Differential Modulation of Motor Unit Behavior When a Fatiguing Contraction Is Matched for Torque versus EMG
Bibliographic record
Abstract
INTRODUCTION: When an isometric contraction is sustained at a submaximal torque, activation of the motoneuron pool increases, making it difficult to interpret neural excitability alterations. Thus, more recently, isometric contractions with maintained electromyographic (EMG) activity (matched-EMG) are being used to induce fatigue; however, little is known about the neurophysiological adjustments that occur to satisfy the requirements of the task. METHODS: For our study, 16 participants performed a 10-min sustained isometric elbow flexion contraction at 20% maximal voluntary contraction (MVC) torque or the level of integrated biceps brachii EMG recorded at 20% MVC torque. Surface EMG was used to assess global median frequency, and four fine-wire electrode pairs were used to obtain motor unit (MU) discharge rate from biceps brachii. Torque or EMG steadiness was also assessed throughout the fatiguing contractions. RESULTS: MU discharge rate increased and torque steadiness decreased during the matched-torque contraction; however, MU discharge rate decreased during the matched-EMG contraction, and no changes occurred for EMG steadiness. Data pooled for the two contractions revealed a decrease in global median frequency. Lastly, a greater loss of MVC torque was observed immediately after the matched-torque compared with matched-EMG contraction. CONCLUSIONS: These findings indicate that, during a matched-torque fatiguing contraction, the nervous system increases MU discharge rates at the cost of poorer steadiness to maintain the requisite torque. In contrast, during a matched-EMG fatiguing contraction, a reduction of MU discharge rates allows for maintenance of EMG steadiness.
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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) | 0.002 | 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".