Specific behaviors during auditory fear conditioning and postsynaptic expression of AMPA receptors in the basolateral amygdala predict interindividual differences in fear generalization in male rats
Bibliographic record
Abstract
Auditory fear conditioning in rats is a widely used method to study learning, memory, and emotional responding. Despite procedural standardizations and optimizations, there is substantial interindividual variability in fear expression during test, notably in terms of fear expressed toward the testing context alone. To better understand which factors could explain this variation between subjects, we here explored whether behavior during training and expression of AMPA receptors (AMPARs) after long-term memory formation in the amygdala could predict freezing during test. We studied outbred male rats and found strong variation in fear generalization to a different context. Hierarchical clustering of these data identified two distinct groups of subjects that independently correlated with a specific pattern of behaviors expressed during initial training (i.e., rearing and freezing). The extent of fear generalization correlated positively with postsynaptic expression of GluA1-containing AMPA receptors in the basolateral nucleus of the amygdala. Our data thus identify candidate behavioral and molecular predictors of fear generalization that may inform our understanding of some anxiety-related disorders, such as posttraumatic stress disorder (PTSD), that are characterized by overgeneralized fear.
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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 | 0.001 | 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".