Polygenic Risk-Informed White Matter Integrity Improves Deep Learning-Based Prediction of Youth Depression

Early detection of youth depression is crucial, given its rising prevalence and long-term consequences. Although genetic factors contribute significantly to youth depression, their integration with neuroimaging remains limited. We present a deep learning framework using polygenic scores (PGS) to pretrain a 3D convolutional neural network on diffusion MRI (track-weighted fractional anisotropy), capturing gene–brain associations from a multi-ethnic cohort in the Adolescent Brain Cognitive Development Study (N=4,741). Fine-tuned for predicting depression, the model improved cross-sectional (N=266; AUC=0.62) and two-year predictions of depression and suicidality (AUC=0.61–0.66). It outperformed unimodal models, increasing accuracy by up to 24% over PGS-only and 5.8% over brain-only models. Explainable artificial intelligence identified key white matter tracts—superior longitudinal fasciculus, cingulum and corpus callosum—as predictive features. Decision curve analysis showed greater clinical utility. The model generalized to an independent Korean youth sample (N=108; AUC=0.67), supporting the cross-ethnic scalability of PGS-informed diffusion MRI for precision psychiatry