Regional correlations between [11C]PIB PET and post-mortem burden of amyloid-beta pathology in a diverse neuropathological cohort
Bibliographic record
Abstract
Imaging-pathological correlation studies show that in vivo amyloid-β (Aβ) positron emission tomography (PET) strongly predicts the presence of significant Aβ pathology at autopsy. We sought to determine whether regional PiB-PET uptake would improve sensitivity for amyloid detection in comparison with global measures (experiment 1), and to estimate the relative contributions of different Aβ aggregates to in vivo PET signal (experiment 2). In experiment 1, 54 subjects with [11C] PiB-PET during life and postmortem neuropathologic examination (85.2% with dementia, interval from PET to autopsy 3.1 ± 1.9 years) were included. We assessed Thal amyloid phase (N = 36) and CERAD score (N = 54) versus both global and regional PiB SUVRs. In experiment 2 (N = 42), PiB SUVR and post-mortem amyloid β burden was analyzed in five customized regions of interest matching regions sampled at autopsy. We assessed the relative contribution of neuritic plaques (NPs), diffuse plaques (DPs) and cerebral amyloid angiopathy (CAA) to regional PIB SUVR using multi-linear regression. In experiment 1, there were no differences in Area Under the Curve for amyloid phase ≥ A2 and CERAD score ≥ C2 between global and highest regional PiB SUVR (p = 0.186 and 0.230). In experiment 2, when NPs, DPs, and/or CAA were included in the same model, moderate to severe NPs were independently correlated with PiB SUVR in all regions except for the inferior temporal and calcarine ROI (β = 0.414–0.804, p < 0.05), whereas DPs were independently correlated with PiB SUVR in the angular gyrus ROI (β = 0.446, p = 0.010). CAA was also associated with PiB SUVR in the inferior temporal and calcarine ROI (β = 0.222–0.355, p < 0.05). In conclusion, global PiB-PET SUVR performed as well as regional values for amyloid detection in our cohort. The substrate-specific binding of PiB might differ among the brain specific regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".