Data‐driven approaches for tau‐PET imaging biomarkers in Alzheimer's disease
Bibliographic record
Abstract
Abstract Previous positron emission tomography (PET) studies have quantified filamentous tau pathology using regions‐of‐interest (ROIs) based on observations of the topographical distribution of neurofibrillary tangles in post‐mortem tissue. However, such approaches may not take full advantage of information contained in neuroimaging data. The present study employs an unsupervised data‐driven method to identify spatial patterns of tau‐PET distribution, and to compare these patterns to previously published “pathology‐driven” ROIs. Tau‐PET patterns were identified from a discovery sample comprised of 123 normal controls and patients with mild cognitive impairment or Alzheimer's disease (AD) dementia from the Swedish BioFINDER cohort, who underwent [ 18 F]AV1451 PET scanning. Associations with cognition were tested in a separate sample of 90 individuals from ADNI. BioFINDER [ 18 F]AV1451 images were entered into a robust voxelwise stable clustering algorithm, which resulted in five clusters. Mean [ 18 F]AV1451 uptake in the data‐driven clusters, and in 35 previously published pathology‐driven ROIs, was extracted from ADNI [ 18 F]AV1451 scans. We performed linear models comparing [ 18 F]AV1451 signal across all 40 ROIs to tests of global cognition and episodic memory, adjusting for age, sex, and education. Two data‐driven ROIs consistently demonstrated the strongest or near‐strongest effect sizes across all cognitive tests. Inputting all regions plus demographics into a feature selection routine resulted in selection of two ROIs (one data‐driven, one pathology‐driven) and education, which together explained 28% of the variance of a global cognitive composite score. Our findings suggest that [ 18 F]AV1451‐PET data naturally clusters into spatial patterns that are biologically meaningful and that may offer advantages as clinical tools.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".