Correlation between nerve atrophy, brain grey matter volume and pain severity in patients with primary trigeminal neuralgia
Bibliographic record
Abstract
BackgroundRecent neuroimaging studies have reported grey matter alterations in primary trigeminal neuralgia patients. However, few studies have focused on quantitative measurements of trigeminal nerves and the interaction between trigeminal nerve volume and brain morphology, particularly grey matter volume. In this study, we investigated the link between trigeminal nerves and grey matter volume changes in primary trigeminal neuralgia patients compared to healthy controls. Moreover, we explored the association of structure of trigeminal nerves and grey matter to collected pain clinical variables.MethodsEighty participants (40 patients and 40 controls) were recruited for the study. All participants underwent MRI sessions and clinical pain assessment. Trigeminal nerve volume and whole brain grey matter volume were evaluated using quantitative imaging techniques. Sensory and affective pain rating indices were assessed using the visual analog scale and short-form McGill Pain Questionnaire. Mediation analysis was conducted to investigate the relationship between clinical pain variables and volumetric changes in trigeminal nerves and grey matter.ResultsDecreased trigeminal nerve volume was detected in primary trigeminal neuralgia patients compared to controls. Additionally, reduced grey matter volume was found in several regions associated with pain in primary trigeminal neuralgia subjects, including the insula, secondary somatosensory cortex, hippocampus, dorsal anterior cingulate cortex, precuneus, and several areas of the temporal lobe. Mediation analysis revealed that decreased trigeminal nerve volume drove grey matter volume abnormality of the left insula, and further led to increased pain ratings.ConclusionThis study showed a predominantly direct effect of trigeminal nerve atrophy on clinical pain variables in primary trigeminal neuralgia patients, providing new insight into the pathophysiology of the disease.Trial registrationClinicalTrials.gov ID: NCT02713646
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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 teacher head, 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".