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EEG Connectivity Signatures in Migraine and Tension-Type Headache: A Multimethod ROI-Based Functional Connectivity
Zeynep Selcan Sanlı1, Ismail Çalıkuşu2, Pamir Bastin1
1Department of Neurology, Adana City Training and Research Hospital, 01230 Adana, Türkiye.
Journal of Clinical Medicine
|August 13, 2026
Summary
This study explored electroencephalography (EEG) network differences in migraine and tension-type headache (TTH) using functional connectivity. Findings suggest subtle, method-dependent EEG signatures, not yet ready for clinical diagnosis.
Area of Science:
- Neuroscience
- Medical Imaging
- Computational Biology
Background:
- Migraine and tension-type headache (TTH) are prevalent primary headache disorders.
- Underlying network-level electroencephalography (EEG) patterns for these conditions are not fully understood.
- Resting-state functional connectivity analysis offers a potential method to investigate these patterns.
Purpose of the Study:
- To determine if resting-state functional connectivity can differentiate between migraine, TTH, and healthy controls.
- To explore various connectivity estimators, regions of interest (ROIs), graph features, and machine learning models for classification.
Main Methods:
- 150 participants (61 migraine, 47 TTH, 42 controls) were included.
- EEG functional connectivity was computed using coherence, imaginary coherence, weighted phase-lag index (wPLI), and debiased wPLI across five frequency bands (delta, theta, alpha, beta, gamma).
- Global, regional, topographic, and graph-theoretical features were analyzed, with ROC-AUC and nested cross-validation for machine learning models.
Main Results:
- Eight candidate ROI features remained significant after FDR correction.
- Specific differences were observed: TTH showed higher gamma temporo-parietal coherence; migraine exhibited higher gamma temporo-parietal wPLI and debiased wPLI.
- Healthy controls displayed higher delta fronto-temporal and gamma fronto-temporal/temporo-parietal imaginary coherence; machine learning models showed modest classification performance.
Conclusions:
- Sensor-level EEG functional connectivity reveals method-dependent, region-specific differences between migraine, TTH, and control groups.
- These findings represent potential neurophysiological signatures but require further validation and are not yet clinically applicable biomarkers.
- Future research should include external validation, clinical covariate assessment, source-level analyses, and investigation into aura status and headache chronicity.
