Evaluating the effects of single, multiple, and delayed systemic rapamycin injections to contextual fear reconsolidation: Implications for the neurobiology of memory and the treatment of PTSD-like re-experiencing
Bibliographic record
Abstract
The mechanistic target of rapamycin (mTOR) kinase is known to mediate the formation and persistence of aversive memories. Rapamycin, an mTOR inhibitor, administered around the time of reactivation blocks retrieval-induced mTOR activity and de novo protein synthesis in the brains of rodents, while correspondingly diminishing subsequent fear memory. The goal of the current experiments was to further explore rapamycin's effects on fear memory persistence. First, we examined whether mTOR blockade at different time-points after reactivation attenuates subsequent contextual fear memory. We show that rapamycin treatment 3 or 12 h post-reactivation disrupts memory persistence. Second, we examined whether consecutive days of reactivation paired with rapamycin had additive effects over a single pairing at disrupting a contextual fear memory. We show that additional reactivation-rapamycin pairings exacerbates the reconsolidation impairment. Finally, we examined if impaired reconsolidation of a contextual fear memory from rapamycin treatment had any after-effects on learning and recalling a new fear association. We show that rapamycin-impaired reconsolidation does not affect new learning or recall and protects against fear generalization. Our findings improve our understanding of mTOR- dependent fear memory processes, as well as provide insight into potentially novel treatment options for stress-related psychopathologies such as posttraumatic stress disorder.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".