Adult hippocampal glucocorticoid receptor expression and dentate synaptic plasticity correlate with maternal care received by individuals early in life
Bibliographic record
Abstract
Maternal care in mammals is the prevailing environmental influence during perinatal development. The adult rat offspring of mothers exhibiting increased levels of pup licking/grooming (LG; High LG mothers), compared to those reared by Low LG dams, show increased hippocampal glucocorticoid receptor expression, complex dendritic tree structure, and an enhanced capacity for synaptic potentiation. However, these data were derived from studies using the total amount of maternal care directed toward the entire litter, thus ignoring possible within-litter variation. We show that the amount of LG received by individual pups within a litter varies considerably. Therefore, we questioned if the amount of LG received by individual pups correlates with and thus putatively predicts later hippocampal structure and function. To this end, LG-scores were determined during the first postnatal week for all pups in 32 litters and correlated with neuroendocrine and hippocampal parameters in young-adulthood. Pup LG-score positively correlated with the glucocorticoid receptor mRNA expression in the adult hippocampus. Moreover, the ability to induce synaptic potentiation in the dentate gyrus in vitro was enhanced in animals with high LG-scores. Structural plasticity correlated less reliably with LG-scores early in life and differed between sexes. Male offspring with high LG-scores displayed fewer newborn neurons, higher brain derived neurotrophic factor expression and tended to have more complex granule cell dendritic trees. We conclude that even moderate variations in early life environment have a major impact on adult hippocampal function. This principle could provide a mechanistic basis for individual differences in susceptibility to psychopathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".