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

Area of Science:

• Neuroscience
• Computational Neuroscience
• Medical Imaging

Background:

• Migraine is a complex neurological disorder characterized by cyclic phases.
• Microstate analysis of electroencephalography (EEG) data offers insights into brief, critical brain activity fluctuations.
• Understanding migraine phases is crucial for developing targeted interventions.

Purpose of the Study:

• To investigate dynamic changes in brain microstate features across different migraine phases.
• To develop and validate a classification model for identifying the pre-ictal phase of migraine.
• To explore the potential of microstate analysis in understanding migraine pathophysiology.

Main Methods:

• Resting-state EEG data collected from 174 individuals with migraine and 50 healthy controls.
• Comparison of microstate features, Lempel-Ziv complexity, and sample entropy across five groups (pre-ictal, ictal, post-ictal, inter-ictal, healthy controls).
• Development and validation of a machine learning model to predict the pre-ictal phase using microstate features.

Main Results:

• Significant alterations in microstate A and B durations and coverage were observed across migraine phases.
• The pre-ictal phase showed reduced microstate A coverage and increased microstate B occurrence and duration.
• The classification model achieved robust performance, with an AUROC of 0.84 and AUPRC of 0.86 on the test set for pre-ictal phase identification.

Conclusions:

• Cyclic alterations in brain microstates, specifically reduced microstate A and enhanced microstate B, characterize the pre-ictal phase in migraine.
• These microstate changes may reflect heightened sensory sensitivity and altered cortical activity.
• The developed classification model shows promise for early detection of the pre-ictal phase, enabling timely intervention.