ArticleThe journal of headache and pain2023
Increased MRI-based Brain Age in chronic migraine patients.
Article in The journal of headache and pain, 2023. 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, 10 citations in OpenAlex.
- Impaired glymphatic function and advanced brain age gap in chronic migraine: a multimodal MRI study.The journal of headache and pain · 2026Article
- Cellular senescence in migraine - a hypothesis-driven narrative review.The journal of headache and pain · 2026Review
- Fifteen years on: a review of the Cam-CAN study of the cognitive neuroscience of ageing.GeroScience · 2026Review
- Increased Brain-Age Gap in Young Adults With Psychotic Experiences.Biological psychiatry global open science · 2026Article
- Accelerated brain ageing in migraine: a multilevel MRI-based brain-age modelling study.Brain communications · 2026Article
- Mind the Metabolic Gap: Bridging Migraine and Alzheimer's disease through Brain Insulin Resistance.Aging and disease · 2024Review
- Genetic evidence for the causal relationships between migraine, dementia, and longitudinal brain atrophy.The journal of headache and pain · 2024Article
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Authors and funding
6 authors at 1 institution in 2 countries.
Funding
Abstract
introductionNeuroimaging has revealed that migraine is linked to alterations in both the structure and function of the brain. However, the relationship of these changes with aging has not been studied in detail. Here we employ the Brain Age framework to analyze migraine, by building a machine-learning model that predicts age from neuroimaging data. We hypothesize that migraine patients will exhibit an increased Brain Age Gap (the difference between the predicted age and the chronological age) compared to healthy participants.
methodsWe trained a machine learning model to predict Brain Age from 2,771 T1-weighted magnetic resonance imaging scans of healthy subjects. The processing pipeline included the automatic segmentation of the images, the extraction of 1,479 imaging features (both morphological and intensity-based), harmonization, feature selection and training inside a 10-fold cross-validation scheme. Separate models based only on morphological and intensity features were also trained, and all the Brain Age models were later applied to a discovery cohort composed of 247 subjects, divided into healthy controls (HC, n=82), episodic migraine (EM, n=91), and chronic migraine patients (CM, n=74).
resultsCM patients showed an increased Brain Age Gap compared to HC (4.16 vs -0.56 years, P=0.01). A smaller Brain Age Gap was found for EM patients, not reaching statistical significance (1.21 vs -0.56 years, P=0.19). No associations were found between the Brain Age Gap and headache or migraine frequency, or duration of the disease. Brain imaging features that have previously been associated with migraine were among the main drivers of the differences in the predicted age. Also, the separate analysis using only morphological or intensity-based features revealed different patterns in the Brain Age biomarker in patients with migraine.
conclusionThe brain-predicted age has shown to be a sensitive biomarker of CM patients and can help reveal distinct aging patterns in migraine.
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