Neuroinflammation and Tau Colocalize in vivo in Progressive Supranuclear Palsy
Bibliographic record
Abstract
Objective We examined the relationship between tau pathology and neuroinflammation using [ 11 C]PK11195 and [ 18 F]AV‐1451 PET in 17 patients with progressive supranuclear palsy (PSP) Richardson's syndrome. We tested the hypothesis that neuroinflammation and tau protein aggregation colocalize macroscopically, and correlate with clinical severity. Methods Nondisplaceable binding potential (BP ND ) for each ligand was quantified in 83 regions of interest (ROIs). The [ 11 C]PK11195 and [ 18 F]AV‐1451 BP ND values were correlated across all regions. The spatial distributions of [ 11 C]PK11195 and [ 18 F]AV‐1451 binding were determined by principal component analyses (PCAs), and the loading of each spatial component compared against the patients’ clinical severity (using the PSP rating scale). Results Regional [ 11 C]PK11195 and [ 18 F]AV‐1451 binding were positively correlated (R = 0.577, p < 0.0001). The PCA identified 4 components for each ligand, reflecting the relative expression of tau pathology or neuroinflammation in distinct groups of brain regions. Positive associations between [ 11 C]PK11195 and [ 18 F]AV‐1451 components’ loadings were found in both subcortical (R = 0.769, p < 0.0001) and cortical regions (R = 0.836, p < 0.0001). There were positive correlations between clinical severity and both subcortical tau pathology (R = 0.667, p = 0.003) and neuroinflammation (R = 0.788, p < 0.001). Interpretation We show that tau pathology and neuroinflammation colocalize in PSP, and that individual differences in subcortical tau pathology and neuroinflammation are linked to clinical severity. Although longitudinal studies are needed to determine causal associations between these molecular pathologies, we suggest that the combination of tau‐ and immune‐oriented strategies may be useful for effective disease‐modifying treatments in PSP. ANN NEUROL 2020;88:1194–1204
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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 | 0.000 | 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 | 0.001 |
| 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".