• There is some indication from behavioral and molecular genetic research to suggest that the co-occurrence of externalizing and internalizing psychopathology may be genetically independent from internalizing and externalizing behavior in isolation (Hudziak, Althoff, Derks, Faraone, & Boomsma, 2005; McGough et al., 2008).

• Extending these findings, youth exhibiting co-occurring aggression and anxious-depressed symptoms may possess unique structural abnormalities within neural networks posited to mediate behavioral and affective regulation. Such networks include medial prefrontal, orbitofrontal, and dorsolateral prefrontal cortices, as well as the anterior insulae (Davidson, Putnam, & Larson, 2000).

• We predicted that co-occurrence of aggression and anxious-depressed symptoms would be uniquely associated with cerebral cortical morphology in the above regions.

FIGURE 3: The relation between cortical thickness and CBCL Aggressive Behavior at increasing levels of CBCL Anxious/Depressed scores (Note: I = minimum observed A/D score, II = mean level A/D score, III = one standard deviation above mean A/D score, IV = two standard deviations above mean A/D score, V = maximum observed A/D score). Colors represent t-statistic values associated with beta coefficient.  

• The co-occurrence of aggressive behavior and anxious-depressed symptoms was associated with specific morphological characteristics in cortical regions previously implicated in the regulation of negative affect and behavior (Davidson et al., 2000; Phillips et al., 2003a, 2003b; Phillips et al., 2008).

• Co-occurring CBCL AGG and A/D predicted reduced cortical thickness in right lateral prefrontal cortex and right subgenual anterior cingulate cortex, above and beyond what would be expected by the independent effects of AGG and A/D.

• Aggressive behavior occurring in conjunction with anxious-depressed symptoms may represent a class of conditions that is, to some degree, distinct from either “pure” internalizing or externalizing disorders alone.

Automated Image Processing

• Quality controlled native MR images were processed through the CIVET automated pipeline (version 1.1.9, 2006) (Ad-Dab'bagh et al., 2006). This pipeline includes the CLASP algorithm for generating cortical thickness measurements at 40,962 vertices per hemisphere (Ad-Dab'bagh et al., 2006; Collins, Holmes, Peters, & Evans, 1995; Kim et al., 2005; Lyttelton, Boucher, Robbins, & Evans, 2007; MacDonald, Kabani, Avis, & Evans, 2000).

• The NIH MRI Study of Normal Brain Development is a multi-site project that provides a normative database to study relations between healthy brain maturation and behavior (Evans, 2006).

• Subjects were recruited throughout the United States utilizing a population-based sampling method aimed at minimizing selection bias (Waber et al., 2007).

• The Objective 1 database used in this study included 431 children from 4:6 to 18:3  years who underwent extensive cognitive, neuropsychological and behavioral testing along with three MRI brain scans (two years between each visit). Data from the first visit were used in the present study.

• Quality controlled thickness data and CBCL data were available

for 202 subjects (113 females, 89 males).

ANXIOUS-DEPRESSED SYMPTOMS QUALIFY THE RELATION BETWEEN CEREBRAL CORTICAL STRUCTURE AND AGGRESSIVE BEHAVIOR IN TYPICALLY DEVELOPING YOUTHS

Introduction

Results

Analyses

Conclusions

Sample

Albaugh, M.D.,1 Hudziak, J.J,1 Ducharme, S.,2 Karama, S.,2 Botteron, K.N.,3 Althoff, R.R.,1 Evans, A.C.,2 & The Brain Development Cooperative Group

1University of Vermont College of Medicine, 2McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University,

3School of Medicine, Washington University in St. Louis

References

Child Behavior Checklist (CBCL)

• The Child Behavior Checklist (CBCL) asks parents to report on specific behaviors exhibited by their child within the past 6 months (Achenbach, 1991; Achenbach & Rescorla, 2001).

• CBCL Aggressive Behavior (AGG )and Anxious/Depressed (A/D) raw scores were utilized, as well as the cross product of these two scales.

 

 • Statistical analyses were implemented using SurfStat, a toolbox

created for MATLAB 7 (The MathWorks, Inc.) by Dr. Keith Worsley (http://wiki.bic.mni.mcgill.ca/index.php/SurfStat).

• For each subject, cortical thickness measures were linearly regressed against the cross product of CBCL AGG and A/D scores at each point on the cerebral surface.

• Age, gender, total brain volume, IQ, and scanner site were linearly controlled for in this analysis, as were the independent effects of A/D and AGG.

• To account for multiple comparisons, random field theory (RFT) correction was applied to the entire cortical surface (Worsley, Taylor, Tomaiuolo, & Lerch, 2004). In a priori ROIs, an uncorrected p <. 001 threshold was used to identify trends of associations.

• Significant associations were found between cerebral cortical thickness and the cross product of A/D and AGG raw scores.

• Findings that survived whole-cortex RFT correction were in the right lateral prefrontal cortex and right subgenual anterior cingulate cortex (Figure 1).

• Trend-level associations were revealed in the following a priori regions: left anterior insula and left medial prefrontal cortex (Figure 2).

• Across these regions, decomposition of the interaction revealed that greater A/D scores were accompanied by more robust negative associations between AGG and cortical thickness (Figure 3).

FIGURE 2: Cerebral cortical thickness regressed against the interaction of CBCL Anxious/Depressed and Aggressive Behavior (n = 202). Results are shown at a threshold of p < .001, uncorrected.

FIGURE 1: Cerebral cortical  thickness regressed against the interaction of CBCL Anxious/Depressed and Aggressive Behavior (n = 202). Results are displayed at p<.05, RFT corrected.  Blue shades correspond to significant associations at the cluster level and orange shades at the vertex level.

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Achenbach, T. M., & Rescorla, L. A. (2001). Manual for the ASEBA school-age forms & profiles. Burlington VT: University of Vermont, Research Center for Children,Youth, and Families.

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Worsley, K. J., Taylor, J. E., Tomaiuolo, F., & Lerch, J. (2004). Unified univariate and multivariate random field theory. Neuroimage, 23 Suppl 1, S189-195.

Measures

The MRI Study of Normal Brain Development