Comparative analysis of seizure control efficacy of 5Hz and 20Hz responsive deep brain stimulation in rodent models of epilepsy
Bibliographic record
Abstract
We assess the effects of low-frequency (5Hz) responsive stimulation (LFRS) and high-frequency (20Hz) responsive stimulation (HFRS) of the rat hippocampus on the spontaneous seizure suppression in two rodent models of epilepsy. Acute seizures in 12 rats were induced by intra-hippocampal injection of 4-amynopyridine (4-AP) and chronic seizures in six rats were induced by intraperitoneal injection of kainic acid. Two bipolar electrodes were implanted into the CA1 regions of both hippocampi. The electrodes were connected to a custom-built responsive neurostimulator that detects the intracerebral electroencephalographic (icEEE) seizure onset and triggers a responsive electrical stimulation. The rats were randomly divided into two groups: non-stimulation and stimulation group. The non-stimulation group did not receive stimulation, whereas the stimulation group received LFRS and HFRS. The baseline average seizure rate in the non-stimulation group was ~6.5 seizures per 30-minute in the acute model and ~5 seizures per day in the chronic model. The seizure rate in the stimulation group was reduced by 80.8% during the LFRS, while the HFRS reduced seizure frequency only by 26.9% and in the chronic model, 91.6% during the LFRS, while the HFRS reduced seizure frequency only by 15%. The seizure formation was effectively aborted using the LFRS by means of the neural inhibition mechanism, which is similar to that of anti-epileptic drugs. In this responsive stimulation technique, the inhibition lasted only for several seconds, as needed for the seizure suppression, unlike the continuous inhibition (neural activity suppression) in the case of anti-epileptic drugs.
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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) | 0.001 | 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".