Chen Nan, Shi Jie, Li Yongchao, Ji Shanling, Zou Ying, Yang Lin, Yao Zhijun, Hu Bin
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
J Psychiatr Res. 2021 Jan;133:197-204. doi: 10.1016/j.jpsychires.2020.12.018. Epub 2020 Dec 15.
Major Depressive Disorder (MDD) is increasingly recognized as a common brain disorder with aberrant brain networks. Alterations in dynamic functional brain networks have been widely reported in MDD. However, previous studies mainly focused on detecting non-overlapping sub-networks/communities, neglecting the possibility that one brain region may belong to multiple sub-networks/communities. In the present work, we utilized tensor decomposition method to detect overlapping communities and study the dynamism of overlapping sub-networks through 58 patients with MDD and 63 age- and sex-matched healthy controls (HC). The strength vectors of communities were calculated and two-sample t-test was performed to investigate the statistical significance of the differences in dynamism of MDD and HC groups. We found that communities detected in two groups were pairwise region-matching but overlapped brain regions were almost totally different. We considered two region-matching communities in the two groups as a sub-network. Compared to HCs, MDD patients showed significantly decreased dynamism in five sub-networks which could be functionally mapped to Visual Network (VN), Default Mode Network (DMN), Cognitive Control Network (CCN), Bilateral Limbic Network (BLN) and Auditory Network (AN). The results showed that MDD might only have a marginal effect on the holistic detection of communities and the changes of overlapped brain regions in MDD patients might be put down to the alteration of hubs. Further statistical analysis on nine sub-networks showed decreased dynamism of five sub-networks in MDD patients, which might help us achieve a better understanding of mechanism in MDD.
重度抑郁症(MDD)日益被认为是一种伴有大脑网络异常的常见脑部疾病。MDD患者动态功能脑网络的改变已被广泛报道。然而,以往的研究主要集中在检测不重叠的子网/社区,而忽略了一个脑区可能属于多个子网/社区的可能性。在本研究中,我们利用张量分解方法来检测重叠社区,并通过58例MDD患者和63例年龄及性别匹配的健康对照(HC)来研究重叠子网的动态变化。计算社区的强度向量,并进行两样本t检验,以研究MDD组和HC组在动态变化方面差异的统计学意义。我们发现两组中检测到的社区在区域上两两匹配,但重叠的脑区几乎完全不同。我们将两组中两个区域匹配的社区视为一个子网。与HC相比,MDD患者在五个子网中的动态变化显著降低,这些子网在功能上可映射到视觉网络(VN)、默认模式网络(DMN)、认知控制网络(CCN)、双侧边缘网络(BLN)和听觉网络(AN)。结果表明,MDD可能仅对社区的整体检测有轻微影响,MDD患者重叠脑区的变化可能归因于枢纽的改变。对九个子网的进一步统计分析表明,MDD患者五个子网的动态变化降低,这可能有助于我们更好地理解MDD的发病机制。