supports the crucial importance of this region in the regulation of aggressive impulses in the developing brain. Interestingly, decreased right ACC volume had been previously associated with aggression and defiance in healthy teenage males in a smaller study, but not in females (40). Our results were similar in both genders, possibly because of the larger sample size. The association in a quadratic model but not in a linear model could be explained by a gradually stronger negative association between right ACC thickness and aggression as children get closer to pathological levels. Another hypothesis would be that the
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CBCL could not discriminate as precisely small differences between children with very low levels of aggression (AGG 0‐2), making it impossible to detect a small association at these levels.
Although it remains unclear as to why the association between ACC thickness and CBCL AGG was found only in the right hemisphere, previous studies on aggression have evinced similar asymmetrical findings (6, 29). Moreover, there is burgeoning evidence from imaging studies that the neural circuitry mediating response inhibition demonstrates a rightward lateralization (128). Prospective longitudinal studies, employing both structural and functional imaging modalities, are needed to address such possibilities.
The positive bilateral associations between striatal volume and the CBCL AGG suggest that these structures are implicated in the regulation of impulsive aggressive behaviors. In addition, given that the putamen and the head of the caudate fuse to form the ventral striatum (including the nucleus accumbens, a crucial component of the reward system) which has important connections with the limbic circuitry, they might also be associated with increased “bottom‐up” aggressive impulses (117). In that regard, it is interesting to note that larger striatum has also been found in adult psychopathic individuals who often exhibit impulsive aggressions (90). This suggests that conduct disorder and antisocial personality disorder might be on a continuum with normal aggressive traits. The literature on the link between aggression and basal ganglia being less developed
37
than for the ACC/OFC, it is possible that the role of striatum is related to impulsivity in general as opposed to aggression specifically. The absence of correlation between
AGG and globus pallidus is not unexpected given that it had rarely been specifically associated with aggression in previous studies.
The trends of negative associations between aggressive behavior and OFC thickness in males support previous reports that implicated this region in the neurobiology of aggression (6, 7). Although results were not statistically significant, this research suggests that the OFC would also plays a role in normal aggressive traits in developing boys. Decreased “top‐down” control could be linked to a different maturation of this brain region. Our findings interestingly parallel recent reports of structural studies in boys with conduct problems associated with aggression (37, 110). Both studies have identified grey matter volume of the OFC as a correlate of their clinical samples; however the direction of the association was inconsistent. Our findings support the results from Huebner et al. (2008) suggesting that decreased OFC grey matter is associated with increased aggressive tendencies
(37). The results of Huebner at al. (2008), when interpreted in conjunction with the present study’s findings, suggest that normative aggressive behavior and more severe conduct problems may constitute a single underlying dimensional trait that is associated with distinct neurodevelopmental correlates—including difference in grey matter in the OFC in childhood.
In females, the absence of such trends in the OFC and the presence of a
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positive trend in the left subgenual ACC raise the issue of gender specific neuroanatomical correlates of aggression. Observed trends of differences might represent gender specific anatomical correlates of impulsive aggression, but might also be related to the AGG score not capturing as well relational forms of aggression that are more prevalent in females(15).
This cross‐sectional study had several limitations that should be mentioned.
First, although it is a clinically and scientifically well‐validated tool, AGG is a single measure and remains a gross estimate of impulsive aggression. It is not aimed at measuring all aspects such as instrumental aggression. Further, although statistically significant, the associations were of small magnitude. This is expected given the complexity and heterogeneity of the measured phenomenon. The threshold used to identify trends in the ROI (p=0.005) is not the most stringent, but probably yielded valid findings given the strength of the a priori hypothesis, the small variation in the measured phenomenon (aggression in a very healthy cohort of children) and the intrinsic difficulties of measuring cortical thickness in the OFC because of its know susceptibility to field artifacts. In addition, it is important to point out that regions of associations were found primarily in the a priori defined
ROI, increasing the confidence in the validity of the findings. Importantly, the right
ACC’s quadratic association was significant after a stringent RFT correction and the positive striatal correlations were significant after a Bonferroni correction. This study has focused on regions thought to be implicated in the regulation of aggressive impulsions. It would have been interesting to study limbic structures such as the
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amygdala, however the automated measurement of these structures with ANIMAL in children has not yet been validated due to their subjective anatomical boundaries.
Finally, less than half of all the initial sample subjects were used for analysis. These were subjects with the highest imaging quality and they remained representative of the American population in terms of demographics. Cortical thickness measurement being sensitive to very small movement artifact (frequent in children), this loss of subjects was compensated by a more precise and reliable measurement.
This being said, it is the largest study of neuroanatomical correlates of aggressive behaviors in typically developing children from a demographically representative sample. It is also one of the rare studies that looked at aggressive behavior at the trait level (40). Our findings were located in biologically plausible brain regions and mapped a fronto‐striatal network of structures (striatum, right
ACC, OFC) that has been repeatedly associated with regulation of impulses and affect in functional imaging studies (6, 7). Finally, the AGG scale has good correlation with clinical diagnoses of ODD and conduct CD (19). Psychiatric disorders being more and more conceptualized as a continuum from normal to pathological behaviors, these results suggest that the identified structures could play a role in the development of these pathologies.
In summary, several cortical and subcortical neuroanatomical correlates of
AGG scores in healthy children were identified. These findings have significant implications for those who wish to study aggression in genetic, clinical and
40
neuroimaging studies. Future interesting areas of research could include longitudinal investigation of regional developmental changes and their relationship to changes in AGG and functional imaging studies of the network formed by the right
ACC, OFC and striatum and how they interact to regulate aggressive behaviors. A next step in this type of investigation would be to determine the genetic and epigenetic contributions to findings such as these. Finally, we believe that the right
ACC and the striatum should be a focus of attention for future neurobiological studies of pediatric psychiatric diagnoses associated with aggression, mainly CD and
ODD.
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ACKNOWLEDGEMENT AND FINANCIAL DISCLOSURES
This project has been funded in whole or in part with Federal funds from the
National Institute of Child Health and Human Development, the National Institute on
Drug Abuse, the National Institute of Mental Health, and the National Institute of
Neurological Disorders and Stroke (Contract #s N01‐HD02‐3343, N01‐MH9‐0002, and N01‐NS‐9‐2314, ‐2315, ‐2316, ‐2317, ‐2319 and ‐2320).
Dr. Simon Ducharme receives financial support from the Canadian Institutes of
Health Research with a Master’s Award: Frederick Banting and Charles Best Canada
Graduate Scholarship. Dr. Sherif Karama was supported by the Fonds de Recherche en Santé du Québec. Authors have no conflict of interest related to this article.
