Remodeling of Neuronal Circuits After Reach Training in Chronic Capsular Stroke
Bibliographic record
Abstract
BACKGROUND: Subcortical capsular stroke has a poor prognosis, and it is not yet fully understood how and under what circumstances reach training contributes to motor recovery. Objective This study was performed to investigate changes in neuronal circuits and motor recovery in a chronic capsular stroke model in the presence or absence of reach training. METHOD: We generated photothrombotic capsular lesions in 42 Sprague-Dawley rats and evaluated motor recovery with or without daily training in a single-pellet reaching task (SPRT). We used 2-deoxy-2-[18F]-fluoro-D-glucose-microPET (positron emission tomography) to assess remodeling of neuronal circuits. RESULTS: SPRT training was selectively beneficial only for the group with incomplete capsular destruction (P < .05), suggesting the relevance of plasticity in the remaining capsular fibers for motor recovery. Groups that did not receive SPRT training showed no motor recovery at all. The microPET analysis demonstrated that motor recovery was correlated with a reduction in cortical diaschisis in ipsilesional motor and sensory cortices and in the contralesional sensory cortex (Pearson's correlation, P < .05). We also observed training-dependent subcortical activation in the contralesional red nucleus, the internal capsule, and the ventral hippocampus (P < .0025; false discovery rate q < 0.05). The groups without reach training did not show the same degree of reduction in diaschisis or activation of the red nucleus. CONCLUSIONS: Our results suggest that motor recovery and remodeling of neuronal circuits after capsular stroke depend on the magnitude of the capsular lesion and on the presence or absence of reach training. Task-specific training is strongly indicated only when there is incomplete destruction of the capsular fibers.
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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) | 0.000 | 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".