Cortical abnormalities in episodic migraine: A multi-center 3T MRI study
Bibliographic record
Abstract
BACKGROUND: Several previous studies have investigated cortical abnormalities, specifically cortical thickness, in patients with migraine, with variable results. The relatively small sample sizes of most previous studies may partially explain these inconsistencies. OBJECTIVE: To investigate differences of cortical thickness between control subjects and migraineurs in a large cohort. METHODS: Three Tesla MRI data of 131 patients (38 with and 93 without aura) and 115 control subjects were analysed. A vertex-wise linear model was applied controlling for age, gender and MRI scanner to investigate differences between groups and determine the impact of clinical factors on cortical thickness measures. RESULTS: Migraineurs showed areas of thinned cortex compared with controls bilaterally in the central sulcus, in the left middle-frontal gyrus, in left visual cortices and the right occipito-temporal gyrus. Frequency of migraine attacks and the duration of the disorder had a significant impact on cortical thickness in the sensorimotor cortex and middle-frontal gyrus. Patients without aura showed thinner cortex than controls bilaterally in the central sulcus and in the middle frontal gyrus, in the left primary visual cortices, in the left supramarginal gyrus and in the right cuneus. Patients with aura showed clusters of thinner cortex bilaterally in the subparietal sulcus (between the precuneus and posterior cingulate cortex), in the left intraparietal sulcus and in the right anterior cingulate. CONCLUSION: These results indicate cortical abnormalities in specific brain regions in migraineurs. Some of the observed abnormalities may reflect a genetic susceptibility towards developing migraine attacks, while others are probably a consequence of repeated head pain attacks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".