Preliminary prediction of individual response to electroconvulsive therapy using whole-brain functional magnetic resonance imaging data

• Type of Material: Article
• Authors: Hailun Sun, Chinese Academy of Sciences; Rongtao Jiang, Chinese Academy of Sciences; Shile Qi, Georgia State University; Katherine L Narr, University of California Los Angeles; Benjamin SC Wade, University of California Los Angeles; Joel Upston, University of New Mexico; Randall Espinoza, University of California Los Angeles; Tom Jones, University of New Mexico; Vince Calhoun, Emory University; Christopher C Abbott, University of New Mexico; Jing Sui, Chinese Academy of Sciences
• Language: English
• Date: 2020-01-01
• Publisher: ELSEVIER SCI LTD
• Publication Version: Final Published Version
• Copyright Statement: © 2019 The Authors
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.nicl.2019.102080
• Title of Journal or Parent Work: NEUROIMAGE-CLINICAL
• Volume: 26
• Start Page: 102080
• End Page: 102080
• Grant/Funding Information: This work is supported in part by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant No. XDB32040100), China Natural Science Foundation (No. 61773380), Beijing Municipal Science and Technology Commission (Z181100001518005), the National Institute of Health (1R01MH117107, R01EB020407, 1R01EB005846, 1R01MH094524, P20GM103472, P30GM122734, U01 MH111826) and (MH092301, MH110008 and MH102743 to UCLA investigators) and the National Science Foundation (1539067).
• Keywords: Science & Technology; MIRTAZAPINE; CONNECTIVITY; Treatment response; Neuroimaging; Functional connectivity (FC); ECT; Resting-state fMRI; REMISSION; HDRS; Electroconvulsive therapy (ECT); Life Sciences & Biomedicine; Major depressive disorder (DEP); METAANALYSIS; ARTIFACT; Neurosciences & Neurology; DEPRESSION; STAR-ASTERISK-D; Individualized prediction
• Research Categories: Health Sciences, Mental Health

Abstract

Electroconvulsive therapy (ECT) works rapidly and has been 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) with a 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. This study design has limitations regarding the longitudinal design and the absence of a control group that limit the 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 demonstrate the promise and generalizability of biomarkers for advancing personalized treatment strategies.