Statistical analysis and machine learning of TMS-induced MEPs for predicting poststroke motor impairment and performance

Abstract

Stroke severity is associated with the presence or absence of motor-evoked potentials (MEPs) induced by transcranial magnetic stimulation (TMS). However, there is limited evidence regarding the relationship between MEP waveforms, post-stroke motor impairment, and functional performance. This study aimed to evaluate the predictive value of inter-trial correlation (ITC), a novel metric reflecting waveform consistency, along with MEP amplitude and resting motor threshold (rMT), in estimating post-stroke motor outcomes. Thirty-eight stroke participants were enrolled, and TMS was applied to the hotspot of the first dorsal interosseous muscle in the ipsilesional or contralesional hemisphere to elicit MEPs. MEP amplitude, ITC, and rMT were analyzed in 20 participants with detectable MEPs. Pearson correlation coefficient (PCC) analysis assessed the relationships between MEP features and motor outcomes, including the Stroke Impairment Assessment Set (SIAS), Fugl-Meyer Assessment (FMA), and Action Research Arm Test (ARAT). A linear support vector machine (SVM) was trained using leave-one-subject-out cross-validation to predict the motor outcomes. Participants without detectable MEPs (n = 18) had significantly lower motor scores than those with detectable MEPs did. MEP amplitude from the contralesional side was positively correlated with SIAS, FMA, and ARAT (PCC = 0.51, 0.47, and 0.55, respectively), whereas LICI amplitude and ITC from the ipsilesional side were negatively correlated with motor scores. The SVM model predicted motor outcomes with an R² of 0.42 and a normalized root mean square error of 0.26. A Gaussian classifier achieved 75 % accuracy in classifying motor outcome improvements. These findings suggest that bilateral MEP features, particularly those from the contralesional hemisphere, offer valuable prognostic information. This study proposes a practical framework for post-stroke motor outcome prediction based on MEP analysis with potential utility in individualized rehabilitation planning.