<i>How</i> does different types of exercise promote cognitive function? What do we know?
Bibliographic record
Abstract
Abstract Our current understanding of how exercise promotes cognitive function largely stems from animal studies and is restricted to aerobic exercise training (AT). It is widely hypothesized that AT induces neurotrophic factor cascades (i.e., BDNF, IGF‐1, and VEGF), a central mechanism mediating exercise‐dependent benefits in cognitive performance. However, evidence regarding the effect of AT on neurotrophic factors in humans is equivocal. Notably, whether AT‐induced changes in neurotrophic factors are associated with AT‐induced changes in cognitive function is largely unknown. The dearth of mechanistic evidence is even more pronounced for resistance training (RT). Both AT and RT may also promote cognitive function by reducing peripheral cardiometabolic risk factors for neurodegeneration (e.g., hypertension, type 2 diabetes, hypercholesterolemia), as well as systemic inflammation associated with these risk factors. Specifically, cardiovascular disease and type 2 diabetes are associated with chronic low‐grade systemic inflammation reflected by elevated levels of C reactive protein and cytokines. Systemic inflammation can impair neurotrophic factor signaling, exacerbate the metabolic syndrome, and accelerate cognitive decline. Similar to neurotrophic factors, the roles of cardiometabolic risk and systemic inflammation in exercise‐induced improvements in human cognitive performance are not well established. A better understanding of the underlying biological mechanisms of different types of exercise training will greatly advance our ability to refine and develop novel exercise (and other) strategies for dementia prevention. In this session, we will discuss/debate the selection and measurement of biomarkers in randomized controlled trials of exercise in individuals with or without cognitive impairment.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".