Evoked potential studies in migraine: A systematic review of neurophysiological patterns across migraine subtypes
Bibliographic record
Abstract
BackgroundEvoked potentials are widely used to investigate sensory and nociceptive processing abnormalities in migraine. However, electrophysiological distinctions between migraine subtypes remain insufficiently characterized in the literature. The aim was to systematically review and summarize neurophysiological abnormalities in evoked potential studies (visual, auditory, brainstem, somatosensory and laser) in migraine patients, with a particular focus on latency, amplitude, habituation and clinical correlations across subtypes and healthy controls.MethodsFollowing PRISMA guidelines, we searched PubMed, EMBASE and Web of Science for studies, terms included "Migraine Disorders," "Migraine," "Vestibular Diseases" and "Evoked Potentials", which were published from 2000 to 2024 were included. Risk of bias was assessed using a modified Newcastle-Ottawa Scale.ResultsIn total, 813 studies were screened, resulting in 55 studies meeting the inclusion criteria. Patients with migraine with aura demonstrated higher amplitudes and asymmetry of visual evoked potentials compared to those with migraine without aura. Habituation deficits were particularly evident across all types of evoked potentials. A few studies compared chronic and episodic migraine, reporting higher brainstem and somatosensory evoked potential amplitudes in chronic migraine.ConclusionsMigraine patients have a consistent habituation deficit on all evoked potential parameters. Migraine with aura and chronic migraine may have higher cortical excitability. Further research with larger sample sizes, standardized methodologies and an accurate comparison of migraine phases will enlighten our understanding of the migraine subtypes.Trial RegistrationPROSPERO ID: CRD42024502803.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".