Neural Coupling Between the Arms and Legs During Rhythmic Locomotor-Like Cycling Movement
Bibliographic record
Abstract
Neuronal coupling between the arms and legs allowing coordinated rhythmic movement during locomotion is poorly understood. We used the modulation of cutaneous reflexes to probe this neuronal coupling between the arms and legs using a cycling paradigm. Participants performed rhythmic cycling with arms, legs, or arms and legs together. We hypothesized that any contributions from the arms would be functionally linked to locomotion and would thus be phase-dependent. Reflexes were evoked by electrical stimulation of the superficial peroneal nerve at the ankle, and electromyography (EMG) was recorded from muscles in the arms and legs. The main finding was that the relative contribution from the arms and legs was linked to the functional state of the legs. For example, in tibialis anterior, the largest contribution from arm movement [57% variance accounted for (VAF), P < 0.05] was during the leg power phase, whereas the largest from leg movement (71% VAF, P < 0.05) was during leg cycling recovery. Thus the contribution from the arms was functionally gated throughout the locomotor cycle in a manner that appears to support the action of the legs. Additionally, the effect of arm cycling on reflexes in leg muscles when the legs were not moving was relatively minor; full expression of the effect of rhythmic arm movement was only observed when both the arms and legs were moving. Our findings provide experimental support for the interaction of rhythmic arm and leg movement during human locomotion.
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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".