Resting-state electroencephalogram microstate to evaluate post-stroke rehabilitation and associate with clinical scales

• Published in: Frontiers in neuroscience 2022-11, Vol.16, p.1032696-1032696
• Main Authors: Wang, Zhongpeng, Liu, Zhaoyang, Chen, Long, Liu, Shuang, Xu, Minpeng, He, Feng, Ming, Dong
• Format: Article
• Language: English
• Subjects: Activities of daily living; Algorithms; Alzheimer's disease; clinical scales; Clustering; Dyskinesia; EEG; Electrodes; Electroencephalography; microstate analysis; Movement disorders; Neuroscience; Patients; post-stroke; Rehabilitation; rehabilitation assessment; resting-state EEG; Statistical analysis; Stroke; Topography
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2022.1032696

Stroke is usually accompanied by a range of complications, like post-stroke motor disorders. So far, its evaluation of motor function is developed on clinical scales, such as FMA, IADL, etc. These scale results from behavior and kinematic assessment are inevitably influenced by subjective factors, like experience of patients and doctors, lacking neurological correlations and evidence. In this paper, we applied a modified k-means clustering based microstate model to analyze 64-channel EEG from nine stroke patients and nine healthy volunteers, respectively. We aimed at finding some possible differences between stroke and healthy individuals in resting-state EEG microstate features. We further explored the correlations between EEG microstate features and scales within stroke group. By statistical analysis, we obtained significant differences in EEG microstate features between stroke and healthy group, and significant correlations between microstate features and scales within stroke group. These results might provide some neurological evidence of EEG microstate analysis for stroke rehabilitation. resting-state EEG microstate analysis is a promising method to assist clinical diagnosis and assessment application as a neurological marker.