Special thanks to the NIH contracting officers for their support.
We also acknowledge the important contribution and remarkable spirit of John
Haselgrove, Ph.D. (deceased).
Disclaimer
The views herein do not necessarily represent the official views of the National
Institute of Child Health and Human Development, the National Institute on Drug
Abuse, the National Institute of Mental Health, the National Institute of Neurological
Disorders and Stroke, the National Institutes of Health, the U.S. Department of
Health and Human Services, or any other agency of the United States Government.
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TABLES
Table 1. Pearson’s correlation coefficient between CBCL Aggressive Behavior raw scores and basal ganglia volumes in mm3 (n=193) Basal Caudate Caudate Putamen Putamen Globus Globus Striatum Striatum right left Ganglia right left right left pallidus pallidus right left Pearson’s r=0.207 r=0.191 r=0.193 r=0.134 r=0.056 r=0.036 r=0.238 r=0.188 p=0.001 p=0.010 Coefficient p=0.004 p=0.008 p=0.008 p=0.066 p=0.447 p=0.626 α=0.0235. Controlled for age, scanner, gender and total brain volume.
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FIGURES
Figure 1.
Brain areas where local cortical thickness is associated with CBCL Aggressive
Behavior raw scores in a second‐order (quadratic) model over the whole sample
(n=193). Random field theory was used to correct for multiple comparisons over the whole cortical mantle.
Figure is shown at p≤0.05, RFT corrected. Blue areas are significant at the cluster level and red color corresponds to areas significant at the vertex level (none in this figure).
Controlled for age, gender, scanner and total brain volume.
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Figure 2.
Scatterplots of CBCL Aggressive Behavior raw scores against local cortical thickness
(mm).
Controlled for age, gender, scanner and total brain volume.
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Figure 3.
Brain areas where local cortical thickness is associated with the ‘CBCL Aggressive
Behavior by gender’ interaction term (contrast females – males) over the whole sample (n=193). A first‐order linear model was used. Threshold for trends was set at p≤0.005 uncorrected for regions of interest (ACC/OFC).
Figure is shown at p≤0.005, uncorrected. This figure includes only onesided associations because trends in the regions of interest were all in the same direction.
Associations outside of the regions of interest were not significant after a random field theory correction. Controlled for age, gender, scanner and total brain volume.
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Figure 4.
Scatterplots of CBCL Aggressive Behavior raw scores against the left and right striatum volume (mm3)
Controlled for age, gender, scanner and total brain volume.
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SUPPLEMENTAL INFORMATION
S1. Demographic comparisons of the complete NIH Objective 1 cohort (n=431) and cortical thickness analysis sample (n=193) n=431 n=193 Age in Years* 10.4 ± 0.20 (4.6‐18.3) 11.8 ± 0.25 (618) p<0.001 Gender Females=52.0% Females=54.4% Males=48.0% Males=45.6% Proportions** χ2=0.446 p>0.50 Race** White=81.7% White=83.4% African American=9.5% African Other or N/A=8.8% American=8.8% Other or N/A=7.8% χ2=0.396 p>0.95 Household Income Less than 35=8.8% Less than 35=6.2% 35 to 50=19.0% 35 to 50=18.1% in 1,000$** 50‐75=24.1% 50‐75=22.2% 75‐100=23.7% 75‐100=25.9% Over 100=24.4% Over 100=27.5% χ2=2.941 p>0.50 Hand used to write Right=88.9% Right=90.1% Left=10.4% Left=9.9% N/A=0.7% χ2=1.441 p>0.30 CBCL AGG Average 2.86 ± 0.15 (range 0‐ 2.47 ± 0.19 (011) 21***) p=0.047 Score* *One sample t test with the standard error comparing the utilized sample (n=193) to the average of the complete NIH cohort, which is deemed to be representative of the population **χ2 for goodness of fit using the full NIH cohort proportions as the predicted model ***Only one subject had a very high score, with most CBCL Aggressive Behavior raw scores between 0‐12
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S2. Demographic comparisons of males (n=88) versus females (n=105) subjects Males Females n=88 n=105 Age in Years* 11.7 ± 0.35 (6‐18) 11.9 ± 0.34 (6‐18) p=0.768 Race** White=82.9% White=84.0% African American=9.5% African American=8.0% Other or N/A=7.6% Other or N/A=8.0% χ2=0.150 p=0.928 Household Income in Less than 35=3.4% Less than 35=8.6% 1,000$** 35 to 50=23.9% 35 to 50=13.3% 50‐75=26.1% 50‐75=19.0% 75‐100=23.9% 75‐100=27.6% Over 100=22.7% Over 100=31.4% χ2=7.64 p=0.105
Hand used to write Right=87.5% Right=92.4% Left=12.5% Left=7.6% χ2=1.285 p=0.257 CBCL AGG Average Score* 2.76 ± 0.32 (range 0‐11) 2.23 ± 0.24 (range 0‐10) p=0.058 * t test for independent samples with the standard error comparing males versus females average age and CBCL Aggressive Behavior raw scores **χ2 for independent samples comparing males versus females proportion of race, income and handedness
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S3. Comparisons of the six scanner sites for CBCL Aggressive Behavior raw scores, age, gender distribution and volumetric measurements
Scanner 1 Scanner 2 Scanner 3 Scanner 4 Scanner 5 Scanner 6 Subjects 43 40 42 27 33 8 Gender Male=20 Male=20 Male=20 Male=15 Male=11 Male=2 Female=23 Female=20 Female=22 Female=12 Female=22 Female=6 Mean AGG 2.58±0.4 2.28±0.4 2.48±0.4 3.19±0.6 2.03±0.37 2.25±1.1 score Mean Age 11.0±0.5 12.2±0.6 11.6±0.5 11.9±0.75 12.6±0.6 11.0±1.3 Mean 3.59±0.02 3.61±0.03 3.58±0.03 3.56±0.03 3.54±0.03 3.53±0.06 Cortical Thickness (mm) Mean Left 10606±154* 11154±182* 10928±157 10871±161 11079±187 11073±337 Striatum (mm3) Mean 10699±151 11074±187 10971±171 11096±169 11081±180 11016±344 Right Striatum (mm3) Mean Left 5467±81* 5698±98 5611±88 5572±94 5745±100* 5678±154 Caudate (mm3) Mean 5469±82 5623±102 5582±91 5587±97 5648±103 5576±140 Right Caudate (mm3) Mean Left 5138±90* 5456±98* 5318±91 5299±103 5334±109 5395±198 Putamen (mm3) Mean 5231±86* 5452±100 5390±101 5510±109* 5534±101 5441±217 Right Putamen (mm3) Mean Left 1271±26 1318±23 1275±22 1278±31 1266±31 1294±68 Globus Pallidus (mm3) Mean 1213±31 1287±23** 1207±28 1204±30* 1162±29** 1252±62 Right Globus Pallidus (mm3) Data in bold have significant between sites differences. *p≤0.05 **p≤0.01
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Figure S4.
