Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data
文献类型:期刊论文
作者 | Hailun, Sun1,6; Rongtao, Jiang1,6; Shile, Qi5; Katherine, L., Narr4; Benjamin, SC, Wade4; Joel, Upston3; Randall, Espinoza4; Tom, Jones3; Vince, D., Calhoun5; Christopher, C, Abbott3 |
刊名 | NeuroImage: Clinical |
出版日期 | 2019-11 |
卷号 | 26期号:102080页码:1-9 |
ISSN号 | 2213-1582 |
关键词 | Individualized prediction Electroconvulsive therapy (ECT) Functional connectivity (FC) Major depressive disorder (DEP) Resting-state fMRI HDRS Treatment response |
产权排序 | 1 |
英文摘要 | Electroconvulsive therapy (ECT) works rapidly and is widely used to treat depressive disorders (DEP). However, identifying biomarkers predictive of response to ECT remains a priority to individually tailor treatment and understand treatment mechanisms. This study used a connectome-based predictive modeling (CPM) approach in 122 patients with DEP to determine if pre-ECT whole-brain functional connectivity (FC) predicts depressive rating changes and remission status after ECT (47 of 122 total subjects or 38.5% of sample), and whether pre-ECT and longitudinal changes (pre/post-ECT) in regional brain network biomarkers are associated with treatment-related changes in depression ratings. Results show the networks with the best predictive performance of ECT response were negative (anti-correlated) FC networks, which predict the post-ECT depression severity (continuous measure) and achieve 76.23% accuracy for remission prediction. FC networks with the greatest predictive power were concentrated in the prefrontal and temporal cortices and subcortical nuclei, and include the inferior frontal (IFG), superior frontal (SFG), superior temporal (STG), inferior temporal gyri (ITG), basal ganglia (BG), and thalamus (Tha). Several of these brain regions were also identified as nodes in the FC networks that show significant change pre-/post-ECT, but these networks were not related to treatment response. Our study design has limitations regarding the longitudinal design and absence of a control group that limit causal inference regarding mechanism of post-treatment status. Though predictive biomarkers remained below the threshold of those recommended for potential translation, the analysis methods and results demonstr |
WOS记录号 | WOS:000533149400007 |
源URL | [http://ir.ia.ac.cn/handle/173211/39218] |
专题 | 自动化研究所_脑网络组研究中心 |
通讯作者 | Christopher, C, Abbott; Jing, Sui; Sui, Jing |
作者单位 | 1.Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 2.Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China 3.Department of Psychiatry, University of New Mexico, Albuquerque, NM, USA 4.Departments of Neurology, Psychiatry and Biobehavioral Sciences, University of California, Los Angeles (UCLA), CA, USA 5.Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA 6.University of Chinese Academy of Sciences, Beijing, China |
推荐引用方式 GB/T 7714 | Hailun, Sun,Rongtao, Jiang,Shile, Qi,et al. Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data[J]. NeuroImage: Clinical,2019,26(102080):1-9. |
APA | Hailun, Sun.,Rongtao, Jiang.,Shile, Qi.,Katherine, L., Narr.,Benjamin, SC, Wade.,...&Sun, Hailun.(2019).Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data.NeuroImage: Clinical,26(102080),1-9. |
MLA | Hailun, Sun,et al."Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data".NeuroImage: Clinical 26.102080(2019):1-9. |
入库方式: OAI收割
来源:自动化研究所
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