Behaviour of the motoneurone pool in a fatiguing submaximal contraction
Bibliographic record
Abstract
During fatigue caused by a sustained maximal voluntary contraction (MVC), motoneurones become markedly less responsive when tested during the silent period following transcranial magnetic stimulation (TMS). To determine whether this reduction depends on the repetitive activation of the motoneurones, responses to TMS (motor evoked potentials, MEPs) and to cervicomedullary stimulation (cervicomedullary motor evoked potentials, CMEPs) were tested during a sustained submaximal contraction at a constant level of electromyographic activity (EMG). In such a contraction, some motoneurones are repetitively activated whereas others are not active. On four visits, eight subjects performed a 10 min maintained-EMG elbow flexor contraction of 25% maximum. Test stimuli were delivered with and without conditioning by TMS given 100 ms prior. Test responses were MEPs or CMEPs (two visits each, small responses evoked by weak stimuli on one visit and large responses on the other). During the sustained contraction, unconditioned CMEPs decreased ∼20% whereas conditioned CMEPs decreased ∼75 and 30% with weak and strong stimuli, respectively. Conditioned MEPs were reduced to the same extent as CMEPs of the same size. The data reveal a novel decrease in motoneurone excitability during a submaximal contraction if EMG is maintained. Further, the much greater reduction of conditioned than unconditioned CMEPs shows the critical influence of voluntary drive on motoneurone responsiveness. Strong test stimuli attenuate the reduction of conditioned CMEPs which indicates that low-threshold motoneurones active in the contraction are most affected. The equivalent reduction of conditioned MEPs and CMEPs suggests that, similar to findings with a sustained MVC, impaired motoneurone responsiveness rather than intracortical inhibition is responsible for the fatigue-related impairment of the MEP during a sustained submaximal contraction.
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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".