Meneghetti Nicolò, Lassi Michael, Massa Verediana, Micera Silvestro, Mazzoni Alberto, Alia Claudia, Bandini Andrea
CNR Neuroscience Institute, Pisa, Italy.
APL Bioeng. 2025 Apr 22;9(2):026108. doi: 10.1063/5.0263191. eCollection 2025 Jun.
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.
中风仍然是导致长期残疾的主要原因，这凸显了对运动恢复有效预测指标的迫切需求。了解自发恢复背后的电生理变化，可为恢复机制提供关键见解，并有助于预测个体的康复轨迹。在本研究中，我们调查了在缺血性中风小鼠模型中，中风后2天记录的局部场电位对预测1个月运动恢复的能力。通过采用全面的机器学习方法，我们确定了显著提高预测准确性的关键电生理特征。通过嵌套留一动物交叉验证，我们实现了高预测准确性，在16只小鼠中的15只中正确识别了运动恢复状态。我们的研究结果还表明，中风前的大脑活动对预测准确性没有贡献，这表明中风后的动态变化是恢复的主要决定因素。值得注意的是，我们发现来自对侧半球的特征在预测恢复结果方面特别有影响力，这突出了未受损半球在运动恢复中的关键作用。我们的数据驱动方法强调了平衡特征选择以优化预测性能的重要性，特别是在自发恢复的背景下，对自然恢复过程的洞察可以指导有针对性的康复策略的制定。最终，我们的研究结果主张更深入地了解中风后的大脑动态变化，以改善中风患者的临床结果。
