Optimal hippocampal targeting in responsive neurostimulation for mesial temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify features of responsive neurostimulation (RNS) lead configuration and contact placement associated with greater seizure reduction in mesial temporal lobe epilepsy (MTLE). METHODS: A single-center series of patients with MTLE treated with RNS were retrospectively analyzed to assess the relationship between anatomical targeting and seizure reduction. Targeting was determined according to both the preoperatively conceived lead configuration and the actual placement of RNS contacts. Three lead configurations were used: 1) single bilateral, with 1 depth lead in each hippocampus; 2) single unilateral, with 1 hippocampal depth lead and another implant outside the mesial temporal lobe; and 3) dual unilateral, with 2 leads in 1 hippocampus. Contact placement on postoperative imaging was measured according to the number of hippocampal contacts per targeted hippocampus (contact density) and per patient (contact count), distribution throughout the hippocampus, and proximity to the anteromedial hippocampus. RESULTS: Dual unilateral lead placement resulted in significantly higher hippocampal contact density compared with the single hippocampal approaches, but only showed a nonsignificant trend toward a higher rate of response. However, those patients with more than 4 contacts in a single hippocampus, achievable only with dual unilateral leads, had a significantly higher rate of response. The higher likelihood of response was poorly explained by more widespread hippocampal coverage, but well correlated with proximity to the anteromedial hippocampus. CONCLUSIONS: Dual unilateral hippocampal implantation increased RNS contact density in patients with unilateral MTLE, which contributed to improved outcomes, not by stimulating more of the hippocampus, but instead by being more likely to stimulate a latent subtarget in the anterior hippocampus. It remains to be explored whether a single electrode targeted selectively to this region would also result in improved outcomes.
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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".