Randomized EEG functional brain networks in major depressive disorders with greater resilience and lower rich-club coefficient

被引:58
作者
Zhang, Minghui [1 ,2 ,3 ]
Zhou, Haiyan [1 ,2 ,3 ]
Liu, Liqing [1 ,2 ,3 ]
Feng, Lei [4 ,5 ,6 ,7 ]
Yang, Jie [4 ,5 ,6 ,7 ]
Wang, Gang [4 ,5 ,6 ,7 ]
Zhong, Ning [1 ,2 ,3 ,8 ,9 ]
机构
[1] Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China
[2] Beijing Int Collaborat Base Brain Informat & Wisd, Beijing, Peoples R China
[3] Beijing Key Lab Magnet Resonance Imaging & Brain, Beijing, Peoples R China
[4] Capital Med Univ, Beijing Anding Hosp, Mood Disorders Ctr, Beijing, Peoples R China
[5] Natl Clin Res Ctr Mental Disorders, Beijing, Peoples R China
[6] Beijing Key Lab Mental Disorders, Beijing, Peoples R China
[7] Capital Med Univ, Beijing Anding Hosp, Beijing, Peoples R China
[8] Beijing Univ Technol, Beijing Adv Innovat Ctr Future Internet Technol, Beijing, Peoples R China
[9] Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gunma, Japan
基金
中国国家自然科学基金; 对外科技合作项目(国际科技项目);
关键词
Major depressive disorder; Resting-state EEG; Functional brain network; Graph theoretic analysis; Network metrics; RESTING-STATE; CORTICAL NETWORKS; MEDICATION-NAIVE; LOW-FREQUENCY; CONNECTIVITY; ORGANIZATION; TOPOLOGY; DYNAMICS; PATTERNS; REMOVAL;
D O I
10.1016/j.clinph.2018.01.017
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Objective: Some studies have shown that the functional electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) networks in those with major depressive disorders (MDDs) have an abnormal random topology. In this study we aimed to further investigate the characteristics of the randomized functional brain networks in MDDs by examining resting-state scalp-EEG data. Methods: Based on the methods of independent component analysis (ICA) and graph theoretic analysis, the abnormalities in the power spectral density (PSD) functional brain networks were compared between 13 MDDs and 13 matched healthy controls (HCs). Nonparametric permutation tests were performed to explore the between-group differences in multiple network metrics. The Pearson correlation coefficients were calculated to measure the linear relationships between the clinical symptom and network metrics. Results: Compared with the HCs, the MDDs showed significant randomization of global network metrics, characterized by greater global efficiency, but lower clustering coefficient, characteristic path length, and local efficiency. This randomization was also reflected in the less heterogeneous and less fat-tailed degree distributions in the MDDs. More importantly, the randomized brain networks in MDDs had greater network resilience to both random failure and targeted attack, which might be a protective mechanism to avoid fast deterioration of the integrity of MDDs' brain networks under pathological attack. In addition, the randomized brain networks in MDDs had a lower level of rich-club coefficient, suggesting that the density of connections among rich-club hubs became sparser. Furthermore, some of the network metrics explored in this study were significantly associated with the severity of depression in all participants. Conclusions: A replicable randomization of the brain network is found in MDDs. The randomization is further characterized by more homogeneous degree distribution, greater resilience and lower rich-club coefficient, reflecting the reconfiguration of the brain network caused by the reduction of hub nodes in MDD. Significance: Our results may provide new biomarkers of brain network organization in MDD. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:743 / 758
页数:16
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