Abstract #0700
Machine learning-based prediction of stroke outcome in mice from MRI and behavioral testing
Philipp Boehm-Sturm1,2,3, Felix Knab1,2, Stefan Paul Koch1,2,3, Sebastian Major1,2, Tracy D. Farr1,2,3,4, Susanne Mueller1,2,3, Philipp Euskirchen1, Moritz Eggers1,2, Melanie Kuffner1,2, Josefine Walter1,2,5, Jens P. Dreier1,2,6,7, Matthias Endres1,2,6,8,9,10, Ulrich Dirnagl1,2,6,8,9,10, Nikolaus Wenger1,2,6,11, Christian J. Hoffmann1,2,11, and Christoph Harms1,2,6,8,9
1Klinik und Hochschulambulanz für Neurologie, Department of Experimental Neurology, Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany, 2Center for Stroke Research Berlin, Charité-Universitätsmedizin Berlin, Berlin, Germany, 3NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité-Universitätsmedizin Berlin, Berlin, Germany, 4School of Life Sciences, University of Nottingham, Berlin, Germany, 5QUEST Center for Transforming Biomedical Research, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany, 6Einstein Center for Neuroscience, Berlin, Germany, 7Bernstein Center for Computational Neuroscience, Berlin, Germany, 8German Center for Cardiovascular Research (DZHK), partner site Berlin, Berlin, Germany, 9NeuroCure Clinical Research Center, Charité-Universitätsmedizin Berlin, Berlin, Germany, 10German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany, 11Berlin Institute of Health (BIH), Berlin, Germany
Synopsis
Keywords: Stroke, Animals, Machine learning, Mice, MCAOPrediction of motor-functional outcome in mice could potentially guide researchers in their treatment decisions in preclinical stroke intervention studies and support outcome-dependent stratifications. We pooled 13 studies in which mice underwent identical MRI and behavioral testing protocols. In this large cohort of mice (n=148), we developed and compared machine learning-based predictors of post-stroke recovery.
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