Training-induced alterations in the modulation of human motoneuron discharge patterns with contraction force
Bibliographic record
Abstract
ABSTRACT Motoneurons adapt to both resistance and endurance training in reduced animal preparations, with adaptations seemingly more apparent in higher threshold neurons, but similar evidence in humans is lacking. Here, we compared the identified motor unit (MU) discharge patterns from decomposed electromyography signals acquired during triangular dorsiflexion contractions up to 70% of maximal voluntary force (MVF) between resistance-trained, endurance-trained, and untrained individuals (n=23 in each group). We then estimated intrinsic motoneuron properties and garnered insight about the proportion of excitatory, inhibitory, and neuromodulatory inputs contributing to motor commands across contraction intensities in each group. Participants also performed a task where a triangular contraction was superimposed onto a sustained one designed to challenge inhibitory control of dendritic persistent inward currents (PICs). Both trained groups demonstrated greater MU discharge rates with greater ascending discharge rate modulation during higher contraction forces (≥50% MVF), which were accompanied by more linear MU discharge patterns and greater post-acceleration attenuation slopes of the ascending discharge rates. No differences in discharge rate hysteresis or the discharge rate characteristics during the sombrero tasks between groups, suggesting no differences in neuromodulatory input. Conversely, resistance-compared to endurance-trained individuals exhibited greater acceleration slopes during lower contractions forces (≤50% MVF), indicating the possibility of enhanced initial activation of PICs. Collectively, the greater and more linear MU discharge patterns in the trained groups either suggests a more reciprocal (i.e., push-pull) excitation-inhibition coupling during higher contraction forces or enhanced excitatory synaptic input to the motor pool, which might underpin greater force production of trained individuals.
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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) | 0.002 | 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".