Assessment of Tau Pathology as Measured by 18F-THK5317 and 18F-Flortaucipir PET and Their Relation to Brain Atrophy and Cognition in Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: In Alzheimer's disease (AD), the abnormal aggregation of hyperphosphorylated tau leads to synaptic dysfunction and neurodegeneration. Recently developed tau PET imaging tracers are candidate biomarkers for diagnosis and staging of AD. OBJECTIVE: We aimed to investigate the discriminative ability of 18F-THK5317 and 18F-flortaucipir tracers and brain atrophy at different stages of AD, and their respective associations with cognition. METHODS: Two cohorts, each including 29 participants (healthy controls [HC], prodromal AD, and AD dementia patients), underwent 18F-THK5317 or 18F-flortaucipir PET, T1-weighted MRI, and neuropsychological assessment. For each subject, we quantified regional 18F-THK5317 and 18F-flortaucipir uptake within six bilateral and two composite regions of interest. We assessed global brain atrophy for each individual by quantifying the brain volume index, a measure of brain volume-to-cerebrospinal fluid ratio. We then quantified the discriminative ability of regional 18F-THK5317, 18F-flortaucipir, and brain volume index between diagnostic groups, and their associations with cognition in patients. RESULTS: Both 18F-THK5317 and 18F-flortaucipir outperformed global brain atrophy in discriminating between HC and both prodromal AD and AD dementia groups. 18F-THK5317 provided the highest discriminative ability between HC and prodromal AD groups. 18F-flortaucipir performed best at discriminating between prodromal and dementia stages of AD. Across all patients, both tau tracers were predictive of RAVL learning, but only 18F-flortaucipir predicted MMSE. CONCLUSION: Our results warrant further in vivo head-to-head and antemortem-postmortem evaluations. These validation studies are needed to select tracers with high clinical validity as biomarkers for early diagnosis, prognosis, and disease staging, which will facilitate their incorporation in clinical practice and 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".