Trigeminal nerve microstructure is linked with neuroinflammation and brainstem activity in migraine
Bibliographic record
Abstract
Although the pathophysiology of migraine involves a complex ensemble of peripheral and CNS changes that remain incompletely understood, the activation and sensitization of the trigeminovascular system are believed to play a major role. However, non-invasive, in vivo neuroimaging studies investigating the underlying neural mechanisms of trigeminal system abnormalities in human migraine patients are limited. Here, we studied 60 patients with migraine (55 females, mean ± standard deviation age: 36.28 ± 11.95 years) and 20 age- and sex-matched healthy controls (19 females, age: 35.45 ± 13.30 years) using ultra-high field 7 T diffusion tensor imaging and functional MRI, in addition to PET with the translocator protein ligand 11C-PBR28. We evaluated MRI diffusivity measures and the PET signal at the trigeminal nerve root, in addition to the brainstem functional MRI response to innocuous ophthalmic trigeminal nerve territory stimulation. Patients with migraine demonstrated altered white matter microstructure at the trigeminal nerve root (n = 53), including reduced fractional anisotropy, in comparison to healthy controls (n = 18). Furthermore, in patients, lower fractional anisotropy was accompanied by higher neuroinflammation (i.e. elevated 11C-PBR28 PET signal) at the nerve root (n = 36) and by lower functional MRI activation in an ipsilateral pontine cluster consistent with the spinal trigeminal nucleus (n = 51). These findings were more robust on the right side, which was consistent with the observation that right headache-dominant patients demonstrated higher migraine severity in comparison to left headache-dominant patients in our cohort. Multimodal imaging of the integrated neural mechanisms that characterize migraine underscores the importance of trigeminal system remodelling as both a key aspect of the dynamics underlying migraine pathophysiology and a target for therapeutic interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".