From cortex to cord: motor circuit plasticity after spinal cord injury
Bibliographic record
Abstract
Spinal cord injury is associated with chronic sensorimotor deficits due to the interruption of ascending and descending tracts between the brain and spinal cord. Functional recovery after anatomically complete spinal cord injury is limited due to the lack of long-distance axonal regeneration of severed fibers in the adult central nervous system. Most spinal cord injuries in humans, however, are anatomically incomplete. Although restorative treatment options for spinal cord injury remain currently limited, research from experimental models of spinal cord injury have revealed a tremendous capability for both spontaneous and treatment-induced plasticity of the corticospinal system that supports functional recovery. We review recent advances in the understanding of corticospinal circuit plasticity after spinal cord injury and concentrate mainly on the hindlimb motor cortex, its corticospinal projections, and the role of spinal mechanisms that support locomotor recovery. First, we discuss plasticity that occurs at the level of motor cortex and the reorganization of cortical movement representations. Next, we explore downstream plasticity in corticospinal projections. We then review the role of spinal mechanisms in locomotor recovery. We conclude with a perspective on harnessing neuroplasticity with therapeutic interventions to promote functional recovery.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".