Microbleed Topography, Leukoaraiosis, and Cognition in Probable Alzheimer Disease From the Sunnybrook Dementia Study
Bibliographic record
Abstract
BACKGROUND: Microbleeds are hemosiderin deposits around small vessels and are well visualized with T2*-weighted gradient-recalled echo (GRE) imaging. OBJECTIVES: To determine frequency and topography of microbleeds in Alzheimer disease (AD) and to assess their association with leukoaraiosis and cognition. DESIGN: Case-control cross-sectional analysis. Microbleeds were counted using GRE imaging. Leukoaraiosis was rated on T2-weighted and proton density-weighted scans using the Age-Related White Matter Changes Rating Scale (ARWMC). Neuropsychological tests indexed cognition. SETTING: The Cognitive Neurology Clinic, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada. PATIENTS: Individuals with probable AD (n = 80) and healthy controls (n = 25) from a longitudinal cohort with GRE sequences as part of standard imaging protocol (2002-2006). RESULTS: Microbleeds occurred in 29% of patients with AD and 12% of controls and were multiple (> 1) in 48% of patients with AD and 33% of controls. There was lobar (vs centrencephalic) predominance in 92% of AD patients, with occipital lobes accounting for 57% of these microbleeds. The ARWMC scores (P < .005) were significantly higher in AD patients with microbleeds than in those without, and microbleeds correlated with total (r = 0.39, P = .01) and parietooccipital (r = 0.28, P < .01) ARWMC scores. We were unable to demonstrate an association between microbleeds (or leukoaraiosis) and cognitive performance. CONCLUSIONS: Occipital predominance of microbleeds with corresponding parietooccipital leukoaraiosis has not been well described in prior imaging studies of AD. Microbleeds were frequent, often multiple, and predicted greater leukoaraiosis. These findings illustrate the complexity of AD vasculopathy and the need for additional studies in dementia and stroke populations.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".