Jian Song

Jan 4, 2025 | nature.com | Yudie Hu |Jian Song |Jingsong Wu |Youze He |Zhihan Liu |Baoru Zhao

Accumulating studies have highlighted the links between stress-related networks and the HPA axis for emotion regulation and proved the mapping associations between altered structural and functional networks (called SC-FC coupling) in depression. However, the signatures of SC-FC coupling in subthreshold depression (StD) individuals and their relationships with HPA axis reactivity, as well as the predictive power of these combinations for discriminating StD, remain unclear. This cross-sectional study enrolled 160 adults, including 117 StD and 43 healthy controls (HC). The propensity score matching method was applied for match-pair analysis between StD and HC. Herein, we measured depression level, cortisol level, and brain imaging outcomes. The functional MRI and diffusion tensor imaging methods were employed to acquire the network SC-FC couplings and topological attributes. Support vector machine models were employed to discriminate StD from HC. Herein, 43 pairs were matched, but four participants were excluded due to over-threshold head motion, leaving 41 participants in each group. General linear model results revealed a significant SC-FC coupling increase in the default mode network (DMN) and decrements of global efficiency in DMN and frontoparietal control network (P < 0.05), while the cortisol secretion significantly increased (P < 0.001) in StD individuals. Partial correlation analysis revealed positive associations between DMN coupling and cortisol values (r = 0.298, P = 0.033), and their combination provided greater power for discriminating StD than another single model, with the classification accuracy and AUC value up to 85.71% and 0.894, respectively. In summary, this study clarified the relationship between stress-related network SC-FC coupling and cortisol secretion in influencing depressive symptoms, whose combination would contribute to discriminating subthreshold depressive states in the future.

Oct 27, 2023 | journalofinfection.com | Zhili Jin |Jingxuan Wu |Ying Wang |Tao Huang |Kexin Zhao |Jian Liu | +15 more

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