Brain areas where local cortical thickness is negatively associated with CBCL
Aggressive Behavior raw scores in a first‐order linear model in subjects with AGG scores ≥3 (n=72). Random field theory was used to correct for multiple comparisons over the whole cortical mantle.
Figure is shown at p≤0.05, RFT corrected. Blue areas are significant at the cluster level and red color corresponds to areas significant at the vertex level (none in this figure).
Controlled for age, gender, scanner and total brain volume.
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Figure S5.
t scores map of the ‘gender by CBCL AGG’ interaction (females ‐ males contrast, df=182) against local cortical thickness over the whole sample (n=193). A first‐order linear model was used.
Controlled for age, gender, scanner and total brain volume.
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Figure S6.
Brain areas where local cortical thickness is negatively associated with CBCL
Aggressive Behavior raw scores in a first‐order linear model in male subjects only
(n=88). Threshold for trends was set at p≤0.005 uncorrected for regions of interest
(ACC/OFC).
Figure is shown at p≤0.005, uncorrected. Controlled for age, scanner and total brain volume.
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Figure S7.
Brain areas where local cortical thickness is positively associated with CBCL
Aggressive Behavior raw scores in a first‐order linear model in female subjects only
(n=105). Threshold for trends was set at p≤0.005 uncorrected for regions of interest
(ACC/OFC).
Figure is shown at p≤0.005, uncorrected. Associations outside of the regions of interest were not significant after a random field theory correction. Controlled for age, scanner and total brain volume.
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POSTFACE
As mentioned in the manuscript, this was the largest study to date on neuroanatomical correlates of impulsive aggression in children, and it has yielded innovative insights on the implication of the right ACC and bilateral striatum in this behavior. In a follow up project, our group has investigated further the neurobiology of impulsive/reactive aggression by analyzing the impact of anxious and depressed symptoms on the cortical thickness correlates of AGG.
Impulsive aggression often co‐occurs with internalizing symptoms such as anxiety (10‐14). This is a logical association since reactive aggression has been characterized by a low threshold for experiencing negative affect, with the resultant inability to effectively regulate behavior (26). Children with that phenotype might uniquely have broader problems with affective regulation, and we hypothesized that this might be associated with different cortical development compared to simply looking at CBCL Aggressive Behavior or anxious/depressed symptoms in isolation.
Putative circuits involved in behavioral control and regulation of negative affects include the OFC, lateral PFC, ACC, anterior insular cortex and subcortical structures
(129‐133). These were selected as a priori regions of interest for the analyses. This follow‐up study was lead by one of the US collaborators, Matthew Albaugh (PhD student, University of Vermont), but the author of this thesis was significantly implicated in the development of the study, statistical analyses, and will be second author on an imminently submitted manuscript.
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A similar linear regression methodology was implemented, but including
CBCL Anxious/Depressed scores (AxD) and the ‘AGG by AxD’ interaction in the following model:
Y = 1 + age + gender + total brain volume + scanner + IQ + AGG + AxD + AGG*AxD
The ‘AGG by AxD’ interaction was regressed against local cortical thickness in 202 healthy children from 6 to 18 years of age. Whole brain random field theory corrections were also applied to account for multiple comparisons.
As hypothesized, the ‘AGG by AxD’ interaction was significantly associated
(p≤.05, corrected) with the right subgenual ACC and the right lateral prefrontal cortical thickness (figure 5). There were also trends of associations (p≤.001, uncorrected) in some regions of interest including the left anterior insula and left medial prefrontal cortex (figure 6). Subsequently, an analysis centered at different
AxD levels revealed that, as anxious/depressed symptoms increased, the associations between AGG and cortical thickness became more negative in all four regions described above (figure 7) (134).
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FIGURE 5:
Local cerebral cortical thickness regressed against the interaction of CBCL
Anxious/Depressed and Aggressive Behavior (n = 202). Random field theory corrected results are displayed. Blue taint corresponds to areas significant at the cluster level; yellow‐orange taints correspond to areas significant at the vertex level.
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FIGURE 6:
Local cerebral cortical thickness regressed against the interaction of CBCL
Anxious/Depressed and Aggressive Behavior (n = 202). The color bar corresponds to p‐values and results are shown at a threshold of p≤.001, uncorrected.
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FIGURE 7 :
Associations between cortical thickness and CBCL Aggressive Behavior at increasing levels of CBCL Anxious/Depressed scores (Note: I = minimum observed AxD score, II
= mean level AxD score, III = one standard deviation above mean AxD score, IV = two standard deviations above mean AxD score, V = maximum observed AxD score).
Colors represent t‐statistic values. Blue‐purple taints correspond to negative associations; yellow‐red taints correspond to positive associations.
59
These results support previous research implicating the right SgACC and right lateral prefrontal cortex in the regulation of negative affects. The SgACC having important connections with limbic areas, decreased cortical thickness is compatible with increased affective dysregulation (54, 120, 123). The SgACC is also strongly linked to the hypothalamus and periaqueductal grey, therefore reduced grey matter might also be linked to impaired physiological response to external and internal cues associated with negative affects (135, 136). The lateral prefrontal cortex, particularly in the right hemisphere, has been implicated in response inhibition and behavioral control processes (137‐141). Thinner cortex in this area is compatible with difficulty in negative affect regulation and increased impulsive/reactive aggressive responses. This study finally supports some specificity to a phenotype of co‐occurring anxious/depressed symptoms and impulsive aggressive behaviors that would be in part anatomically distinct from children with high impulsive aggression without internalizing symptoms.
Integrative Models
Various authors have developed heuristic neurobiological models of impulsive aggression centered on the interaction of subcortical structures and prefrontal cortical areas. Coccaro et al. (2011) have divided the neural substrates of impulsive aggression into three systems (see figure below) (25). There would be a system supporting the subjective experience (subcortical including amygdala and hippocampus), a system evaluating consequences of aggressive behavior such as decision‐making circuits and social‐emotional information processing circuits
60
(rostral ACC, vmPFC, OMPFC, anterior insula), and finally a system underlying emotional regulation and impulse control (medial prefrontal structures, dorsal ACC,
DLPFC, dorsomedial PFC).
