Does Covert Cerebral Small Vessel Disease Modulate Early Change in Glymphatic Flow from DTI-ALPS?
Bibliographic record
Abstract
Diffusion tensor image analysis along the perivascular space (DTI-ALPS) has emerged as a method for non-invasively assessing glymphatic flow from magnetic resonance diffusion-weighted images. This index has been applied to the study of neurodegenerative and other brain disorders. It has been found to decrease in most studies of neurodegenerative conditions. Decreases in DTI-ALPS also have been found in healthy adults with age. Changes by sex are less consistently observed but seem to suggest females have higher indices. We explored in a healthy adult cohort whether DTI-ALPS is affected by age and sex, or serves as a surrogate/biomarker of the presence of early, covert small vessel disease (SVD) (white matter hyperintensities, lacunes, microbleeds or enlarged perivascular spaces), and vascular risk factors (VRFs) like diabetes, hypertension, hyperlipidemia, or smoking. Separate linear regressions found a slower decrease in DTI-ALPS with age in females than males (-0.0034±0.0013 units/year vs -0.0048 ±0.0011 units/year, p<0.020 between slopes). DTI-ALPS was associated with SVD in one-way ANOVA testing (F=4.72, p=0.032) but not the presence of one or more VRFs (F=0.955, p=0.330). However, in a multivariable linear model only age was significant (-0.0041 units/year, p = 0.007). The other examined factors (sex, presence of SVD or VRF, and age×sex interaction) were all not significant. In conclusion, only older age was significantly associated with lower DTI-ALPS, suggesting a reduction in glymphatic flow with age. However, male sex, the presence of mild-to-moderate SVD pathology nor VRFs were linked to decreased glymphatic flow in a sample drawn from healthy community-dwelling adults.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".