EEG Connectivity Signatures in Migraine and Tension-Type Headache: A Multimethod ROI-Based Functional Connectivity and Machine-Learning Analysis.

Overview

1. Department of Neurology, Adana City Training and Research Hospital, 01230 Adana, Türkiye; (P.B.); (S.M.B.); (V.D.Y.)
2. Biomedical Device Technology Program, Vocational School, Nevşehir Hacı Bektaş Veli University, 50300 Nevsehir, Türkiye
3. Department of Biostatistics, Çukurova University Faculty of Medicine, 01330 Adana, Türkiye

Abstract

Background/Objectives: Migraine and tension-type headache (TTH) are common primary headache disorders, but their underlying network-level EEG patterns remain incompletely characterized. We examined whether resting-state functional connectivity measured with complementary estimators, predefined sensor-level regions of interest (ROIs), graph features, and machine-learning models could distinguish migraine, TTH, and healthy controls. Methods: The study included 150 participants (61 migraine, 47 TTH, and 42 controls). Connectivity was calculated in the delta, theta, alpha, beta, and gamma bands using coherence, imaginary coherence, weighted phase-lag index (wPLI), and debiased wPLI. We evaluated global, regional, topographic, and graph-theoretical features, performed ROC-AUC analyses, and tested classification models with nested cross-validation. To limit the multiple-testing burden, false discovery rate correction was applied to a prespecified, hypothesis-driven ROI set. Results: Eight candidate ROI features remained significant after correction. TTH showed the highest gamma temporo-parietal coherence, whereas migraine showed higher gamma temporo-parietal wPLI and debiased wPLI and a higher exploratory migraine probability-like score. Controls had higher delta fronto-temporal and gamma fronto-temporal/temporo-parietal imaginary coherence. Single-feature ROC-AUC values were moderate, and the nested machine-learning models showed only modest classification performance. Conclusions: The results point to method-dependent, region-specific sensor-level EEG differences across migraine, TTH, and control groups. They are best viewed as candidate neurophysiological signatures rather than clinically ready biomarkers. External validation, fuller clinical covariate assessment, and source-level analyses are needed before diagnostic use can be considered. Future studies should also examine whether these candidate signatures differ according to aura status and episodic or chronic headache subtype.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request, subject to institutional, ethical, and privacy restrictions. Raw EEG recordings cannot be made publicly available because they were obtained in a routine clinical setting and may contain sensitive health-related information. Derived anonymized feature tables and analysis outputs may be shared upon reasonable request where permitted by the relevant ethical and institutional regulations.

Reproduced under the paper's license (CC BY), from the paper cited above.

2.10. Data, Code, and Protocol Availability

The anonymized data supporting the findings of this study, including derived EEG connectivity features and analysis outputs, will be made available by the corresponding author upon reasonable request, subject to institutional, ethical, and privacy restrictions. Raw clinical EEG recordings cannot be made publicly available because they were obtained in a routine clinical setting and may contain sensitive health-related information. Analysis scripts, feature definitions, and protocol details used for EEG preprocessing, connectivity estimation, statistical analysis, and machine-learning evaluation will be made available upon reasonable request to allow verification and methodological replication.

No large public database accession number is applicable to this study because the dataset was not deposited in a public repository at the time of submission.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 6 authors, 9 keywords, 19 references.

Cite

This paper

Sanlı, Z. S., Çalıkuşu, I., Bastin, P., Bastin, S. M., Binokay, H., & Yerdelen, V. D. (2026). EEG Connectivity Signatures in Migraine and Tension-Type Headache: A Multimethod ROI-Based Functional Connectivity and Machine-Learning Analysis. Journal of clinical medicine, 15(15), 6046. https://doi.org/10.3390/jcm15156046

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