Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning

Fu Jung Hsiao*, Wei Ta Chen, Li Ling Hope Pan, Hung Yu Liu, Yen Feng Wang, Shih Pin Chen, Kuan Lin Lai, Gianluca Coppola, Shuu Jiun Wang

*此作品的通信作者

研究成果: Article同行評審

9 引文 斯高帕斯(Scopus)

摘要

To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1–40 Hz. A classification model that employed a support vector machine was developed using the magnetoencephalographic data to assess the reliability and generalizability of CM identification. In the findings, the discriminative features that differentiate CM from HC were principally observed from the functional interactions between salience, sensorimotor, and part of the default mode networks. The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.

原文English
文章編號130
期刊Journal of Headache and Pain
23
發行號1
DOIs
出版狀態Published - 12月 2022

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