Selective hippocampal subfield volume reductions in classic trigeminal neuralgia
Bibliographic record
Abstract
Trigeminal Neuralgia (TN) is a chronic neuropathic pain syndrome characterized by paroxysmal unilateral shock-like pains in the trigeminal territory most frequently attributed to neurovascular compression of the trigeminal nerve at its root entry zone. Recent advances in the study of TN suggest a possible central nervous system (CNS) role in modulation and maintenance of pain. TN and other chronic pain patients commonly experience alterations in cognition and affect, as well as abnormalities in CNS volume and microstructure in regions associated with pain perception, emotional modulation, and memory consolidation. However, the microstructural changes in the hippocampus, an important structure within the limbic system, have not been previously studied in TN patients. Here, we use grey matter analysis to assess whether TN pain is associated with altered hippocampal subfield volume in patients with classic TN. Anatomical magnetic resonance (MR) images of twenty-two right-sided TN patients and matched healthy controls underwent automated segmentation of hippocampal subfields using FreeSurfer v6.0. Right-sided TN patients had significant volumetric reductions in ipsilateral cornu ammois 1 (CA1), CA4, dentate gyrus, molecular layer, and hippocampus-amygdala transition area - resulting in decreased whole ipsilateral hippocampal volume, compared to healthy controls. Overall, we demonstrate selective hippocampal subfield volume reduction in patients with classic TN. These changes occur in subfields implicated as neural circuits for chronic pain processing. Selective subfield volume reduction suggests aberrant processes and circuitry reorganization, which may contribute to development and/or maintenance of TN symptoms.
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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 | 0.000 |
| 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".