Reaching training in rats with spinal cord injury promotes plasticity and task specific recovery
Bibliographic record
Abstract
In the current study we examined the effects of training in adult rats with a cervical spinal cord injury (SCI). One group of rats received 6 weeks of training in a single pellet reaching task immediately after injury, while a second group did not receive training. Following this period changes in cortical levels of BDNF and GAP-43 were analysed in trained and untrained animals and in a group with training but no injury. In another group of rats, functional recovery was analysed in the reaching task and when walking on a horizontal ladder. Thereupon, the cortical forelimb area was electrophysiologically examined using micro-stimulation followed by tracing of the lesioned corticospinal tract (CST). We found that trained rats improved substantially in the reaching task, when compared to their untrained counterparts. Trained rats however, performed significantly worse with their injured forelimb when walking on a horizontal ladder. In parallel to the improved recovery in the trained task, we found that the cortical area where wrist movements could be evoked by micro-stimulation expanded in trained rats in comparison to both untrained and uninjured rats. Furthermore, collateral sprouting of lesioned CST fibres rostral to the injury was increased in trained rats. Post-injury training was also found to increase cortical levels of GAP-43 but not BDNF. In conclusion we show that training of a reaching task promotes recovery of the trained task following partial SCI by enhancing plasticity at various levels of the central nervous system (CNS), but may come at the cost of an untrained task.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".