Dissociation of Down syndrome and Alzheimer's disease effects with imaging
Bibliographic record
Abstract
INTRODUCTION: Down Syndrome (DS) adults experience accumulation of Alzheimer's disease (AD)-like amyloid plaques and tangles and a high incidence of dementia and could provide an enriched population to study AD-targeted treatments. However, to evaluate effects of therapeutic intervention, it is necessary to dissociate the contributions of DS and AD from overall phenotype. Imaging biomarkers offer the potential to characterize and stratify patients who will worsen clinically but have yielded mixed findings in DS subjects. METHODS: We evaluated 18F fluorodeoxyglucose positron emission tomography (PET), florbetapir PET, and structural magnetic resonance (sMR) image data from 12 nondemented DS adults using advanced multivariate machine learning methods. RESULTS: Our results showed distinctive patterns of glucose metabolism and brain volume enabling dissociation of DS and AD effects. AD-like pattern expression corresponded to amyloid burden and clinical measures. DISCUSSION: These findings lay groundwork to enable AD clinical trials with characterization and disease-specific tracking of DS adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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) | 0.002 | 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".