Long-interval intracortical inhibition to the biceps brachii is present during arm cycling but is not different than a position‑matched tonic contraction
Bibliographic record
Abstract
Locomotor outputs are controlled by a combination of descending input, sensory feedback and networks of cells in the spinal cord called central pattern generators (CPGs). In the absence of descending input, sensory feedback and CPGs are able to produce rhythmic muscle activation, which create complex patterns of locomotor outputs such as crawling, swimming, walking, cycling. Arm cycling is used as a model of locomotion in order to examine various changes in neural excitability as humans require descending input in order to preform successful locomotion. The human nervous system is complex with many different pathways and tracts; one of which, the corticospinal tract, is involved in the voluntary control of human locomotion. Research investigating corticospinal excitability during arm cycling found that supraspinal excitability was greater during arm cycling than a position- and intensity-matched tonic contraction, yet the mechanism(s) are unclear. Various cortical circuits, such as short-interval intracortical inhibition (SICI) and interhemispheric inhibition (IHI), have been investigated. However, these results do not shed light on possible mechanisms for greater supraspinal excitability during arm cycling. Only one study assessed long-interval intracortical inhibition (LICI) during locomotion, but it was during leg cycling and did not assess task-dependency. To date, there has yet to be a study that has assessed LICI during arm cycling. Therefore, the purpose of the study was to determine if LICI is 1) present during arm cycling and 2) task-dependent. It was hypothesized that 1) LICI would be observed during arm cycling and 2) the amount of LICI would be less during arm cycling compared to a tonic contraction.
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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".