Relationship between Perivascular Space Burden, White Matter Hyperintensities, and Cognitive Function
Bibliographic record
Abstract
Introduction: Perivascular space (PVS) is believed to be involved in clearing metabolic waste from the brain. PVS has been found to increase in size in patients with Alzheimer’s disease, but the relationship between PVS enlargement and cognitive function still requires further investigation. This study seeks to clarify the relationship between Montreal Cognitive Assessment (MoCA) scores and the percentage of brain volume occupied by perivascular space. We take into account confounding factors such as white matter hyperintensities (WMH), a biomarker associated with cognitive decline. We hypothesize that PVS enlargement leads to decreased brain waste clearing efficiency, which subsequently leads to cognitive impairment. Methods: The included participants (n = 15, ages 62 to 89, female: n = 9) were recruited for the 3YWU study from the Indiana Alzheimer's Disease Research Center. The sample included the control group (n = 9) and patients with mild cognitive impairment (n = 1) and subjective cognitive decline (n = 5). For imaging processing, the enhanced PVS contrast (EPC) was calculated by dividing T1-weighted images by T2-weighted images, followed by applying the Frangi filter to capture the vessel-like PVS structure. Three trained and blinded reviewers then further manually improved the PVS masks. For the analysis, we calculated the correlation coefficients between the Montreal Cognitive Assessment (MoCA) scores and normalized PVS volume, as well as a partial correlation coefficient while controlling for the normalized WMH volume. Results. We found a negative association between the PVS volume and MoCA scores of -0.28 (p=0.31), and after controlling for WMH, we still observed a negative correlation of -0.34 (pvalue of 0.23). Conclusion. Our results suggest that PVS enlargement is a possible factor in cognitive impairment. However, further investigation is necessary to characterize these correlations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".