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

Abstract

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.

Decoding pain through facial expressions: a study of patients with migraine

“…Finally, this study’s finding of a lack of discernible differences in facial activities between groups may be attributable to the experimental setup involving the resting condition during facial video capture. Given that previous findings indicate altered cortical activation in patients with CM during sensory or nociceptive processing [ 39 , 51 ], additional research exploring the influence of stimulation on changes in facial activities could provide a more comprehensive understanding of this issue.…”

Migraine Aura—Catch Me If You Can with EEG and MRI—A Narrative Review

“…Neurophysiological studies with event-related potentials and neuroimaging studies have been developed in machine learning settings for automated classification of ictal and interictal states in migraine or patients from healthy controls [75][76][77]. An automated classification algorithm based on cortical features, such as cortical thickness, surface area, volume, or folding index from MRI post processing, could distinguish not only between MwA patients and healthy controls but also between migraine patients with simple and complex aura with high accuracy [76].…”

Quantitative electroencephalography as a potential biomarker in migraine

“…In addition to the conventional electroencephalography (EEG) methods, various studies have utilized EEG event-related potentials, somatosensory evoked potentials, and visual evoked potentials in the pursuit of potential biomarkers (Hsiao et al, 2023;2021;Petrusic et al, 2021Petrusic et al, , 2022Zhu et al, 2019). For instance, Zhu et al (2019) demonstrated the efficacy of somatosensory evoked potentials for migraine classification.…”