Abstract

Suicide is one of the leading causes of death in youth in the US. Previous studies have mainly attempted to examine structural neural correlates of suicide risk using pre-specified brain regions. The current study implements a machine-learning (ML) algorithm to examine whole-brain structural brain alterations in adolescents at suicide risk relative to typically developing (TD) adolescents.

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