Harnessing neural activity to promote repair of the damaged corticospinal system after spinal cord injury
Bibliographic record
Abstract
As most spinal cord injuries (SCIs) are incomplete, an important target for promoting neural repair and recovery of lost motor function is to promote the connections of spared descending spinal pathways with spinal motor circuits. Among the pathways, the corticospinal tract (CST) is most associated with skilled voluntary functions in humans and many animals. CST loss, whether at its origin in the motor cortex or in the white matter tracts subcortically and in the spinal cord, leads to movement impairments and paralysis. To restore motor function after injury will require repair of the damaged CST. In this review, I discuss how knowledge of activity-dependent development of the CST—which establishes connectional specificity through axon pruning, axon outgrowth, and synaptic competition among CST terminals—informed a novel activity-based therapy for promoting sprouting of spared CST axons after injur in mature animals. This therapy, which comprises motor cortex electrical stimulation with and without concurrent trans-spinal direct current stimulation, leads to an increase in the gray matter axon length of spared CST axons in the rat spinal cord and, after a pyramidal tract lesion, restoration of skilled locomotor movements. I discuss how this approach is now being applied to a C4 contusion rat model.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".