Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine.

BACKGROUND: Objective and interpretable brain signatures for migraine diagnosis and phenotyping remain limited. Edge-centric connectivity treats interregional connections as primary analytical units and captures higher-order, frequency-resolved coordination patterns that may not be detected by conventional node-based functional connectivity approaches. This study investigated whether resting-state magnetoencephalography-derived oscillatory edge-connectivity features within pain-related regions may provide mechanistic insights into migraine neuropathology and inform future research on complementary approaches to migraine diagnosis. METHODS: This study included 250 individuals, stratified into healthy controls (n = 72), patients with chronic migraine (CM; n = 98), and patients with episodic migraine (EM; n = 80). Resting-state 306-channel magnetoencephalography activity was source-analysed in predefined pain-related regions: the bilateral insula, primary somatosensory cortex, secondary somatosensory cortex, primary motor cortex, and anterior cingulate cortex (ACC). Dynamic connectivity was estimated using amplitude envelope correlation, with symmetric orthogonalisation to reduce leakage. Edge-centric functional connectivity was calculated from co-fluctuation time series across frequency bands. Seven classifiers were trained using fivefold nested cross-validation with independent testing (80/20 split). Model predictions were further interpreted using Shapley values to identify candidate neurophysiological signatures at the edge-and frequency-band levels. RESULTS: Compared with HCs, both migraine groups exhibited significantly higher anxiety and depression scores (all p < 0.001), whereas the CM group demonstrated greater headache frequency and migraine-related disability than did the EM group. Using oscillatory edge-connectivity features derived from resting-state magnetoencephalography, multiple machine-learning models successfully discriminated patients with migraine from healthy controls. In the validation and independent test datasets, six models, particularly support vector machine, k-nearest neighbour, and ensemble approaches, achieved the predefined performance criteria (accuracy and weighted F1-score ≥ 0.75). Receiver operating characteristic analysis demonstrated good discriminative performance, with area under the curve values ranging from 0.783 to 0.841 in the validation dataset and from 0.808 to 0.854 in the independent test dataset. Shapley analysis consistently revealed that elevated ACC-centred edges linking the somatosensory cortex, insula, and motor cortex to the ACC contributed most strongly to migraine classification, particularly within alpha-to-gamma frequency bands. Region-level contribution analyses further demonstrated that the ACC and primary somatosensory cortex together accounted for more than 60% of the total feature importance. By contrast, machine learning models failed to reliably distinguish CM from EM under the nested cross-validation framework, with no model meeting the predefined criteria, indicating substantial overlap between migraine phenotypes. CONCLUSION: Applying interpretable machine learning classification to magnetoencephalography-derived oscillatory edge-centric connectivity yields frequency-specific, connection-level signatures that reveal aberrant coordination within pain-related regions. These MEG signatures may warrant future translational research toward complementary tools for migraine diagnosis. CLINICAL TRIAL NUMBER: Not applicable.