From Coccaro E, Sekhar Sripada C, Yanovitch R, Phan K. Corticolimbic function in impulsive aggresive behavior. Biol Psychiatry. 2011:1553-9.
In that model, aggressive impulses generated in the amygdala and the hippocampus secondary to environmental emotional cues and past memories are modulated by a neural network including the ACC, and cognitively appraised in the DLPFC. The decision to act out or retain aggressive impulses is influenced by value and social judgments primarily happening in the medial OFC, subgenual ACC and pregenual
ACC. Although there are significant interactions among prefrontal structures, Sterzer et al. (2009) have also suggested some specialized functions in the following schema
(7):
61
From Sterzer P, Stadler C. Neuroimaging of aggressive and violent behaviour in children and adolescents. Behav Neurosci. 2009;3:1-8.
According to these models, increased impulsive aggression levels could result from highly reactive to threats subcortical structures, and/or inadequate ‘top‐down’ modulation of amygdala activity by prefrontal structures. Siever et al. (2008) provided a similar schematized representation of the sequence of events leading to aggression, from stimuli perception to action (6). This schema also includes various internal and external factors influencing neural networks, and potential neurotransmitters involved at each steps (6).
62
From Siever L. Neurobiology of aggression and violence. Am J Psychiatry. 2008;165(4):429-42.
Our results, in conjunction with the Boes et al. (2008) results, support the crucial role of subgenual, rostral and dorsal ACC in the regulation of non‐ pathological aggression across development (40). One hypothesis would be that an underdeveloped ACC associated with less grey matter might be less efficient in tempering amygdala activity, although this is speculative with our observational design. This was a static finding that did not change as children became older, suggesting that neurobiological substrates linked to impulsive aggression are formed early in a child’s development. It further adds the idea that the striatum
63
plays a part in normative aggression. Given the role of the ventral striatum in reward prediction, neural activity of this area could be involved in both generation of impulses and modulation of the amygdala (142). Although this is the first description of a positive volumetric association between striatum and impulsive aggression among healthy children, striatum volume has been previously found to be larger in individuals with antisocial personality disorder (90). The precise role of basal ganglia in the integrated neural networks related to impulsive aggression should be investigated in future functional imaging studies.
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FINAL CONCLUSIONS
In the largest neuroanatomical study of impulsive aggression in healthy children, variations in structure of the right anterior cingulate cortex and bilateral striatum were found to be associated with CBCL Aggressive Behavior scores. More precisely, the right ACC had a quadratic association in which little association was present for subjects with very low scores (0‐2), while the association with cortical thickness was strongly negative at higher levels (3‐11). Bilateral striatal volume was positively associated with AGG scores in a first‐order linear model. Trends of gender‐specific associations between cortical thickness and AGG were also found in bilateral OFC and bilateral ACC. A follow‐up study determined that the presence of anxiety and depressive symptoms influenced the relationship between AGG and cortical thickness in the right subgenual ACC, right lateral prefrontal cortex, with trends in the left anterior insula and left medial prefrontal cortex. The associations between AGG and cortical thickness were more negative with increased anxious/depressed symptoms, suggesting broader deficits in negative affect regulation. This characterization of brain development in relationship with aggressive behaviors has the potential to guide future neuroimaging studies on clinical disorders such as oppositional‐defiant and conduct disorders, and to better understand comorbidities with anxiety and mood disorders.
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ELEMENTS OF ORIGINALITY
‐ Largest neuroanatomical study of aggressive behaviors in healthy
children.
‐ First study to demonstrate a quadratic relationship between impulsive
aggression and right anterior cingulate cortical thickness.
‐ First study to demonstrate an association between bilateral striatal
volume and impulsive aggression in healthy children.
‐ First study to demonstrate the impact of anxious/depressed symptoms on
the relationship between impulsive aggressive behavior and cortical
thickness.
66
REFERENCES
1. Evans A, Group BDC. The NIH MRI study of normal brain development.
NeuroImage. 2006;30:184-202.
2. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th Edition, Text Revision ed. Washington, DC: American Psychiatric
Association; 2000.
3. Atkins M, Stoff D. Instrumental and hostile aggression in childhood disruptive disorders. Journal of Abnormal Child Psychology. 1993;21(2):165-78.
4. Foster E, Jones D, The Conduct Problems Prevention Group. The high costs of aggression: public expenditures resulting from conduct disorder. American Journal of
Public Health. 2005;95(10):1767-72.
5. Soares R. The welfare cost of violence across countries. Journal of Health
Economics. 2006;25(5):821-46.
6. Siever L. Neurobiology of aggression and violence. Am J Psychiatry.
2008;165(4):429-42.
7. Sterzer P, Stadler C. Neuroimaging of aggressive and violent behaviour in children and adolescents. Behav Neurosci. 2009;3:1-8.
8. Vitiello B, Stoff D. Subtypes of aggression and their relevance to child psychiatry.
J Am Acad Child Adolesc Psychiatry. 1997;36(3):307-15.
9. Blair R. Neurocognitive models of aggression, the antisocial personality disorders, and psychopathy. J Neurol Neurosurg Psychiatry. 2001;71:727-31.
67
10. Fite PJ, Colder CR, Lochman JE, Wells KC. The relation between childhood proactive and reactive aggression and substance use initiation. J Abnorm Child Psychol.
2008;36(2):261-71.
11. Fite PJ, Stoppelbein L, Greening L. Proactive and reactive aggression in a child psychiatric inpatient population. J Clin Child Adolesc Psychol. 2009;38(2):199-205.
12. Murrie DC, Cornell DG, Kaplan S, McConville D, Levy-Elkon A. Psychopathy scores and violence among juvenile offenders: a multi-measure study. Behav Sci Law.
2004;22(1):49-67.
13. Vitacco MJ, Neumann CS, Caldwell MF, Leistico AM, Van Rybroek GJ. Testing factor models of the Psychopathy Checklist: Youth Version and their association with instrumental aggression. J Pers Assess. 2006;87(1):74-83.
14. Raine A, Dodge K, Loeber R, Gatzke-Kopp L, Lynam D, Reynolds C, et al. The
Reactive-Proactive Aggression Questionnaire: Differential Correlates of Reactive and
Proactive Aggression in Adolescent Boys. Aggress Behav. 2006;32(2):159-71.
