Intrinsic connectivity of the human brain provides scaffold for tau aggregation in clinical variants of Alzheimer's disease
Bibliographic record
Abstract
Abstract Background Preclinical models suggest that tau pathology spreads intracellularly, thought to explain the topographical distribution of tau observed in Alzheimer’s disease (AD). However, these findings have yet to be extended to humans. Method We assessed 131 cognitively unimpaired elderly and 63 AD individuals who underwent amyloid‐PET with [18F]AZD4694, tau‐PET with [18F]MK6240, structural MRI, fMRI and diffusion‐weighted MRI. Of the subjects with AD, 11 had behavioural/ dysexecutive AD, 18 had PCA and 11 had Logopenic variant PPA, while 23 subjects had an amnestic presentation (all were Ab+/Tau+). A voxelwise multivariate regression model was employed to determine the peak difference in [18F]MK6240 SUVR between each AD variant and CU elderly, with each clinical diagnosis entered as a different categorical variable and correcting for age, gender and MMSE score. Within each AD variant, the peak voxels derived from the regression model were used to compute the correlation between [18F]MK6240 SUVR in the seed voxel and [18F]MK6240 SUVR in every voxel, thus generating an [18F]MK6240 covariance network for each AD group. The same seeds were also employed in functional connectivity and diffusion tractography analyses in the CU elderly group. To determine whether the topographical distribution of tau pathology is related to connectivity properties of the human brain, we applied regression models to assess the relationship between functional/structural connectivity values from the CU population and [18F]MK6240 in each variant of AD. Result Tau organization differed between AD groups, reflecting clinical phenotypes and organizing within distinct brain networks. Furthermore, structural (Figure 1) and functional (Figure 2) connectivity patterns of the human brain predicted in vivo [18F]MK6240 SUVR across the cerebral cortex in each variant of AD. Conclusion These results support a framework in which the intrinsic connectivity of the human brain provides a scaffold for tau pathology to spread to anatomically distant regions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".