Exploring the alterations in microstate dynamics during the migraine cycle and detecting pre-ictal phases.

Background: Microstate analysis captures brief but critical fluctuations in brain activity, making it a powerful tool for exploring the cyclic nature of migraine. In this study, we aimed to investigate microstate features during different migraine phases and develop a classification model to identify the pre-ictal phase.

Methods: From May 2023 to June 2024, we conducted a cross-sectional study with consecutive recruitment, collecting resting-state electroencephalography data from 174 individuals with migraine without aura and 50 healthy controls, followed by classification of migraine phases. Microstate features, Lempel-Ziv complexity, and sample entropy were compared across five groups. A model was developed to identify the pre-ictal phase and validated on a test set.

Results: Microstate features, particularly for microstates A and B, exhibited dynamic changes across the migraine cycle. The duration of microstate A was significantly longer in the inter-ictal phase than in the pre-ictal phase, whereas microstate B showed prolonged duration in the pre-ictal phase compared to healthy controls and the post-ictal phase. Microstate A displayed reduced coverage in the pre-ictal phase, whereas microstate B had increased occurrence and coverage during the pre-ictal and ictal phases. Transition probabilities also varied significantly: the pre-ictal phase showed elevated transitions from microstates A, C, and D to B, and the post-ictal phase showed reduced transitions from C and D to A. A classification model based on these microstate features achieved an area under the receiver operating characteristic curve (AUROC) of 0.85 (0.73-0.95), an area under the precision-recall curve (AUPRC) of 0.83 (0.66-0.95), and an F1 score of 0.78 (0.62-0.90) in the training set; and an AUROC of 0.84 (0.69-0.97), an AUPRC of 0.86 (0.67-0.98), and an F1 score of 0.81 (0.65-0.93) in the test set, indicating robust performance in identifying the pre-ictal phase.

Conclusion: Through the observation of cyclic alterations in the microstates of patients with migraine, we identified a reduction in microstate A and an enhancement in microstate B during the pre-ictal phase. These changes may indicate a heightened sensitivity to auditory stimuli and increased activity in the visual cortex, providing new insights into migraine pathophysiology. Our model effectively identified the pre-ictal phase, offering a promising approach for early intervention in migraine attacks.