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Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis

Item Type:Article
Title:Predicting response to repetitive transcranial magnetic stimulation in patients with schizophrenia using structural magnetic resonance imaging: a multisite machine learning analysis
Creators Name:Koutsouleris, N. and Wobrock, T. and Guse, B. and Langguth, B. and Landgrebe, M. and Eichhammer, P. and Frank, E. and Cordes, J. and Woelwer, W. and Musso, F. and Winterer, G. and Gaebel, W. and Hajak, G. and Ohmann, C. and Verde, P.E. and Rietschel, M. and Ahmed, R. and Honer, W.G. and Dwyer, D. and Ghaseminejad, F. and Dechent, P. and Malchow, B. and Kreuzer, P.M. and Poeppl, T.B. and Schneider-Axmann, T. and Falkai, P. and Hasan, A.
Abstract:Background: The variability of responses to plasticity-inducing repetitive transcranial magnetic stimulation (rTMS) challenges its successful application in psychiatric care. No objective means currently exists to individually predict the patients' response to rTMS. Methods: We used machine learning to develop and validate such tools using the pre-treatment structural Magnetic Resonance Images (sMRI) of 92 patients with schizophrenia enrolled in the multisite RESIS trial (http://clinicaltrials.gov, NCT00783120): patients were randomized to either active (N = 45) or sham (N = 47) 10-Hz rTMS applied to the left dorsolateral prefrontal cortex 5 days per week for 21 days. The prediction target was nonresponse vs response defined by a ≥20% pre-post Positive and Negative Syndrome Scale (PANSS) negative score reduction. Results: Our models predicted this endpoint with a cross-validated balanced accuracy (BAC) of 85% (nonresponse/response: 79%/90%) in patients receiving active rTMS, but only with 51% (48%/55%) in the sham-treated sample. Leave-site-out cross-validation demonstrated cross-site generalizability of the active rTMS predictor despite smaller training samples (BAC: 71%). The predictive pre-treatment pattern involved gray matter density reductions in prefrontal, insular, medio-temporal, and cerebellar cortices, and increments in parietal and thalamic structures. The low BAC of 58% produced by the active rTMS predictor in sham-treated patients, as well as its poor performance in predicting positive symptom courses supported the therapeutic specificity of this brain pattern. Conclusions: Individual responses to active rTMS in patients with predominant negative schizophrenia may be accurately predicted using structural neuromarkers. Further multisite studies are needed to externally validate the proposed treatment stratifier and develop more personalized and biologically informed rTMS interventions.
Keywords:Schizophrenia, Repetitive Transcranial Magnetic Stimulation, Neuroanatomical Pattern Classification, Machine Learning, Voxel-Based Morphometry, Treatment Outcome Prediction, Response Heterogeneity
Source:Schizophrenia Bulletin
ISSN:0586-7614
Publisher:Oxford Universal Press (U.K.)
Volume:44
Number:5
Page Range:1021-1034
Date:20 August 2018
Official Publication:https://doi.org/10.1093/schbul/sbx114
PubMed:View item in PubMed

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