Molecular mechanisms underlying physical exercise-induced brain BDNF overproduction
Bibliographic record
Abstract
Accumulating evidence supports that physical exercise (EX) is the most effective non-pharmacological strategy to improve brain health. EX prevents cognitive decline associated with age and decreases the risk of developing neurodegenerative diseases and psychiatric disorders. These positive effects of EX can be attributed to an increase in neurogenesis and neuroplastic processes, leading to learning and memory improvement. At the molecular level, there is a solid consensus to involve the neurotrophin brain-derived neurotrophic factor (BDNF) as the crucial molecule for positive EX effects on the brain. However, even though EX incontestably leads to beneficial processes through BDNF expression, cellular sources and molecular mechanisms underlying EX-induced cerebral BDNF overproduction are still being elucidated. In this context, the present review offers a summary of the different molecular mechanisms involved in brain's response to EX, with a specific focus on BDNF. It aims to provide a cohesive overview of the three main mechanisms leading to EX-induced brain BDNF production: the neuronal-dependent overexpression, the elevation of cerebral blood flow (hemodynamic hypothesis), and the exerkine signaling emanating from peripheral tissues (humoral response). By shedding light on these intricate pathways, this review seeks to contribute to the ongoing elucidation of the relationship between EX and cerebral BDNF expression, offering valuable insights into the potential therapeutic implications for brain health enhancement.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".