Assessment of the Virchow-Robin Spaces in Alzheimer Disease, Mild Cognitive Impairment, and Normal Aging, Using High-Field MR Imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: VRSs are the perivascular spaces surrounding the deep perforating arteries in the brain. Although VRS variations with age and disease pathologies have been reported previously, the radiologic characteristics of the VRS in relation to AD are poorly understood. This study investigated the prevalence, spatial distribution, and severity of the VRS in AD, MCI, and older adults who were CN. It also investigated the relationship of the VRS to white matter changes. MATERIALS AND METHODS: Structural MR imaging data were acquired from 158 participants (AD = 37, MCI = 71, CN = 50, mean age = 74.97 ± 7.20 years) who had undergone T1WI at 3T. The severity of VRS in the white matter, basal ganglia, hippocampus, and brain stem structures was evaluated by using a semiquantitative scale, adapted from existing rating scales. A VRS total score summarizing the subscales was calculated to assess the whole-brain VRSs. RESULTS: VRSs were observed in multiple brain regions of all participants, typically presented as <2-mm well-margined symmetric round-, oval- and linear-shaped hypointensities on T1WI. The VRS total score increased with leukoaraiosis, atrophy, and advanced age (P < .001). Individuals with AD and MCI showed greater levels of VRS than control subjects. The VRS total score discriminated individuals with AD and those who were CN with an accuracy of 0.79 (95% CI, 0.69-0.89). CONCLUSIONS: VRSs are common in older adults and are more severe in AD and MCI than in CN. Whether increased VRSs can be reliably used to aid in AD diagnosis warrants further investigation.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".