Turning on the central contribution to contractions evoked by neuromuscular electrical stimulation
Bibliographic record
Abstract
Neuromuscular electrical stimulation can generate contractions through peripheral and central mechanisms. Direct activation of motor axons (peripheral mechanism) recruits motor units in an unnatural order, with fatigable muscle fibers often activated early in contractions. The activation of sensory axons can produce contractions through a central mechanism, providing excitatory synaptic input to spinal neurons that recruit motor units in the natural order. Presently, we quantified the effect of stimulation frequency (10-100 Hz), duration (0.25-2 s of high-frequency bursts, or 20 s of constant-frequency stimulation), and intensity [1-5% maximal voluntary contraction (MVC) torque generated by a brief 100-Hz train] on the torque generated centrally. Electrical stimulation (1-ms pulses) was delivered over the triceps surae in eight subjects, and plantar flexion torque was recorded. Stimulation frequency, duration, and intensity all influenced the magnitude of the central contribution to torque. Central torque did not develop at frequencies < or = 20 Hz, and it was maximal at frequencies > or = 80 Hz. Increasing the duration of high-frequency stimulation increased the central contribution to torque, as central torque developed over 11 s. Central torque was greatest at a relatively low contraction intensity. The largest amount of central torque was produced by a 20-s, 100-Hz train (10.7 +/- 5.5 %MVC) and by repeated 2-s bursts of 80- or 100-Hz stimulation (9.2 +/- 4.8 and 10.2 +/- 8.1% MVC, respectively). Therefore, central torque was maximized by applying high-frequency, long-duration stimulation while avoiding antidromic block by stimulating at a relatively low intensity. If, as hypothesized, the central mechanism primarily activates fatigue-resistant muscle fibers, generating muscle contractions through this pathway may improve rehabilitation applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".