Cadence-dependent changes in corticospinal excitability of the biceps brachii during arm cycling
Bibliographic record
Abstract
This is the first study to report the influence of different cadences on the modulation of supraspinal and spinal excitability during arm cycling. Supraspinal and spinal excitability were assessed using transcranial magnetic stimulation of the motor cortex and transmastoid electrical stimulation of the corticospinal tract, respectively. Transcranial magnetic stimulation-induced motor evoked potentials and transmastoid electrical stimulation-induced cervicomedullary evoked potentials (CMEPs) were recorded from the biceps brachii at two separate positions corresponding to elbow flexion and extension (6 and 12 o'clock relative to a clock face, respectively) while arm cycling at 30, 60 and 90 rpm. Motor evoked potential amplitudes increased significantly as cadence increased during both elbow flexion (P < 0.001) and extension (P = 0.027). CMEP amplitudes also increased with cadence during elbow flexion (P < 0.01); however, the opposite occurred during elbow extension (i.e., decreased CMEP amplitude; P = 0.01). The data indicate an overall increase in the excitability of corticospinal neurons which ultimately project to biceps brachii throughout arm cycling as cadence increased. Conversely, changes in spinal excitability as cadence increased were phase dependent (i.e., increased during elbow flexion and decreased during elbow extension). Phase- and cadence-dependent changes in spinal excitability are suggested to be mediated via changes in the balance of excitatory and inhibitory synaptic input to the motor pool, as opposed to changes in the intrinsic properties of spinal motoneurons.
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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".