Longitudinal progression and harmonization of tau‐PET tracers
Bibliographic record
Abstract
BACKGROUND: Tau-PET tracers have been used to monitor the progression of Alzheimer's disease (AD). However, different tracers present distinct patterns of binding throughout the brain, challenging the harmonization of their findings. Leveraging the HEAD Study, the largest head-to-head study of tau-PET tracers, we recently developed the Uniτ scale, which cross-sectionally harmonizes Flortaucipir and MK6240 onto a universal tau-PET measurement. Here, we provide a preliminary evaluation of the Uniτ scale's longitudinal performance in HEAD and two independent cohorts. METHODS: We assessed 422 individuals across the AD spectrum with longitudinal tau-PET from three cohorts: HEAD [13 cognitively unimpaired (CU), 9 cognitively impaired (CI) individuals, scanned head-to-head with Flortaucipir and MK6240], ADNI [74 CU, 208 CI, tracer: Flortaucipir], and TRIAD [72 CU, 46 CI, tracer: MK6240]. Standardized uptake ratios (SUVRs) were harmonized to Uniτ using the Uniτ Ecosystem (unitau.app). Braak I-II and the Meta-Temporal regions were used as regions of interest. Annual tau-PET uptake change was calculated, and the effect size was defined as the mean annual change divided by its standard deviation. RESULTS: In HEAD, annual change in tau-PET uptake in Braak I-II across CU and CI was only detectable in MK6240 (Figure 1A). However, both tracers detected significant annual changes in the Meta-Temporal ROI for CI (Figure 1B). In both ADNI (Flortaucipir) and TRIAD (MK6240), changes in tau-PET uptake in Braak I-II regions were not significant (Figures 2-3A). However, across all cohorts, the Meta-Temporal ROI consistently showed detectable annual tau-PET increases-most pronounced among CI-leading to larger effect sizes than in Braak I-II (Figures 1-3B). The Uniτ harmonization did not fundamentally alter the pattern of the findings. However, in ADNI (Flortaucipir), Uniτ yielded a slightly higher effect size than SUVR in CU for both Braak I-II and Meta-Temporal regions. CONCLUSION: In this large longitudinal sample, our findings confirm that Flortaucipir and MK6240 can detect tau PET changes over time likely associated with the progression of tau tangle pathology. While MK6240 appears to show greater progression in CU, both tracers progress similarly in CI. Our data also suggested that Uniτ harmonized tau PET measurements maintain the longitudinal characteristics of each tau PET tracer for use in clinical 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 | 0.049 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".