Stroke remains a leading cause of long-term disability, underscoring the urgent need for effective predictors of motor recovery. Understanding the electrophysiological changes underlying spontaneous recovery could offer critical insight into recovery mechanisms and aid in predicting individual rehabilitation trajectories. In this study, we investigated the predictive power of local field potentials recorded 2 days post-stroke to forecast 1 month motor recovery in a mouse model of ischemic stroke. By employing a comprehensive machine learning approach, we identified key electrophysiological features that significantly enhanced prediction accuracy. Through nested leave-one-animal-out cross-validation, we achieved high prediction accuracy, correctly identifying motor recovery status in 15 out of 16 mice. Our findings also revealed that pre-stroke brain activity did not contribute to prediction accuracy, suggesting that post-stroke dynamics are the primary determinants of recovery. Notably, we found that features from the contralesional hemisphere were particularly influential in predicting recovery outcomes, underscoring the critical role of the non-lesioned hemisphere in motor recovery. Our data-driven methodology underscores the importance of balancing feature selection to optimize predictive performance, particularly in the context of spontaneous recovery, where insight into natural recovery processes can guide the development of targeted rehabilitation strategies. Ultimately, our findings advocate for a deeper understanding of post-stroke brain dynamics to improve clinical outcomes for stroke patients.
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