15. Lagerspetz K, Björkqvist K, Peltonen T. Is indirect aggression typical of females?
Gender differences in aggressiveness in 11- to 12-year-old children. Aggressive Behavior.
1988;14(6):403-14.
16. Ligthart L, Bartels M, Hoekstra R, Hudziak J, Boomsma D. Genetic contributions to subtypes of aggression. Twin Research and Human Genetics. 2005;8(5):483-91.
17. Nock M, Kazdin A, Hiripi E, Kessler R. Lifetime prevalence, correlates, and persistence of oppositional defiant disorder: results from the National Comorbidity
Survey Replication. Journal of Child Psychology and Psychiatry. 2007;48(7):703-13.
68
18. Burke J, Loeber R, Birmaher B. Oppositional defiant disorder and conduct disorder: a review of the past 10 years, part II. J Am Acad Child Adolesc Psychiatry.
2002;41(11):1275-93.
19. Hudziak J, Copeland W, Stanger C, Wadsworth M. Screening for DSM-IV externalizing disorders with the Child Behavior Checklist: a receiver-operating characteristic analysis. Journal of Child Psychology and Psychiatry. 2004;45(7):1299-
307.
20. Frick P, Cornell A, Barry C, Bodin B, Dane H. Callous-unemotional traits and conduct problems in the prediction of conduct problem severity, aggression, and self- report of delinquency. Journal of Abnormal Child Psychology. 2002;31(4):457-70.
21. Moffitt T, Caspi A, Dickson N, Silva P, Stanton W. Childhood-onset versus adolescent-onset antisocial conduct problems in males: natural history from ages 3 to 18 years. Development and Psychopathology. 1996;8:399-424.
22. Biederman J, Faraone S. Attention-deficit hyperactivity disorder. Lancet.
2005;366:237-48.
23. Faraone S, Sergeant J, Gillberg C, Biederman J. The worldwide prevalence of
ADHD: is it an American condition? World Psychiatry. 2003;2(2):104-13.
24. Larson K, Russ S, Kahn R, Halfon N. Patterns of comorbidity, functioning, and service use for US children with ADHD, 2007. Pediatrics. 2011;127(3):462-70.
25. Coccaro E, Sekhar Sripada C, Yanovitch R, Phan K. Corticolimbic function in impulsive aggresive behavior. Biol Psychiatry. 2011:1553-9.
26. Davidson R, Putnam K, Larson C. Dysfunction in the neural circuitry of emotion- regulation-a possible preclude to violence. Science. 2000;289:591-4.
69
27. Nelson R, Trainor B. Neural mechanisms of aggression. Nat Rev Neurosci.
2007;8:536-46.
28. Coccaro E, McCloskey M, Fitzgerald D, Phan K. Amygdala and orbitofrontal reactivity to social threat in individuals with impulsive aggression. 62. 2007:168-78.
29. Amen D, Stubblefield M, Carmichael B, Thisted R. Brain SPECT findings and aggressiveness. Annals of Clinical Psychiatry. 1996;8(3):129-37.
30. Visser J, Bär P, Jinnah H. Lesch-Nyhan disease and the basal ganglia. Brain
Research Reviews. 2000;32:449-75.
31. Raine A, Meloy J, Bihrle S, Stoddard J, LaCasse L, Bushsbaum M. Reduced prefrontal and increased subcortical brain functioning assessed using positron emission tomography in predatory and affective murderers. Behav Sci Law. 1998;16:319-32.
32. Raine A, Lencz T, Bhihrle S, LaCasse L, Coletti P. Reduced prefrontal grey matter volume and reduced autonomic activity in antisocial personality disorder. Arch
Gen Psychiatry. 2000;57:119-27.
33. Raine A. Annotation: the role of prefrontal deficits, low autonomic arousal, and early heath factors in the development of antisocial and aggressive behaviour in children.
J Child Psychol Psychiat. 2002;43:417-34.
34. Brower M, Price B. Neuropsychiatry of frontal lobe dysfunction in violent and criminal behaviour: a critical review. J Neurol Neurosurg Psychiatry. 2001;71:720-6.
35. Vloet T, Konrad K, Huebner T, Herpertz S, Herpertz-Dahlmann B. Structural and functional MRI-findings in children and adolescents with antisocial behavior. Behav Sci
Law. 2008;26:99-111.
70
36. Blair R. The role of orbital frontal cortex in the modulation of antisocial behavior.
Brain and Cognition. 2004;55:198-208.
37. Huebner T, Vloet T, Marx I, Konrad K, Fink G, Herpetz S, et al. Morphometric brain abnormalities in boys with conduct disorder. J Am Acad Child Adolesc Psychiatry.
2008;47(5):540-7.
38. Sterzer P, Stadler C, Poustka F, Kleinschmidt A. A structural neural deficit in adolescents with conduct disorder and its association with lack of empathy. NeuroImage.
2007;37(1):335-42.
39. De Brito S, Mechelli A, Wilke M, Laurens K, Jones A, Barker G, et al. Size matters: increased grey matter in boys with conduct problems and callous-unemotional traits. Brain. 2009;132:843-52.
40. Boes A, Tranel D, Anderson S, Nopoulos P. Right anterior cingulate: a neuroanatomical correlate of aggression and defiance in boys. Behavioral Neuroscience.
2008;122(3):677-84.
41. Sterzer P, Stadler C, Krebs A, Kleinschmidt A, Poustka F. Abnormal neural responses to emotional visual stimuli in adolescents with conduct disorder. Biol
Psychiatry. 2005;57:7-15.
42. Herpertz S, Huebner T, Marx I, Vloet T, Fink G, Stoecker T, et al. Emotional processing in male adolescents with childhood-onset conduct disorder. J Child Psychol
Psychiat. 2008;49:781-91.
43. Foland-Ross L, Altshuler L, Bookheimer S, Lieberman M, Townsend J, Penfold
C, et al. Amygdala reactivity in healthy adults is correlated with prefrontal cortical thickness. J Neurosci. 2010;30(49):16673-8.
71
44. Ochsner K, Bunge S, Gross J, Gabrieli J. Rethinking feelings: An fMRI study of the cognitive regulation of emotion. J Cogn Neurosci. 2002;14:1215-29.
45. Etkin A, Egner T, Peraza D, Kandel E, Hirsch J. Resolving emotional conflict: a role for the rostral anterior cingulate cortex. Neuron. 2006;51(6):871-82.
