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, we analyzed in 343 adolescents with MDD aged 10 − 18 years, including 211 with a history of childhood trauma (MDD-CT) and 106 without childhood trauma (MDD-NCT), as well as 149 healthy controls. Machine learning models were applied to baseline functional network data to distinguish between treatment responders and non-responders. 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. Childhood trauma is an important risk factor for depression in adolescents. We used brain network analysis and machine learning to compare brain connection patterns in adolescents with depression who had or did not have childhood trauma, as well as healthy controls. We also examined whether treatment could improve these brain changes. We found that childhood trauma was linked to changes in brain networks involved in memory and self-related thinking. After treatment, some of these changes moved toward normal, especially in two brain regions involved in memory and emotion. We also found that baseline brain connection patterns could help predict who would respond to antidepressant treatment. These findings may support more personalized diagnosis and treatment for adolescent depression. Zhu, Huang, Li et al. investigate how childhood trauma influences functional brain network alterations and treatment response in adolescents with major depressive disorder using graph-theoretical analyses. Findings reveal disrupted connectivity within default mode networks, particularly in patients with childhood trauma.
Zhu et al. (Fri,) studied this question.