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August 6, 2026Journal of Clinical MedicineOpen Access

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

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Authors

ZŞZeynep Selcan ŞanlıİÇİsmail ÇalıkuşuPBPamir Bastin

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Overview

Randomized trial examines EEG connectivity differences in migraine, TTH, and healthy controls, suggesting potential neurophysiological signatures.

Key Points

  • The study aims to identify EEG connectivity patterns that differentiate migraine, tension-type headache, and healthy controls.
  • Included 150 participants (61 migraine, 47 TTH, 42 controls)
  • Measured connectivity across multiple frequency bands using coherence, wPLI, and graph features
  • Employed machine-learning models with ROC-AUC analyses and false discovery rate correction.
  • TTH exhibited highest gamma temporo-parietal coherence; migraine had higher gamma wPLI and exploratory scores
  • Controls showed greater delta fronto-temporal and gamma coherence
  • Machine-learning models demonstrated modest classification performance with moderate ROC-AUC values.

Cite This Study

Şanlı et al. (2026) studied this question.

synapsesocial.com/papers/6a7437ba764cddc9499d544ahttps://doi.org/10.3390/jcm15156046
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Disrupted functional network topology in tension-type headache: A cross-sectional magnetoencephalography study2025 · 1 citations
  2. 2Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine2026
  3. 3Differential Cortical Connectivity in Migraine: Insights from High-Density EEG and Steady-State Visual Evoked Potentials2024
  4. 4MEG Analysis of Cortical Activity and Network Topology During the Interictal Period of Migraine Without Aura2025 · 1 citations
  5. 5The Role of Electroencephalography in the Evaluation of Patients With Migraine and Tension-Type Headache2026