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SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets
Jiang, Rongtao1,2,3; Abbott, Christopher C.4; Jiang, Tianzi1,2,3,5; Du, Yuhui6,7; Espinoza, Randall8; Narr, Katherine L.8,9; Wade, Benjamin9; Yu, Qingbao6; Song, Ming1,2; Lin, Dongdong6; Chen, Jiayu6; Jones, Thomas4; Argyelan, Miklos10,11,12,13; Petrides, Georgios10,11,12,13; Sui, Jing1,2,3,5,6; Calhoun, Vince D.4,6,14
AbstractOwing to the rapid and robust clinical effects, electroconvulsive therapy (ECT) represents an optimal model to develop and test treatment predictors for major depressive disorders (MDDs), whereas imaging markers can be informative in identifying MDD patients who will respond to a specific antidepressant treatment or not. Here we aim to predict post-ECT depressive rating changes and remission status using pre-ECT gray matter (GM) in 38 MDD patients and validate in two independent data sets. Six GM regions including the right hippocampus/parahippocampus, right orbitofrontal gyrus, right inferior temporal gyrus (ITG), left postcentral gyrus/precuneus, left supplementary motor area, and left lingual gyrus were identified as predictors of ECT response, achieving accuracy of 89, 90 and 86% for remission prediction in three independent, age-matched data sets, respectively. For MDD patients, GM density increases only in the left supplementary motor cortex and left postcentral gyrus/precuneus after ECT. These results suggest that treatment-predictive and treatment-responsive regions may be anatomically different but functionally related in the context of ECT response. To the best of our knowledge, this is the first attempt to quantitatively identify and validate the ECT treatment biomarkers using multi-site GM data. We address a major clinical challenge and provide potential opportunities for more effective and timely interventions for electroconvulsive treatment.
WOS HeadingsScience & Technology ; Life Sciences & Biomedicine
Indexed BySCI
Funding OrganizationNational High Tech Program (863)(2015AA020513) ; China National Natural Science Foundation(81471367) ; Strategic Priority Research Program of the Chinese Academy of Sciences(XDB02060005) ; Natural Science Foundation of Shanxi Province(2016021077) ; National Institute of Health(1R01EB005846 ; 1R01MH094524 ; P20GM103472)
WOS Research AreaNeurosciences & Neurology ; Pharmacology & Pharmacy ; Psychiatry
WOS SubjectNeurosciences ; Pharmacology & Pharmacy ; Psychiatry
WOS IDWOS:000427484600017
Citation statistics
Cited Times:6[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Univ New Mexico, Dept Psychiat, Albuquerque, NM 87131 USA
5.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci, Beijing, Peoples R China
6.Mind Res Network & Lovelace Biomed & Environm Res, Albuquerque, NM USA
7.Shanxi Univ, Sch Comp & Informat Technol, Taiyuan, Shanxi, Peoples R China
8.Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90024 USA
9.Univ Calif Los Angeles, Dept Neurol, Ahmanson Lovelace Brain Mapping Ctr, Los Angeles, CA 90024 USA
10.Feinstein Inst Med Res, Ctr Psychiat Neurosci, Manhasset, NY USA
11.Northwell Syst, Zucker Hillside Hosp, Div Psychiat Res, Glen Oaks, NY USA
12.Hofstra Northwell Sch Med, Dept Psychiat, Hempstead, NY USA
13.Hofstra Northwell Sch Med, Dept Mol Med, Hempstead, NY USA
14.Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
Recommended Citation
GB/T 7714
Jiang, Rongtao,Abbott, Christopher C.,Jiang, Tianzi,et al. SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets[J]. NEUROPSYCHOPHARMACOLOGY,2018,43(5):1078-1087.
APA Jiang, Rongtao.,Abbott, Christopher C..,Jiang, Tianzi.,Du, Yuhui.,Espinoza, Randall.,...&Calhoun, Vince D..(2018).SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets.NEUROPSYCHOPHARMACOLOGY,43(5),1078-1087.
MLA Jiang, Rongtao,et al."SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets".NEUROPSYCHOPHARMACOLOGY 43.5(2018):1078-1087.
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