Exercise Training Increases Parietal Lobe Cerebral Blood Flow in Chronic Stroke: An Observational Study
Bibliographic record
Abstract
Exercise is increasingly recommended as an essential component of stroke rehabilitation, yet uncertainty remains with respect to its direct effect on the cerebral vasculature. The current study first demonstrated the repeatability of pseudo-continuous arterial spin labeling (ASL) magnetic resonance imaging in older adults with stroke, and then investigated the change in cerebrovascular function following a 6-month cardiovascular rehabilitation program. In the repeatability study, 12 participants at least 3 months post-stroke underwent 2 ASL imaging scans 1 month apart. In the prospective observational study, eight individuals underwent ASL imaging and aerobic fitness testing before and after a 6-month cardiovascular rehabilitation program. Cerebral blood flow (CBF) and the spatial coefficient of variation of CBF (sCoV) were quantified to characterize tissue-level perfusion and large cerebral artery transit time properties, respectively. Group mean interscan differences for both grey matter CBF and sCoV metrics were less than 1% of the single scan values, suggesting good repeatability in individuals with chronic stroke. In the observational study, grey matter CBF increased after training (baseline: 40±13 vs. 6-month: 46±12 ml/100g/min, P=0.036). The greatest change occurred in the parietal lobe (+18±12%). Grey matter sCoV, however, did not change following training (P=0.31). This study provides preliminary evidence that exercise-based rehabilitation in chronic stroke enhances tissue-level perfusion, without changing the relative hemodynamic properties of the large cerebral arteries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".