Aerobic exercise interacts with neurotrophic factors to predict cognitive functioning in adolescents
Bibliographic record
Abstract
Recent findings have suggested that aerobic exercise may have a positive effect on brain functioning, in addition to its well-recognized beneficial effects on human physiology. This study confirmed the cognitive effects of aerobic exercise on the human brain. It also examined the relationships between exercise and the serum levels of neurotrophic factors (BDNF, IGI-1, and VEGF). A total of 91 healthy teens who exercised regularly participated in this study. A between-group design was adopted to compare cognitive functioning subserved by the frontal and temporal brain regions and the serum levels of neurotrophic factors between 45 regular exercisers and 46 matched controls. The exercisers performed significantly better than the controls on the frontal and temporal functioning parameters measured. This beneficial cognitive effect was region-specific because no such positive cognitive effect on task-tapping occipital functioning was observed. With respect to the serum levels of the neurotrophic factors, a negative correlation between neurotrophic factors (BDNF and VEGF) with frontal and medial-temporal lobe function was revealed. Furthermore, the levels of BDNF and VEGF interacted with exercise status in predicting frontal and temporal lobe function. This is the first report of the interaction effects of exercise and neurotrophic factors on cognitive functioning. Herein, we report preliminary evidence of the beneficial effects of regular aerobic exercise in improving cognitive functions in teens. These beneficial effects are region-specific and are associated with the serum levels of neurotrophic factors. Our findings lay the path for future studies looking at ways to translate these beneficial effects to therapeutic strategies for adolescents.
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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 | 0.002 |
| 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 | 0.000 |
| 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".