A bi-cortical neuroprosthesis to modulate locomotion after incomplete spinal cord injury
Bibliographic record
Abstract
Neuroprosthetic strategies seek to immediately alleviate deficits and reinstate voluntary control of movement. To facilitate recovery, it is crucial to gain a comprehensive understanding of the mechanisms involved in the return of intentional movement. Nevertheless, the precise relationship between the resurgence of cortical commands and the recovery of locomotion remains somewhat elusive. In the study conducted by Duguay, Bonizzato, Delivet-Mongrain, Fortier-Lebel and Martinez, we introduced a neuroprosthesis designed to deliver precise bi-cortical stimulation in a clinically relevant contusive spinal cord injury model. We conducted experiments in both healthy and spinal cord injured cats, where we fine-tuned the timing, duration, amplitude, and site of stimulation to modulate hindlimb locomotor output. In healthy cats, we observed a wide range of motor programs. However, after spinal cord injury, the induced hindlimb movements became highly stereotyped but were effective in modulating gait and reducing bilateral foot dragging. These results suggest that the neural basis for motor recovery traded off selectivity for effectiveness. Through a series of longitudinal assessments, we found that the restoration of locomotion following spinal cord injury was closely linked to the recovery of the descending neural drive. This underscores the importance of directing rehabilitation interventions toward the cortical target. The study results are discussed in terms of their impact and limitations.
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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".