Investigating the Associations Between the Perivascular Space, Age, and Cognitive Function in Cognitively Normal and Impaired Individuals
Bibliographic record
Abstract
Background and Hypothesis: Perivascular spaces (PVS) are a component of the glymphatic system, which is believed to help remove waste products from the brain. Dilated PVS have been correlated with normal aging and neurological conditions. In this study, we sought to evaluate the associations between age, PVS volume, and Montreal Cognitive Assessment (MoCA) score in cognitively normal participants (CN), as well as those with subjective cognitive decline (SCD), and mild cognitive impairment (MCI). We hypothesize that greater PVS volumes will be associated with lower MoCA scores. Methods: PVS delineation was conducted on MRI images of participants from the Indiana Alzheimer’s Disease Research Center (ages 62-89; with 9 CN, 5 SCD, 1 MCI). For image processing, the enhanced PVS contrast (EPC) was calculated by dividing T1-weighted images by T2-weighted images, followed by Frangi filter to capture the vessel-like PVS structure. Three blinded reviewers used the imaging software FSLeyes to analyze EPC and FLAIR images to correct the mask. Partial correlation was applied to PVS volume normalized to total brain volume and MoCA scores while controlling for age. Results: A partial correlation between MoCA score and PVS volume was -0.24 (p=0.40) after controlling for age. The sample sizes were not sufficient to investigate if these correlations varied among the different research groups. Scientific Implications: Our analysis suggests that enlarged PVS volume might contribute to decline in cognitive function, however, further work is needed to provide more insights to the pathologic alterations of PVS in individuals with cognitive complaints or impairment.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".