The effects of forearm position and contraction intensity on cortical and spinal excitability during a submaximal force steadiness task of the elbow flexors
Bibliographic record
Abstract
Elbow flexor force steadiness is less with the forearm pronated (PRO) compared with neutral (NEU) or supinated (SUP) and may relate to neural excitability. Although not tested in a force steadiness paradigm, lower spinal and cortical excitability was observed separately for biceps brachii in PRO, possibly dependent on contractile status at the time of assessment. This study aimed to investigate position-dependent changes in force steadiness as well as spinal and cortical excitability at a variety of contraction intensities. Thirteen males (26 ± 7 yr; means ± SD) performed three blocks (PRO, NEU, and SUP) of 24 brief (~6 s) isometric elbow flexor contractions (5, 10, 25 or 50% of maximal force). During each contraction, transcranial magnetic stimulation or transmastoid stimulation was delivered to elicit a motor-evoked potential (MEP) or cervicomedullary motor-evoked potential (CMEP), respectively. Force steadiness was lower in PRO compared with NEU and SUP ( P ≤ 0.001), with no difference between NEU and SUP. Similarly, spinal excitability (CMEP/maximal M wave) was lower in PRO than NEU (25 and 50% maximal force; P ≤ 0.010) and SUP (all force levels; P ≤ 0.004), with no difference between NEU and SUP. Cortical excitability (MEP/CMEP) did not change with forearm position ( P = 0.055); however, a priori post hoc testing for position showed excitability was 39.8 ± 38.3% lower for PRO than NEU at 25% maximal force ( P = 0.006). The data suggest that contraction intensity influences the effect of forearm position on neural excitability and that reduced spinal and, to a lesser extent, cortical excitability could contribute to lower force steadiness in PRO compared with NEU and SUP. NEW & NOTEWORTHY To address conflicting reports about the effect of forearm position on spinal and cortical excitability of the elbow flexors, we examine the influence of contraction intensity. For the first time, excitability data are considered in a force steadiness context. Motoneuronal excitability is lowest in pronation and this disparity increases with contraction intensity. Cortical excitability exhibits a similar pattern from 5 to 25% of maximal force. Lower corticospinal excitability likely contributes to relatively poor force steadiness in pronation.
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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".