Koutsouleris, Nikolaos and Wobrock, Thomas and Guse, Birgit and Langguth, Berthold and Landgrebe, Michael and Eichhammer, Peter and Frank, Elmar and Cordes, Joachim and Woelwer, Wolfgang and Musso, Francesco and Winterer, Georg and Gaebel, Wolfgang and Hajak, Goeran and Ohmann, Christian and Verde, Pablo E. and Rietschel, Marcella and Ahmed, Raees and Honer, William G. and Dwyer, Dominic and Ghaseminejad, Farhad and Dechent, Peter and Malchow, Berend and Kreuzer, Peter M. and Poeppl, Tim B. and Schneider-Axmann, Thomas and Falkai, Peter and Hasan, Alkomiet (2018) Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis. SCHIZOPHRENIA BULLETIN, 44 (5). pp. 1021-1034. ISSN 0586-7614, 1745-1701
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | TREATMENT-RESISTANT DEPRESSION; PREDOMINANT NEGATIVE SYMPTOMS; RANDOMIZED CONTROLLED-TRIAL; SHAM-CONTROLLED TRIAL; AUDITORY HALLUCINATIONS; PREFRONTAL CORTEX; NEUROANATOMICAL BIOMARKERS; FUNCTIONAL CONNECTIVITY; FREQUENCY RTMS; BRAIN CHANGES; schizophrenia; repetitive transcranial magnetic stimulation; neuroanatomical pattern classification; machine learning; voxel-based morphometry; treatment outcome prediction; response heterogeneity |
| Subjects: | 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Psychiatrie und Psychotherapie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 09 Jan 2020 08:06 |
| Last Modified: | 09 Jan 2020 08:06 |
| URI: | https://pred.uni-regensburg.de/id/eprint/13921 |
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