Disruptions of deep medullary veins and MRI indices of glymphatic function in cerebral small vessel disease
Bibliographic record
Abstract
BackgroundDisruptions of deep medullary veins (DMV) have been associated with the radiological severity and cognitive impairment observed in cerebral small vessel disease (SVD). Glymphatic dysfunction may serve as a potential mechanism underlying these associations.ObjectiveWe aimed to clarify the associations between DMV disruptions, MRI indices previously hypothesized as related to glymphatic function, white matter hyperintensities (WMH), and cognitive impairment in SVD.MethodsThis cross-sectional study included 133 SVD participants. DMV disruptions were visually rated on susceptibility-weighted imaging (SWI). Five MRI indices related to glymphatic function were measured: the diffusion tensor imaging along the perivascular space (DTI-ALPS), free water (FW) fraction, choroid plexus (Cp) volumes, perivascular spaces in basal ganglia (BG-PVS) and white matter (WM-PVS).ResultsHigher DMV scores were associated with higher WMH volumes (β = 0.55, p < 0.001) and lower Montreal Cognitive Assessment (MoCA) scores (β = -0.24, p = 0.003). Higher DMV scores were correlated with lower DTI-ALPS values, higher FW fraction, higher volumes in Cp, BG-PVS, and WM-PVS (all p < 0.001). DTI-ALPS values and BG-PVS volumes mediated the associations between DMV scores and WMH volumes, with only BG-PVS volumes mediating the associations between DMV scores and MoCA scores.ConclusionsOur results suggested that DMV disruptions contribute to WMH burden and cognitive impairment in SVD. This effect could be mediated by MRI markers indicative of glymphatic dysfunction, particularly the enlargement of BG-PVS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".