Altered activity within the social behavior neural network in adolescent rats following prenatal alcohol exposure and/or early‐life adversity
Bibliographic record
Abstract
BACKGROUND: Social behavior relies on the dynamic, complex, and coordinated activity of a highly conserved "social behavior neural network," which includes the olfactory bulb (OB), piriform cortex (PCX), lateral septum, medial prefrontal cortex (mPFC), amygdala, paraventricular nucleus of the hypothalamus (PVN), and ventral hippocampus. Prenatal alcohol exposure (PAE) is known to disrupt social behavior development, leading to lifelong social functioning impairments. Individuals with PAE are at heightened risk of experiencing early-life adversity (ELA), which independently affects social behavior development; however, little is known about the combined effects of PAE and ELA on social behavior. METHODS: We previously demonstrated that PAE and ELA impact social recognition memory throughout adolescence; here, we combine animal models of PAE and ELA to gain insight into both independent and interactive effects of these insults on social behavior network neural activity in both early and late adolescent male and female rats. We measured neural activity (c-fos mRNA expression) across the network following social recognition memory testing. RESULTS: Our findings indicate that both PAE and ELA are associated with altered neural activity in regions supporting social recognition memory, notably the OB, PCX, mPFC, amygdala, and PVN. The direction of these effects and specific regions impacted vary by sex and age at assessment. Importantly, different brain areas exhibit distinct sensitivities to each type of insult, with PAE generally resulting in hypoactivity of the amygdala and ELA altering mPFC activity. CONCLUSIONS: These data contribute to a more complete social neurobehavioral profile, accounting for both PAE and ELA, to support earlier diagnoses and interventions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".