ArticleFrontiers in pain research (Lausanne, Switzerland)2022
The value of brain MRI functional connectivity data in a machine learning classifier for distinguishing migraine from persistent post-traumatic headache.
Article in Frontiers in pain research (Lausanne, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- A foundation-model approach to pediatric headache classification from resting-state fMRI.ArXiv · 2026Article
- Headache associated with adverse cognitive trajectories in Chinese aging cohort: a group-based trajectory modeling study.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026Article
- Shared neural signatures of photophobia in migraine and post-traumatic headache: a task-based fMRI study.The journal of headache and pain · 2025Article
- Current and prospective roles of magnetic resonance imaging in mild traumatic brain injury.Brain communications · 2025Review
- Migraine headache (MH) classification using machine learning methods with data augmentation.Scientific reports · 2024Article
- A Narrative Review of Visual Hallucinations in Migraine and Epilepsy: Similarities and Differences in Children and Adolescents.Brain sciences · 2023Review
- A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis.Frontiers in human neuroscience · 2023Review
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5 authors.
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Abstract
Background: Post-traumatic headache (PTH) and migraine often have similar phenotypes. The objective of this exploratory study was to develop classification models to differentiate persistent PTH (PPTH) from migraine using clinical data and magnetic resonance imaging (MRI) measures of brain structure and functional connectivity (fc). Methods: Thirty-four individuals with migraine and 48 individuals with PPTH attributed to mild TBI were included. All individuals completed questionnaires assessing headache characteristics, mood, sensory hypersensitivities, and cognitive function and underwent brain structural and functional imaging during the same study visit. Clinical features, structural and functional resting-state measures were included as potential variables. Classifiers using ridge logistic regression of principal components were fit on the data. Average accuracy was calculated using leave-one-out cross-validation. Models were fit with and without fc data. The importance of specific variables to the classifier were examined. Results: With internal variable selection and principal components creation the average accuracy was 72% with fc data and 63.4% without fc data. This classifier with fc data identified individuals with PPTH and individuals with migraine with equal accuracy. Conclusion: Multivariate models based on clinical characteristics, fc, and brain structural data accurately classify and differentiate PPTH vs. migraine suggesting differences in the neuromechanism and clinical features underlying both headache disorders.
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