Brain morphology of childhood aggressive behavior: A multi-informant study in school-age children

Cognitive, affective & behavioral neuroscience(2015)

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摘要
Objective Few studies have focused on the neuroanatomy of aggressive behavior in children younger than 10 years. Here, we explored the neuroanatomical correlates of aggression in a population-based sample of 6- to 9-year-old children using a multiple-informant approach. Methods Magnetic resonance (MR) scans were acquired from 566 children from the Generation R study who participated in the Berkeley Puppet Interview and whose parents had completed the Child Behavior Checklist. Linear regression analyses were used to examine associations between aggression and amygdala and hippocampal volume. We performed surface-based analyses to study the association between aggression and cortical thickness, surface area, and gyrification. Results Aggressive behavior was associated with smaller amygdala ( p < .05) but not hippocampal volume. Aggression was associated with a thinner cortex in the left precentral cortex ( p < .01) and in a cluster including the right inferior parietal, supramarginal, and postcentral cortex ( p < .001). Gender moderated the association between aggression and cortical thickness in the right medial posterior cortex ( p = .001) and the right prefrontal cortex ( p < .001). Aggression was associated with decreased gyrification in a large cluster including the right precentral, postcentral, frontal, and parietal cortex ( p = .01). Moreover, aggression was associated with decreased gyrification in the right occipital and parietal cortex ( p = .02). Conclusion We found novel evidence that childhood aggressive behavior is related to decreased amygdala volume, decreased sensorimotor cortical thickness, and decreased global right hemisphere gyrification. Aggression is related to cortical thickness in regions associated with the default mode network, with negative associations in boys and positive associations in girls.
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关键词
Childhood aggression,Structural MRI,Sensorimotor cortex,Default mode network,Multiple-informant
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