Improved Working Memory Following Novel Combinations of Physical and Cognitive Activity
Bibliographic record
Abstract
BACKGROUND: In humans, retrospective studies suggest that habitual physical activity (PA) or cognitive activity (CA) can help maintain or improve cognitive function. Similar findings have been reported using physical exercise in animal studies; however, the exercise paradigms differ markedly in duration and frequency, making extrapolation difficult. Here, the authors present a novel PA and CA paradigm that combines voluntary wheel running with Hebb-Williams and radial arm maze (RAM) training. METHODS: A total of 57 male Sprague-Dawley rats were divided into 4 treatment groups: the PA, CA, and combined PA and CA groups and sedentary controls. PA (voluntary wheel running) and CA (Hebb-Williams mazes) consisted of a moderate 2 h/d, 5 d/wk treatment paradigm. RESULTS: Animals exposed to a combination of PA and CA made significantly fewer working memory errors and exhibited superior choice accuracy when compared with animals exposed to either PA or CA alone in the 8-arm baited configuration of the RAM. Additional analyses revealed that the cognitive improvements were independent of exercise intensity/duration. Assessment of brain-derived neurotrophic factor (BDNF) levels revealed a significant increase in hippocampal BDNF only in the PA-alone group. CONCLUSION: A novel combination of PA and CA improves learning and memory abilities independent of activity intensity, BDNF, or phosphorylated cyclic AMP response element binding protein levels. This is the first report of significant changes in cognitive ability using a paradigm involving moderate levels of PA plus cognitive stimulation. An adaptation of this paradigm may be particularly beneficial in slowing the development of mild cognitive impairment and subsequent dementia in elderly people.
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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) | 0.000 | 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 | 0.001 |
| 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".