Paper Info
Title
Abnormal Dynamic Functional Network Connectivity and Graph Theoretical Analysis in Major Depressive Disorder.
Abstract
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.
Year
DOI
Venue
2018
10.1109/EMBC.2018.8512340
EMBC
Field
DocType
Volume
Computer vision,Mood,Neuroscience,Functional magnetic resonance imaging,Computer science,Brain activity and meditation,Correlation,Artificial intelligence,Major depressive disorder,Cognition,Effects of sleep deprivation on cognitive performance,Sample size determination
Conference
2018
Citations 
PageRank 
References 
0
0.34
0
Authors
14
Name
Order
Citations
PageRank
Dongmei Zhi100.68
Xiaohong Ma240.71
Luxian Lv300.34
Qing Ke401.01
Yongfeng Yang562.21
Xiao Yang62210.06
Miao Pan7234.58
Shile Qi800.34
Rongtao Jiang900.68
Yuhui Du10477.60
Qingbao Yu1152.21
Vince D Calhoun122769268.91
T. Jiang131419128.59
Jing Sui1418717.11