Pain Relief Reverses Hippocampal Abnormalities in Trigeminal Neuralgia
Bibliographic record
Abstract
Chronic pain patients frequently report memory and concentration difficulties. Objective testing in this population points to poor performance on memory and cognitive tests, and increased comorbid anxiety and depression. Recent evidence has suggested convergence between chronic pain and memory deficits onto the hippocampus. The hippocampus consists of heterogenous subfields involved in memory consolidation, behavior regulation, and stress modulation. Despite significant studies outlining hippocampal changes in human and chronic pain animal models, the effect of pain relief on hippocampal abnormalities remains unknown. Trigeminal neuralgia (TN) is a chronic neuropathic pain disorder which is highly amenable to surgical interventions, providing a unique opportunity to investigate the effect of pain relief. This study investigates the effect of pain relief on hippocampal subfields in TN. Anatomical MR images of 61 TN patients were examined before and 6 months after surgery. Treatment responders (n = 47) reported 95% pain relief, whereas non-responders (n = 14) reported 40% change in pain on average. At baseline, patients had smaller hippocampal volumes, compared to controls. After surgery, responders' hippocampal volumes normalized, largely driven by CA2/3, CA4, and dentate gyrus, which are involved in memory consolidation and neurogenesis. We propose that hippocampal atrophy in TN is pain-driven and successful treatment normalizes such abnormalities. PERSPECTIVE: Chronic pain patients have structural abnormalities in the hippocampus and its subfields. Pain relief normalizes these structural abnormalities and impacts patients in a sex-dependent manner.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".