Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder

作者：Hou, Zhenghua; Wang, Zan; Jiang, Wenhao; Yin, Yingying; Yue, Yingying; Zhang, Yuqun; Song, Xiaopeng; Yuan, Yonggui^*

来源：Scientific Reports, 2016, 6(1): 39243.

DOI：10.1038/srep39243

• GRAPH-THEORETICAL ANALYSIS
• FUNCTIONAL CONNECTIVITY
• EARLY IMPROVEMENT
• BRAIN NETWORKS
• ORGANIZATION
• DYSFUNCTION
• CINGULATE
• BURDEN
• MATTER
• MEMORY

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (D-nodal) in the left anterior cingulate gyrus; as for RD, the Dnodal of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P < 0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the. and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication.

• 出版日期2016-12-14
• 单位上海交通大学; 东南大学; 北京大学