Glymphatic system dysfunction mediates the relationship between deep medullary vein alterations and cognitive impairment in cerebral small vessel disease
Bibliographic record
Abstract
BACKGROUND: This study investigates how structural changes in deep medullary vein (DMV), glymphatic system dysfunction, and cognitive decline are interconnected in cerebral small vessel disease (CSVD), with a focus on whether impaired glymphatic function acts as a mediator in this relationship. METHODS: Clinical and MRI data from 93 CSVD patients were retrospectively analyzed. DMV burden was assessed using a semiquantitative scoring system (0-3 points per region), based on the visibility of DMVs in six anatomical regions on susceptibility-weighted imaging, yielding a total score ranging from 0 to 18. Glymphatic system function was evaluated using the diffusion tensor image analysis along the perivascular space (DTI-ALPS) index. Global cognitive function was assessed with the Montreal Cognitive Assessment (MoCA). Spearman correlation analysis, general linear modeling, and mediation analysis were conducted to examine the relationships among the variables. RESULTS: DMV scores(which higher scores indicate poorer venous visibility)were significantly negatively correlated with MoCA scores (r = -0.48, p< 0.001) and with the DTI-ALPS index (r = -0.28, p < 0.001), while the DTI-ALPS index was positively correlated with MoCA scores (r= 0.35, p < 0.05). Mediation analysis indicated that the DTI-ALPS index partially mediated the effect of DMV burden on cognitive performance, accounting for 14.08% of the total effect. CONCLUSIONS: This study suggests that DMV structural abnormalities may exacerbate CSVD-related cognitive impairment by disrupting glymphatic function. DMV scoring may serve as a potential imaging biomarker, providing a foundation for early identification and intervention.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".