The Effects of Ageing on Motor Cortical Representations Assessed by Transcranial Magnetic Stimulation (TMS) Mapping: A Systematic Literature Review
Bibliographic record
Abstract
Transcranial Magnetic Stimulation (TMS) is a non-invasive tool used to study sensorimotor cortical excitability, structure, and function of the human brain. Using TMS to map the motor cortex amongst different age groups may provide insight into a potential relationship between physiological changes in the motor cortex and muscle function with age. This systematic review presents an analysis of the existing literature on age-related differences in motor cortical representations of the human brain examined by TMS. Two independent reviewers conducted a literature review using PubMed. They extracted eligible articles that reported the usage of TMS to map the muscle cortex corresponding to specific muscles according to pre-established inclusion and exclusion criteria. The search criteria yielded a total of 667 citations, from which five cross-sectional papers were included in the analysis. Four of these papers demonstrated either no change or inconclusive results regarding the impact of age on motor cortical map size as assessed by TMS. The current literature does not suggest a relationship between age and motor cortical map size within the human motor cortex as assessed by TMS. However, further research is necessary to draw a definitive conclusion. Additional investigation is required to determine if the age-related decrease in muscle function is connected to changes in motor cortical map size. A deeper understanding of alterations in motor map sizes as a function of age may provide valuable insights into various age-related motor deficits and neurodegenerative conditions.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".