Maternal Care and Hippocampal Plasticity: Evidence for Experience-Dependent Structural Plasticity, Altered Synaptic Functioning, and Differential Responsiveness to Glucocorticoids and Stress
Bibliographic record
Abstract
Maternal licking and grooming (LG) in infancy influences stress responsiveness and cognitive performance in the offspring. We examined the effects of variation in the frequency of pup LG on morphological, electrophysiological, and behavioral aspects of hippocampal synaptic plasticity under basal and stress-like conditions. We found shorter dendritic branch length and lower spine density in CA1 cells from the adult offspring of low compared with high LG offspring. We also observed dramatic effects on long-term potentiation (LTP) depending on corticosterone treatment. Low LG offspring, in contrast to those of high LG mothers, displayed significantly impaired LTP under basal conditions but surprisingly a significantly enhanced LTP in response to high corticosterone in vitro. This enhanced plasticity under conditions that mimic those of a stressful event was apparent in vivo. Adult low LG offspring displayed enhanced memory relative to high LG offspring when tested in a hippocampal-dependent, contextual fear-conditioning paradigm. Hippocampal levels of glucocorticoid and mineralocorticoid receptors were reduced in low compared with high LG offspring. Such effects, as well as the differences in dendritic morphology, likely contribute to LTP differences under resting conditions, as well as to the maternal effects on synaptic plasticity and behavior in response to elevated corticosterone levels. These results suggest that maternal effects may modulate optimal cognitive functioning in environments varying in demand in later life, with offspring of high and low LG mothers showing enhanced learning under contexts of low and high stress, respectively.
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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".