Seizure Suppression Efficacy of Closed-Loop Versus Open-Loop Deep Brain Stimulation in a Rodent Model of Epilepsy
Bibliographic record
Abstract
We assess and compare the effects of both closed-loop and open-loop neurostimulation of the rat hippocampus by means of a custom low-power programmable therapeutic neurostimulation device on the suppression of spontaneous seizures in a rodent model of epilepsy. Chronic seizures were induced by intraperitoneal kainic acid injection. Two bipolar electrodes were implanted into the CA1 regions of both hippocampi. The electrodes were connected to the custom-built programmable therapeutic neurostimulation device that can trigger an electrical stimulation either in a periodic manner or upon detection of the intracerebral electroencephalographic (icEEE) seizure onset. This device includes a microchip consisting of a 256-channel icEEG recording system and a 64-channel stimulator, and a programmable seizure detector implemented in a field-programmable gate array (FPGA). The neurostimulator was used to evaluate seizure suppression efficacy in ten epileptic rats for a total of 240 subject-days (5760 subject-hours). For this purpose, all rats were randomly divided into two groups: the no-stimulation group and the stimulation group. The no-stimulation group did not receive stimulation. The stimulation group received, first, closed-loop stimulation and, next, open-loop stimulation. The no-stimulation and stimulation groups had a similar seizure frequency baseline, averaging five seizures per day. Closed-loop stimulation reduced seizure frequency by 90% and open-loop stimulation reduced seizure frequency by 17%, both in the stimulation group as compared to the no-stimulation group.
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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 | 0.000 | 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".