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

Abstract

Background

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.

Methods

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.

Results

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.

Conclusion

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.

Plain Language Summary

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.