Graph theory reveals functional connectome disruptions in adolescent major depressive disorder with childhood trauma

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Abstract
Childhood trauma (CT) is a major risk factor for adolescent major depressive disorder (MDD), yet its neurobiological underpinnings and longitudinal treatment effects remain poorly characterized. Leveraging graph theory and resting-state fMRI in 343 adolescents with MDD (211 with CT history [MDD-CT], 106 without [MDD-NCT]) and 149 healthy controls, we identified CT-associated functional connectome disruptions marked by increased network randomness and topological deficits in default mode network (DMN) hubs (left parahippocampal gyrus, posterior cingulate gyrus, temporal pole). Longitudinal neuroimaging revealed post-treatment normalization of these abnormalities, particularly in the left precuneus and amygdala, paralleling symptom improvement. Machine learning models using baseline connectomes predicted antidepressant response with 82% accuracy. Our findings establish CT-driven connectome disturbances in adolescent MDD, map dynamic network reorganization to therapeutic recovery, and position functional connectivity as a clinically actionable biomarker. This work bridges neurobiological mechanisms of trauma-related depression with precision treatment strategies, offering a path toward biomarker-guided interventions.

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