Brain-Derived Neurotrophic Factor Contributes to Recovery of Skilled Reaching After Focal Ischemia in Rats
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Brain-derived neurotrophic factor (BDNF) is involved in neuronal survival, synaptic plasticity, learning and memory, and neuroplasticity. Further, exogenous treatment with BDNF or exposing animals to enrichment and exercise regimens, which also increase BDNF, enhances behavioral recovery after brain injury. Thus, the beneficial effects of rehabilitation in promoting recovery after stroke may also depend on BDNF. We tested this hypothesis by evaluating the contribution of BDNF to motor skill relearning after endothelin-1-induced middle cerebral artery occlusion in rats. METHODS: Antisense BDNF oligonucleotide, which blocks the expression of BDNF (or saline vehicle) was infused into the contralateral lateral ventricle for 28 days after ischemia. Animals received either a graduated rehabilitation program, including running exercise and skilled reaching training, which simulates clinical practice, or no rehabilitation. Functional recovery was assessed with a battery of tests that measured skilled reaching, forelimb use asymmetry, and foraging ability. RESULTS: Rehabilitation significantly improved skilled reaching ability in the staircase task. Antisense BDNF oligonucleotide effectively blocked BDNF mRNA, and negated the beneficial effects of rehabilitation on recovery of skilled reaching. Importantly, antisense BDNF oligonucleotide did not affect reaching with the unaffected limb, body weight, infarct size, or foraging ability, indicating the treatment was specific to relearning of motor skill after ischemia. CONCLUSIONS: This study is the first to identify a critical role for BDNF in rehabilitation-induced recovery after stroke, and our results suggest that new treatments to enhance BDNF would constitute a promising therapy for promoting recovery of function after stroke.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".