Unraveling the relationship between white matter lesions in MRI and migraine: a systematic review
Bibliographic record
Abstract
White matter hyperintensities (WMH) are commonly detected on brain magnetic resonance imaging (MRI) scans of migraine patients, but their clinical relevance and underlying mechanisms remain uncertain.To systematically review the relationship between WMH and migraine, focusing on prevalence, progression, and associations with clinical and demographic characteristics.We conducted a systematic review of observational studies published between 1990 and May 2025, including adult patients with migraine (with or without aura) who underwent brain MRIs with at least 1.5T scanners. Data extraction was performed by two independent reviewers, with disagreements resolved by a third. Study quality was assessed using the Newcastle-Ottawa scale for observational studies.A total of 25 studies were included, comprising approximately 3,600 participants, of whom 1,725 had migraine. Most participants were women and reported age means or medians typically between 30 and 60 years. Frequently, WMHs were observed in migraine patients, particularly in those with aura, longer disease duration, and higher headache frequency. No consistent association was found between WMH and comorbidities. Significant heterogeneity in imaging protocols, lesion quantification methods, and study design limited data comparability and precluded meta-analysis.Migraine patients often present with WMHs, but their clinical significance remains unclear. Future studies should employ standardized MRI protocols, volumetric lesion quantification, and consistent migraine phenotyping to clarify its pathophysiological role in migraine and potential implications for diagnosis and management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".