Tau‐related reduction of glucose metabolism in mild cognitive impairment occurs independently of <i>APOE</i> ε4 genotype and is influenced by Aβ
Bibliographic record
Abstract
INTRODUCTION: Positron emission tomography (PET) imaging studies have shown that amyloid beta (Aβ) is significantly correlated with glucose metabolism in mild cognitive impairment independently of the apolipoprotein E (APOE) ε4 genotype. METHODS: We used a singular value decomposition (SVD) approach to pairwise cross-correlation among tau, Aβ, and fluorodeoxyglucose PET images. The resulting SVD-based tau and Aβ scores as well as the APOE ε4 genotype, were entered as predictors in a voxelwise general linear model for statistical assessment of their effect on FDG. RESULTS: We found cortical regions where a reduced glucose metabolism was maximally correlated with distributed patterns of tau, accounting for the effect of Aβ and APOE ε4 genotype. DISCUSSION: By highlighting the more significant role of tau, rather than Aβ, in the reduction of glucose metabolism, our results provide a better understanding of their combined effect in the development and progression of Alzheimer's disease. HIGHLIGHTS: This study uses a data-driven singular value decomposition approach to the cross-correlation matrix between tau and fluorodeoxyglucose (FDG) images, as well as between FDG and amyloid beta (Aβ) positron emission tomography (PET) images. From a population of mild cognitive impairment subjects, we found that spatially distributed scores of tau PET are associated with an even stronger reduction of glucose metabolism, independent of the apolipoprotein E ε4 genotype and confounded by Aβ. By highlighting the more significant role of tau, rather than Aβ, on the reduction of glucose metabolism, our results provide a better understanding of their combined effects in the development of Alzheimer's disease.
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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 | 0.001 |
| 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".