The influence of an acute bout of moderate‐intensity cycling exercise on sensorimotor integration
Bibliographic record
Abstract
Acute cycling exercise can modulate motor cortical circuitry in the non-exercised upper-limb. Within the primary motor cortex, measures of intracortical inhibition are reduced and intracortical facilitation is enhanced following acute exercise. Further, acute cycling exercise decreases interhemispheric inhibition between the motor cortices and lowers cerebellar-to-motor cortex inhibition. Yet, investigations into the effects of acute exercise on sensorimotor integration, referring to the transfer of incoming afferent information from the primary somatosensory cortex to motor cortex, are lacking. The current work addresses this gap in knowledge with two experimental sessions. In the first session, we tested the exercise-induced changes in somatosensory and motor excitability by assessing somatosensory (SEP) and motor evoked potentials (MEPs). In the second session, we explored the effects of acute cycling exercise on short- (SAI) and long-latency afferent inhibition (LAI), and afferent facilitation. In both experimental sessions, neurophysiological measures were obtained from the non-exercised upper-limb muscle, tested at two time points pre-exercise separated by a 25-min period of rest. Next, a 25-min bout of moderate-intensity lower-limb cycling was performed with measures assessed at two time points post-exercise. Acute lower-limb cycling increased LAI, without modulation of SAI or afferent facilitation. Further, there were no exercise-induced changes to SEP or MEP amplitudes. Together, these results suggest that acute exercise has unique effects on sensorimotor integration, which are not accompanied by concurrent changes in somatosensory or motor cortical excitability.
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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 | 0.001 |
| 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".