Effect of Neurofibrillary Tangles on Behavioural Flexibility in Rats: Animal Models for Fronto-Temporal Dementia and Alzheimer’s Disease
Bibliographic record
Abstract
Neurofibrillary tangles (NFTs) are aggregations of abnormal tau protein, which are implicated in neurodegenerative diseases such as behavioural-variant frontotemporal dementia (bvFTD) and Alzheimer’s disease (AD). NFTs are prominent in the prefrontal and entorhinal cortices in early-stage bvFTD and AD, respectively. We modeled this site-specific neurodegeneration in rats via microinjection of a viral vector to express an excess of mutated tau protein in these target areas. We then examined the impact of this somatic gene transfer on performance in a behavioural flexibility task. The rats were initially trained to find food rewards in a plus maze using a place strategy: to always go to the same place in the environment, i.e., north or south. Subsequently, they switched to a response strategy: to always make the same body response, i.e., turn right or left. Rats made a total of six switches between place and response strategies, and their acquisition and retention of each switch was measured and compared to controls. We found that bvFTD model rats could acquire the place strategy, but were impaired when acquiring the response strategy. AD model rats were impaired in acquiring both strategies. Despite differences in ability to acquire strategies, there were no impairments in retention of either strategy for both groups. Together, these findings suggest that patterns of performance on a behavioural flexibility paradigm can differentiate between animal models of early bvFTD and AD. Future research can use these findings to screen novel candidate drugs, and study mechanisms underlying AD and bvFTD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".