CNS neuroscience & therapeutics · 2026
Abnormal Dynamic Functional Connectivity Within the Fronto-Limbic Network Mediates the Association Between Depressive Symptoms and Non-Suicidal Self-Injury in Adolescents.
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Zhou Yongjie, Ni Shengmiao, Han Junrong, Yuan Binke, Luo Xinni, Li Haiyan, Yan Nan, Wang Lan, Zhang Wei, Zeng Lingyun, Weng Xuchu, Huo Lijuan
Аннотация
AIM: Non-suicidal self-injury (NSSI) is a prevalent behavior among adolescents with major depressive disorder (MDD), yet the precise neural mechanisms remain unclear. This study aimed to investigate the temporal dynamics of brain connectivity associated with NSSI in adolescents with MDD using dynamic functional connectivity (dFC) analysis.
METHODS: Resting-state fMRI data from 204 adolescents (154 with NSSI, 50 without) were analyzed. dFC variability within the fronto-limbic network was assessed using a seed-based dynamic conditional correlation approach. Group differences in dFC variability were examined, and a machine-learning model was used to predict NSSI based on dFC features. Mediation analysis explored the dFC's role in the relationship between depressive symptoms and NSSI.
RESULTS: Adolescents with NSSI exhibited reduced dFC variability, which mediated the relationship between depressive severity and NSSI behavior (a*b = 0.144; p = 0.001). Key connections-insula, anterior cingulate cortex, orbitofrontal cortex, and hippocampus-were critical in distinguishing NSSI from non-NSSI groups. Machine learning models based on these connections achieved robust and stable performance with mean AUC of 0.84 and PR-AUC of 0.94 in predicting NSSI.
CONCLUSIONS: Altered dFC within the fronto-limbic network may underlie NSSI in adolescents with MDD, identifying preliminary neural features for targeted interventions and highlighting neurobiological heterogeneity associated with NSSI in adolescents with MDD.
METHODS: Resting-state fMRI data from 204 adolescents (154 with NSSI, 50 without) were analyzed. dFC variability within the fronto-limbic network was assessed using a seed-based dynamic conditional correlation approach. Group differences in dFC variability were examined, and a machine-learning model was used to predict NSSI based on dFC features. Mediation analysis explored the dFC's role in the relationship between depressive symptoms and NSSI.
RESULTS: Adolescents with NSSI exhibited reduced dFC variability, which mediated the relationship between depressive severity and NSSI behavior (a*b = 0.144; p = 0.001). Key connections-insula, anterior cingulate cortex, orbitofrontal cortex, and hippocampus-were critical in distinguishing NSSI from non-NSSI groups. Machine learning models based on these connections achieved robust and stable performance with mean AUC of 0.84 and PR-AUC of 0.94 in predicting NSSI.
CONCLUSIONS: Altered dFC within the fronto-limbic network may underlie NSSI in adolescents with MDD, identifying preliminary neural features for targeted interventions and highlighting neurobiological heterogeneity associated with NSSI in adolescents with MDD.