Differential Regulation of Cutaneous and H-Reflexes During Leg Cycling in Humans
Bibliographic record
Abstract
Reflexes undergo modulation according to task and timing during standing, walking, running, and leg cycling in humans. Both cutaneous and Hoffman (H-) reflexes are modulated by movement and task. However, recent evidence suggests that the modulation pattern for cutaneous and H-reflexes may be different. We sought to clarify this issue by reducing the effect of movement phase and altering the level of background muscle activation (low and high) in static and dynamic (leg cycling) conditions. Electromyography was recorded from the ankle extensors soleus and medial gastrocnemius (MG) and the knee extensor vastus lateralis (VL). Reflexes were evoked during the downstroke of stationary leg cycling. Cutaneous reflexes were evoked with trains of 5 x 1.0 ms pulses at 300 Hz delivered to the distal tibial nerve, whereas H-reflexes were evoked in soleus by stimulation with single 1.0-ms pulses. There were two main observations in this study: 1) middle latency cutaneous reflexes were facilitatory during static contraction but were dramatically attenuated or reversed to suppressive responses during cycling (task-dependent modulation); 2) soleus H-reflexes were larger in the high muscle activation condition but were unaffected by task (no task-dependent modulation). Thus opposite results were obtained in the two reflex pathways. It is concluded that cutaneous and H-reflexes are modulated by different mechanisms during active locomotor-like movements.
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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) | 0.001 | 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".