Effect of the closed‐loop hippocampal low‐frequency stimulation on seizure severity, learning, and memory in pilocarpine epilepsy rat model
Bibliographic record
Abstract
AIMS: In this study, the anticonvulsant action of closed-loop, low-frequency deep brain stimulation (DBS) was investigated. In addition, the changes in brain rhythms and functional connectivity of the hippocampus and prefrontal cortex were evaluated. METHODS: Epilepsy was induced by pilocarpine in male Wistar rats. After the chronic phase, a tripolar electrode was implanted in the right ventral hippocampus and a monopolar electrode in medial prefrontal cortex (mPFC). Subjects' spontaneous seizure behaviors were observed in continuous video recording, while the local field potentials (LFPs) were recorded simultaneously. In addition, spatial memory was evaluated by the Barnes maze test. RESULTS: Applying hippocampal DBS, immediately after seizure detection in epileptic animals, reduced their seizure severity and duration, and improved their performance in Barnes maze test. DBS reduced the increment in power of delta, theta, and gamma waves in pre-ictal, ictal, and post-ictal periods. Meanwhile, DBS increased the post-ictal-to-pre-ictal ratio of theta band. DBS decreased delta and increased theta coherences, and also increased the post-ictal-to-pre-ictal ratio of coherence. In addition, DBS increased the hippocampal-mPFC coupling in pre-ictal period and decreased the coupling in the ictal and post-ictal periods. CONCLUSION: Applying closed-loop, low-frequency DBS at seizure onset reduced seizure severity and improved memory. In addition, the changes in power, coherence, and coupling of the LFP oscillations in the hippocampus and mPFC demonstrate low-frequency DBS efficacy as an antiepileptic treatment, returning LFPs to a seemingly non-seizure state in subjects that received DBS.
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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 | 0.001 |
| 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".