Probing the Human Spinal Locomotor Circuits by Phasic Step-Induced Feedback and by Tonic Electrical and Pharmacological Neuromodulation
Bibliographic record
Abstract
The mammalian lumbar spinal cord experimentally isolated from supraspinal and afferent feedback input remains capable of expressing some basic locomotor function when appropriately stimulated. This ability has been attributed to spinal neural circuits referred to as central pattern generators (CPGs). In individuals with a severe spinal cord injury, rhythmic activity in paralyzed leg muscles can be generated by phasic proprioceptive feedback during therapist- or robotic-assisted stepping on a motorized treadmill. Here, we critically review to what extent the resulting motor output represents locomotor-like activity, and whether these motor patterns are the result of activation of CPGs, as commonly suggested in the literature. Attempts will be made to further delineate the pivotal roles played by mechanisms such as spinal proprioceptive reflexes and their alterations after spinal cord injury, the central excitability level, and by neurotransmitters critical for spinal locomotor activity. We will discuss the view that the muscle activity produced during assisted passive treadmill stepping is resulting from the entrainment of spinal reflex circuits by the cyclically generated proprioceptive feedback. We suggest that the activation of CPG circuits depends rather on the presence of a sustained tonic excitatory drive, as can be provided by electrical spinal cord stimulation, or by specific combinations of dopaminergic agonists, adrenergic/ dopaminergic precursors and/or 5-HT receptor agonists. Novel rehabilitation strategies using spinal cord stimulation and rhythmic-activity producing drugs during locomotor therapy will pave the way for clinically relevant advances in restoration of motor function in people with severe spinal cord injury.
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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".