Effect of the phase of force production on corticomuscular coherence with agonist and antagonist muscles
Bibliographic record
Abstract
During isometric contractions, the net joint torque stability is modulated with the force production phases, i.e., increasing (IFP), holding (HFP), and decreasing force (DFP) phases. It was hypothesized that this modulation results from an altered cortical control of agonist and antagonist muscle activations. Eleven healthy participants performed 50 submaximal isometric ankle plantar flexion contractions. The force production phase effect (IFP, HFP and DFP) was assessed on the net joint torque stability, agonist and antagonist muscles activations, cortical activation, and corticomuscular coherence (CMC) with agonist and antagonist muscles. In comparison to HFP, the net joint torque stability, the agonist muscles activation and the CMC with agonist muscles were lower during IFP and even more during DFP. Antagonist muscle activations, cortical activations and CMC with antagonist muscles were higher during HFP than during IFP only. Increased CMC with agonist and antagonist muscles appeared to enhance the fine motor control. At a cortical level, agonist and antagonist muscle activations seemed to be controlled independently according to their muscle function and the phase of force production. Results revealed that CMC was an adequate measure to investigate the cortical regulation of agonist and antagonist muscle activations. This may have potential applications for patients with altered muscle activations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.003 | 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".