Zhi Dongmei, Ma Xiaohong, Lv Luxian, Ke Qing, Yang Yongfeng, Yang Xiao, Pan Miao, Qi Shile, Jiang Rongtao, Du Yuhui, Yu Qingbao, Calhoun Vince D, Jiang Tianzi, Sui Jing
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:558-561. doi: 10.1109/EMBC.2018.8512340.
Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified abnormalities in large scale functional brain networks in MDD, yet most of them were based on static functional connectivity. By contrast, here we explored disrupted topological organization of dynamic functional network connectivity (dFNC) in MDD based on graph theory. 182 MDD patients and 218 healthy controls were included in this study, all Chinese Han people. By applying group information guided independent component analysis (GIG-ICA) on resting-state fMRI data, the dFNCs of each subject were estimated using a sliding window method and k-means clustering. Five dynamic functional states were identified, three of which demonstrated significant group difference on the percentage of state occurrence. Interestingly, MDD patients spent much more time in a weakly-connected state 2, which is associated with self-focused thinking, a representative feature of depression. In addition, the abnormal FNCs in MDD were observed connecting different networks, especially among prefrontal, sensorimotor and cerebellum networks. As to network properties, MDD patients exhibited increased node efficiency in prefrontal and cerebellum. Moreover, three dFNCs with disrupted node properties were commonly identified in different states, which are also correlated with depressive symptom severity and cognitive performance. This study is the first attempt to investigate the dynamic functional abnormalities in Chinese MDD using a relatively large sample size, which provides new evidence on aberrant time-varying brain activity and its network disruptions in MDD, which might underscore the impaired cognitive functions in this mental disorder.
重度抑郁症（MDD）是一种复杂的情绪障碍，其特征为持续且强烈的抑郁情绪。以往研究已发现MDD患者大脑大规模功能网络存在异常，但其中大多数基于静态功能连接。相比之下，我们在此基于图论探讨了MDD患者动态功能网络连接性（dFNC）的拓扑组织破坏情况。本研究纳入了182例MDD患者和218名健康对照，均为中国汉族人群。通过对静息态功能磁共振成像（fMRI）数据应用组信息引导的独立成分分析（GIG-ICA），采用滑动窗口法和k均值聚类估计每个受试者的dFNC。识别出五种动态功能状态，其中三种状态的出现百分比存在显著组间差异。有趣的是，MDD患者在弱连接状态2下花费的时间更多，该状态与自我聚焦思维相关，而自我聚焦思维是抑郁症的一个典型特征。此外，观察到MDD患者不同网络之间存在异常的功能连接，尤其是前额叶、感觉运动和小脑网络之间。在网络属性方面，MDD患者在前额叶和小脑的节点效率增加。此外，在不同状态下通常能识别出三种节点属性被破坏的dFNC，它们也与抑郁症状严重程度和认知表现相关。本研究首次尝试使用相对较大样本量调查中国MDD患者的动态功能异常，为MDD患者异常的随时间变化的脑活动及其网络破坏提供了新证据，这可能突出了这种精神障碍中受损的认知功能。
