Brivaracetam, but not ethosuximide, reverses memory impairments in an Alzheimer’s disease mouse model
Bibliographic record
Abstract
INTRODUCTION: Recent studies have shown that several strains of transgenic Alzheimer's disease (AD) mice overexpressing the amyloid precursor protein (APP) have cortical hyperexcitability, and their results have suggested that this aberrant network activity may be a mechanism by which amyloid-β (Aβ) causes more widespread neuronal dysfunction. Specific anticonvulsant therapy reverses memory impairments in various transgenic mouse strains, but it is not known whether reduction of epileptiform activity might serve as a surrogate marker of drug efficacy for memory improvement in AD mouse models. METHODS: Transgenic AD mice (APP/PS1 and 3xTg-AD) were chronically implanted with dural electroencephalography electrodes, and epileptiform activity was correlated with spatial memory function and transgene-specific pathology. The antiepileptic drugs ethosuximide and brivaracetam were tested for their ability to suppress epileptiform activity and to reverse memory impairments and synapse loss in APP/PS1 mice. RESULTS: We report that in two transgenic mouse models of AD (APP/PS1 and 3xTg-AD), the presence of spike-wave discharges (SWDs) correlated with impairments in spatial memory. Both ethosuximide and brivaracetam reduce mouse SWDs, but only brivaracetam reverses memory impairments in APP/PS1 mice. CONCLUSIONS: Our data confirm an intriguing therapeutic role of anticonvulsant drugs targeting synaptic vesicle protein 2A across AD mouse models. Chronic ethosuximide dosing did not reverse spatial memory impairments in APP/PS1 mice, despite reduction of SWDs. Our data indicate that SWDs are not a reliable surrogate marker of appropriate target engagement for reversal of memory dysfunction in APP/PS1 mice.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".