Dilated brain perivascular spaces and their role in Alzheimer disease
Bibliographic record
Abstract
Abstract Background Dilated small perivascular spaces (SPVS) have gained interest as MRI markers of cerebrovascular disease and possibly dementia. Method We leveraged existing data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) to extract information regarding participants’ demographics, vascular risks, cognitive profile, APOE4 genotype and PET imaging for beta amyloid (AV‐45) and phospho tau (AV‐1451). For cognition and dementia status, we used the total Montreal Cognitive Assessment (MOCA), Alzheimer's Disease Assessment Scale–Cognitive Subscale (ADAS‐Cog), and clinical dementia rating (CDR) scores. We used composite gray matter regions for PET measurements of amyloid and tau. We rated SPVS using a previously validated and reliable visual scale that assesses 14 brain regions for presence of SPVS. Each region was given a score of 0 (no SPVS), 1 (1‐3 SPVS), or 2(>3 SPVS). Regional scores were combined to create global SPVS scores. We built multivariate regression models using sex, age, education (in years), hypertension, prior stroke, white matter hypointensities (WMH) volume and APOE4 carrier status. Result We included 1,278 participants (mean age 75±8, 48%women, 89% non‐Hispanic white). SPVS were present in 85% of participants (median 6, range 0‐25). In multivariate analysis, SPVS score was associated with older age (B=0.05, P=0.01), history of stroke (B=6.9, P<0.001), WMH (B=0.33 per standard deviation, P=0.05) and marginally with hypertension (B=0.51, P=0.08). SPVS score, however, was associated with a higher MOCA score (B=0.06, P=0.03). There were no associations between SPVS with total ADAS score (B=‐0.02, P=0.71), CDR score (B= ‐0.03, P=0.07), amyloid (B=0.001, P=0.94) or tau (B=0.01, P=0.45). Conclusion Brain SPVS are associated with cerebrovascular disease and aging, but their additional value compared to other imaging markers of cerebrovascular disease as predictors of poorer cognition, dementia or Alzheimer disease is not supported by these data.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".