Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning

Abstract

This study applied machine learning regression to predict motor function after stroke based on multimodal magnetic resonance imaging. Fifty-four stroke patients, who underwent T1 weighted, diffusion tensor, and resting state functional magnetic resonance imaging were retrospectively included. The kernel rigid regression machine algorithm was applied to gray and white matter maps in T1 weighted, fractional anisotropy and mean diffusivity maps in diffusion tensor, and two motor-related independent component analysis maps in resting state functional magnetic resonance imaging to predict Fugl-Meyer motor assessment scores with the covariate as the onset duration after stroke. The results were validated using the leave-one-subject-out cross-validation method. This study is the first to apply machine learning in this area using multimodal magnetic resonance imaging data, which constitutes the main novelty. Multimodal magnetic resonance imaging correctly predicted the Fugl-Meyer motor assessment score in 72 % of cases with a normalized mean squared error of 5.93 (p value = 0.0020). The ipsilesional premotor, periventricular, and contralesional cerebellar areas were shown to be of relatively high importance in the prediction. Machine learning using multimodal magnetic resonance imaging data after a stroke may predict motor outcome.