Tau-PET imaging predicts cognitive decline and brain atrophy progression in early Alzheimer’s disease
Bibliographic record
Abstract
OBJECTIVES: To explore whether regional tau binding measured at baseline is associated with the rapidity of Alzheimer's disease (AD) progression over 2 years, as assessed by the decline in specified cognitive domains, and the progression of regional brain atrophy, in comparison with amyloid-positron emission tomography (PET), MRI and cerebrospinal fluid (CSF) biomarkers. METHODS: F]-flortaucipir PET imaging, and were monitored annually over 2 years, with a second brain MRI after 2 years. We used mixed effects models to explore the relations between tau-PET, amyloid-PET, CSF biomarkers and MRI at baseline and cognitive decline and the progression of brain atrophy over 2 years in patients with AD. RESULTS: Baseline tau-PET was strongly associated with the subsequent cognitive decline in regions that are usually associated with each cognitive domain. No significant relationship was observed between the cognitive decline and initial amyloid load, regional cortical atrophy or CSF biomarkers. Baseline tau tracer binding in the superior temporal gyrus was associated with subsequent atrophy in an inferomedial temporal volume of interest, as was the voxelwise tau tracer binding with subsequent cortical atrophy in the superior temporal, parietal and frontal association cortices. CONCLUSIONS: These results suggest that tau tracer binding is predictive of cognitive decline in AD in domain-specific brain areas, which provides important insights into the interaction between tau burden and neurodegeneration, and is of the utmost importance to develop new prognostic markers that will help improve the design of therapeutic trials.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".