CASIA OpenIR  > 脑图谱与类脑智能实验室  > 脑网络组研究
Abnormal Dynamic Functional Network Connectivity Estimated from Default Mode Network Predicts Symptom Severity in Major Depressive Disorder
Sendi, Mohammad S. E.1,2,3,4; Zendehrouh, Elaheh5; Sui, Jing4,6,7,8,9; Fu, Zening4; Zhi, Dongmei4,6,7,8; Lv, Luxian10,11; Ma, Xiaohong12,13,14; Ke, Qing15; Li, Xianbin16; Wang, Chuanyue16; Abbott, Christopher C.17; Turner, Jessica A.4,18,19; Miller, Robyn L.4,5; Calhoun, Vince D.1,2,3,4,5,18,19
Source PublicationBRAIN CONNECTIVITY
ISSN2158-0014
2021-12-01
Volume11Issue:10Pages:838-849
Corresponding AuthorSendi, Mohammad S. E.(eslampanahsendi@gmail.com) ; Sui, Jing(jsui@bnu.edu.cn) ; Calhoun, Vince D.(vcalhoun@gsu.edu)
AbstractBackground: 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.
Keyworddefault mode network dynamic functional network connectivity machine learning major depressive disorder resting-state functional magnetic resonance imaging
DOI10.1089/brain.2020.0748
WOS KeywordRESTING-STATE ; TREATMENT-RESISTANT ; RATING-SCALE ; CINGULATE CORTEX ; BRAIN ACTIVITY ; STIMULATION ; COMPONENTS ; SELECTION ; ANATOMY ; PATTERN
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational Institute of Health
WOS Research AreaNeurosciences & Neurology
WOS SubjectNeurosciences
WOS IDWOS:000756940700008
PublisherMARY ANN LIEBERT, INC
Sub direction classification脑网络分析
Citation statistics
Cited Times:19[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/47627
Collection脑图谱与类脑智能实验室_脑网络组研究
Corresponding AuthorSendi, Mohammad S. E.; Sui, Jing; Calhoun, Vince D.
Affiliation1.Georgia Inst Technol, Wallace H Coulter Dept Biomed Engn, Atlanta, GA 30332 USA
2.Emory Univ, Atlanta, GA 30322 USA
3.Georgia Inst Technol, Dept Elect & Comp Engn, Atlanta, GA 30332 USA
4.Emory Univ, Georgia State Univ, Georgia Inst Technol, Triinst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA 30322 USA
5.Georgia State Univ, Dept Comp Sci, Atlanta, GA 30303 USA
6.Chinese Acad Sci, Brainnetome Ctr, Inst Automat, Beijing, Peoples R China
7.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
8.Univ Chinese Acad Sci, Sch Future Technol, Beijing, Peoples R China
9.Beijing Normal Univ, State Key Lab Cognit Neurosci & Learning, Beijing, Peoples R China
10.Xinxiang Med Univ, Affiliated Hosp 2, Henan Mental Hosp, Dept Psychiat, Xinxiang, Henan, Peoples R China
11.Xinxiang Med Univ, Henan Key Lab Biol Psychiat, Xinxiang, Henan, Peoples R China
12.Sichuan Univ, West China Hosp, Psychiat Lab, Chengdu, Peoples R China
13.Sichuan Univ, West China Hosp, Mental Hlth Ctr, State Key Lab Biotherapy, Chengdu, Peoples R China
14.Sichuan Univ, West China Hosp, Huaxi Brain Res Ctr, Chengdu, Peoples R China
15.Zhejiang Univ, Affiliated Hosp 1, Sch Med, Dept Neurol, Hangzhou, Peoples R China
16.Capital Med Univ, Beijing Anding Hosp, Beijing Key Lab Mental Disorders, Beijing, Peoples R China
17.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA
18.Georgia State Univ, Dept Psychol, Atlanta, GA 30303 USA
19.Georgia State Univ, Neurosci Inst, Atlanta, GA 30303 USA
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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