Identification of patients with chronic migraine by using sensory-evoked oscillations from the electroencephalogram classifier

Background: To examine whether the modulating evoked cortical oscillations could be brain signatures among patients with chronic migraine, we investigated cortical modulation using an electroencephalogram with machine learning techniques. Methods: We directly record evoked electroencephalogram activity during nonpainful, painful, and repetitive painful electrical stimulation tasks. Cortical modulation for experimental pain and habituation processing was analyzed and used to differentiate patients with chronic migraine from healthy controls using a validated machine-learning model. Results: This study included 80 participants: 40 healthy controls and 40 patients with chronic migraine. Evoked somatosensory oscillations were dominant in the alpha band. Longer latency (nonpainful and repetitive painful) and augmented power (nonpainful and repetitive painful) were present among patients with chronic migraine. However, for painful tasks, alpha increases were observed among healthy controls. The oscillatory activity ratios between repetitive painful and painful tasks represented the frequency modulation and power habituation among healthy controls, respectively, but not among patients with chronic migraine. The classification models with oscillatory features exhibited high performance in differentiating patients with chronic migraine from healthy controls. Conclusion: Altered oscillatory characteristics of sensory processing and cortical modulation reflected the neuropathology of patients with chronic migraine. These characteristics can be reliably used to identify patients with chronic migraine using a machine-learning approach.