Epidural stimulation restores muscle synergies by modulating neural drives in participants with motor/sensory complete spinal cord injuries
Bibliographic record
Abstract
Multiple studies have corroborated restored volitional motor control after motor-complete spinal cord injury (SCI) through the use of spinal cord stimulation (SCS/eSCS) but rigorous quantitative descriptions have been lacking. Using a structured surface electromyogram based (sEMG) task with and without SCS during the Epidural Stimulation After Neurological Damage (ESTAND) study in participants with chronic, motor and sensory complete SCI, we investigated muscle activity complexity and muscle synergies to better characterize neuro-muscular control. In addition, competition exists between the task and neural origin hypotheses underlying muscle synergies, and this analysis in humans with motor and sensory complete chronic injury provided an opportunity to test these hypotheses. Muscle activity complexity was computed with Higuchi Fractal Dimensional analysis (HFD), and muscle synergies were estimated using non-negative matrix factorization (NNMF) in six participants with AIS A chronic SCI. We found that the complexity of muscle activity is immediately reduced with SCS in the SCI participants. We also found that over the follow-up sessions, the muscle synergy structure of the SCI participants became more defined, and the number of synergies decreased over time, indicating improved coordination between the muscle groups. Lastly, we found that the muscle synergies were restored with SCS, supporting the neural hypothesis of muscle synergies. We conclude that SCS restores muscle movements and muscle synergies that are distinct from healthy, able-bodied controls.
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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".