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Background <p>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.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月24日

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  • Tong Zhu,
  • Yang Huang,
  • Xuemei Li,
  • Mengqi Liu,
  • Jingbo Zhang,
  • Taocui Yan,
  • Yuhang Yang,
  • Wenjing Wang,
  • Linlin Hu,
  • Jie Wang,
  • Qian Li,
  • Chao Li,
  • Robert K. McNamara,
  • Melissa P. DelBello,
  • Xinyu Zhou,
  • Du Lei

摘要

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.