Optogenetic intervention of seizures improves spatial memory in a mouse model of chronic temporal lobe epilepsy
Bibliographic record
Abstract
OBJECTIVE: To determine if closed-loop optogenetic seizure intervention, previously shown to reduce seizure duration in a well-established mouse model chronic temporal lobe epilepsy (TLE), also improves the associated comorbidity of impaired spatial memory. METHODS: Mice with chronic, spontaneous seizures in the unilateral intrahippocampal kainic acid model of TLE, expressing channelrhodopsin in parvalbumin-expressing interneurons, were implanted with optical fibers and electrodes, and tested for response to closed-loop light intervention of seizures. Animals that responded to closed-loop optogenetic curtailment of seizures were tested in the object location memory test and then given closed-loop optogenetic intervention on all detected seizures for 2 weeks. Following this, they were tested with a second object location memory test, with different objects and contexts than used previously, to assess if seizure suppression can improve deficits in spatial memory. RESULTS: Animals that received closed-loop optogenetic intervention performed significantly better in the second object location memory test compared to the first test. Epileptic controls with no intervention showed stable frequency and duration of seizures, as well as stable spatial memory deficits, for several months after the precipitating insult. SIGNIFICANCE: Many currently available treatments for epilepsy target seizures but not the associated comorbidities, therefore there is a need to investigate new potential therapies that may be able to improve both seizure burden and associated comorbidities of epilepsy. In this study, we showed that optogenetic intervention may be able to both shorten seizure duration and improve cognitive outcomes of spatial memory.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".