Gray and white matter abnormalities in primary trigeminal neuralgia with and without neurovascular compression
Bibliographic record
Abstract
PURPOSE: Previous researches have reported gray and white matter microalterations in primary trigeminal neuralgia (TN) with neurovascular compression (NVC). The central mechanism underlying TN without NVC are unknown but may include changes in specific brain regions or circuitries. This study aimed to investigate abnormalities in the gray matter (GM) and white matter (WM) of the whole brain and the possible pathogenetic mechanism underlying this disease. METHODS: We analyzed brain morphologic images of TN patients, 23 with NVC (TN wNVC) and 22 without NVC (TN wNVC) compared with 45 healthy controls (HC). All subjects underwent 3T-magnetic resonance imaging and pain scale evaluation. Voxel-based morphometry (VBM) and surface-based morphometry (SBM) were used to investigate whole brain grey matter quantitatively; graph theory was applied to obtain network measures based on extracted DTI data based on DTI data of the whole brains. Sensory and affective pain rating indices were assessed using the visual analog scale (VAS) and short-form McGill Pain Questionnaire (SF-MPQ). RESULTS: The VBM and SBM analyses revealed widespread decreases in GM volume and cortical thickness in TN wNVC compared to TN woNVC, and diffusion metrics measures and topology organization changes revealed DTI showed extensive WM integrity alterations. However, above structural changes differed between TN wNVC and TN woNVC, and were related to specific chronic pain modulation mechanism. CONCLUSION: Abnormalities in characteristic regions of GM and WM structural network were found in TN woNVC compared with TN wNVC group, suggesting differences in pathophysiology of two types of TN.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".