Amyloid and tau pathology are associated with white matter properties in cognitively unimpaired older adults at risk of AD dementia
Bibliographic record
Abstract
Abstract Background White matter changes are hypothesized to be among the earliest changes occurring in the course of Alzheimer’s disease (AD). We investigated associations between AD pathology, namely amyloid and tau, and white matter microstructural changes in cognitively unimpaired individuals at risk of AD dementia. We expected higher levels of pathology to be related to lower free‐water‐corrected fractional anisotropy. Method We studied 223 cognitively normal older adults with PET and multi‐shell diffusion MRI from the PREVENT‐AD cohort. We first measured standardized uptake value ratios (SUVRs) in 40 brain regions for amyloid‐ß (Aß, i.e., lateral and medial prefrontal, parietal, lateral temporal, and cingulate cortical regions in both hemispheres) and 26 brain regions for tau (i.e., Braak stages I, III, and IV). We then used TractSeg, a deep learning framework, to segment 49 well‐established white matter bundles. We mapped free‐water‐corrected fractional anisotropy (FAT) in those bundles. Lastly, to explore the relationships between AD pathology and white matter microstructure across bundles, we applied two separate partial least squares analyses: one for Aß pathology and one for tau (Figure 1). Result We found one significant latent variable relating Aß pathology to FAT. Specifically, higher levels of Aß pathology were associated with the combination of higher levels of FAT within the rostral body of the uncinate fascicle and superior longitudinal fascicle (Figure 2A). We found two significant latent variables relating tau pathology to FAT for which higher levels of tau pathology were associated with elevated FAT within the uncinate fascicle, thalamo‐occipital bundle, optic radiation, inferior occipitofrontal fascicle, inferior longitudinal fascicle, middle longitudinal fascicle, etc. (Figure 2B). Conclusion In cognitively unimpaired individuals at risk of AD, increased Aß and tau pathology were associated with higher levels of FAT in AD‐related bundles. These unexpected findings may suggest an inverted U‐shape pattern between AD pathology and FAT in the preclinical phase of AD for which FAT values would start by increasing before decreasing later in the course of the disease. This first increase might be a marker of early pathological processes such as neuroinflammation or swelling.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".