Endurance exercise facilitates relearning of forelimb motor skill after focal ischemia
Bibliographic record
Abstract
Endurance exercise (i.e. running), by up-regulating brain-derived neurotrophic factor (BDNF) and other modulators of synaptic plasticity, improves attention and learning, both critical components of stroke rehabilitation. We hypothesized that, following middle cerebral artery occlusion in male Sprague-Dawley rats, endurance exercise would act synergistically with a challenging skilled forelimb task to facilitate motor recovery. Animals were randomly assigned to one of four rehabilitation conditions: no rehabilitation, running only, reach training only, and reach training preceded by running (run/reach training) for 5 weeks beginning 5 days after stroke. The behavioral outcome, morphological change and mRNA expression of proteins implicated in neuroplasticity (BDNF, synapsin I and microtubule-associated protein 2) were compared. Endurance exercise on a motorized running wheel, prior to reach training, enhanced recovery of skilled reaching ability but did not transfer to gross motor skills such as postural support (forelimb asymmetry test) and gait (ladder rung walking test). Microtubule-associated protein 2 staining density in the run/reach group was slightly enhanced in the contralateral motor cortex compared with the contralateral sensory and ipsilateral cingulate cortices, suggesting that running preceding reach training may have resulted in more dendritic branching within the motor cortex in this group. No significant differences in mRNA levels were detected among the training paradigms; however, there was a trend toward greater BDNF and synapsin I mRNA in the reaching groups. These findings suggest that exercise facilitates learning of subsequent challenging reaching tasks after stroke, which has the potential to optimize outcomes in patients with stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".