Predicting Response to Repetitive Transcranial Magnetic Stimulation in Patients With Schizophrenia Using Structural Magnetic Resonance Imaging: A Multisite Machine Learning Analysis
| Autor: | Dominic B. Dwyer, Tim B Poeppl, Pablo E. Verde, Raees Ahmed, Michael Landgrebe, Wolfgang Wölwer, Peter Eichhammer, Thomas Schneider-Axmann, Alkomiet Hasan, Christian Ohmann, Göran Hajak, Birgit Guse, Berthold Langguth, Marcella Rietschel, Peter Dechent, Francesco Musso, Farhad Ghaseminejad, Thomas Wobrock, Joachim Cordes, Peter M. Kreuzer, Georg Winterer, Elmar Frank, Wolfgang Gaebel, Nikolaos Koutsouleris, Peter Falkai, Berend Malchow, William G. Honer |
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| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
Adult
Male Support Vector Machine medicine.medical_treatment Prefrontal Cortex Machine learning computer.software_genre law.invention 03 medical and health sciences Young Adult 0302 clinical medicine Randomized controlled trial law Outcome Assessment Health Care Medicine Humans Generalizability theory In patient Young adult Prefrontal cortex medicine.diagnostic_test Positive and Negative Syndrome Scale business.industry Brain Magnetic resonance imaging Middle Aged Prognosis Magnetic Resonance Imaging Transcranial Magnetic Stimulation 3. Good health 030227 psychiatry Transcranial magnetic stimulation Psychiatry and Mental health Schizophrenia Female Artificial intelligence business computer 030217 neurology & neurosurgery Regular Articles |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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