On task: Considerations and future directions for studies of corticospinal excitability in exercise neuroscience and related disciplines
Bibliographic record
Abstract
Over the last few decades, transcranial magnetic stimulation (TMS) has emerged as a conventional laboratory technique in human neurophysiological research. Exercise neuroscientists have used TMS to study central nervous system contributions to fatigue, training, and performance in health, injury, and disease. In such studies, corticospinal excitability is often assessed at rest or during simple isometric tasks with the implication that the results may be extrapolated to more functional and complex movement outside of the laboratory. However, the neural mechanisms that influence corticospinal excitability are both state- and task-dependent. Furthermore, there are many sites of modulation along the pathway from the motor cortex to the muscle; a fact that is somewhat obscured by the all-encompassing and poorly defined term "corticospinal excitability". Therefore, the tasks we use to assess corticospinal excitability and the conclusions that we draw from such a global measure of the motor pathway must be taken into consideration. The overall objective of this review is to highlight the task-dependent nature of corticospinal excitability and the tools used to assess modulation at cortical and spinal sites of modulation. By weighing the advantages and constraints of conventional approaches to studying corticospinal excitability, and considering some new and novel approaches, we will continue to advance our understanding of the neural control of movement during exercise.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".