Shen Yuedi, Yao Jiashu, Jiang Xueyan, Zhang Lei, Xu Luoyi, Feng Rui, Cai Liqiang, Liu Jing, Wang Jinhui, Chen Wei
The Affiliated Hospital of Hangzhou Normal University, Hangzhou Normal University, Hangzhou, Zhejiang, China.
Department of Psychiatry, Sir Run Run Shaw Hospital, Zhejiang University School of Medical and the Collaborative Innovation Center for Brain Science, Hangzhou, Zhejiang, China.
Hum Brain Mapp. 2015 Aug;36(8):2915-27. doi: 10.1002/hbm.22817. Epub 2015 Apr 30.
Accumulating evidence suggests that early improvement after two-week antidepressant treatment is predictive of later outcomes of patients with major depressive disorder (MDD); however, whether this early improvement is associated with baseline neural architecture remains largely unknown. Utilizing resting-state functional MRI data and graph-based network approaches, this study calculated voxel-wise degree centrality maps for 24 MDD patients at baseline and linked them with changes in the Hamilton Rating Scale for Depression (HAMD) scores after two weeks of medication. Six clusters exhibited significant correlations of their baseline degree centrality with treatment-induced HAMD changes for the patients, which were mainly categorized into the posterior default-mode network (i.e., the left precuneus, supramarginal gyrus, middle temporal gyrus, and right angular gyrus) and frontal regions. Receiver operating characteristic curve and logistic regression analyses convergently revealed excellent performance of these regions in discriminating the early improvement status for the patients, especially the angular gyrus (sensitivity and specificity of 100%). Moreover, the angular gyrus was identified as the optimal regressor as determined by stepwise regression. Interestingly, these regions possessed higher centrality than others in the brain (P < 10(-3)) although they were not the most highly connected hubs. Finally, we demonstrate a high reproducibility of our findings across several factors (e.g., threshold choice, anatomical distance, and temporal cutting) in our analyses. Together, these preliminary exploratory analyses demonstrate the potential of neuroimaging-based network analysis in predicting the early therapeutic improvement of MDD patients and have important implications in guiding earlier personalized therapeutic regimens for possible treatment-refractory depression.
越来越多的证据表明,重度抑郁症(MDD)患者在接受两周抗抑郁治疗后的早期改善情况可预测其后期预后;然而,这种早期改善是否与基线神经结构相关,在很大程度上仍不清楚。本研究利用静息态功能磁共振成像数据和基于图谱的网络分析方法,计算了24例MDD患者基线时的体素水平度中心性图谱,并将其与用药两周后的汉密尔顿抑郁量表(HAMD)评分变化相关联。六个脑区簇的基线度中心性与患者治疗引起的HAMD变化存在显著相关性,这些脑区主要分为后默认模式网络(即左侧楔前叶、缘上回、颞中回和右侧角回)和额叶区域。受试者工作特征曲线和逻辑回归分析一致显示,这些脑区在区分患者早期改善状态方面表现出色,尤其是角回(敏感性和特异性均为100%)。此外,通过逐步回归确定角回为最佳回归变量。有趣的是,这些脑区在大脑中的中心性高于其他脑区(P < 10^(-3)),尽管它们并非连接性最高的枢纽。最后,我们在分析中证明了我们的研究结果在几个因素(如阈值选择、解剖距离和时间截断)上具有高度可重复性。总之,这些初步探索性分析证明了基于神经影像学的网络分析在预测MDD患者早期治疗改善方面的潜力,对指导可能难治性抑郁症的早期个性化治疗方案具有重要意义。