Physical Exercise and Spatial Training: A Longitudinal Study of Effects on Cognition, Growth Factors, and Hippocampal Plasticity
Bibliographic record
Abstract
Abstract Physical exercise has been suggested to improve cognitive performance through various neurobiological mechanisms, mediated by growth factors such as BDNF, IGF-I, and VEGF. Moreover, animal research has demonstrated that combined physical and cognitive stimulation leads to increased adult neurogenesis as compared to either experimental condition alone. In the present study, we therefore investigated whether a sequential combination of physical and spatial training in young, healthy adults elicits an additive effect on training and transfer gains. To this end, we compared the effects of (i) eight 20-minute sessions of cycling, (ii) sixteen 30-minute sessions of spatial training, (iii) a combination of both, and included (iv) a passive control cohort. We assessed longitudinal changes in cognitive performance, growth factor levels, and T1relaxation of hippocampal subfields (acquired with 7 T MRI). While substantial physical and spatial training gains were elicited in all trained groups, longitudinal transfer changes did not differ between these groups. Notably, we found no evidence for an additive effect of sequential physical and spatial training. These results challenge the extrapolation from the findings reported in animals to young, healthy adults.
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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 | 0.001 | 0.000 |
| 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".