46. Decety J, Michalska K, Akitsuki Y, Lahey B. Atypical empathic responses in adolescents with aggressive conduct disorder: a functional MRI investigation. Biol
Psychol. 2009;80:203-11.
47. Marsh A, Finger E, Mitchell D, Reid M, Sims C, Kosson D, et al. Reduced amygdala response to fearful expressions in children and adolescents with callous- unemotional traits and disruptibe behavior disorders. Am J Psychiatry. 2008;165:712-20.
48. Jones A, Laurens K, Herba C, Barker G, Viding E. Amygdala hypoactivity to fearful faces in boys with conduct problems and callous-unemotional traits. Am J
Psychiatry. 2009;166:95-102.
49. Blair R. Psychopathy, frustration, and reactive aggression: the role of ventromedial prefrontal cortex. Br J Psychol. 2010;101(383-99).
50. Rubia K, Halari R, Smith A, Mohammed M, Scott S, Giampietro V, et al.
Dissociated functional brain abnormalities of inshibition in boys with pure conduct disorder and in boys with pure attention deficit hyperactivity disorder. Am J Psychiatry.
2008;165:889-97.
51. Stevens F, Hurley R, Taber K. Anterior cingulate cortex: unique role in cognition and emotion. J Neuropsychiatry Clin Neurosci. 2011;23(2):120-5.
72
52. Palomero-Gallagher N, Vogt B, Schleider A, Mayberg H, Zilles K. Receptor architecture of human cingulate cortex: evaluation of the four-region neurobiological model. Hum Brain Mapp. 2009;30(8):2336-55.
53. Drevets W, Savitz J, Trimble M. The subgenual anterior cingulate cortex in mood disorders. CNS Spectr. 2008;13(8):663-81.
54. Hamani C, Mayberg H, Stone S, Laxton A, Haber S, Lozano A. The subcallosal cingulate gyrus in the context of major depression. Biol Psychiatry. 2011;69:301-8.
55. Price J, Drevets W. Neurocircuitry of mood disorders.
Neuropsychopharmacology. 2010;35:192-216.
56. Etkin A, Egner T, Kalisch R. Emotional processing in anterior cingulate and medial prefrontal cortex. Trends Cogn Sci. 2011;15:85-93.
57. Nimchinsky E, Glissen E, Allman J, Perl D, Erwin J, Hof P. A neuronal morphologic type unique to humans and great apes. Proc Natl Acad Sci USA.
1999;96(9):5268-73.
58. Allman J, Hakeem A, Erwin J, Nimchinsky E, Hof P. The anterior cingulate cortex. The evolution of an interface between emotion and cognition. Ann N Y Acad Sci.
2001;935:107-17.
59. Kringelbach M. The human orbitofrontal cortex: linking reward to hedonic experience. Nature Reviews Neuroscience. 2005;6:691-702.
60. Semendeferi K, Armstrong E, Schleider A, Zilles K, Van Hoesen G. Prefrontal cortex in humans and apes: a comparative study of area 10. American Journal of Physical
Anthropology. 2001;114:224-41.
73
61. Ongur D, Price J. The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex. 2000;10:206-19.
62. Drevets W. Orbitofrontal cortex function and structure in depression. Ann NY
Acad Sci. 2007;1121:499-527.
63. Rolls E. The orbitofrontal cortex and reward. Cereb Cortex. 2000;10(3):284-94.
64. Bechara A, Damasio H, Damasio A. Emotion, decision making and the orbitofrontal cortex. Cereb Cortex. 2000;10(3):295-307.
65. Petrovic P, Kalso E, Petersson K, Ingvar M. Placebo and opiod analgesia - imaging a shared neuronal network. Science. 2002;295:1737-40.
66. Shorter E. A history of psychiatry: From the era of asylum to the age of prozac
Wiley; 1998.
67. Giedd J, Blumenthal J, Jeffries N, Castellanos F, Liu H, Zijdenbos A, et al. Brain development during childhood and adolescence: a longitudinal MRI study. Nature
Neuroscience. 1999;2(10):861-3.
68. Shaw P, Eckstrand K, Sharp W, Blumenthal J, Lerch J, Greenstein D, et al.
Attention-deficit/hyperactivity disorder is characterized by a delay in cortical maturation.
PNAS. 2007;104(49):19649-54.
69. Blakemore S, Choudhury S. Brain development during puberty: state of the science. Developmental Science. 2006;9(1):11-4.
70. Sowell E, Thompson P, Tessner K, Toga A. Mapping continued brain growth and gray matter density reduction in dorsal frontal cortex: inverse relationships during postadolescent brain maturation. Journal of Neuroscience. 2001;21(22):8819-29.
74
71. Kim J, Singh V, MacDonald D, Lee J, Kim S, Evans A. Automated 3D extraction and evaluation of the outer cortical surface using a Laplacian map and partial volume effect classification. NeuroImage. 2005;27:210-21.
72. MacDonald D, Kabani N, Avis D, Evans A. Automated 3D extraction of inner and outer surfaces of cerebral cortex from MRI. NeuroImage. 2000;13:340-56.
73. Collins DL, Evans AC. Animal: Validation and Applications of Nonlinear
Registration-Based Segmentation. International Journal of Pattern Recognition and
Artificial Intelligence. 1997;11(8):1271-94.
74. Collins D, Holmes C, Peters T, Evans A. Automatic 3D model-based neuroanatomical segmentation. Hum Brain Mapp. 1995;3:190-208.
75. Karama S, Ad-Dab'bagh Y, Haier R, Deary I, Lyttelton O, Lepage C, et al.
Positive association between cognitive ability and cortical thickness in a representative
US sample of healthy 6 to 18 year-olds. Intelligence. 2009;37(2):145-55.
76. Pine D, Cohen E, Cohen P, Brook J. Adolescent depressive symptoms as predictors of adult depression: moodiness or mood disorder? Am J Psychiatry.
1999;156:133-5.
77. Bell-Dolan D, Last C, Strauss C. Symptoms of anxiety disorders in normal children. J Am Acad Child Adolesc Psychiatry. 1990;29(5):759-65.
78. Shaw P, Gilliam M, Liverpool M, Weddle C, Malek M, Sharp W, et al. Cortical development in typically developing children with symptoms of hyperactivity and impulsivity: support for a dimensional view of attention deficit hyperactivity disorder.
Am J Psychiatry. 2011;168:143-51.
75
79. Ducharme S, Hudziak J, Botteron K, Ganjavi H, Lepage C, Collins D, et al. Right anterior cingulate cortical thickness and bilateral striatal volume correlate with Child
Behavior Checklist aggressive behavior scores in healthy children. Biol Psychiatry.
