Effect of afferent feedback and central motor commands on soleus H-reflex suppression during arm cycling
Bibliographic record
Abstract
Suppression of soleus H-reflex amplitude in stationary legs is seen during rhythmic arm cycling. We examined the influence of various arm-cycling parameters on this interlimb reflex modulation to determine the origin of the effect. We previously showed the suppression to be graded with the frequency of arm cycling but not largely influenced by changes in peripheral input associated with crank length. Here, we more explicitly explored the contribution of afferent feedback related to arm movement on the soleus H-reflex suppression. We explored the influence of load and rate of muscle stretch by manipulating crank-load and arm-muscle vibration during arm cycling. Furthermore, internally driven ("Active") and externally driven ("Passive") arm cycling was compared. Soleus H-reflexes were evoked with tibial nerve stimulation during stationary control and rhythmic arm-cycling conditions, including: 1) six different loads; 2) with and without vibration to arm muscles; and 3) Active and Passive conditions. No significant differences were seen in the level of suppression between the different crank loads or between conditions with and without arm-muscle vibration. Furthermore, in contrast to the clear effect seen during active cycling, passive arm cycling did not significantly suppress the soleus H-reflex amplitude. Current results, in conjunction with previous findings, suggest that the afferent feedback examined in these studies is not the primary source responsible for soleus H-reflex suppression. Instead, it appears that central motor commands (supraspinal or spinal in origin) associated with frequency of arm cycling are relatively more dominant sources.
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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) | 0.001 | 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".