Exploring potential resting-state EEG biomarkers of obsessive-compulsive disorder based on explainable machine learning analysis of independent training and test samples
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Le résumé fourni par la source
BACKGROUND: At present, the clinical diagnosis of obsessive-compulsive disorder (OCD) is primarily based on interviews with patients by experienced psychiatrists, which is inherently limited by its subjectivity. Therefore, there is a pressing clinical need to identify objective biomarkers of OCD. Although the combination of EEG and data-driven method based on machine learning offers promise for identifying objective neurophysiological biomarkers, current researches either rely on only small sample sizes, lack interpretability, use only one single dataset which cannot validate model generalizability, or utilize only one type of EEG feature which cannot evaluate the relative utility of different features as potential biomarkers of OCD under a unified data-driven framework. METHOD: This study employed two independent datasets (Dataset 1: OCD = 35, healthy controls = 37; Dataset 2: OCD = 21, healthy controls = 21). Eight EEG features were extracted and were sent to six machine learning (ML) classifiers to classify OCD from healthy controls. After optimizing the hyperparameters and the most relevant feature set on Dataset 1, each ML model was retrained on Dataset 1 and then were tested on an independent external test set (Dataset 2) to assess their generalizability. Feature contributions were interpreted using SHapley Additive Explanations (SHAP) analysis. RESULT: Among the eight EEG feature sets, the phase-locking value (PLV) features achieved the highest classification performance across all machine learning models. The LightGBM classifier outperformed others ML classifiers, reaching an accuracy of 86.6% on Dataset 1 and 83.3% on Dataset 2 (independent external test). The data-driven method based on machine learning selected the most important 16 PLV features and SHAP-based feature importance analysis identified alpha band PLV (F4-P3), delta band PLV (P3-O1, F3-O1), and theta band PLV (C3-T4) as the most influential contributors to model predictions. CONCLUSION: This study developed an explainable machine learning framework based on PLV functional connectivity feature to enable accurate and generalizable OCD classification. These results highlight the potential of PLV especially long-range PLV connectivity between frontal and parietal/occipital lobe as a reliable EEG biomarker for objective and clinically applicable OCD diagnosis.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Exploring potential resting-state EEG biomarkers of obsessive-compulsive disorder based on explainable machine learning analysis of independent training and test samples
- Date Crossref
- 15/11/2025
- 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 Medical University pays non établi dans la noticeUniversité ou école supérieure
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Tianjin University Institute of Disaster and Emergency Medicine pays non établi dans la noticeUniversité ou école supérieure
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Henan Psychiatric Hospital pays non établi dans la noticeÉtablissement de santé
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First Affiliated Hospital of Xinxiang Medical University Henan Key Laboratory of Neurorestoratology pays non établi dans la noticeÉtablissement de santé
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Capital Medical University 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 Engineering Research Center of Physical Diagnostics and Treatment Technology for the Mental and Neurological Diseases 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 Mathematical Medicine pays non établi dans la noticeUniversité ou école supérieure
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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
Henan Medical University, Institute of Disaster and Emergency Medicine — Tianjin University et Henan Psychiatric Hospital, avec 8 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.