Bilateral Knee Extensor Fatigue Modulates Force and Responsiveness of the Corticospinal Pathway in the Non-fatigued, Dominant Elbow Flexors
Bibliographic record
Abstract
Exercise-induced fatigue affects muscle performance and modulates corticospinal excitability in non-exercised muscles. The purpose of this study was to investigate the effect of bilateral knee extensor fatigue on dominant elbow flexor (EF) maximal voluntary force production and corticospinal excitability. Transcranial magnetic, transmastoid electrical and brachial plexus electrical stimulation (BPES) were used to investigate corticospinal, spinal, and muscle excitability of the dominant EF before and after a bilateral knee extensor fatiguing protocol or time matched rest period (control). For both sessions three stimuli were delivered every 1.5 s during the three pre-test time points and during the 1st, 3rd, 6th, 9th and 12th post-test 5 s EF isometric maximal voluntary contractions (MVC). In both conditions, overall, EF MVC force (p < 0.001) decreased progressively from repetition #1 to #12 during the post-test MVC protocol. EF MVC force (p < 0.001, ES = 0.9, Δ10.3%) decrements were more pronounced in the knee extensor fatigue intervention condition. In addition, there were no significant differences between conditions for biceps brachii electromyographic (EMG) activity (p = 0.43), motor evoked potentials (MEPs) amplitude (p = 0.908) or MEP silent period (SP; p = 0.776). However, the fatigue condition exhibited a lower MEP/cervicomedullary MEP (CMEP) ratio (p = 0.042, ES = 2.5, Δ25%) and a trend toward higher CMEP values (p = 0.08, ES = 0.5, Δ20.4%). These findings suggest that bilateral knee extensor fatigue can impair performance and modulate corticospinal excitability of the EF.
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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 | 0.000 |
| 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) | 0.001 | 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".