White matter brain and trigeminal nerve abnormalities in temporomandibular disorder
Bibliographic record
Abstract
Temporomandibular disorder (TMD) is a prevalent chronic pain disorder that remains poorly understood. Recent imaging studies reported functional and gray matter abnormalities in brain areas implicated in sensorimotor, modulatory, and cognitive function in TMD, but it is not known whether there are white matter (WM) abnormalities along the trigeminal nerve (CNV) or in the brain. Here, we used diffusion tensor imaging, and found that, compared to healthy controls, TMD patients had 1) lower fractional anisotropy (FA) in both CNVs; 2) a negative correlation between FA of the right CNV and pain duration; and 3) diffuse abnormalities in the microstructure of WM tracts related to sensory, motor, cognitive, and pain functions, with a highly significant focal abnormality in the corpus callosum. Using probabilistic tractography, we found that the corpus callosum in patients had a higher connection probability to the frontal pole, and a lower connection probability to the dorsolateral prefrontal cortex, compared to controls. Finally, we found that 1) FA in tracts adjacent to the ventrolateral prefrontal cortex and tracts coursing through the thalamus negatively correlated with pain intensity; 2) FA in the internal capsule negatively correlated with pain intensity and unpleasantness; and 3) decreases in brain FA were associated with increases in mean diffusivity and radial diffusivity, markers of inflammation and oedema. These data provide novel evidence for CNV microstructural abnormalities that may be caused by increased nociceptive activity, accompanied by abnormalities along central WM pathways in TMD.
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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 | 0.001 |
| 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".