Visible Virchow-Robin Spaces on Magnetic Resonance Imaging of Alzheimer's Disease Patients and Normal Elderly from the Sunnybrook Dementia Study
Bibliographic record
Abstract
BACKGROUND: Visible Virchow-Robin spaces (VRS) are commonly used markers for small vessel disease in aging and dementia. OBJECTIVE: However, as previous reports were based on subjective visual ratings, the goal of this project was to validate and apply an MRI-based quantitative measure of VRS as a potential neuroimaging biomarker. METHODS: A modified version of Lesion Explorer was applied to MRIs from Alzheimer's disease patients (AD: n = 203) and normal elderly controls (NC: n = 94). Inter-rater reliability, technique validity, group/gender differences, and correlations with other small vessel disease markers were examined (lacunes and white matter hyperintensities, WMH). RESULTS: Inter-rater reliability and spatial congruence was excellent (ICC = 0.99, SI = 0.96), and VRS volumes were highly correlated with established rating scales (CS: ρ = 0.84, p < 0.001; BG: ρ = 0.75, p < 0.001). Compared to NC, AD had significantly greater volumes of WMH (p < 0.01), lacunes (p < 0.001), and VRS in the white matter (p < 0.01), but not in the basal ganglia (n.s.). Compared to women, demented and non-demented men had greater VRS in the white matter (p < 0.001), but not in the basal ganglia (n.s.). Additionally, VRS were correlated with lacunes and WMH, but only in AD (r = 0.3, p < 0.01). CONCLUSION: Compared to women, men may be more susceptible to greater volumes of VRS, particularly in the white matter. RESULTS support the hypothesis that VRS in the white matter may be more related to AD-related vascular pathology compared to VRS found in the basal ganglia. Future work using this novel VRS segmentation tool will examine its potential utility as an imaging biomarker of vascular rather than parenchymal amyloid.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".