Stimulation Techniques used to Assess Corticospinal Excitability Alters an Attentional Focus Maximal Voluntary Contraction of the Elbow Flexors.
Bibliographic record
Abstract
PURPOSE: To investigate the role of attentional focus on force output by assessing 1) force output during maximal voluntary elbow flexion contractions, 2) corticospinal excitability and 3) motor unit activation patterns METHODS: 7 resistance-trained males completed two experimental sessions. Each session consisted of 12 maximum voluntary contractions (MVC) with 180s rest of recovery between MVC. Participants were given counter-balanced external and internal attentional focus conditions prior to each MVC to direct attention. Force output and electromyography (EMG) of the biceps brachii, triceps brachii, and brachioradialis were recorded for both sessions. Transcranial magnetic stimulation, transmastoid electrical stimulation, and brachial plexus electrical stimulation were used to produce motor evoked potentials (MEPs), cervicomedullary motor evoked potentials (CMEPs) and maximal M-waves (Mmax) in the biceps brachii during each MVC in one of the two sessions. All MEPs and CMEPs were normalized to Mmax. RESULTS: Forces produced during the stimulation session were not significantly different between external and internal focus conditions (p = 0.20). However, forces produced during the non-stimulation session were 19.9% higher with an external cue compared to internal cues (p < 0.05). As well, forces produced with external cues were 13.2% greater during the non-stimulation session compared to the stimulation session. (p<0.05). EMG activity was not found to be significantly different between attention focus cues (p≥0.1). CONCLUSIONS: The usage of stimulation techniques likely distracted participants from the attentional focus cues provided during the stimulation session. Therefore, we were unable to successfully assess changes in corticospinal excitability between focus cues. However, we were still able to show that external cues direct greater force production of the elbow flexors compared to internal cues.
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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) | 0.002 | 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".