ArticleThe journal of headache and pain2022
Resting-state magnetoencephalographic oscillatory connectivity to identify patients with chronic migraine using machine learning.
Article in The journal of headache and pain, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- 'Man vs. Machine: can ML algorithms diagnose headaches as accurately as clinicians? A systematic review.'Journal of neurology · 2026Pooled it
- Artificial intelligence in headache medicine: between automation and the doctor-patient relationship. A systematic review.The journal of headache and pain · 2025Pooled it
- Magnetoencephalography studies in migraine and headache disorders: A systematic review.Headache · 2025Pooled it
- Oscillatory edge connectivity in pain-related regions supports machine learning identification of migraine.The journal of headache and pain · 2026Article
- Unmasking the noise: aberrant cortical oscillations in visual snow syndrome.The journal of headache and pain · 2026Article
- Central executive network-related neuromagnetic abnormalities in chronic and episodic migraine: a resting-state magnetoencephalography study.Frontiers in neuroscience · 2026Article
- Temporal stability and neural complexity in resting-state MEG predict migraine phenotypes.The journal of headache and pain · 2025Article
- Aberrant static and dynamic functional connectivity of auditory processing in migraine: A millisecond-scale magnetoencephalography study.Journal of biomedical research · 2025Article
- Artificial neural networks applied to somatosensory evoked potentials for migraine classification.The journal of headache and pain · 2025Article
- Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data.PloS one · 2025Article
- Hallmarks of primary headache: part 1 - migraine.The journal of headache and pain · 2024Review
- Altered brainstem-cortex activation and interaction in migraine patients: somatosensory evoked EEG responses with machine learning.The journal of headache and pain · 2024Article
- Influence of next-generation artificial intelligence on headache research, diagnosis and treatment: the junior editorial board members' vision - part 1.The journal of headache and pain · 2024Review
- Decoding pain through facial expressions: a study of patients with migraine.The journal of headache and pain · 2024Article
- The Clinical Relevance of Artificial Intelligence in Migraine.Brain sciences · 2024Review
- Characteristic oscillatory brain networks for predicting patients with chronic migraine.The journal of headache and pain · 2023Article
- Application of EEG in migraine.Frontiers in human neuroscience · 2023Review
- Classifying migraine using PET compressive big data analytics of brain'sFrontiers in pharmacology · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
To identify and validate the neural signatures of resting-state oscillatory connectivity for chronic migraine (CM), we used machine learning techniques to classify patients with CM from healthy controls (HC) and patients with other pain disorders. The cross-sectional study obtained resting-state magnetoencephalographic data from 240 participants (70 HC, 100 CM, 35 episodic migraine [EM], and 35 fibromyalgia [FM]). Source-based oscillatory connectivity of relevant cortical regions was calculated to determine intrinsic connectivity at 1-40 Hz. A classification model that employed a support vector machine was developed using the magnetoencephalographic data to assess the reliability and generalizability of CM identification. In the findings, the discriminative features that differentiate CM from HC were principally observed from the functional interactions between salience, sensorimotor, and part of the default mode networks. The classification model with these features exhibited excellent performance in distinguishing patients with CM from HC (accuracy ≥ 86.8%, area under the curve (AUC) ≥ 0.9) and from those with EM (accuracy: 94.5%, AUC: 0.96). The model also achieved high performance (accuracy: 89.1%, AUC: 0.91) in classifying CM from other pain disorders (FM in this study). These resting-state magnetoencephalographic electrophysiological features yield oscillatory connectivity to identify patients with CM from those with a different type of migraine and pain disorder, with adequate reliability and generalizability.
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