Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness
IntroductionIschemic 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 diso...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-10-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1257511/full |
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| author | Fang Yu Yanzhe Gao Fenglian Li Xueying Zhang Fengyun Hu Wenhui Jia Xiaohui Li |
| author_facet | Fang Yu Yanzhe Gao Fenglian Li Xueying Zhang Fengyun Hu Wenhui Jia Xiaohui Li |
| author_sort | Fang Yu |
| collection | DOAJ |
| description | IntroductionIschemic 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.MethodsIn 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.ResultsBoth 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%).DiscussionOur 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. |
| first_indexed | 2024-03-11T20:25:01Z |
| format | Article |
| id | doaj.art-22e92fe90cec48728b27168248ad9628 |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-11T20:25:01Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-22e92fe90cec48728b27168248ad96282023-10-02T15:39:51ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-10-011710.3389/fnins.2023.12575111257511Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousnessFang Yu0Yanzhe Gao1Fenglian Li2Xueying Zhang3Fengyun Hu4Wenhui Jia5Xiaohui Li6College of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Life Sciences, Nankai University, Tianjin, ChinaCollege of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, ChinaCollege of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, ChinaThe Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People’s Hospital, Taiyuan, ChinaThe Fifth Clinical Medical College of Shanxi Medical University, Department of Neurology, Shanxi Provincial People’s Hospital, Taiyuan, ChinaCollege of Electronic Information and Optical Engineering, Taiyuan University of Technology, Taiyuan, ChinaIntroductionIschemic 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.MethodsIn 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.ResultsBoth 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%).DiscussionOur 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.https://www.frontiersin.org/articles/10.3389/fnins.2023.1257511/fullmicrostatesdisorder of consciousnessEEGpost-strokebiomarkers |
| spellingShingle | Fang Yu Yanzhe Gao Fenglian Li Xueying Zhang Fengyun Hu Wenhui Jia Xiaohui Li Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness Frontiers in Neuroscience microstates disorder of consciousness EEG post-stroke biomarkers |
| title | Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness |
| title_full | Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness |
| title_fullStr | Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness |
| title_full_unstemmed | Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness |
| title_short | Resting-state EEG microstates as electrophysiological biomarkers in post-stroke disorder of consciousness |
| title_sort | resting state eeg microstates as electrophysiological biomarkers in post stroke disorder of consciousness |
| topic | microstates disorder of consciousness EEG post-stroke biomarkers |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1257511/full |
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