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Adolescent psychiatric disorders arise from intricate interactions of clinical histories and disruptions in brain development. While connections between psychopathology and brain functional connectivity are studied, the use of deep learning to elucidate overlapping neural mechanisms through multimodal brain images remains nascent. Utilizing two adolescent datasets—the Philadelphia Neurodevelopmental Cohort (PNC, n = 1100) and the Adolescent Brain Cognitive Development (ABCD, n = 7536)—this study employs interpretable neural networks and demonstrates that incorporating brain morphology, along with functional and structural networks, augments traditional clinical characteristics (age, gender, race, parental education, medical history, and trauma exposure). Predictive accuracy reaches 0.37–0.464 between real and predicted general psychopathology and four psychopathology dimensions (externalizing, psychosis, anxiety, and fear). The brain morphology and connectivities within the frontoparietal, default mode network, and visual associate networks are recurrent across general psychopathology and four psychopathology dimensions. Unique structural and functional pathways originating from the cerebellum, amygdala, and visual‐sensorimotor cortex are linked with these individual dimensions. Consistent findings across both PNC and ABCD affirm the generalizability. The results underscore the potential of diverse sensory inputs in steering executive processes tied to psychopathology dimensions in adolescents, hinting at neural avenues for targeted therapeutic interventions and preventive strategies.
Xia et al. (Thu,) studied this question.