Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

Yicheng LONG, Hengyi CAO*, Chaogan YAN, Xiao CHEN, Le LI, Francisco Xavier CASTELLANOS, Tongjian BAI, Qijing BO, Guanmao CHEN, Ningxuan CHEN, Wei CHEN, Chang CHENG, Yuqi CHENG, Xilong CUI, Jia DUAN, Yiru FANG, Qiyong GONG, Wenbin GUO, Zhenghua HOU, Lan HULi KUANG, Feng LI, Kaiming LI, Tao LI, Yansong LIU, Qinghua LUO, Huaqing MENG, Daihui PENG, Haitang QIU, Jiang QIU, Yuedi SHEN, Yushu SHI, Tianmei SI, Chuanyue WANG, Fei WANG, Kai WANG, Li WANG, Xiang WANG, Ying WANG, Xiaoping WU, Xinran WU, Chunming XIE, Guangrong XIE, Haiyan XIE, Peng XIE, Xiufeng XU, Hong YANG, Jian YANG, Jiashu YAO, Shuqiao YAO, Yingying YIN, Yonggui YUAN, Aixia ZHANG, Hong ZHANG, Kerang ZHANG, Lei ZHANG, Zhijun ZHANG, Rubai ZHOU, Yiting ZHOU, Junjuan ZHU, Chaojie ZOU, Yufeng ZANG, Jingping ZHAO, Calais Kin-yuen CHAN, Weidan PU, Zhening LIU

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

12 Citations (Scopus)

Abstract

Background: Major depressive disorder (MDD) is known to be characterized by altered brain functional connectivity (FC) patterns. However, whether and how the features of dynamic FC would change in patients with MDD are unclear. In this study, we aimed to characterize dynamic FC in MDD using a large multi-site sample and a novel dynamic network-based approach. 

Methods: Resting-state functional magnetic resonance imaging (fMRI) data were acquired from a total of 460 MDD patients and 473 healthy controls, as a part of the REST-meta-MDD consortium. Resting-state dynamic functional brain networks were constructed for each subject by a sliding-window approach. Multiple spatio-temporal features of dynamic brain networks, including temporal variability, temporal clustering and temporal efficiency, were then compared between patients and healthy subjects at both global and local levels. 

Results: The group of MDD patients showed significantly higher temporal variability, lower temporal correlation coefficient (indicating decreased temporal clustering) and shorter characteristic temporal path length (indicating increased temporal efficiency) compared with healthy controls (corrected p < 3.14×10−3). Corresponding local changes in MDD were mainly found in the default-mode, sensorimotor and subcortical areas. Measures of temporal variability and characteristic temporal path length were significantly correlated with depression severity in patients (corrected p < 0.05). Moreover, the observed between-group differences were robustly present in both first-episode, drug-naïve (FEDN) and non-FEDN patients. 

Conclusions: Our findings suggest that excessive temporal variations of brain FC, reflecting abnormal communications between large-scale bran networks over time, may underlie the neuropathology of MDD.

Original languageEnglish
Article number102163
JournalNeuroImage: Clinical
Volume26
Early online date7 Jan 2020
DOIs
Publication statusPublished - 2020
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the China Precision Medicine Initiative (2016YFC0906300), the National Natural Science Foundation of China ( 81561168021 , 81671335 , 81701325 , 81671774 , 81630031 and 81801353 ), the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, the Beijing Nova Program of Science and Technology (Z191100001119104) and the Brain and Behavioral Research Foundation NARSAD Young Investigator Grant (No. 27068). The data of this study were provided by the members of the REST-meta-MDD Consortium. We thank Dr. Alan Anticevic and Jie Lisa Ji from Department of Psychiatry, Yale University School of Medicine, as well as Dr. Xinian Zuo from the Institute of Psychology, Chinese Academy of Sciences, for their suggestions in the preparation of the manuscript. We also thank Zhimin Xue, Xuan Ouyang and Guowei Wu from Department of Psychiatry, The Second Xiangya Hospital of Central South University for their assistance in data collection.

Publisher Copyright:
© 2020 The Authors

Keywords

  • Connectome
  • Default-mode
  • Depression
  • Dynamic functional connectivity
  • FMRI
  • Temporal variability

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