Changes in Corticospinal and Spinal Excitability to the Biceps Brachii with a Neutral vs. Pronated Handgrip Position Differ between Arm Cycling and Tonic Elbow Flexion
Bibliographic record
Abstract
The purpose of this study was to examine the influence of neutral and pronated handgrip positions on corticospinal excitability to the biceps brachii during arm cycling. Corticospinal and spinal excitability were assessed using motor evoked potentials (MEPs) elicited via transcranial magnetic stimulation (TMS) and cervicomedullary-evoked potentials (CMEPs) elicited via transmastoid electrical stimulation (TMES), respectively. Participants were seated upright in front on arm cycle ergometer. Responses were recorded from the biceps brachii at two different crank positions (6 and 12 o'clock positions relative to a clock face) while arm cycling with neutral and pronated handgrip positions. Responses were also elicited during tonic elbow flexion to compare/contrast the results to a non-rhythmic motor output. MEP and CMEP amplitudes were significantly larger at the 6 o'clock position while arm cycling with a neutral handgrip position compared to pronated (45.6 and 29.9%, respectively). There were no differences in MEP and CMEP amplitudes at the 12 o'clock position for either handgrip position. For the tonic contractions, MEPs were significantly larger with a neutral vs. pronated handgrip position (32.6% greater) while there were no difference in CMEPs. Corticospinal excitability was higher with a neutral handgrip position for both arm cycling and tonic elbow flexion. While spinal excitability was also higher with a neutral handgrip position during arm cycling, no difference was observed during tonic elbow flexion. These findings suggest that not only is corticospinal excitability to the biceps brachii modulated at both the supraspinal and spinal level, but that it is influenced differently between rhythmic arm cycling and tonic elbow flexion.
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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".