IEEE Trans Neural Syst Rehabil Eng. 2023;31:3760-3771. doi: 10.1109/TNSRE.2023.3316210. Epub 2023 Sep 28.
Stroke often leads to permanent impairment in motor function. Accurate and quantitative prognosis of potential motor recovery before rehabilitation intervention can help healthcare centers improve resources organization and enable individualized intervention. The context of this paper investigated the potential of using electroencephalography (EEG) functional connectivity (FC) measures as biomarkers for assessing and prognosing improvement of Fugl-Meyer Assessment in upper extremity motor function ( ∆FMU) among participants with chronic stroke. EEG data from resting and motor imagery task were recorded from 13 participants with chronic stroke. Three functional connectivity methods, which were Pearson correlation measure (PCM), weighted Phase Lag Index (wPLI) and phase synchronization index (PSI), were investigated, under three regions of interest (inter-hemispheric, intra-hemispheric, and whole-brain), in two statues (resting and motor imagery), with 15 refined center frequencies. We applied correlation analysis to identify the optimal center frequencies and pairs of synchronized channels that were consistently associated with ∆FMU . Predictive models were generated using regression analysis algorithms based on optimized center frequencies and channel pairs identified from the proposed analysis method, with leave-one-out cross-validation. We found that PSI in the Alpha band (with center frequency of 9Hz) was the most sensitive FC measures for prognosing motor recovery. Strong and significant correlations were identified between the predictions and actual ∆FMU scores both in the resting state ( [Formula: see text], [Formula: see text], N=13) and motor imagery ( [Formula: see text], [Formula: see text], N=13). Our results suggested that EEG connectivity measured with PSI in resting state could be a promising biomarker for quantifying motor recovery before motor rehabilitation intervention.
中风常导致运动功能永久受损。在康复干预前准确、定量地预测潜在的运动恢复情况，有助于医疗中心改善资源组织，并实现个体化干预。本文研究了使用脑电图（EEG）功能连接（FC）测量作为评估和预测慢性中风患者上肢运动功能（∆FMU）的 Fugl-Meyer 评估改善的生物标志物的潜力。从 13 名慢性中风患者中记录了静息和运动想象任务的 EEG 数据。研究了三种功能连接方法，即 Pearson 相关系数测量（PCM）、加权相位滞后指数（wPLI）和相位同步指数（PSI），在三个感兴趣区域（半球间、半球内和全脑）、两种状态（静息和运动想象）下，使用 15 个细化的中心频率。我们应用相关分析来确定与 ∆FMU 始终相关的最佳中心频率和同步通道对。使用回归分析算法基于从提出的分析方法中确定的优化中心频率和通道对生成预测模型，采用留一法交叉验证。我们发现，在 Alpha 波段（中心频率为 9Hz）的 PSI 是预测运动恢复的最敏感 FC 测量方法。在静息状态下（[公式：见文本]，[公式：见文本]，N=13）和运动想象状态下（[公式：见文本]，[公式：见文本]，N=13），预测值与实际 ∆FMU 得分之间均存在强烈且显著的相关性。我们的结果表明，静息状态下 PSI 测量的 EEG 连接性可能是量化运动康复前的一种很有前途的生物标志物，可用于运动康复干预前。
