Effects of trunk neuromuscular electrical stimulation on the motor circuits of able-bodied individuals
Bibliographic record
Abstract
Upper- and lower-limb neuromuscular electrical stimulation (NMES) is known to modulate the excitability of the neural motor circuits. However, it remains unclear whether short-duration trunk muscle NMES could achieve similar neuromodulation effects. We assessed motor evoked potentials (MEPs) elicited through transcranial magnetic stimulation of the primary motor cortex representation of the trunk extensor muscles to evaluate corticospinal excitability. Moreover, cervicomedullary motor evoked potentials (CMEPs) were assessed through cervicomedullary junction magnetic stimulation to evaluate subcortical excitability. Twelve able-bodied individuals participated in the MEP study, and another twelve in the CMEP study. During the interventions, NMES was applied bilaterally to activate the erector spinae muscle and produce intermittent contractions (20 s ON/20 s OFF) for a total of 20 min while participants remained seated. Assessments were performed: (i) before; (ii) during (in brief periods when NMES was OFF); and (iii) immediately after the interventions to compare MEP or CMEP excitability. Our results showed that MEP responses were not affected by trunk NMES, while CMEP responses were facilitated for approximately 8 min during the intervention, and returned to baseline before the end of the 20 min stimulating period. Our findings therefore suggest that short-duration NMES of the trunk extensor muscles likely does not affect the corticospinal excitability, but it has a potential to facilitate subcortical neural circuits immediately after starting the intervention. These findings indicate that short-duration application of NEMS may be helpful in rehabilitation to enhance neuromodulation of the trunk subcortical neural motor circuits.
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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".