Journal of affective disorders · 2026
The impact of childhood trauma on brain structural connectivity in adolescent major depressive disorder: A graph-based analysis.
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Zhang Jingbo, Li Xuemei, Liu Mengqi, Huang Yang, Wang Wenjing, Zhu Tong, Yan Taocui, Yang Yuhang, Hu Linlin, Wang Jie, Li Qian, Qin Kun, Li Chao, McNamara Robert K, DelBello Melissa P, Lei Du, Zhou Xinyu
Аннотация
BACKGROUND: Childhood trauma (CT) significantly increases vulnerability to adolescent major depressive disorder (MDD), yet the underlying neurobiological mechanisms remain unclear.
METHODS: This study applied graph-theoretical analysis to structural MRI data to examine how CT influences brain structural networks and antidepressant treatment outcomes in adolescent MDD. A total of 339 adolescents with MDD and 152 healthy controls (HC) were recruited. The MDD group was subdivided into those with (MDD-CT; n = 209) and without (MDD-nCT; n = 106) a history of CT.
RESULTS: Compared with HC, both MDD subgroups exhibited a shift toward more randomized network properties. Notably, MDD-CT was characterized by more severe depressive and anxiety symptoms and showed focal disruptions within the default mode network (DMN), whereas MDD-nCT involved broader alterations across the central executive, salience, and DMN networks. Following antidepressant treatment, MDD-CT had a lower response rate than MDD-nCT (48.24% vs. 65.38%). Significant group-by-time interactions were observed in the right amygdala and caudate nucleus. Moreover, a machine learning model combining autoencoder-based feature extraction with support vector machine classification predicted treatment response with 89.8% accuracy.
CONCLUSIONS: This study revealed unique and common structural network abnormalities in MDD-CT and MDD-nCT, suggesting that childhood trauma leads to focused disruptions in the DMN. Treatment-induced changes in the amygdala and caudate nucleus may contribute to poorer outcomes in MDD-CT. These findings provide insights into the neurobiological underpinnings of trauma-related depression and highlight the potential of structural connectivity markers for guiding biomarker-informed treatment strategies in adolescent MDD.
METHODS: This study applied graph-theoretical analysis to structural MRI data to examine how CT influences brain structural networks and antidepressant treatment outcomes in adolescent MDD. A total of 339 adolescents with MDD and 152 healthy controls (HC) were recruited. The MDD group was subdivided into those with (MDD-CT; n = 209) and without (MDD-nCT; n = 106) a history of CT.
RESULTS: Compared with HC, both MDD subgroups exhibited a shift toward more randomized network properties. Notably, MDD-CT was characterized by more severe depressive and anxiety symptoms and showed focal disruptions within the default mode network (DMN), whereas MDD-nCT involved broader alterations across the central executive, salience, and DMN networks. Following antidepressant treatment, MDD-CT had a lower response rate than MDD-nCT (48.24% vs. 65.38%). Significant group-by-time interactions were observed in the right amygdala and caudate nucleus. Moreover, a machine learning model combining autoencoder-based feature extraction with support vector machine classification predicted treatment response with 89.8% accuracy.
CONCLUSIONS: This study revealed unique and common structural network abnormalities in MDD-CT and MDD-nCT, suggesting that childhood trauma leads to focused disruptions in the DMN. Treatment-induced changes in the amygdala and caudate nucleus may contribute to poorer outcomes in MDD-CT. These findings provide insights into the neurobiological underpinnings of trauma-related depression and highlight the potential of structural connectivity markers for guiding biomarker-informed treatment strategies in adolescent MDD.