Zhijun Yao1, Jie Shi1, Zhe Zhang2, Weihao Zheng2, Tao Hu3, Yuan Li4, Yue Yu1, Zicheng Zhang1, Yu Fu1, Ying Zou1, Wenwen Zhang5, Xia Wu6, Bin Hu7. 1. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, PR China. 2. College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang Province 310027, PR China. 3. Graduate School of Informatics, Nagoya University, Nagoya, Aichi 464-8601, Japan. 4. School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong Province 250358, PR China. 5. Department of Radiology, Gansu Provincial Hospital, Lanzhou, Gansu Province 730000, PR China. 6. College of Information Science and Technology, Beijing Normal University, Beijing 100000, PR China. Electronic address: wuxia@bnu.edu.cn. 7. School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu Province 730000, PR China. Electronic address: bh@lzu.edu.cn.
Abstract
OBJECTIVE: Major depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state). METHODS: We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDD patients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored. RESULTS: Two distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDD patients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity. CONCLUSIONS: Our findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD. SIGNIFICANCE: These findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.
OBJECTIVE: Major depressive disorder (MDD) is accompanied by abnormal changes in dynamic functional connectivity (FC) among brain regions. The aim of this study is to investigate whether the abnormalities of dynamic FC in MDD are state-dependent (related to a specific connectivity state). METHODS: We performed time-varying connectivity analysis on resting-state functional magnetic resonance imaging (rs-fMRI) of 49 MDDpatients and 54 matched healthy controls (HCs). FC differences between groups in each connectivity state were analyzed and associations between disease severity and dynamics of aberrant FC were explored. RESULTS: Two distinct connectivity states (i.e., weakly-connected and strongly-connected state) were identified. Compared to HCs, MDDpatients were associated with increased mean dwell time and decreased FC between and within subnetworks in the weakly-connected state. Dynamics of reduced FC between cognitive control network and default mode network as well as within cognitive control network predicted individual differences in depression symptom severity. CONCLUSIONS: Our findings suggested that the MDD-caused FC alterations mostly appeared in the weakly-connected state, which might contribute to clinical diagnosis of MDD. SIGNIFICANCE: These findings provide new perspectives for understanding the state-dependent neurophysiological mechanisms in MDD.
Authors: Dmitry D Bezmaternykh; Mikhail Ye Melnikov; Andrey A Savelov; Lyudmila I Kozlova; Evgeniy D Petrovskiy; Kira A Natarova; Mark B Shtark Journal: Neural Plast Date: 2021-01-15 Impact factor: 3.599