Deep Venous Perivascular Space Dysfunction Reflects Glymphatic Aging and Predicts Cognitive Vulnerability: In Vivo Human Evidence
Bibliographic record
Abstract
Impaired glymphatic clearance has been recognized as a fundamental mechanism of cognitive decline. While models suggest cerebrospinal fluid influx along arterial perivascular spaces (aPVS) and efflux along venous PVS (vPVS), their differential roles in humans are unclear. We aimed to visualize directional glymphatic flow in vivo, classify human PVS functionally and determine their associations with cognition. This study included 91 patients undergoing intrathecal gadodiamide with serial MRI at baseline, 4.5 hours, 15 hours and 39 hours post-injection. PVS showing early (4.5h) and delayed (39h) enhancement peaks were defined as aPVS and vPVS, respectively. Among 742 basal ganglia (BG) and 1380 centrum semiovale (CSO) PVS analyzed, 10.4% and 21.6% were aPVS, while 62.7% and 52.0% were vPVS, respectively. BG-vPVS burden correlated with age (r=0.275, p<0.001) and hypertension. Among 60 patients with cognitive assessment (telephone Montreal Cognitive Assessment, T-MoCA) data, only BG-vPVS burden independently correlated with lower scores after adjusting for age and education (β=-0.16, p=0.041). This study provided direct in vivo MRI evidence of glymphatic flow within human PVS. We introduced a novel functional classification method to differentiate arterial from venous PVS, finding their different role in cognitive impairment, which may represent a potential target for therapeutic intervention.
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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".