Hippocampal abnormality and response to vagus nerve stimulation in epilepsy
Bibliographic record
Abstract
Abstract Vagus nerve stimulation (VNS) reduces seizure frequency and severity in some, but not all, individuals with epilepsy. The hippocampus has been implicated in VNS response, but is yet to be studied structurally using T1‐weighted (T1w) magnetic resonance imaging (MRI). In this study we hypothesized greater hippocampal abnormality in VNS non‐responders. Using hippocampal morphometrics, we extracted the volumes of four hippocampal regions from T1w MRI across three groups; VNS responders (), non‐responders (), and healthy controls (). We first calculated the multivariate Mahalanobis distance using z‐scores from all four hippocampal regions to measure abnormality relative to controls. We then compared traditional univariate measures to the Mahalanobis distance. Response to VNS was defined as having a seizure reduction 2 years post‐implantation. Hippocampal morphometrics were significantly more abnormal in non‐responders than responders () using the multivariate Mahalanobis distance. Univariate approaches did not differ significantly between responders and non‐responders (). At the group level, non‐responders to VNS had greater structural hippocampal abnormality when using the multivariate approach. Conversely, this effect was lost with the univariate analysis. This suggests that abnormality is likely present in different parts of the hippocampus in different individuals. Future studies should incorporate multivariate, and potentially multi‐modal, information to better characterize the mechanisms of VNS response.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".