Abnormal trigeminal nerve microstructure and brain white matter in idiopathic trigeminal neuralgia
Bibliographic record
Abstract
Idiopathic trigeminal neuralgia (TN) is classically associated with neurovascular compression (NVC) of the trigeminal nerve at the root entry zone (REZ), but NVC-induced structural alterations are not always apparent on conventional imaging. Previous studies report lower fractional anisotropy (FA) in the affected trigeminal nerves of TN patients using diffusion tensor imaging (DTI). However, it is not known if TN patients have trigeminal nerve abnormalities of mean, radial, or axial diffusivity (MD, RD, AD - metrics linked to neuroinflammation and edema) or brain white matter (WM) abnormalities. DTI scans in 18 right-sided TN patients and 18 healthy controls were retrospectively analyzed to extract FA, RD, AD, and MD from the trigeminal nerve REZ, and Tract-Based Spatial Statistics (TBSS) was used to assess brain WM. In patients, the affected trigeminal nerve had lower FA, and higher RD, AD, and MD was found bilaterally compared to controls. Group TBSS (P<0.05, corrected) showed patients had lower FA and increased RD, MD, and AD in brain WM connecting areas involved in the sensory and cognitive-affective dimensions of pain, attention, and motor functions, including the corpus callosum, cingulum, posterior corona radiata, and superior longitudinal fasciculus. These data indicate that TN patients have abnormal tissue microstructure in their affected trigeminal nerves, and as a possible consequence, WM microstructural alterations in the brain. These findings suggest that trigeminal nerve structural abnormalities occur in TN, even if not apparent on gross imaging. Furthermore, MD and RD findings suggest that neuroinflammation and edema may contribute to TN pathophysiology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".