Increased Diameters of the Internal Cerebral Veins and the Basal Veins of Rosenthal Are Associated with White Matter Hyperintensity Volume
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: White matter hyperintensities on T2-weighted MR imaging are typical in older adults and have been linked to several poor health outcomes, including cognitive impairment and Alzheimer disease. The presence and severity of white matter hyperintensities have traditionally been attributed to occlusive arteriopathy, but recent evidence also implicates deep medullary venule collagenosis and associated vasogenic edema. Historically, postmortem analyses have been the sole way to analyze cerebral veins, but SWI can be now used to examine cortical veins in vivo. The aim of the current study was to determine whether there is an association between the diameters of the large draining cerebral veins/sinuses and white matter hyperintensity volume. MATERIALS AND METHODS: T2-weighted FLAIR and SWI were performed in 682 older adults without dementia (mean age, 73.9 ± 5.9 years; 59.1% women). Total and regional white matter hyperintensity volume was derived. We measured the diameters of 5 regions of the cerebral venous draining system: internal cerebral veins, basal veins of Rosenthal, superior sagittal sinus, vein of Galen, and straight sinus terminus. RESULTS: Increased diameter of the internal cerebral veins was associated with greater total white matter hyperintensity volume (β = 0.09, P = .02) and regionally in the parietal (β = 0.10, P = .006), frontal (β = 0.09, P = .02), and temporal (β = 0.09, P = .02) lobes. Increased diameter of the basal veins of Rosenthal was associated with greater total (β = 0.10, P = .01), frontal (β = 0.11, P = .003), and temporal (β = 0.09, P = .02) white matter hyperintensity volume. CONCLUSIONS: Our results suggest that the caliber of the internal cerebral veins and of the basal veins of Rosenthal relates to regional white matter disease.
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) | 0.002 | 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".