Eccentric cycling enhances primary motor cortex excitability
Bibliographic record
Abstract
Abstract Acute aerobic exercise (AAE) can modulate primary motor cortex (M1) excitability. To date, studies evaluating its effects have focused almost exclusively on concentric cycling. Critically, we found that eccentric AAE enhances motor learning more than concentric AAE, possibly explained by enhanced frontal-parietal brain activation during eccentric cycling. Yet, M1 excitability mechanisms underlying this eccentric AAE-enhanced motor learning remain unknown. Thus, this study aimed to evaluate the effect of eccentric cycling AAE on M1 excitability using transcranial magnetic stimulation (TMS). Thirty adults performed three 20 min-conditions: i) eccentric cycling AAE, ii) concentric cycling AAE, and iii) rest. Cycling AAE was carried out at a workload corresponding to 70% of peak heart rate (%HR peak ) measured during concentric incremental cycling exercise. TMS assessments were conducted before (Pre), immediately (Post 0 ) and 20 minutes after (Post 20 ) AAE/rest to evaluate changes in corticospinal excitability (CSE) and short-interval intracortical inhibition (SICI). Overall, we found CSE increased and intracortical inhibition (SICI) was reduced at Post 20 to a comparable extent following eccentric and concentric cycling AAE compared to rest. Also, %HR peak , muscle pain and perceived effort were lower during eccentric cycling AAE compared to concentric cycling AAE. Our results showed that eccentric cycling impacted M1 excitability change to a comparable degree as concentric cycling, while requiring less cardiovascular response, eliciting less muscle pain and lower perceived effort. Taken together, our results suggest that eccentric cycling AAE may be a valuable intervention to modulate M1 excitability for populations with limited cardiovascular capacity and may have potential implications in clinical and sports-related contexts.
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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) | 0.000 | 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) | 0.002 | 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".