Long-term exercise is needed to enhance synaptic plasticity in the hippocampus
Bibliographic record
Abstract
Exercise can have many benefits for the body, but it also benefits the brain by increasing neurogenesis, synaptic plasticity, and performance on learning and memory tasks. The period of exercise needed to realize the structural and functional benefits for the brain have not been well delineated, and previous studies have used periods of exercise exposure that range from as little as 3 d to up to 6 mo. In this study, we systematically evaluated the effects of differential running periods (3, 7, 14, 28, and 56 d) on both structural (cell proliferation and maturation) and functional (in vivo LTP) changes in the dentate gyrus of adult male Sprague-Dawley rats. We found that voluntary access to a running wheel for both short- and long-term periods can increase cell proliferation in the adult DG; however, increases in neurogenesis required longer term exposure to exercise. Increases in immature neurons were not observed until animals had been running for a minimum of 14 d. Similarly, short-term periods of wheel running did not facilitate LTP in the DG of adult animals, and reliable increases in LTP were only observed with 56 d of running. These results provide us with a greater understanding of the time course of wheel running access needed to enhance DG function. Furthermore, the results indicate that the new neurons produced in response to exercise in rats do not contribute significantly to synaptic plasticity until they mature.
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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) | 0.001 | 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".