Large-Scale Functional Brain Networks of Maladaptive Childhood Aggression Identified by Connectome-Based Predictive Modeling

Abstract Disruptions in frontoparietal networks supporting emotion regulation have been long implicated in maladaptive childhood aggression. However, the association of connectivity between large-scale functional networks in the human connectome with aggressive behavior has not been tested. By using a data-driven, machine learning approach, we show that the functional organization of the connectome during emotion processing predicts severity of aggression in children (n=129). Connectivity predictive of aggression was identified within and between large-scale networks implicated in cognitive control (frontoparietal), social functioning (default mode), and emotion processing (subcortical). Out-of-sample replication and generalization of findings predicting aggression from the functional connectome was conducted in an independent sample of children from the Adolescent Brain Cognitive Development study (n=1,791; n=1,701). These results define novel connectivity-based networks of child aggression that can serve as biomarkers to inform targeted treatments for aggression.