Impaired Cognitive Flexibility With Preserved Learning in an Amyloid Precursor Protein Knock‐In Mouse Model of Amyloidopathy
Bibliographic record
Abstract
ABSTRACT Alzheimer's disease is a debilitating neurodegenerative condition characterized by amyloid beta plaques and tau neurofibrillary tangles, which leads to progressive cognitive decline. Several new mouse models of fast amyloid deposition have been generated with compound mutations, but how these affect high‐level cognitive function is still not fully understood. Four cohorts of a second‐generation amyloid precursor protein knock‐in mouse model, App NL‐G‐F/NL‐G‐F , which develops aggressive amyloidopathy, were compared with two different control groups that do not produce plaques ( App NL/NL and wildtype littermates), on touchscreen‐based tests of learning and cognitive flexibility. App NL‐G‐F/NL‐G‐F mice learned to discriminate between two visual stimuli during the pairwise visual discrimination (PVD) task but were impaired when the reward contingencies were reversed (the PVR task). Analyses of the correction trials indicated perseverative behavior. One cohort was further tested on the touchscreen Extinction test, which isolates the ability to withhold responding to a previously rewarded stimulus. The App NL‐G‐F/NL‐G‐F mice extinguished their responding no differently than the App NL/NL control group. These results indicate that compound mutations in App driving fast accumulation of plaques in this mouse model impair cognitive flexibility and may serve as a preclinical target for putative therapeutic drugs.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".