Identifying Prepubertal Children with Risk for Suicide Using Deep Neural Network Trained on Multimodal Brain Imaging

Suicide is among the leading causes of death in youth worldwide. Early identification of children with high risk for suicide is a key to effective screening and intervention strategies. Yet, little is known about the neural pathways to the clinical outcomes of youth suicide. In this study, we tested brain functional substrates associated with the risk for youth suicidality. Based on the large, multi-site, multi-ethnic, representative, and prospective developmental population study in the US, we trained a state-of-the-art interpretable deep neural network on functional brain imaging, behavioral, and self-reported questionnaires. Our best model contains the functional estimates of key brain regions important for attention, emotion regulation, and motor coordination, such as the anterior cingulate cortex, temporal gyrus, and precentral gyrus. The interpretable neural network shows that these brain functional features interact with depression and impulsivity, the known risk factors of youth suicidality. This study demonstrates a novel application of the interpretable deep neural network to childhood suicidal research, uncovering the complex interactions between psychological and neural factors underlying youth suicidality.