Abnormal nonlinear features of EEG microstate sequence in obsessive–compulsive disorder
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Le résumé fourni par la source
BACKGROUND: At present, only a few studies have explored electroencephalography (EEG) microstates of patients with obsessive-compulsive disorder (OCD) and the results are inconsistent. Additionally, the nonlinear features of EEG microstate sequences contain rich information about the brain, yet how the nonlinear features of EEG microstate sequences abnormally change in patients with OCD is still unknown. METHODS: Resting-state EEG data were collected from 48 OCD patients and macheted 48 healthy controls (HC). Subsequently, EEG microstate analysis was used to extract the microstate temporal parameters (duration, occurrence, coverage) and nonlinear features of EEG microstate sequences (sample entropy, Lempel-Ziv complexity, Hurst index). Finally, the temporal parameters and nonlinear features of EEG microstate sequences were sent to three kinds of machine learning models to classify OCD patients. RESULTS: Both groups obtained four typical EEG microstate topographies. The duration of microstates A, B, and C in OCD patients decreased significantly, while the occurrence of microstate D increased significantly compared to HC. Sample entropy and Lempel-Ziv complexity of microstate sequences in OCD patients increased significantly, while Hurst index decreased significantly compared to HC. The classification accuracy using the nonlinear features of microstate sequences reached up to 85%, significantly higher than that based on microstate temporal parameter models. CONCLUSION: This study provides supplementary findings on EEG microstates in OCD patients with a larger sample size. We found that the nonlinear features of EEG microstate sequences in OCD patients can serve as potential electrophysiological biomarkers for distinguishing OCD patients.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Abnormal nonlinear features of EEG microstate sequence in obsessive–compulsive disorder
- Date Crossref
- 04/12/2024
- Éditeur
- Springer Science and Business Media LLC
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Henan Psychiatric Hospital pays non établi dans la noticeÉtablissement de santé
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Henan Medical University pays non établi dans la noticeUniversité ou école supérieure
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Henan Institute of Technology pays non établi dans la noticeUniversité ou école supérieure
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First Affiliated Hospital of Xinxiang Medical University pays non établi dans la noticeÉtablissement de santé
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The Second Affiliated Hospital of Xinxiang Medical University Henan Collaborative Innovation Center of Prevention and Treatment of Mental Disorder pays non établi dans la noticeUniversité ou école supérieure
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Engineering Technology Research Center of Neurosense and Control of Henan Province pays non établi dans la noticeStructure de recherche
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Henan Engineering Research Center of Medical VR Intelligent Sensing Feedback pays non établi dans la noticeStructure de recherche
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Henan International Joint Laboratory of Neural Information Analysis and Drug Intelligent Design pays non établi dans la noticeStructure de recherche
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School of Medical Engineering pays non établi dans la noticeUniversité ou école supérieure
Henan Psychiatric Hospital, Henan Medical University et Henan Institute of Technology, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.