Gastrin-Releasing Peptide Signaling Plays a Limited and Subtle Role in Amygdala Physiology and Aversive Memory
Bibliographic record
Abstract
Links between synaptic plasticity in the lateral amygdala (LA) and Pavlovian fear learning are well established. Neuropeptides including gastrin-releasing peptide (GRP) can modulate LA function. GRP increases inhibition in the LA and mice lacking the GRP receptor (GRPR KO) show more pronounced and persistent fear after single-trial associative learning. Here, we confirmed these initial findings and examined whether they extrapolate to more aspects of amygdala physiology and to other forms of aversive associative learning. GRP application in brain slices from wildtype but not GRPR KO mice increased spontaneous inhibitory activity in LA pyramidal neurons. In amygdala slices from GRPR KO mice, GRP did not increase inhibitory activity. In comparison to wildtype, short- but not long-term plasticity was increased in the cortico-lateral amygdala (LA) pathway of GRPR KO amygdala slices, whereas no changes were detected in the thalamo-LA pathway. In addition, GRPR KO mice showed enhanced fear evoked by single-trial conditioning and reduced spontaneous firing of neurons in the central nucleus of the amygdala (CeA). Altogether, these results are consistent with a potentially important modulatory role of GRP/GRPR signaling in the amygdala. However, administration of GRP or the GRPR antagonist (D-Phe(6), Leu-NHEt(13), des-Met(14))-Bombesin (6-14) did not affect amygdala LTP in brain slices, nor did they affect the expression of conditioned fear following intra-amygdala administration. GRPR KO mice also failed to show differences in fear expression and extinction after multiple-trial fear conditioning, and there were no differences in conditioned taste aversion or gustatory neophobia. Collectively, our data indicate that GRP/GRPR signaling modulates amygdala physiology in a paradigm-specific fashion that likely is insufficient to generate therapeutic effects across amygdala-dependent disorders.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".