2011;70(3):283-90.
80. Kruesi M, Casanova M, Mannheim G, Johnson-Bilder A. Reduced temporal lobe volume in early onset conduct disorder. Psychiatry Research: Neuroimaging. 2004;132:1-
11.
81. Hudziak J, van Beijsterveldt C, Bartels M, Rietveld M, Rettew D, Derks E, et al.
Individual differences in aggression: genetic analyses by age, gender, and informant in 3-,
7-, and 10-year-old Dutch twins. Behav Genet. 2003;33(5):575-89.
82. Grafman J, Schwab K, Warden D, Pridgen A, Brown H, Salazar A. Frontal lobe injuries, violence and aggression. Neurology. 1996;46.
83. Damasio H, Grabowski T, Frank R, Galaburda A, Damasio A. The return of
Phineas Gage: clues about the brain from the skull of a famous patient. Science.
1994;264(5162):1102-5.
84. Frankle W, Lombardo I, New A, Goodman M, Talbot P, Huang Y, et al. Brain serotonin transporter distribution in subjects with impulsive aggressivity: a position emission study with [11C]McN 5652. Am J Psychiatry. 2005;162(5):915-23.
85. Pietrini P, Guazzelli M, Basso G, Jaffe K, Grafman J. Neural correlates of imaginal aggressive behavior assessed by position emission tomography in healthy subjects. Am J Psychiatry. 2000;157(11):1772-81.
76
86. Kolb B. Studies on the caudate-putamen and the dorsomedial thalamic nucleus of the rat: implication for mammalian frontal-lobe functions. Physiology & Behavior.
1977;18:237-44.
87. de Almeida R, Benini Q, Betat J, Hipolide D, Miczek K, Svensson A. Heightened aggression after chronic flunitrazepam in male rates: potential links to cortical and caudate-putamen-binding sites. Psychopharmacology. 2008;197:309-18.
88. Chow T, Cummins J. Frontal-subcortical circuits. In: Miller B, Cummings J, editors. The human frontal lobe lobes-functions and disorders. New York, NY: Guilford
Press; 1999. p. 3.
89. Krishnan K. Brain imaging correlates. J Clin Psychiatry. 1999;60((suppl 15)):50-
4.
90. Glenn A, Raine A, Yaralian P, Yang Y. Increased volume of striatum in psychopathic individuals. Biol Psychiatry. 2010;67:52-8.
91. Nyhan W. Behavior in the Lesch-Nyhan syndrome. Journal of Autism and
Childhood Schizophrenia. 1976;6(3):235-52.
92. Stephens R, Sandor P. Aggressive behaviour in children with Tourette syndrome and comorbid attention-deficit hyperactivity disorder and obsessive-compulsive disorder.
Can J Psychiatry. 1999;44:1036-42.
93. Rosenblatt A, Leroi I. Neuropsychiatry of Huntington's disease and other basal ganglia disorders. Psychosomatics. 2000;41:24-30.
94. Petty R, Bonner D, Mouratoglou V, Silverman M. Acute frontal lobe syndrome and dyscontrol associated with bilateral caudate nucleus infarctions. British Journal of
Psychiatry. 1996;168:237-40.
77
95. Achenbach T. Manual for the Child Behavior Checklist/4-18.
Burlington:University of Vermont1991.
96. Achenbach T, Ruffle T. The Child Behavior Checklist and related forms for assessing behavioral/emotional problems and competencies Pediatrics in Review.
2000;21(1):265-71.
97. Achenbach T, Rescorla L. Manual for the ASEBA school-age forms & profiles.
Burlington: University of Vermont: Research Center for Children, Youth & Families;
2001.
98. Crijnen A, Achenbach T, Verlhuist F. Problems reported by parents of children in multiple cultures: The Child Behavior Checklist syndrome constructs. Am J Psychiatry.
1999;156:569-74.
99. Heubeck B. Cross-cultural generalizability of CBCL syndromes across three continents: from the USA and Holland to Australia. Journal of Abnormal Child
Psychology. 2000;28(5):439-50.
100. Stanger C, Achenbach T, Verhulst F. Accelerated longitudinal comparisons of aggressive versus delinquent syndromes. Developmental Psychopathology. 1997;9:43-58.
101. Hopwood C, Burt A, Markowitz J, Yen S, Shea M, Sanislow C, et al. The construct validity of rule-breaking and aggression in an adult clinical sample. Journal of
Psychiatric Research. 2009;43(8):803-8.
102. Bartels M, Hudziak J, van den Ord E, van Beijsterveldt C, Rietveld M, Boomsma
D. Co-occurence of aggressive behavior and rule-breaking behavior at age 12: multi-rater analyses. Behav Genet. 2003;33(5):607-21.
78
103. Hudziak J, van Beijsterveldt C, Bartels M, Rietveld M, Rettew D, Derks E, et al.
Individual differences in aggression: genetic analyses by age, gender, and informant in 3-,
7-, and 10-year-old Dutch twins. Behavior Genetics. 2003;33(5):575-89.
104. Hudziak J, Derks E, Althoff R, Copeland W, Boomsma D. The genetic and environmental contributions to oppositional defiant behavior: A multi-informant twin study. J Am Acad Child Adolesc Psychiatry. 2005;44:907-14.
105. Craig I, Halton K. Genetics of human aggressive behaviour. Hum Genet.
2009;126:101-13.
106. Rhee S, Waldman I. Genetics and environmental influences on antisocial behavior: a meta-analysis of twin and adoption studies. Psychol Bull. 2002;128:490-529.
107. Moffitt T. The new look of behavioral genetics in developmental psychopathology: Gene-environment interplay in antisocial behaviors. Psychological
Bulletin. 2005;131:533-54.
108. Elliott R, Dolan R, Frith C. Dissociable functions in the medial and lateral orbitofrontal cortex: evidence from human neuroimaging studies. Cereb Cortex.
2000;10(3):308-17.
109. Petrides M, Pandya D. Comparative architechtonics analysis of the human and macaque frontal cortex. In: Boller F, Grafman J, editors. Handbook of neuropsychology.
Amsterdam: Elsevier Science BV; 1994. p. 17-58.
110. De Brito SA, Mechelli A, Wilke M, Laurens KR, Jones AP, Barker GJ, et al. Size matters: increased grey matter in boys with conduct problems and callous-unemotional traits. Brain. 2009;132(Pt 4):843-52.
