Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes

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 groups as well as between the CM and EM groups by using principal component analysis, 5-fold cross-validation, and 10% independent test datasets. Results In the spectral power dynamics, the migraine group exhibited elevated entropy in the theta band, particularly in the right insula, along with reduced temporal standard deviation in the delta band within the insula, in the alpha band within the lateral frontal cortex, and in the beta band within the posterior cingulate and lateral frontal regions (corrected p < 0.05). Node strength dynamics of functional connectivity revealed reduced entropy in the migraine group, particularly in the alpha, beta, gamma, and high-frequency oscillation bands across distinct brain networks (corrected p < 0.05). In the classification model between the migraine and HC groups, SVM model achieved a validation accuracy of 79.1% (sensitivity: 82.0%; specificity: 71.9%; AUC value: 0.8507) and a test accuracy of 76%. By contrast, in comparisons of the CM and EM groups, SVM models achieved a validation accuracy of 80.7% (sensitivity: 80.9%; specificity: 80.6%; AUC value: 0.9117) and a test accuracy of 76.5%. Feature importance indicated migraine is linked to disrupted spectral complexity and dynamic coupling across sensory–cognitive networks. Conclusion Our MEG-based temporal dynamics approach exhibited good classification accuracy, revealing distinct homeostatic disruptions in individuals with migraine. The findings may guide the development of objective, brain-based signatures, advancing the development of personalised strategies for migraine management. Future research should validate the findings and assess their applicability to scalable modalities such as EEG.