ArticleBMC neurology2023
Functional connectivity of the language area in migraine: a preliminary classification model.
Article in BMC neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Changes in Somatomotor Network Connectivity Differentiate Better Versus Worse Recovery of Hand Dexterity After Brain Tumor Surgery: A Cohort Study.Neural plasticity · 2026Article
- Cortical network dysfunction in migraine: linking genes, metabolism and clinical disability.The journal of headache and pain · 2025Article
- EEG Spectral Analysis in Chronic Pain During Rest and Cognitive Reasoning.Sensors (Basel, Switzerland) · 2025Article
- Migraine aura discrimination using machine learning: an fMRI study during ictal and interictal periods.Medical & biological engineering & computing · 2024Article
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Authors and funding
14 authors.
Funding
Abstract
backgroundMigraine is a complex disorder characterized by debilitating headaches. Despite its prevalence, its pathophysiology remains unknown, with subsequent gaps in diagnosis and treatment. We combined machine learning with connectivity analysis and applied a whole-brain network approach to identify potential targets for migraine diagnosis and treatment.
methodsBaseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI(rfMRI), and diffusion weighted scans were obtained from 31 patients with migraine, and 17 controls. A recently developed machine learning technique, Hollow Tree Super (HoTS) was used to classify subjects into diagnostic groups based on functional connectivity (FC) and derive networks and parcels contributing to the model. PageRank centrality analysis was also performed on the structural connectome to identify changes in hubness.
resultsOur model attained an area under the receiver operating characteristic curve (AUC-ROC) of 0.68, which rose to 0.86 following hyperparameter tuning. FC of the language network was most predictive of the model's classification, though patients with migraine also demonstrated differences in the accessory language, visual and medial temporal regions. Several analogous regions in the right hemisphere demonstrated changes in PageRank centrality, suggesting possible compensation.
conclusionsAlthough our small sample size demands caution, our preliminary findings demonstrate the utility of our method in providing a network-based perspective to diagnosis and treatment of migraine.
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