Trigeminal neuralgia diffusivities using Gaussian process classification and merged group tractography
Bibliographic record
Abstract
ABSTRACT: Imaging of trigeminal neuralgia (TN) has demonstrated key diffusion tensor imaging-based diffusivity alterations in the trigeminal nerve; however, imaging has primarily focused on the peripheral nerve segment because of previous limitations in reliably segmenting small fiber bundles across multiple subjects. We used Selective Automated Group Integrated Tractography to study 36 subjects with TN (right-sided pain) and 36 sex-matched controls to examine the trigeminal nerve (fifth cranial nerve [CN V]), pontine decussation (TPT), and thalamocortical fibers (S1). Gaussian process classifiers were trained by scrolling a moving window over CN V, TPT, and S1 tractography centroids. Fractional anisotropy (FA), generalized FA, radial diffusivity, axial diffusivity, and mean diffusivity metrics were evaluated for both groups, analyzing TN vs control groups and affected vs unaffected sides. Classifiers that performed at greater-than-or-equal-to 70% accuracy were included. Gaussian process classifier consistently demonstrated bilateral trigeminal changes, differentiating them from controls with an accuracy of 80%. Affected and unaffected sides could be differentiated from each other with 75% accuracy. Bilateral TPT could be distinguished from controls with at least 85% accuracy. TPT left-right classification achieved 98% accuracy. Bilateral S1 could be differentiated from controls, where the affected S1 radial diffusivity classifier achieved 87% accuracy. This is the first TN study that combines group-wise merged tractography, machine learning classification, and analysis of the complete trigeminal pathways from the peripheral fibers to S1 cortex. This analysis demonstrates that TN is characterized by bilateral abnormalities throughout the trigeminal pathway compared with controls and abnormalities between affected and unaffected sides. This full pathway tractography study of TN demonstrates bilateral changes throughout the trigeminal pathway and changes between affected and unaffected sides.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".