Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia
Background Subclinical insomnia (sINSO) represents an early stage of insomnia but lacks effective biomarkers for its recognition. The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO pat...
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| Format: | Article |
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Taylor & Francis Group
2024-12-01
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| Series: | Brain-Apparatus Communication |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/27706710.2024.2388106 |
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| author | Yujie Shi Mengqi Ji Fan Zhong Rui Jiang Zhuhong Chen Chi Zhang Yuting Li Junpeng Zhang Wen Wang |
| author_facet | Yujie Shi Mengqi Ji Fan Zhong Rui Jiang Zhuhong Chen Chi Zhang Yuting Li Junpeng Zhang Wen Wang |
| author_sort | Yujie Shi |
| collection | DOAJ |
| description | Background Subclinical insomnia (sINSO) represents an early stage of insomnia but lacks effective biomarkers for its recognition. The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO patients and healthy controls.Methods Resting-state EEG data from 20 sINSO subjects and 20 healthy controls, under both open and closed eye conditions, were analyzed using microstate clustering (labeled A, B, C, and D) and machine learning to evaluate their discriminative power.Results The microstate global explained variance of the eyes-closed data was better than that of the eyes-open data. In the sINSO group under closed-eye conditions, the tendencies and transition probabilities for microstate changes are as follows: A to D at 7.7%, B to D at 10.7%, C to A at 7.3%, and D to B at 10.8%. Under open-eye conditions, they are: A to C at 9.1%, B to C at 8.4%, C to D at 9.4%, and D to C at 8.9%. Machine learning classification showed higher accuracy in closed-eye conditions, reaching 77.6%.Conclusion Resting-state EEG microstates exhibit significant differences between sINSO and healthy individuals. These microstates are promising biomarkers for distinguishing sINSO, with closed-eye data providing the most reliable discrimination. |
| format | Article |
| id | doaj-art-9dbe3ad744b94d6195cd352508961d21 |
| institution | DOAJ |
| issn | 2770-6710 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Brain-Apparatus Communication |
| spelling | doaj-art-9dbe3ad744b94d6195cd352508961d212025-08-20T02:49:39ZengTaylor & Francis GroupBrain-Apparatus Communication2770-67102024-12-013110.1080/27706710.2024.2388106Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomniaYujie Shi0Mengqi Ji1Fan Zhong2Rui Jiang3Zhuhong Chen4Chi Zhang5Yuting Li6Junpeng Zhang7Wen Wang8College of Electrical Engineering, Sichuan University, Chengdu, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, ChinaFunctional and Molecular Imaging Key Lab of Shanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, ChinaFunctional and Molecular Imaging Key Lab of Shanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, ChinaFunctional and Molecular Imaging Key Lab of Shanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu, ChinaFunctional and Molecular Imaging Key Lab of Shanxi Province, Department of Radiology, Tangdu Hospital, Fourth Military Medical University, Xi’an, ChinaBackground Subclinical insomnia (sINSO) represents an early stage of insomnia but lacks effective biomarkers for its recognition. The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO patients and healthy controls.Methods Resting-state EEG data from 20 sINSO subjects and 20 healthy controls, under both open and closed eye conditions, were analyzed using microstate clustering (labeled A, B, C, and D) and machine learning to evaluate their discriminative power.Results The microstate global explained variance of the eyes-closed data was better than that of the eyes-open data. In the sINSO group under closed-eye conditions, the tendencies and transition probabilities for microstate changes are as follows: A to D at 7.7%, B to D at 10.7%, C to A at 7.3%, and D to B at 10.8%. Under open-eye conditions, they are: A to C at 9.1%, B to C at 8.4%, C to D at 9.4%, and D to C at 8.9%. Machine learning classification showed higher accuracy in closed-eye conditions, reaching 77.6%.Conclusion Resting-state EEG microstates exhibit significant differences between sINSO and healthy individuals. These microstates are promising biomarkers for distinguishing sINSO, with closed-eye data providing the most reliable discrimination.https://www.tandfonline.com/doi/10.1080/27706710.2024.2388106Subclinical insomniaresting-state EEGmicrostatemachine learning |
| spellingShingle | Yujie Shi Mengqi Ji Fan Zhong Rui Jiang Zhuhong Chen Chi Zhang Yuting Li Junpeng Zhang Wen Wang Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia Brain-Apparatus Communication Subclinical insomnia resting-state EEG microstate machine learning |
| title | Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia |
| title_full | Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia |
| title_fullStr | Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia |
| title_full_unstemmed | Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia |
| title_short | Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia |
| title_sort | resting state eeg microstate analysis reveals potential biomarkers for subclinical insomnia |
| topic | Subclinical insomnia resting-state EEG microstate machine learning |
| url | https://www.tandfonline.com/doi/10.1080/27706710.2024.2388106 |
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