Larger perivascular space volume fraction is associated with worse post-stroke sensorimotor outcomes: An ENIGMA analysis
Bibliographic record
Abstract
BACKGROUND: Perivascular Spaces (PVS) are a marker of cerebral small vessel disease (CSVD) that are visible on brain imaging. Larger PVS has been associated with poor quality of life and cognitive impairment post-stroke. However, the association between PVS and post-stroke sensorimotor outcomes has not been investigated. METHODS: 602 individuals with a history of stroke across 24 research cohorts from the ENIGMA Stroke Recovery Working Group were included. PVS volume fractions were obtained using a validated, automated segmentation pipeline from the basal ganglia (BG) and white matter centrum semiovale (CSO), separately. Robust mixed effects regressions were used to a) examine the cross-sectional association between PVS volume fraction and post-stroke sensorimotor outcomes and b) to examine whether PVS volume fraction was associated with other measures of CSVD and overall brain health (e.g., white matter hyperintensities [WMHs], brain age [measured by predicted age difference, brain-PAD]). RESULTS: Larger PVS volume fraction in the CSO, but not BG, was associated with worse post-stroke sensorimotor outcomes (b = -0.06, p = 0.047). Higher burden of deep WMH (b = 0.25, p <0.001), periventricular WMH (b = 0.16, p <0.001) and higher brain-PAD (b = 0.09, p <0.001) were associated with larger PVS volume fraction in the CSO. CONCLUSIONS: Our data show that PVS volume fraction in the CSO is cross-sectionally associated with sensorimotor outcomes after stroke, above and beyond standard lesion metrics. PVS may provide insight into how the overall vascular health of the brain impacts inter-individual differences in post-stroke sensorimotor outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".