Influence of the contractile properties of muscle on motor unit firing rates during a moderate-intensity contraction in vivo
Bibliographic record
Abstract
It is suggested that firing rate characteristics of motor units (MUs) are influenced by the physical properties of the muscle. However, no study has correlated MU firing rates at recruitment, targeted force, or derecruitment with the contractile properties of the muscle in vivo. Twelve participants (age = 20.67 ± 2.35 yr) performed a 40% isometric maximal voluntary contraction of the leg extensors that included linearly increasing, steady force, and decreasing segments. Muscle biopsies were collected with myosin heavy chain (MHC) content quantified, and surface electromyography (EMG) was recorded from the vastus lateralis. The EMG signal was decomposed into the firing events of single MUs. Slopes and y-intercepts were calculated for 1) firing rates at recruitment vs. recruitment threshold, 2) mean firing rates at steady force vs. recruitment threshold, and 3) firing rates at derecruitment vs. derecruitment threshold relationships for each subject. Correlations among type I %MHC isoform content and the slopes and y-intercepts from the three relationships were examined. Type I %MHC isoform content was correlated with MU firing rates at recruitment (y-intercepts: r = -0.577; slopes: r = 0.741) and targeted force (slopes: r = 0.853) vs. recruitment threshold and MU firing rates at derecruitment (y-intercept: r = -0.597; slopes: r = 0.701) vs. derecruitment threshold relationships. However, the majority of the individual MU firing rates vs. recruitment and derecruitment relationships were not significant (P > 0.05) and, thus, revealed no systematic pattern. In contrast, MU firing rates during the steady force demonstrated a systematic pattern with higher firing rates for the lower- than higher-threshold MUs and were correlated with the physical properties of MUs in vivo.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".