Working memory impairment in a transgenic amyloid precursor protein TgCRND8 mouse model of Alzheimer's disease
Bibliographic record
Abstract
The most profound deficits observed in Alzheimer's disease (AD) are in domains of episodic and working memory systems. Transgenic (Tg) mice expressing mutated human amyloid precursor protein (APP) genes offer a model to study the effect of AD pathology on cognition. We reported previously that APP TgCRND8 mice showed deficits in a reference and working memory evaluated in a Morris water-maze test. In this study, we evaluated the working memory of TgCRND8 mice comparing two training paradigms in a six-arm radial water maze. In the first paradigm, the exploration of the maze was constrained, forcing the mice to use a spatial mapping strategy. In the second paradigm, mice were unconstrained in their exploration of the maze. TgCRND8 mice proved to be significantly impaired in spatial working memory in both paradigms as compared with their non-transgenic littermates. The analysis of data revealed that forcing mice to use a spatial strategy during training caused only a moderate improvement in the performance of all mice. However, unconstrained exploration of the maze not only resulted in a fast learning in control mice, but also facilitated the development of a chaining strategy in spatially impaired TgCRND8 mice. In conclusion, TgCRND8 mice showed impairment in spatial working memory but retained a plasticity to choose alternative search strategies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".