Grey matter changes of the pain matrix in patients with burning mouth syndrome
Bibliographic record
Abstract
Burning mouth syndrome (BMS) is characterized by a burning sensation in the mouth, usually in the absence of clinical and laboratory findings. Latest findings indicate that BMS could result from neuropathic trigeminal conditions. While many investigations have focused on the periphery, very few have examined possible central dysfunctions. To highlight changes of the central system of subjects with BMS, we analysed the grey matter concentration in 12 subjects using voxel-based morphometry. Data were compared with a control group (Ct). To better understand the brain mechanisms underlying BMS, the grey matter concentration of patients was also compared with those of dysgeusic patients (Dys). Dysgeusia is another oral dysfunction condition, characterized by a distorted sense of taste and accompanied by a reduced taste function. We found that a major part of the 'pain matrix' presented modifications of the grey matter concentration in subjects with BMS. Six regions out of eight were affected [anterior and posterior cingulate gyrus, lobules of the cerebellum, insula/frontal operculum, inferior temporal area, primary motor cortex, dorsolateral pre-frontal cortex (DLPFC)]. In the anterior cingulate gyrus, the lobules of the cerebellum, the inferior temporal lobe and the DLPFC, pain intensity correlated with grey matter concentration. Dys also presented changes in grey matter concentration but in different areas of the brain. Our results suggest that a deficiency in the control of pain could in part be a cause of BMS and that BMS and dysgeusia conditions are not linked to similar structural changes in the brain.
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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 | 0.000 |
| 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) | 0.002 | 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".