Billot, AnneLai, ShaVarkanitsa, MariaBraun, Emily JaneRapp, BrendaParrish, ToddHiggins, JamesKurani, AjayCaplan, DavidThompson, CynthiaIshwar, PrakashBetke, MargritKiran, Swathi2023-03-292023-03-292022-01-26A. Billot, S. Lai, M. Varkanitsa, E. Braun, B. Rapp, T. Parrish, J. Higgins, A. Kurani, D. Caplan, C. Thompson, P. Ishwar, M. Betke, S. Kiran. 2022. "Multimodal neural and behavioral data predict response to rehabilitation in chronic post-stroke aphasia" Stroke, Volume 53, Issue 5, pp.1606-1614. https://doi.org/10.1161/STROKEAHA.121.0367490039-24991524-4628https://hdl.handle.net/2144/45974BACKGROUND: Poststroke recovery depends on multiple factors and varies greatly across individuals. Using machine learning models, this study investigated the independent and complementary prognostic role of different patient-related factors in predicting response to language rehabilitation after a stroke. METHODS: Fifty-five individuals with chronic poststroke aphasia underwent a battery of standardized assessments and structural and functional magnetic resonance imaging scans, and received 12 weeks of language treatment. Support vector machine and random forest models were constructed to predict responsiveness to treatment using pretreatment behavioral, demographic, and structural and functional neuroimaging data. RESULTS: The best prediction performance was achieved by a support vector machine model trained on aphasia severity, demographics, measures of anatomic integrity and resting-state functional connectivity (F1=0.94). This model resulted in a significantly superior prediction performance compared with support vector machine models trained on all feature sets (F1=0.82, P<0.001) or a single feature set (F1 range=0.68–0.84, P<0.001). Across random forest models, training on resting-state functional magnetic resonance imaging connectivity data yielded the best F1 score (F1=0.87). CONCLUSIONS: While behavioral, multimodal neuroimaging data and demographic information carry complementary information in predicting response to rehabilitation in chronic poststroke aphasia, functional connectivity of the brain at rest after stroke is a particularly important predictor of responsiveness to treatment, both alone and combined with other patient-related factors.p. 1606-1614Print-ElectronicenAttribution-NonCommercial-NoDerivatives 4.0 International© 2022 The Authors. Stroke is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution Non-Commercial-NoDerivs License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited, the use is noncommercial, and no modifications or adaptations are made. Stroke is available at www.ahajournals.org/journal/strhttp://creativecommons.org/licenses/by-nc-nd/4.0/AphasiaLanguageMachine learningMagnetic resonance imagingNeuroimagingRehabilitationCardiorespiratory medicine and haematologyClinical sciencesNeurosciencesNeurology & neurosurgeryAphasiaBrainHumansMagnetic Resonance ImagingNeuroimagingStrokeArticle2023-01-1910.1161/STROKEAHA.121.0367490000-0002-4491-6868 (Betke, Margrit)0000-0003-1200-5936 (Kiran, Swathi)701016