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- Title
Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning.
- Authors
Hsiao, Fu-Jung; Chen, Wei-Ta; Pan, Li-Ling Hope; Liu, Hung-Yu; Wang, Yen-Feng; Chen, Shih-Pin; Lai, Kuan-Lin; Coppola, Gianluca; Wang, Shuu-Jiun
- Abstract
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.
- Subjects
MIGRAINE diagnosis; SUPPORT vector machines; NEUROPHYSIOLOGY; CHRONIC diseases; CROSS-sectional method; MACHINE learning; FIBROMYALGIA; PSYCHOLOGICAL tests; DIARY (Literary form); DESCRIPTIVE statistics; QUESTIONNAIRES; RECEIVER operating characteristic curves; HEADACHE; DATA analysis software; SENSITIVITY &; specificity (Statistics); NEUROLOGIC examination; CEREBRAL cortex
- Publication
Journal of Headache & Pain, 2022, Vol 23, Issue 1, p1
- ISSN
1129-2369
- Publication type
Article
- DOI
10.1186/s10194-022-01500-1