Effects of neuromuscular electrical stimulation and voluntary commands on the spinal reflex excitability of remote limb muscles
Bibliographic record
Abstract
It is well known that contracting the upper limbs can affect spinal reflexes of the lower limb muscle, via intraneuronal networks within the central nervous system. However, it remains unknown whether neuromuscular electrical stimulation (NMES), which can generate muscle contractions without central commands from the cortex, can also play a role in such inter-limb facilitation. Therefore, the objective of this study was to compare the effects of unilateral upper limb contractions using NMES and voluntary unilateral upper limb contractions on the inter-limb spinal reflex facilitation in the lower limb muscles. Spinal reflex excitability was assessed using transcutaneous spinal cord stimulation (tSCS) to elicit responses bilaterally in multiple lower limb muscles, including ankle and thigh muscles. Five interventions were applied on the right wrist flexors for 70 s: (1) sensory-level NMES; (2) motor-level NMES; (3) voluntary contraction; (4) voluntary contraction and sensory-level NMES; (5) voluntary contraction and motor-level NMES. Results showed that spinal reflex excitability of ankle muscles was facilitated bilaterally during voluntary contraction of the upper limb unilaterally and that voluntary contraction with motor-level NMES had similar effects as just contracting voluntarily. Meanwhile, motor-level NMES facilitated contralateral thigh muscles, and sensory-level NMES had no effect. Overall, our results suggest that inter-limb facilitation effect of spinal reflex excitability in lower limb muscles depends, to a larger extent, on the presence of the central commands from the cortex during voluntary contractions. However, peripheral input generated by muscle contractions using NMES might have effects on the spinal reflex excitability of inter-limb muscles via spinal intraneuronal networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".