Staging of tau accumulation associated with cognitive decline in AD progression using 18F‐MK‐6240 PET data
Bibliographic record
Abstract
Abstract Background Alzheimer's disease is characterized by the accumulation of amyloid beta and the formation of tau neurofibrillary tangles (NFTs), leading to irreversible neurodegeneration. The formation of NFTs is believed to follow a stereotypical pattern known as Braak stages. Here, using tau‐PET tracer 18F‐MK‐6240 we aim to analyze patterns of Tau accumulation associated with AD‐related cognitive decline and build an in‐vivo, data‐driven staging system based on longitudinal data, using an estimated latent time of disease onset based on cognitive scores to place all subjects on a common timeline. Method We used 18F‐MK‐6240 scans from the TRIAD dataset, including 194 cognitively normal, 99 mild cognitive impairment and 77 with Alzheimer’s disease dementia. We used a trajectory model (Kühnel et al. 2021) to align patients based on their longitudinal cognitive scores along a continuous latent disease timeline. ADAS‐cog‐13, CDR‐SB and MMSE were used simultaneously to estimate time‐shifts for each subject. As there were not enough longitudinal time‐points in the TRIAD dataset to directly apply this method, we first applied the method to the full ADNI dataset and then used a nearest‐neighbour technique to impute the disease offset for TRIAD subjects from the closest 38 subjects in the ADNI cohort. (n=38 was found to be optimal through cross‐validation within ADNI.) This supervised imputation model used baseline cognitive test scores (MMSE and CDR‐SB) along with the age of participants to impute their latent disease onset. Result We defined 5 2‐year windows on the 10‐year span of the estimated latent disease offset timeline from 4y before onset and up to 6y afterwards. Tau PET scans for subjects within each window were averaged, resulting in 5 average Tau templates, in addition to an initial template generated using amyloid negative normal participants. This staging system depicts the incremental tau accumulation along with the decline in cognition. Medial temporal regions show initial accumulation, starting in transentorhinal and entorhinal cortices, and later stages show full brain involvement. Conclusion Using purely data‐driven techniques, this method reveals patterns of tau accumulation associated with cognitive decline. These models will help understand the link between Tau and cognitive decline, even for those subjects that don't fit Braak framework.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".