Comparing and combining TSPO-PET tracers in tauopathies
Bibliographic record
Abstract
Abstract Purpose Neuroinflammation is a key pathological driver in neurodegenerative diseases, including Alzheimer’s disease (AD) and Progressive Supranuclear Palsy (PSP). Positron emission tomography (PET) with tracers targeting the translocator protein (TSPO) enables the in vivo quantification of microgliosis. Different TSPO tracers have shown similar patterns across disease-specific cohorts. However, direct quantitative comparisons between commonly used TSPO-PET tracers in tauopathies have not been performed. Here, we apply our TSPO-PET standardization pipeline across clinically matched AD and PSP cohorts, to quantify, compare and combine multi-centre TSPO-PET data. Methods Patients with PSP were scanned with either [ 11 C]PK11195 or [ 18 F]GE-180 at one of two centres, while patients with AD and control participants were scanned with either [ 11 C]PK11195, [ 18 F]GE-180 or [ 11 C]PBR28 at one of three centres. A standardised pre-processing pipeline was implemented and participant standardised uptake volume ratio (SUVR) values were z-scored using tracer-specific control participant values. In a data-driven approach, dissimilarity analyses were employed to assess differences between tracers across clinically matched cohorts. Results In PSP, dissimilarity analysis suggested that [ 11 C]PK11195 and [ 18 F]GE-180 were comparable following standardisation. In AD, comparability across tracers was less robust, with [ 11 C]PK11195 and [ 18 F]GE-180 being most comparable, followed by [ 18 F]GE-180 vs [ 11 C]PBR28, then by [ 11 C]PK11195 vs [ 11 C]PBR28. Conclusion The pipeline was effective at harmonising TSPO-PET tracers and standardising the regional quantification of neuroinflammation in clinically matched cohorts of PSP, while the standardisation pipeline results were less robust across AD cohorts.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".