Individual prediction of psychotherapy outcome in posttraumatic stress disorder using neuroimaging data

• Type of Material: Article
• Authors: Paul Zhutovsky, University of Amsterdam; Rajat M Thomas, University of Amsterdam; Miranda Olff, University of Amsterdam; Sanne van Rooij, Emory University; Mitzy Kennis, Utrecht University; Guido A van Wingen, University of Amsterdam; Elbert Geuze, Utrecht University Medical Center, Rudolf Magnus Institute of Neuroscience
• Language: English
• Date: 2019-12-02
• Publisher: Nature Publishing Group
• Publication Version: Final Published Version
• Copyright Statement: © The Author(s) 2019
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1038/s41398-019-0663-7
• Title of Journal or Parent Work: Translational Psychiatry
• Volume: 9
• Grant/Funding Information: This study was supported by the Dutch Ministry of Defence, the Netherlands Organization for Scientific Research (NWO/ZonMW Vidi 016.156.318) and the AMC Research Council (150622).
• Keywords: Psychology; Neuroscience
• Research Categories: Health Sciences, Mental Health

Abstract

Trauma-focused psychotherapy is the first-line treatment for posttraumatic stress disorder (PTSD) but 30–50% of patients do not benefit sufficiently. We investigated whether structural and resting-state functional magnetic resonance imaging (MRI/rs-fMRI) data could distinguish between treatment responders and non-responders on the group and individual level. Forty-four male veterans with PTSD underwent baseline scanning followed by trauma-focused psychotherapy. Voxel-wise gray matter volumes were extracted from the structural MRI data and resting-state networks (RSNs) were calculated from rs-fMRI data using independent component analysis. Data were used to detect differences between responders and non-responders on the group level using permutation testing, and the single-subject level using Gaussian process classification with cross-validation. A RSN centered on the bilateral superior frontal gyrus differed between responders and non-responder groups (PFWE < 0.05) while a RSN centered on the pre-supplementary motor area distinguished between responders and non-responders on an individual-level with 81.4% accuracy (P < 0.001, 84.8% sensitivity, 78% specificity and AUC of 0.93). No significant single-subject classification or group differences were observed for gray matter volume. This proof-of-concept study demonstrates the feasibility of using rs-fMRI to develop neuroimaging biomarkers for treatment response, which could enable personalized treatment of patients with PTSD.