Systemic M1 muscarinic acetylcholine receptor activation can restore aging-related object memory updating deficits in mice
Bibliographic record
Abstract
Memory reactivation (RA) can facilitate trace destabilization, enabling memory updating. Some memories, however, are resistant to modification. Specifically, elderly individuals display deficits in declarative memory updating. There is currently no treatment to target this cognitive impairment, likely because factors mediating RA-based memory updating remain unclear. We propose that cholinergic signalling supports RA-based memory changes. Recent findings in our lab suggest that M1-muscarinic acetylcholine receptor (mAChR) signalling within perirhinal cortex supports object memory destabilization and RA-based updating. Correspondingly, aging research indicates a decline in cholinergic transmission across the lifespan. Therefore, we hypothesize that age-related cholinergic system dysfunction underlies memory updating deficits in elderly individuals. To test this, we used a post-RA object memory modification (PROMM) task--where a reactivated object memory is updated with new contextual information--to assess object memory updating in 3-, 6-, and 12-month-old male mice. 3-month-old mice showed intact PROMM task performance, 6-month-old mice showed a mild impairment, while 12-month-old mice were severely impaired. Subsequently, we administered the M1-selective agonist CDD-0102A prior to object memory RA in the PROMM task in the 12-month-old male mice; remarkably, pre-RA systemic M1-mAChR agonism enhanced PROMM task performance. These results therefore suggest a promising neural target for promoting the modification of inflexible memories in aged populations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".