Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder

doi:10.3389/fnhum.2021.689488

	Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder
	Sendi, Mohammad S. E.1,2; Dini, Hossein 3; Sui, Jing 4; Fu, Zening 5; Qi, Shile6 ; Riva-Posse, Patricio 2; Abbott, Christopher 7; Mayberg, Helen 8; Calhoun, Vince 9
发表期刊	BIOLOGICAL PSYCHIATRY
ISSN	0006-3223
	2021-05-01
卷号	89 期号:9 页码:S169-S170
通讯作者	Calhoun, Vince D.(vcalhoun@gsu.edu)
摘要	Background: Electroconvulsive therapy (ECT) is one of the most effective treatments for major depressive disorder. Recently, there has been increasing attention to evaluate the effect of ECT on resting-state functional magnetic resonance imaging (rs-fMRI). This study aims to compare rs-fMRI of depressive disorder (DEP) patients with healthy participants, investigate whether pre-ECT dynamic functional network connectivity network (dFNC) estimated from patients rs-fMRI is associated with an eventual ECT outcome, and explore the effect of ECT on brain network states. Method: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 119 patients with depression or depressive disorder (DEP) (76 females), and 61 healthy (HC) participants (34 females), with an age mean of 52.25 (N = 180) years old. The pre-ECT and post-ECT Hamilton Depression Rating Scale (HDRS) were 25.59 +/- 6.14 and 11.48 +/- 9.07, respectively. Twenty-four independent components from default mode (DMN) and cognitive control network (CCN) were extracted, using group-independent component analysis from pre-ECT and post-ECT rs-fMRI. Then, the sliding window approach was used to estimate the pre-and post-ECT dFNC of each subject. Next, k-means clustering was separately applied to pre-ECT dFNC and post-ECT dFNC to assess three distinct states from each participant. We calculated the amount of time each subject spends in each state, which is called "occupancy rate" or OCR. Next, we compared OCR values between HC and DEP participants. We also calculated the partial correlation between pre-ECT OCRs and HDRS change while controlling for age, gender, and site. Finally, we evaluated the effectiveness of ECT by comparing pre- and post-ECT OCR of DEP and HC participants. Results: The main findings include (1) depressive disorder (DEP) patients had significantly lower OCR values than the HC group in state 2, where connectivity between cognitive control network (CCN) and default mode network (DMN) was relatively higher than other states (corrected p = 0.015), (2) Pre-ECT OCR of state, with more negative connectivity between CCN and DMN components, is linked with the HDRS changes (R = 0.23 corrected p = 0.03). This means that those DEP patients who spent less time in this state showed more HDRS change, and (3) The post-ECT OCR analysis suggested that ECT increased the amount of time DEP patients spent in state 2 (corrected p = 0.03). Conclusion: Our finding suggests that dynamic functional network connectivity (dFNC) features, estimated from CCN and DMN, show promise as a predictive biomarker of the ECT outcome of DEP patients. Also, this study identifies a possible underlying mechanism associated with the ECT effect on DEP patients.
关键词	Resting State fMRI Dynamic Functional Connectivity Major Depressive Disorder (MDD) Electroconvulsive Therapy (ECT) Cognitive Control Network
DOI	10.3389/fnhum.2021.689488
关键词[WOS]	COGNITIVE CONTROL NETWORK ; DEFAULT MODE NETWORK ; AMYGDALA ; FMRI ; SCALE ; MRI
收录类别	SCI
语种	英语
资助项目	[R01EB006841] ; [R01EB020407] ; [R01MH121246] ; [R01MH117107] ; [R01MH118695] ; [U01MH111826]
项目资助者	NIH
WOS研究方向	Neurosciences & Neurology ; Psychiatry
WOS类目	Neurosciences ; Psychiatry
WOS记录号	WOS:000645683800409
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：1[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/44341
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	1.Georgia Inst Technol, Atlanta, GA 30332 USA 2.Emory Univ, Atlanta, GA 30322 USA 3.Amirkabir Univ Technol, Tehran, Iran 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Georgia State Univ, Atlanta, GA 30303 USA 6.GSU GT Emory, TReNDS Ctr, Atlanta, GA USA 7.Univ New Mexico, Albuquerque, NM 87131 USA 8.Icahn Sch Med Mt Sinai, New York, NY 10029 USA 9.Georgia State Georgia Tech Emory, Atlanta, GA USA
推荐引用方式 GB/T 7714	Sendi, Mohammad S. E.,Dini, Hossein,Sui, Jing,et al. Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder[J]. BIOLOGICAL PSYCHIATRY,2021,89(9):S169-S170.
APA	Sendi, Mohammad S. E..,Dini, Hossein.,Sui, Jing.,Fu, Zening.,Qi, Shile.,...&Calhoun, Vince.(2021).Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder.BIOLOGICAL PSYCHIATRY,89(9),S169-S170.
MLA	Sendi, Mohammad S. E.,et al."Dynamic Functional Connectivity Predicts Treatment Response to Electroconvulsive Therapy in Major Depressive Disorder".BIOLOGICAL PSYCHIATRY 89.9(2021):S169-S170.