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 HU; Li 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

doi:10.1016/j.nicl.2020.102163

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 Publications › Journal Article (refereed) › peer-review

54 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 language	English
Article number	102163
Journal	NeuroImage: Clinical
Volume	26
Early online date	7 Jan 2020
DOIs	https://doi.org/10.1016/j.nicl.2020.102163
Publication status	Published - 2020
Externally published	Yes

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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LONG, Y., CAO, H., YAN, C., CHEN, X., LI, L., CASTELLANOS, F. X., BAI, T., BO, Q., CHEN, G., CHEN, N., CHEN, W., CHENG, C., CHENG, Y., CUI, X., DUAN, J., FANG, Y., GONG, Q., GUO, W., HOU, Z., ... LIU, Z. (2020). Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium. NeuroImage: Clinical, 26, Article 102163. Advance online publication. https://doi.org/10.1016/j.nicl.2020.102163

@article{eb4d146b8ab04dd7a76d027d18189e1c,

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

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-na{\"i}ve (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.",

keywords = "Connectome, Default-mode, Depression, Dynamic functional connectivity, FMRI, Temporal variability",

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

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: {\textcopyright} 2020 The Authors",

year = "2020",

doi = "10.1016/j.nicl.2020.102163",

language = "English",

volume = "26",

journal = "NeuroImage: Clinical",

issn = "2213-1582",

publisher = "Elsevier BV",

}

LONG, Y, CAO, H, YAN, C, CHEN, X, LI, L, CASTELLANOS, FX, BAI, T, BO, Q, CHEN, G, CHEN, N, CHEN, W, CHENG, C, CHENG, Y, CUI, X, DUAN, J, FANG, Y, GONG, Q, GUO, W, HOU, Z, HU, L, KUANG, L, LI, F, LI, K, LI, T, LIU, Y, LUO, Q, MENG, H, PENG, D, QIU, H, QIU, J, SHEN, Y, SHI, Y, SI, T, WANG, C, WANG, F, WANG, K, WANG, L, WANG, X, WANG, Y, WU, X, WU, X, XIE, C, XIE, G, XIE, H, XIE, P, XU, X, YANG, H, YANG, J, YAO, J, YAO, S, YIN, Y, YUAN, Y, ZHANG, A, ZHANG, H, ZHANG, K, ZHANG, L, ZHANG, Z, ZHOU, R, ZHOU, Y, ZHU, J, ZOU, C, ZANG, Y, ZHAO, J, CHAN, CK, PU, W & LIU, Z 2020, 'Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium', NeuroImage: Clinical, vol. 26, 102163. https://doi.org/10.1016/j.nicl.2020.102163

TY - JOUR

T1 - Altered resting-state dynamic functional brain networks in major depressive disorder: Findings from the REST-meta-MDD consortium

AU - LONG, Yicheng

AU - CAO, Hengyi

AU - YAN, Chaogan

AU - CHEN, Xiao

AU - LI, Le

AU - CASTELLANOS, Francisco Xavier

AU - BAI, Tongjian

AU - BO, Qijing

AU - CHEN, Guanmao

AU - CHEN, Ningxuan

AU - CHEN, Wei

AU - CHENG, Chang

AU - CHENG, Yuqi

AU - CUI, Xilong

AU - DUAN, Jia

AU - FANG, Yiru

AU - GONG, Qiyong

AU - GUO, Wenbin

AU - HOU, Zhenghua

AU - HU, Lan

AU - KUANG, Li

AU - LI, Feng

AU - LI, Kaiming

AU - LI, Tao

AU - LIU, Yansong

AU - LUO, Qinghua

AU - MENG, Huaqing

AU - PENG, Daihui

AU - QIU, Haitang

AU - QIU, Jiang

AU - SHEN, Yuedi

AU - SHI, Yushu

AU - SI, Tianmei

AU - WANG, Chuanyue

AU - WANG, Fei

AU - WANG, Kai

AU - WANG, Li

AU - WANG, Xiang

AU - WANG, Ying

AU - WU, Xiaoping

AU - WU, Xinran

AU - XIE, Chunming

AU - XIE, Guangrong

AU - XIE, Haiyan

AU - XIE, Peng

AU - XU, Xiufeng

AU - YANG, Hong

AU - YANG, Jian

AU - YAO, Jiashu

AU - YAO, Shuqiao

AU - YIN, Yingying

AU - YUAN, Yonggui

AU - ZHANG, Aixia

AU - ZHANG, Hong

AU - ZHANG, Kerang

AU - ZHANG, Lei

AU - ZHANG, Zhijun

AU - ZHOU, Rubai

AU - ZHOU, Yiting

AU - ZHU, Junjuan

AU - ZOU, Chaojie

AU - ZANG, Yufeng

AU - ZHAO, Jingping

AU - CHAN, Calais Kin-yuen

AU - PU, Weidan

AU - LIU, Zhening

N1 - 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

PY - 2020

Y1 - 2020

N2 - 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.

AB - 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.

KW - Connectome

KW - Default-mode

KW - Depression

KW - Dynamic functional connectivity

KW - FMRI

KW - Temporal variability

UR - http://www.scopus.com/inward/record.url?scp=85077928651&partnerID=8YFLogxK

U2 - 10.1016/j.nicl.2020.102163

DO - 10.1016/j.nicl.2020.102163

M3 - Journal Article (refereed)

C2 - 31953148

AN - SCOPUS:85077928651

SN - 2213-1582

VL - 26

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

M1 - 102163

ER -

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

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Keywords

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