Postnatal Aversive Experience Impairs Sensitivity to Natural Rewards and Increases Susceptibility to Negative Events in Adult Life
Bibliographic record
Abstract
Evidence shows that maternal care and postnatal traumatic events can exert powerful effects on brain circuitry development but little is known about the impact of early postnatal experiences on processing of rewarding and aversive stimuli related to the medial prefrontal cortex (mpFC) function in adult life. In this study, the unstable maternal environment induced by repeated cross-fostering (RCF) impaired palatable food conditioned place preference and disrupted the natural preference for sweetened fluids in the saccharin preference test. By contrast, RCF increased sensitivity to conditioned place aversion (CPA) and enhanced immobility in the forced swimming test. Intracerebral microdialysis data showed that the RCF prevents mpFC dopamine (DA) outflow regardless of exposure to rewarding or aversive stimuli, whereas it induces a strong and sustained prefrontal norepinephrine (NE) release in response to different aversive experiences. Moreover, the selective mpFC NE depletion abolished CPA, thus indicating that prefrontal NE is required for motivational salience attribution to aversion-related stimuli. These findings demonstrate that an unstable maternal environment impairs the natural propensity to seek pleasurable sources of reward, enhances sensitivity to negative events in adult life, blunts prefrontal DA outflow, and modulates NE release in the reverse manner depending on the exposure to rewarding or aversive stimuli.
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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) | 0.001 | 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".