Exercise-Induced Neuroplasticity: A Mechanistic Model and Prospects for Promoting Plasticity
Bibliographic record
Abstract
Aerobic exercise improves cognitive and motor function by inducing neural changes detected using molecular, cellular, and systems level neuroscience techniques. This review unifies the knowledge gained across various neuroscience techniques to provide a comprehensive profile of the neural mechanisms that mediate exercise-induced neuroplasticity. Using a model of exercise-induced neuroplasticity, this review emphasizes the sequence of neural events that accompany exercise, and ultimately promote changes in human performance. This is achieved by differentiating between neuroplasticity induced by acute versus chronic aerobic exercise. Furthermore, this review emphasizes experimental considerations that influence the opportunity to observe exercise-induced neuroplasticity in humans. These include modifiable factors associated with the exercise intervention and nonmodifiable factors such as biological sex, ovarian hormones, genetic variations, and fitness level. To maximize the beneficial effects of exercise in health, disease, and following injury, future research should continue to explore the mechanisms that mediate exercise-induced neuroplasticity. This review identifies some fundamental gaps in knowledge that may serve to guide future research in this area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".