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Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder

文献类型：期刊论文

作者Sendi, Mohammad S. E.16,17,18,19; Zendehrouh, Elaheh15; Sui, Jing11,12,13,14,16; Fu, Zening16; Zhi, Dongmei12,13,14,16; Lv, Luxian9,10; Ma, Xiaohong6,7,8; Ke, Qing5; Li, Xianbin4; Wang, Chuanyue4
刊名BRAIN CONNECTIVITY
出版日期2021-12-01
卷号11期号:10页码:838-849
关键词default mode network dynamic functional network connectivity machine learning major depressive disorder resting-state functional magnetic resonance imaging
ISSN号2158-0014
DOI10.1089/brain.2020.0748
通讯作者Sendi, Mohammad S. E.(eslampanahsendi@gmail.com) ; Sui, Jing(jsui@bnu.edu.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
英文摘要Background: Major depressive disorder (MDD) is a severe mental illness marked by a continuous sense of sadness and a loss of interest. The default mode network (DMN) is a group of brain areas that are more active during rest and deactivate when engaged in task-oriented activities. The DMN of MDD has been found to have aberrant static functional network connectivity (FNC) in recent studies. In this work, we extend previous findings by evaluating dynamic functional network connectivity (dFNC) within the DMN subnodes in MDD. Methods: We analyzed resting-state functional magnetic resonance imaging data of 262 patients with MDD and 277 healthy controls (HCs). We estimated dFNCs for seven subnodes of the DMN, including the anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), and precuneus (PCu), using a sliding window approach, and then clustered the dFNCs into five brain states. Classification of MDD and HC subjects based on state-specific FC was performed using a logistic regression classifier. Transition probabilities between dFNC states were used to identify relationships between symptom severity and dFNC data in MDD patients. Results: By comparing state-specific FNC between HC and MDD, a disrupted connectivity pattern was observed within the DMN. In more detail, we found that the connectivity of ACC is stronger, and the connectivity between PCu and PCC is weaker in individuals with MDD than in those of HC subjects. In addition, MDD showed a higher probability of transitioning from a state with weaker ACC connectivity to a state with stronger ACC connectivity, and this abnormality is associated with symptom severity. This is the first research to look at the dFC of the DMN in MDD with a large sample size. It provides novel evidence of abnormal time-varying DMN configuration in MDD and offers links to symptom severity in MDD subjects. Impact Statement This study is the first attempt that explored the temporal change on default mode network (DMN) connectivity in a relatively large cohort of patients with major depressive disorder (MDD). We also introduced a new hypothesis that explains the inconsistency in DMN functional network connectivity (FNC) comparison between MDD and healthy control based on static FNC in the previous literature. Additionally, our findings suggest that within anterior cingulate cortex connectivity and the connectivity between the precuneus and posterior cingulate cortex are the potential biomarkers for the future intervention of MDD.
WOS关键词RESTING-STATE ; TREATMENT-RESISTANT ; RATING-SCALE ; CINGULATE CORTEX ; BRAIN ACTIVITY ; STIMULATION ; COMPONENTS ; SELECTION ; ANATOMY ; PATTERN
资助项目National Institute of Health[R01EB006841] ; National Institute of Health[R01EB020407] ; National Institute of Health[R01MH121246] ; National Institute of Health[R01MH117107] ; National Institute of Health[R01MH118695] ; National Institute of Health[U01MH111826]
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:000756940700008
出版者MARY ANN LIEBERT, INC
资助机构National Institute of Health
源URL[http://ir.ia.ac.cn/handle/173211/47626]  
专题自动化研究所_脑网络组研究中心
通讯作者Sendi, Mohammad S. E.; Sui, Jing; Calhoun, Vince D.
作者单位1.Georgia State Univ, Neurosci Inst, Atlanta, GA 30303 USA
2.Georgia State Univ, Dept Psychol, Atlanta, GA 30303 USA
3.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA
4.Capital Med Univ, Beijing Anding Hosp, Beijing Key Lab Mental Disorders, Beijing, Peoples R China
5.Zhejiang Univ, Affiliated Hosp 1, Sch Med, Dept Neurol, Hangzhou, Peoples R China
6.Sichuan Univ, West China Hosp, Huaxi Brain Res Ctr, Chengdu, Peoples R China
7.Sichuan Univ, West China Hosp, Mental Hlth Ctr, State Key Lab Biotherapy, Chengdu, Peoples R China
8.Sichuan Univ, West China Hosp, Psychiat Lab, Chengdu, Peoples R China
9.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China
10.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China
推荐引用方式
GB/T 7714		 Sendi, Mohammad S. E.,Zendehrouh, Elaheh,Sui, Jing,et al. Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder[J]. BRAIN CONNECTIVITY,2021,11(10):838-849.
APA Sendi, Mohammad S. E..,Zendehrouh, Elaheh.,Sui, Jing.,Fu, Zening.,Zhi, Dongmei.,...&Calhoun, Vince D..(2021).Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder.BRAIN CONNECTIVITY,11(10),838-849.
MLA Sendi, Mohammad S. E.,et al."Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder".BRAIN CONNECTIVITY 11.10(2021):838-849.

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