Muscle length and joint angle influence spinal but not corticospinal excitability to the biceps brachii across forearm postures
Bibliographic record
Abstract
Forearm rotation (supination/pronation) alters corticospinal excitability to the biceps brachii, but it is unclear whether corticospinal excitability is influenced by joint angle, muscle length, or both. Thus the purpose of this study was to separately examine elbow joint angle and muscle length on corticospinal excitability. Corticospinal excitability to the biceps and triceps brachii was measured using motor evoked potentials (MEPs) elicited via transcranial magnetic stimulation. Spinal excitability was measured using cervicomedullary motor evoked potentials (CMEPs) elicited via transmastoid electrical stimulation. Elbow angles were manipulated with a fixed biceps brachii muscle length (and vice versa) across five unique postures: 1) forearm neutral, elbow flexion 90°; 2) forearm supinated, elbow flexion 90°; 3) forearm pronated, elbow flexion 90°; 4) forearm supinated, elbow flexion 78°; and 5) forearm pronated, elbow flexion 113°. A musculoskeletal model determined biceps brachii muscle length for postures 1–3, and elbow joint angles ( postures 4–5) were selected to maintain biceps length across forearm orientations. MEPs and CMEPs were elicited at rest and during an isometric contraction of 10% of maximal biceps muscle activity. At rest, MEP amplitudes to the biceps were largest during supination, which was independent of elbow joint angle. CMEP amplitudes were not different when the elbow was fixed at 90° but were largest in pronation when muscle length was controlled. During an isometric contraction, there were no significant differences across forearm postures for either MEP or CMEP amplitudes. These results highlight that elbow joint angle and biceps brachii muscle length can each independently influence spinal excitability. NEW & NOTEWORTHY Changes in upper limb posture can influence the responsiveness of the central nervous system to artificial stimulations. We established a novel approach integrating neurophysiology techniques with biomechanical modeling. Through this approach, the effects of elbow joint angle and biceps brachii muscle length on corticospinal and spinal excitability were assessed. We demonstrate that spinal excitability is uniquely influenced by joint angle and muscle length, and this highlights the importance of accounting for muscle length in neurophysiological studies.
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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".