Characterizing the spatiotemporal variability of Alzheimer’s disease pathology
Bibliographic record
Abstract
Abstract Alzheimer’s disease (AD) is characterized by the progressive spread of tau pathology throughout the cerebral cortex. The pattern of spread is thought to be fairly consistent across individuals, though more recent work has demonstrated substantial variability in the AD population that is often associated with distinct clinical phenotypes. Still, a systematic, unbiased, wholebrain characterization of spatiotemporal variation in tau deposition in AD is lacking. We analyzed 1612 tau-PET scans and applied to this sample a disease progression modeling framework designed to identify spatiotemporal trajectories of pathological progression. We identified four distinct trajectories of tau progression, ranging in prevalence from 18–33%, with no one progession predominating. We replicated previously described limbic-predominant and medial temporal lobe-sparing variants, while also discovering posterior and lateral temporal subtypes resembling atypical clinical variants of AD. These “subtypes” were stable during longitudinal follow-up, and could be replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes, however, no “typical” variant predominated. Across all subtypes, younger age was related to worse cognition and more rapid tau accumulation. Additionally, network diffusion models implicated that pathology originates and spreads through distinct corticolimbic in the different subtypes. Together, our results suggest variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of “typical AD”, and a revisiting of tau pathological staging.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".