Stages of tau aggregation associated with amyloidosis reflected in non‐negative matrix factorization components
Bibliographic record
Abstract
Abstract Background Tau aggregation, when accompanied by amyloid deposition, is a key neuropathological signature of Alzheimer’s disease (AD). The regional distribution of tau may be a more sensitive marker of AD pathology than total tau level, however the spread of disease is unlikely to conform to common atlas‐based regions of interest. As such, methods for evaluating tau’s regional distribution could be useful for predicting AD staging or disease progression. In this analysis, we derive regional patterns of tau aggregation in AD using nonnegative matrix factorization (NNMF), a data‐driven method of identifying areas of the brain that consistently covary across individuals. Method We used data from 269 ‐amyloid (A) positive subjects with 18F‐flortaucipir‐PET data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database and the AVID study (138 females, mean age = 73.09; 110 healthy controls, 118 MCI, and 41 AD). PET data were registered to Standard Montreal Neurological Institute (MNI152) 182x218x182 grid with 1mm resolution. SUVR maps were computed with a cerebellar gray matter reference and NNMF was used to estimate common patterns of tau aggregation. Once these patterns were identified, mean SUVR for each component computed for each participant. We performed Gaussian mixture modeling for each component to determine optimal tau positivity cutpoint, and subjects were classified as positive or negative for each component. Components were ordered by frequency of appearance in subjects in order to determine staging. Result 12 regional patterns of tau binding were identified (Figure 1). The patterns most commonly expressed among subjects reflected regions of the temporal lobe (Figure 2a). This was particularly noted in those with AD (Figure 2b). These components were followed by a component including the lingual gyrus, and then by components involving frontal lobes, precuneus, and parietal lobes. Conclusion NNMF‐derived tau PET components provide a data‐driven method for identifying common patterns of tau aggregation, producing stages that are consistent with previous research on tau pathology in AD. These components may provide a more sensitive method for identifying disease progression in AD.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".