Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Fang Yu; Yanzhe Gao; Fenglian Li; Xueying Zhang; Fengyun Hu; Wenhui Jia; Xiaohui Li

doi:10.3389/fnins.2023.1257511

Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness

Front Neurosci. 2023 Oct 2:17:1257511. doi: 10.3389/fnins.2023.1257511. eCollection 2023.

Authors

Fang Yu¹, Yanzhe Gao², Fenglian Li¹, Xueying Zhang¹, Fengyun Hu³, Wenhui Jia³, Xiaohui Li¹

Affiliations

¹ College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, China.
² College of Life Sciences, Nankai University, Tianjin, China.
³ The Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People's Hospital, Taiyuan, China.

Abstract

Introduction: Ischemic stroke patients commonly experience disorder of consciousness (DOC), leading to poorer discharge outcomes and higher mortality risks. Therefore, the identification of applicable electrophysiological biomarkers is crucial for the rapid diagnosis and evaluation of post-stroke disorder of consciousness (PS-DOC), while providing supportive evidence for cerebral neurology.

Methods: In our study, we conduct microstate analysis on resting-state electroencephalography (EEG) of 28 post-stroke patients with awake consciousness and 28 patients with PS-DOC, calculating the temporal features of microstates. Furthermore, we extract the Lempel-Ziv complexity of microstate sequences and the delta/alpha power ratio of EEG on spectral. Statistical analysis is performed to examine the distinctions in features between the two groups, followed by inputting the distinctive features into a support vector machine for the classification of PS-DOC.

Results: Both groups obtain four optimal topographies of EEG microstates, but notable distinctions are observed in microstate C. Within the PS-DOC group, there is a significant increase in the mean duration and coverage of microstates B and C, whereas microstate D displays a contrasting trend. Additionally, noteworthy variations are found in the delta/alpha ratio and Lempel-Ziv complexity between the two groups. The integration of the delta/alpha ratio with microstates' temporal and Lempel-Ziv complexity features demonstrates the highest performance in the classifier (Accuracy = 91.07%).

Discussion: Our results suggest that EEG microstates can provide insights into the abnormal brain network dynamics in DOC patients post-stroke. Integrating the temporal and Lempel-Ziv complexity microstate features with spectral features offers a deeper understanding of the neuro mechanisms underlying brain damage in patients with DOC, holding promise as effective electrophysiological biomarkers for diagnosing PS-DOC.

Keywords: EEG; biomarkers; disorder of consciousness; microstates; post-stroke.