Neurogenesis in the Maternal Rodent Brain: Impacts of Gestation-Related Hormonal Regulation, Stress, and Obesity
Bibliographic record
Abstract
In order to maintain maternal behavior, it is important that the maternal rodent brain promotes neurogenesis. Maternal neurogenesis is altered by the dynamic shifts in reproductive hormone levels during pregnancy. Thus, lifestyle events such as gestational stress and obesity that can affect hormone production will affect neuroendocrine control of maternal neurogenesis. However, there is a lack of information about the regulation of maternal neurogenesis by placental hormones, which are key components of the reproductive hormonal profile during pregnancy. There is also little known about how maternal neurogenesis can be affected by health concerns such as gestational stress and obesity, and its relationship to peripartum mental health disorders. This review summarizes the changing levels of neurogenesis in mice and rats during gestation and postpartum as well as regulation of neurogenesis by pregnancy-related hormones. The influence of neurogenesis on maternal behavior is also discussed while bringing attention to the effect of health-related concerns during gestation, such as stress and obesity on neuroendocrine control of maternal neurogenesis. In doing so, this review identifies the gaps in the literature and specifically emphasizes the importance of further research on maternal brain physiology to address them.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".