Minghui Zhang1, Haiyan Zhou2, Liqing Liu1, Lei Feng3, Jie Yang3, Gang Wang3, Ning Zhong4. 1. Faculty of Information Technology, Beijing University of Technology, Beijing, China; Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing China. 2. Faculty of Information Technology, Beijing University of Technology, Beijing, China; Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing China. Electronic address: zhouhaiyan@bjut.edu.cn. 3. Mood Disorders Center, Beijing Anding Hospital, Capital Medical University, Beijing, China; The National Clinical Research Center for Mental Disorders, China; Beijing Key Laboratory of Mental Disorders, China; Beijing Anding Hospital, Capital Medical University, Beijing, China. 4. Faculty of Information Technology, Beijing University of Technology, Beijing, China; Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing China; Beijing Advanced Innovation Center for Future Internet Technology, Beijing University of Technology, Beijing, China; Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan. Electronic address: zhong@maebashi-it.ac.jp.
Abstract
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.
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.