Spinal reciprocal inhibition in human locomotion
Bibliographic record
Abstract
The purpose of this paper was to study spinal inhibition during several different motor tasks in healthy human subjects. The short-latency, reciprocal inhibitory pathways from the common peroneal (CP) nerve to the soleus muscle and from the tibial nerve to the tibialis anterior muscle were studied as a depression of ongoing voluntary electromyograph (EMG) activity. First, the effect of stimulus intensity on the amount of inhibition was examined to decide an appropriate stimulation to study the task-dependent modulation of inhibition. Then, the inhibition at one level of stimulation (1.5 x motor threshold) was investigated during standing, walking, and running. The change in slope of inhibition vs. EMG level, which approximates the fraction of ongoing activity that is inhibited, decreased with CP stimulation from 0.52 during standing to 0.30 during fast walking (6 km/h) to 0.17 during running at 9 km/h. Similarly, the slope decreased with tibial nerve stimulation from 0.68 (standing) to 0.42 (fast walking) to 0.35 (running at 9 km/h). All differences, except the last one, were highly significant (P < 0.01, Student's t-test). However, the difference between walking (0.42) and running (0.36) at the same speed (6 km/h) was not significant with tibial nerve stimulation and only significant at P < 0.05 with CP nerve stimulation (0.30, 0.20). Also, the difference between standing (0.52) and slow walking (3 km/h; 0.41) with CP stimulation was not significant, but it was significant (P < 0.01) with tibial nerve stimulation (0.68, 0.49). In conclusion, our findings indicate that spinal reciprocal inhibition decreases substantially with increasing speed and only changes to a lesser extent with task.
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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".