Moving forward: methodological considerations for assessing corticospinal excitability during rhythmic motor output in humans
Bibliographic record
Abstract
The use of transcranial magnetic stimulation to assess the excitability of the central nervous system to further understand the neural control of human movement is expansive. The majority of the work performed to-date has assessed corticospinal excitability either at rest or during relatively simple isometric contractions. The results from this work are not easily extrapolated to rhythmic, dynamic motor outputs, given that corticospinal excitability is task-, phase-, intensity-, direction-, and muscle-dependent (Power KE, Lockyer EJ, Forman DA, Button DC. Appl Physiol Nutr Metab 43: 1176–1185, 2018). Assessing corticospinal excitability during rhythmic motor output, however, involves technical challenges that are to be overcome, or at the minimum considered, when attempting to design experiments and interpret the physiological relevance of the results. The purpose of this narrative review is to highlight the research examining corticospinal excitability during a rhythmic motor output and, importantly, to provide recommendations regarding the many factors that must be considered when designing and interpreting findings from studies that involve limb movement. To do so, the majority of work described herein refers to work performed using arm cycling (arm pedaling or arm cranking) as a model of a rhythmic motor output used to examine the neural control of human locomotion.
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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 | 0.029 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".