Exploring the landscape of physical activity and neuroplasticity research: a comprehensive bibliometric review
Bibliographic record
Abstract
Neuroplasticity, the nervous system's ability to adapt structurally and functionally to environmental changes, is essential for learning, memory, recovery, and overall brain health. Physical activity is recognized as an effective intervention to promote neuroplasticity, enhancing cognitive function and neural health. However, research on the overall landscape of "physical activity and neuroplasticity" is limited. This study used bibliometric analysis to elucidate the development status and evolution of the knowledge structure in this field. Data from the Web of Science Core Collection database covering literature published from January 1, 2005, to December 31, 2024, were analyzed using software such as Excel, Scimago Graphica, VOSviewer, and CiteSpace. A total of 1,854 publications meeting the selection criteria were identified, showing a gradual increase in annual publications from 2005 to 2019 and a significant surge starting from 2020. The United States ranked first in publications and citations, followed by China and Canada. The University of California, Los Angeles, and the University of Illinois had the highest number of publications, with the latter leading in citation impact. Neuroscience was the journal with the most publications and citations. Research hotspots included the mechanisms of how physical activity enhances neuroplasticity and optimal intervention modalities, with keywords like "rehabilitation" and "cognition" frequently appearing. Future research should focus on clinical validation of multimodal interventions in the elderly, optimization of experimental designs in animal models, and exploration of mechanisms in neurodegenerative diseases, contributing to the translation of research findings from basic research to clinical applications, ultimately promoting brain health and cognitive function across the lifespan.
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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 | 0.025 | 0.114 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.236 | 0.348 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".