重性抑郁障碍
萧条(经济学)
功能连接
多元分析
心理学
医学
精神科
神经科学
内科学
认知
宏观经济学
经济
作者
Song Cheng,Henry H. Y. Tong,Chaoqing Zhang,Lingyu Jian,Junjun Ji,Ting Li,Yu Wang,Junfeng Li,Kefeng Li
摘要
ABSTRACT Background and Aims Adolescent major depressive disorder (MDD) is prevalent globally but often goes unnoticed due to differences in symptoms compared to adult criteria. Analyzing the brain from a network perspective provides new insights into higher‐level brain functions and its pathophysiology. This study aimed to investigate changes in the topological organization of functional networks in adolescents with first‐episode, treatment‐naïve MDD. Method The study included 23 adolescents with depression and 27 matched healthy controls (HCs). Resting‐state functional MRI (rs‐fMRI) was conducted, and whole‐brain functional networks were constructed. Graph theory analysis was used to evaluate network topological properties. A machine‐learning multivariate diagnostic model was developed using network metrics associated with depression severity. Results Both the MDD and HC groups displayed small‐world topology, with male MDD patients showing reduced global clustering efficiency (Cp). The nodal Cp (NCp) and local efficiency (NLE) in the bilateral pallidum were significantly positively correlated with depression severity. In contrast, nodal efficiency (NE) in the left medial orbital superior frontal gyri (ORBsupmed) showed a negative correlation with disease severity. A machine‐learning multivariate model using regional network topological features produced an AUROC of 0.71 (95% CI: 0.54–0.92) and an F1 score of 0.65, successfully differentiating adolescent MDD from HCs. Conclusion Our findings suggest disruptions in small‐world topology in both global and local brain networks in adolescent depression. These abnormal nodal properties may serve as novel neural markers of the disorder.
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