Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes
Abstract
BACKGROUND: Objective brain signatures for migraine, a leading cause of global disability, have yet to be identified, which limits objective diagnosis and personalised management for the condition. Herein, we introduce a novel magnetoencephalography (MEG)-based approach to capturing dynamic temporal signatures of brain activity in this cross-sectional study. We leveraged MEG’s high temporal and spatial resolution to investigate migraine-related disruptions in neural homeostasis within classification models. METHODS: Resting-state MEG data were collected from 250 right-handed individuals (age: 20–60 years; women: 80%), including 72 healthy control ([HC] group) and 178 migraine patients (diagnosed per the International Classification of Headache Disorders, 3rd edition). Within the migraine group, 98 patients were diagnosed with chronic migraine (CM), and 80 with episodic migraine (EM). MEG data were collected during the interictal period by using a 306-channel system. Time-resolved spectral power and dynamic functional connectivity were analysed across frequency bands ranging from delta to high-frequency oscillations in the default mode, salience, central executive, pain-related, sensorimotor, auditory, and visual networks. Neural stability and complexity were assessed by calculating the temporal standard deviation and entropy of spectral power, region-to-region connectivity, and node strength. Machine learning models were used to differentiate between the migraine and HC…
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 178 participants or records.
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