Exercise and circulating BDNF: Mechanisms of release and implications for the design of exercise interventions
Bibliographic record
Abstract
Engagement in regular bouts of exercise confers numerous positive effects on brain health across the lifespan. Acute bouts of exercise transiently improve cognitive function, while long-term exercise training stimulates brain plasticity, improves brain function, and helps to stave off neurological disease. The action of brain-derived neurotrophic factor (BDNF) is a candidate mechanism underlying these exercise-induced benefits and is the subject of considerable attention in the exercise-brain health literature. It is well established that acute exercise increases circulating levels of BDNF and numerous studies have sought to characterize this response for the purpose of improving brain health. Despite the interest in BDNF responses to exercise, little focus has been given to understanding the sources and mechanisms that underlie this response for the purpose of deliberately increasing circulating levels of BDNF. Here we review evidence to support that exploiting these mechanisms of BDNF release can help to optimize brain plasticity outcomes via exercise interventions, which could be especially relevant in the context of multimodal training (i.e., exercise and cognitive stimulation). Therefore, the purpose of this paper is to review the candidate sources of BDNF during exercise and the mechanisms of release. As well, we discuss strategies for maximizing BDNF responses to exercise, and propose novel research directions for advancing our understanding of these mechanisms.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".