Transcutaneous spinal stimulation alters cortical and subcortical activation patterns during mimicked-standing: A proof-of-concept fMRI study
Bibliographic record
Abstract
Transcutaneous spinal stimulation (TSS) is a non-invasive neuromodulation technique that has been used to facilitate the performance of voluntary motor functions such as trunk control and self-assisted standing in individuals with spinal cord injury. Although it is hypothesized that TSS amplifies signals from supraspinal motor control networks, the effect of TSS on supraspinal activation patterns is presently unknown. The purpose of this study was to investigate TSS-induced activity in supraspinal sensorimotor regions during a lower-limb motor task. Functional magnetic resonance imaging (fMRI) was used to assess changes in neural activation patterns as eleven participants performed mimicked-standing movements in the scanner. Movements were performed without stimulation, as well as in the presence of (1) TSS, (2) stimulation applied to the back muscle, (3) paresthesia stimulation, and (4) neuromuscular electrical stimulation. TSS was associated with greater activation in subcortical and cortical sensorimotor regions involved in relay and processing of movement-related somatosensory information (e.g., thalamus, caudate, pallidum, putamen), as compared to the other stimulation paradigms. TSS also resulted in deactivation in both nucleus accumbens and posterior parietal cortex, suggesting a shift toward somatosensory feedback-based mechanisms and more reflexive motor control. Together, these findings demonstrate that spinal stimulation can alter the activity within supraspinal sensorimotor networks and promote the use of somatosensory feedback, thus providing a plausible neural mechanism for the stimulation-induced improvements of sensorimotor function observed in participants with neurological injuries and disorders.
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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".