Intensity of acute aerobic exercise but not aerobic fitness impacts on corticospinal excitability
Bibliographic record
Abstract
Aerobic exercise (AE) modulates cortical excitability. It can alter both corticospinal excitability and intra-cortical networks, which has implications for its use as a tool to facilitate processes such as motor learning, where increased levels of excitability are conducive to the induction of neural plasticity. Little is known about how different intensities of AE modulate cortical excitability or how individual-level characteristics impact on it. Therefore, we investigated whether AE intensities, lower than those previously employed, would be effective in increasing cortical excitability. We also examined whether the aerobic fitness of individual participants was related to the magnitude of change in AE-induced cortical excitability. In both experiments we employed transcranial magnetic stimulation to probe corticospinal excitability before and after AE. We show that 20 min of continuous moderate- (40% and 50% of heart rate reserve, HRR), but not low- (30% HRR) intensity AE was effective at increasing corticospinal excitability. We also found that while we observed increased corticospinal excitability following 20 min of continuous moderate-intensity (50% HRR) AE, aerobic fitness was not related to the magnitude of change. Our results suggest that there is a lower bound intensity of AE that is effective at driving changes in cortical excitability, and that while individual-level characteristics are important predictors of response to AE, aerobic fitness is not. Overall these findings have implication for the way that AE is used to facilitate processes such as motor learning, where increased levels of cortical excitability and plasticity are favourable.
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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 | 0.001 |
| 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) | 0.003 | 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".