Amyloid and tau pathologies are drivers of white matter damage in aging and Alzheimer’s disease
Bibliographic record
Abstract
Abstract BACKGROUND White matter hyperintensities (WMHs) are increasingly recognized as markers of cerebrovascular pathology in Alzheimer’s disease (AD), yet their temporal relationship with amyloid and tau accumulation remains unclear. While previous studies suggest bidirectional associations between WMHs and AD pathology, regional associations between WMHs and AD pathology have yet to be examined. This study investigated the temporal and regional associations between PET measures of amyloid (Aβ) and tau pathology and WMH burden in older adults. METHODS Data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) included 1,241 older adults with Aβ and 636 with tau for cross-sectional analyses. Longitudinal analyses included 670 participants for Aβ change and 1,079 for WMH change (Aβ group), and 199 for tau change and 356 for WMH change (tau cohort). Linear models were used to i) assess associations between baseline regional WMH and Aβ and tau pathology, and ii) examine whether baseline pathology in one measure was associated with change in the other measure over two years. RESULTS Baseline analyses revealed significant bidirectional associations between WMH burden and both Aβ ( t =2.09-4.16, p <.05) and tau pathology ( t =2.44-2.87, p <.04), with stronger effects in posterior brain regions. Longitudinal analyses showed that baseline Aβ levels were associated with future WMH progression in frontal and occipital regions ( t =2.44-3.27, p <.03), while baseline tau was linked to WMH increases in frontal and parietal regions ( t =2.48-3.51, p <.03). However, baseline WMH burden was not associated with future accumulation of either Aβ or tau pathology in any region. CONCLUSIONS These findings suggest that Aβ and tau pathology drive future WMH progression rather than the reverse, with distinct regional patterns for each pathology type.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".