High Estradiol reduces adult neurogenesis but strengthens functional connectivity within the hippocampus during spatial pattern separation in adult female rats
Bibliographic record
Abstract
Abstract Adult neurogenesis in the dentate gyrus plays an important role for pattern separation, the process of separating similar inputs and forming distinct neural representations. Estradiol modulates neurogenesis and hippocampus function, but to date no examination of estradiol’s effects on pattern separation have been conducted. Here, we examined estrogenic regulation of adult neurogenesis and functional connectivity in the hippocampus after the spatial pattern separation task in female rats. Ovariectomized Sprague-Dawley rats received daily injections of vehicle, 0.32µg (Low) or 5 µg (High) of estradiol benzoate until the end of experiment. A single bromodeoxyuridine (BrdU) was injected one day after initiation of hormone or vehicle treatment and rats were tested in the delayed nonmatching to position spatial pattern separation task in the 8-arm radial maze for 14 days beginning two weeks after BrdU injection. Rats were perfused 90 minutes after the final trial and brain sections were immunohistochemically stained for BrdU/neuronal nuclei (NeuN) (new neurons), Ki67 (cell proliferation), and the immediate early gene, zif268 (activation). Results showed that only high estradiol reduced the density of BrdU/NeuN-ir cells and had significant inter-regional correlations of zif268-ir cell density in the hippocampus following pattern separation. Estradiol treatment did not influence pattern separation performance or strategy use. These results show that higher doses of estradiol can reduce neurogenesis but at the same time increases correlations of activity of neurons within the hippocampus during spatial pattern separation.
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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) | 0.002 | 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".