Trunk Stability Enabled by Noninvasive Spinal Electrical Stimulation after Spinal Cord Injury
Bibliographic record
Abstract
Electrical neuromodulation of spinal networks improves the control of movement of the paralyzed limbs after spinal cord injury (SCI). However, the potential of noninvasive spinal stimulation to facilitate postural trunk control during sitting in humans with SCI has not been investigated. We hypothesized that transcutaneous electrical stimulation of the lumbosacral enlargement can improve trunk posture. Eight participants with non-progressive SCI at C3-T9, American Spinal Injury Association Impairment Scale (AIS) A or C, performed different motor tasks during sitting. Electromyography of the trunk muscles, three-dimensional kinematics, and force plate data were acquired. Spinal stimulation improved trunk control during sitting in all tested individuals. Stimulation resulted in elevated activity of the erector spinae, rectus abdominis, and external obliques, contributing to improved trunk control, more natural anterior pelvic tilt and lordotic curve, and greater multi-directional seated stability. During spinal stimulation, the center of pressure (COP) displacements decreased to 1.36 ± 0.98 mm compared with 4.74 ± 5.41 mm without stimulation (p = 0.0156) in quiet sitting, and the limits of stable displacement increased by 46.92 ± 35.66% (p = 0.0156), 36.92 ± 30.48% (p = 0.0156), 54.67 ± 77.99% (p = 0.0234), and 22.70 ± 26.09% (p = 0.0391) in the forward, backward, right, and left directions, respectively. During self-initiated perturbations, the correlation between anteroposterior arm velocity and the COP displacement decreased from r = 0.5821 (p = 0.0007) without to r = 0.5115 (p = 0.0039) with stimulation, indicating improved trunk stability. These data demonstrate that the spinal networks can be modulated transcutaneously with tonic electrical spinal stimulation to physiological states sufficient to generate a more stable, erect sitting posture after chronic paralysis.
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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 | 0.000 |
| 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".