79
111. Waber D, De Moor C, Forbes P, Almli C, Botteron K, Leonard G, et al. The NIH
MRI study of normal brain development: Performance of a population based sample of healthy children aged 6 to 18 years on a neuropsychological battery. Journal of the
International Neuropsychological Society. 2007;13(5):729-46.
112. Sadock B, Sadock V, Ruiz P. Kaplan & Sadocks's comprehensive textbook of psychiatry. Philadelphia, PA: Lippincott Williams & Wilkins; 2009.
113. Achenbach TM. Manual for the Child behavior Checklist/4-18 and 1991 Profile.
Burlington, VT: University of Vermont Department of Psychiatry; 1991.
114. Ad-Dab'bagh Y, Lyttelton O, Muehlboeck J, Lepage C, Einarson D, Mok K, editors. The CIVET image-processing environment: A fully automated comprehensive pipeline for anatomical neuroimaging research. Proceedings of the 12th annual meeting of the organization for human brain mapping; 2006; Florence, Italy.
115. Lyttelton O, Boucher M, Robbins S, Evans A. An unbiased iterative group registration template for cortical surface analysis. NeuroImage. 2007;34(4):1535-44.
116. Worsley K, Taylor J, Tomaiuolo F, Lerch J. Unified univariate and multivariate random field theory. Neuroimage. 2004;23(Suppl 1):189-95.
117. Blumenfeld H. Neuroanatomy through clinical cases. Sunderland, MA: Sinauer
Associates, Inc.; 2002.
118. Mayberg H, Liotti M, Brannan S, McGinnis S, Mahurin R, Jerabek P, et al.
Reciprocal limbic-cortical function and negative mood: converging PET findings in depression and normal sadness. Am J Psychiatry. 1999;156(5):675-82.
80
119. Allman J, Hakeem A, Erwin J, Nimchinsky E, Hof P. The evolution of an interface between emotion and cognition. Annals New York Academy of Sciences.
2001;935:107-17.
120. Bush G, Luu P, Posner M. Cognitive and emotional influences in anterior cingulate cortex. Trends Cognitive Sci. 2000;4:215-22.
121. Drevets W. Prefrontal cortical-amygdalar metabolism in major depression. Ann
NY Acad Sci. 1999;877:614-37.
122. Ongür D, Drevets W, Price J. Glial reduction in the subgenual prefrontal cortex in mood disorders. PNAS. 1998;95(22):13290-5.
123. Vogt B, Pandya D. Cingulate cortex of the rhesus monkey: II. Cortical afferents. J
Comp Neurol. 1987;262:271-89.
124. Hazlett E, New A, Newmark R, Haznedar M, Lo J, Speiser L, et al. Reduced anterior and posterior cingulate gray matter in borderline personality disorder. Biol
Psychiatry. 2005;58:614-23.
125. Whittle S, Chanen A, Fornito A, McGorry P, Pantelis C, Yücel M. Anterior cingulate volume in adolescents with first-presentation borderline personality disorder.
Psychiatry Research: Neuroimaging. 2009;172:155-60.
126. Tebartz van Elst L, Hesslinger B, Thiel T, Geiger E, Haegele K, Lemieux L, et al.
Frontolimbic brain abnormalities in patients with borderline personality disorder: a volumetric magnetic resonance imaging study. Biol Psychiatry. 2003;54(2):163-71.
127. Goodman M, Hazlett E, Avedon J, Siever D, Chu K, New A. Anterior cingulate volume reduction in adolescents with borderline personality disorder and co-morbid depression. Journal of Psychiatric Research. 2010.
81
128. Robbins TW. Shifting and stopping: fronto-striatal substrates, neurochemical modulation and clinical implications. Philos Trans R Soc Lond B Biol Sci.
2007;362(1481):917-32.
129. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception II: Implications for major psychiatric disorders. Biol Psychiatry.
2003;54(5):515-28.
130. Davidson RJ, Putnam KM, Larson CL. Dysfunction in the neural circuitry of emotion regulation--a possible prelude to violence. Science. 2000;289(5479):591-4.
131. Lane RD, Reiman EM, Bradley MM, Lang PJ, Ahern GL, Davidson RJ, et al.
Neuroanatomical correlates of pleasant and unpleasant emotion. Neuropsychologia.
1997;35(11):1437-44.
132. Phillips ML, Drevets WC, Rauch SL, Lane R. Neurobiology of emotion perception I: The neural basis of normal emotion perception. Biol Psychiatry.
2003;54(5):504-14.
133. Phillips ML, Ladouceur CD, Drevets WC. A neural model of voluntary and automatic emotion regulation: implications for understanding the pathophysiology and neurodevelopment of bipolar disorder. Mol Psychiatry. 2008;13(9):829, 33-57.
134. Aiken L, West S. Multiple regression: testing and interpreting interactions.
Thousand Oaks, CA: Sage Publications, Inc; 1991.
135. Ongur D, An X, Price JL. Prefrontal cortical projections to the hypothalamus in macaque monkeys. J Comp Neurol. 1998;401(4):480-505.
82
136. An X, Bandler R, Ongur D, Price JL. Prefrontal cortical projections to longitudinal columns in the midbrain periaqueductal gray in macaque monkeys. J Comp
Neurol. 1998;401(4):455-79.
137. Aron AR, Fletcher PC, Bullmore ET, Sahakian BJ, Robbins TW. Stop-signal inhibition disrupted by damage to right inferior frontal gyrus in humans. Nat Neurosci.
2003;6(2):115-6.
138. Chikazoe J, Konishi S, Asari T, Jimura K, Miyashita Y. Activation of right inferior frontal gyrus during response inhibition across response modalities. J Cogn
Neurosci. 2007;19(1):69-80.
139. Garavan H, Ross TJ, Stein EA. Right hemispheric dominance of inhibitory control: an event-related functional MRI study. Proc Natl Acad Sci U S A.
1999;96(14):8301-6.
140. Konishi S, Nakajima K, Uchida I, Kikyo H, Kameyama M, Miyashita Y.
Common inhibitory mechanism in human inferior prefrontal cortex revealed by event- related functional MRI. Brain. 1999;122 ( Pt 5):981-91.
141. Konishi S, Nakajima K, Uchida I, Sekihara K, Miyashita Y. No-go dominant brain activity in human inferior prefrontal cortex revealed by functional magnetic resonance imaging. Eur J Neurosci. 1998;10(3):1209-13.
142. Pagnoni G, Zink C, Montague P, Berns G. Activity in human ventral striatum locked to errors of reward prediction. Nat Neurosci. 2002;5(2):97-8.
83