Magnetic Resonance Imaging-Negative Cerebral Amyloid Angiopathy: Cerebrospinal Fluid Amyloid-β42 over Amyloid Positron Emission Tomography
Bibliographic record
Abstract
BACKGROUND: Cerebral amyloid angiopathy (CAA) pathology is becoming increasingly important in Alzheimer's disease (AD) because of its potential link to amyloid-related imaging abnormalities, a critical side effect observed during AD immunotherapy. Identification of CAA without typical magnetic resonance imaging (MRI) markers (MRI-negative CAA) is challenging, and novel detection biomarkers are needed. METHODS: We included 69 participants with high neuritic plaques (NP) burden, with and without CAA pathology (NP with CAA vs. NP without CAA) based on autopsy data from the Alzheimer's Disease Neuroimaging Initiative. Two participants with hemorrhagic CAA markers based on MRI were excluded and the final analysis involved 36 NP without CAA and 31 NP with CAA. A logistic regression model was used to compare the cerebrospinal fluid (CSF) amyloid-β42 (Aβ42), phosphorylated tau181, and total tau levels, the amyloid positron emission tomography (PET) standardized uptake ratio (SUVR), and cognitive profiles between NP with and without CAA. Regression models for CSF and PET were adjusted for age at death, sex, and the last assessed clinical dementia rating sum of boxes score. Models for cognitive performances was adjusted for age at death, sex, and education level. RESULTS: NP with CAA had significantly lower CSF Aβ42 levels when compared with those without CAA (110.5 pg/mL vs. 134.5 pg/mL, p-value = 0.002). Logistic regression analysis revealed that low CSF Aβ42 levels were significantly associated with NP with CAA (odds ratio [OR]: 0.957, 95% confidence interval [CI]: 0.928, 0.987, p-value = 0.005). However, amyloid PET SUVR did not differ between NP with CAA and those without CAA (1.39 vs. 1.48, p-value = 0.666). Logistic regression model analysis did not reveal an association between amyloid PET SUVR and NP with CAA (OR: 0.360, 95% CI: 0.007, 1.741, p-value = 0.606). CONCLUSIONS: CSF Aβ42 is more sensitive to predict MRI-negative CAA in high NP burden than amyloid PET.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".