Levetiracetam induction of theta frequency oscillations in rodent hippocampus in vitro
Bibliographic record
Abstract
Levetiracetam (LEV) has been demonstrated to improve cognitive function. Hippocampal theta rhythm (4–12 Hz) is associated with a variety of cognitively related behaviors, such as exploration in both humans and animal models. We investigated the effects of LEV on the theta rhythm in the rat hippocampal CA3 in hippocampal slices in vitro. We found that LEV increased the theta power in a dose-dependent manner. The increase in theta power can be blocked by GABA A receptor (GABA A R) or NMDA receptor (NMDAR) antagonists but not by AMPA receptor antagonist, indicating the involvement of GABA A R and NMDAR in the induction of theta activity. Interestingly, LEV enhancement of theta power can be also blocked by taurine or GABA-A agonist THIP, indicating that LEV induction of theta may be related to the indirect boosting of GABA action via reduction of extrasynaptic GABA A R activation. Furthermore, the increased theta power can be partially reduced by the mACh receptor (mAChR) antagonist atropine but not by nACh receptor antagonists, suggesting that mAChR activation provides excitatory input into local network responsible for LEV-induced theta. Our study demonstrated that LEV induced a novel theta oscillation in vitro, which may have implications in the treatment of the neuronal disorders with impaired theta oscillation and cognitive function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".