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

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 f

SUBMITTER: Hsiao FJ 

PROVIDER: S-EPMC9531441 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature