Imaging pathological tau in atypical parkinsonisms: A review
Bibliographic record
Abstract
Atypical parkinsonisms (APs) are a group of diseases linked to tau pathology. These include progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). In the initial stages, these APs may have similar clinical manifestations to Parkinson's disease (PD) and other parkinsonisms: bradykinesia, postural instability, tremor, and cognitive decline. Because of this, one major hurdle is the accurate early diagnosis of APs. Recent advances in positron emission tomography (PET) radiotracer development have allowed for targeting pathological tau in Alzheimer's disease (AD). Currently, work is still in progress for identifying a first-in-class radiotracer for imaging tau in APs. In this review, we evaluate the literature on in vitro and in vivo testing of current tau PET radiotracers in APs. The tau PET tracers assessed include both first-generation tracers ([18F]AV-1451, [18F]FDDNP, [18F]THK derivatives, and [11C]PBB3) and second-generation tracers ([18F]PM-PBB3, [18F]PI-2620, [18F]RO-948, [18F]JNJ-067, [18F]MK-6240, and [18F]CBD-2115). Concerns regarding off-target binding to cerebral white matter and the basal ganglia are still prominent with first-generation tracers, but this seems to have been mediated in a handful of second-generation tracers, including [18F]PI-2620 and [18F]PM-PBB3. Additionally, these two tracers and [18F]MK-6240 show promising results for imaging PSP- and CBD-tau. Overall, [18F]AV-1451 is the most widely studied tracer but the mixed results regarding its efficacy for use in imaging AP-tau is a cause for concern moving forward. Instead, future work may benefit from focusing on the second-generation radiotracers which seem to have a higher specificity for AP-tau than those originally developed for imaging AD-tau.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".