A pipeline for comparing and combining TSPO‐PET tracers in Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Neuroinflammation is a key pathological driver of neurodegenerative diseases, including Alzheimer's disease (AD). Positron emission tomography (PET) with tracers targeting the translocator protein (TSPO) enables the in vivo quantification of microglial activation. Currently, direct comparison between TSPO‐PET tracers in AD have not been performed. Here, we tested a pipeline to quantitatively compare different TSPO‐PET tracers in clinically‐matched cohorts of patients with AD across multi‐centre data. Method 32 people with AD and 15 controls underwent [ 11 C]PK11195‐PET at the University of Cambridge, 45 people with AD and 19 controls underwent [ 18 F]GE180‐PET at Ludwig‐Maximilians‐University of Munich, and 25 people with AD and 25 controls underwent [ 11 C]PBR28‐PET at McGill University. Participants across the centres were matched for age, sex, and clinical severity. Pre‐processing of scans was harmonised across centres, and regional SUVr of tracers were obtained using a shared reference region and atlas. Z‐scores of regional SUVr values for each participant were calculated based on centre‐specific controls. Dissimilarity and clustering analyses were performed to assess the effectiveness of the standardisation pipeline. Figure 1 outlines the methodology. Result Clustering analyses identified no tracer‐specific patterns in the distribution of z‐scores following standardisation. Across all tracers, regional z‐scores of the AD groups were significantly different between tracers in 7 of 41 brain regions, while no differences were found for controls (Figure 2). Full factorial analysis found a main effect of tracer; however, these were due to interaction effects with disease group, sex, age, and brain region and explained very little of the variance. Pattern similarity between representational similarity matrices found moderate correlations between the three tracers in patient and control groups. Conclusion These results suggest that our pipeline is effective at harmonising TSPO‐PET tracers and standardising the regional quantification of microglial activation in the context of different AD cohorts. Dissimilarity analyses identified small tracer‐specific effects, however. Ongoing work aims to optimize this pipeline in order to compare and combine TSPO‐PET tracers in other tauopathies (ie PSP) and to identify thresholds of “inflammation severity” related to clinical outcomes.
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".