IIT@Unife Center for Translational Neurophysiology, Istituto Italiano di Tecnologia, Ferrara, Italy.
Department of Neuroscience and Rehabilitation, Ferrara University Hospital, Ferrara, Italy.
Brain Topogr. 2022 Nov;35(5-6):651-666. doi: 10.1007/s10548-022-00915-y. Epub 2022 Sep 22.
Current clinical practice does not leverage electroencephalography (EEG) measurements in stroke patients, despite its potential to contribute to post-stroke recovery predictions. We review the literature on the effectiveness of various quantitative and qualitative EEG-based measures after stroke as a tool to predict upper limb motor outcome, in relation to stroke timeframe and applied experimental tasks. Moreover, we aim to provide guidance on the use of EEG in the assessment of upper limb motor recovery after stroke, suggesting a high potential for some metrics in the appropriate context. We identified relevant papers (N = 16) from databases ScienceDirect, Web of Science and MEDLINE, and assessed their methodological quality with the Joanna Briggs Institute (JBI) Critical Appraisal. We applied the Preferred Reporting Systems for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) Framework. Identified works used EEG to identify properties including event-related activation, spectral power in physiologically relevant bands, symmetry in brain dynamics, functional connectivity, cortico-muscular coherence and rhythmic coordination. EEG was acquired in resting state or in relation to behavioural conditions. Motor outcome was mainly evaluated with the Upper Limb Fugl-Meyer Assessment. Despite great variability in the literature, data suggests that the most promising EEG quantifiers for predicting post-stroke motor outcome are event-related measures. Measures of spectral power in physiologically relevant bands and measures of brain symmetry also show promise. We suggest that EEG measures may improve our understanding of stroke brain dynamics during recovery, and contribute to establishing a functional prognosis and choosing the rehabilitation approach.
目前的临床实践并未利用脑电图(EEG)测量来评估中风患者,尽管其可能有助于预测中风后的恢复情况。我们回顾了中风后各种基于定量和定性 EEG 测量的文献,这些测量可作为预测上肢运动功能恢复的工具,涉及中风时间和应用的实验任务。此外,我们旨在提供关于 EEG 在中风后上肢运动恢复评估中的使用指南,提出在适当的情况下某些指标具有很高的潜力。我们从 ScienceDirect、Web of Science 和 MEDLINE 数据库中确定了相关论文(N=16),并使用 Joanna Briggs 研究所(JBI)的批判性评估工具评估了它们的方法学质量。我们应用了系统评价和荟萃分析扩展的首选报告系统(PRISMA-ScR)框架。确定的研究使用 EEG 来识别与事件相关的激活、生理相关频带中的光谱功率、大脑动力学的对称性、功能连接、皮质-肌肉相干性和节律协调等特性。EEG 是在静息状态或与行为条件下采集的。上肢 Fugl-Meyer 评估主要用于评估运动功能。尽管文献中有很大的差异,但数据表明,最有希望用于预测中风后运动功能恢复的 EEG 量化指标是与事件相关的测量。生理相关频带的光谱功率测量和大脑对称性测量也显示出前景。我们认为,EEG 测量可能有助于我们了解中风后恢复期间的大脑动态,并有助于建立功能预后和选择康复方法。