Reactivation-dependent transfer of fear memory between contexts requires M1 muscarinic receptor stimulation in dorsal hippocampus
Bibliographic record
Abstract
Abstract Background Memory updating is essential for integrating new information into existing representations. However, this process could become maladaptive in conditions like post-traumatic stress disorder (PTSD), when fear memories generalize to neutral contexts. Previously, we have shown that contextual fear memory malleability in rats requires activation of M1 muscarinic acetylcholine receptors in the dorsal hippocampus. Here, we investigated the involvement of this mechanism in transfer of contextual fear memories to other contexts using a novel fear memory updating paradigm. Methods Following brief re-exposure to a previously fear conditioned context, male rats (n=8-10/group) were placed into a neutral context to evaluate the transfer of fear memory. We also infused the selective M1 receptor antagonist pirenzepine into the dorsal hippocampus prior to memory reactivation to try to block this effect. Results Results support the hypothesis that fear memory can be updated with novel contextual information, but only if rats are re-exposed to the originally trained context relatively recently prior to the neutral context; evidence for transfer was not seen if the fear memory reactivation was omitted or if it occurred 6h prior to neutral context exposure. The transferred fear persisted for four weeks, and the effect was blocked by M1 antagonism. Conclusions These findings strongly suggest that fear transfer requires reactivation and destabilization of the original fear memory. Specific parameters likely dictate similar generalization in disorders like PTSD. The novel preclinical model introduced here, and its implication of muscarinic receptors in this process, should inform therapeutic strategies in this area.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".