Distinct stages of adult hippocampal neurogenesis are regulated by running and the running environment
Bibliographic record
Abstract
Hippocampal neurogenesis continues into adulthood in mammalian vertebrates, and in experimental rodent models it is powerfully stimulated by exposure to a voluntary running wheel. In this study, we demonstrate that exposure to a running wheel environment, in the absence of running, is sufficient to regulate specific aspects of hippocampal neurogenesis. Adult mice were provided with standard housing, housing enriched with a running wheel or housing enriched with a locked wheel (i.e., an environment comparable to that of running animals, without the possibility of engaging in running). We found that mice in the running wheel and locked wheel groups exhibited equivalent increases in proliferation within the neurogenic niche of the dentate gyrus; this included comparable increases in the proliferation of radial glia-like stem cells and the number of proliferating neuroblasts. However, only running animals displayed increased numbers of postmitotic neuroblasts and mature neurons. These results demonstrate that the running wheel environment itself is sufficient for promoting proliferation of early lineage hippocampal precursors, while running per se enables newly generated neuroblasts to survive and mature into functional hippocampal neurons. Thus, both running-independent and running-dependent stimuli are integral to running wheel-induced hippocampal neurogenesis.
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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".