Cognitive dispersion is elevated in amyloid-positive older adults and associated with regional hypoperfusion
Bibliographic record
Abstract
OBJECTIVE: Cognitive dispersion across neuropsychological measures within a single testing session is a promising marker predictive of cognitive decline and development of Alzheimer's disease (AD). However, little is known regarding brain changes underlying cognitive dispersion, and the association of cognitive dispersion with in vivo AD biomarkers and regional cerebral blood flow (CBF) has received limited study. We therefore examined associations among cognitive dispersion, amyloid-beta (Aβ) positivity, and regional CBF among older adults free of dementia. METHOD: One hundred and forty-eight Alzheimer's Disease Neuroimaging Initiative (ADNI) participants underwent neuropsychological testing and neuroimaging. Pulsed arterial spin labeling (ASL) magnetic resonance imaging (MRI) was acquired to quantify CBF. Florbetapir positron emission tomography (PET) imaging determined Aβ positivity. RESULTS: Adjusting for age, gender, education, and mean cognitive performance, older adults who were Aβ+ showed higher cognitive dispersion relative to those who were Aβ-. Across the entire sample, higher cognitive dispersion was associated with reduced CBF in inferior parietal and temporal regions. Secondary analyses stratified by Aβ status demonstrated that higher cognitive dispersion was associated with reduced CBF among Aβ+ individuals but not among those who were Aβ-. CONCLUSIONS: Cognitive dispersion may be sensitive to early Aβ accumulation and cerebrovascular changes adjusting for demographics and mean neuropsychological performance. Associations between cognitive dispersion and CBF were observed among Aβ+ individuals, suggesting that cognitive dispersion may be a marker of brain changes among individuals on the AD continuum. Future studies should examine whether cognitive dispersion predicts brain changes in diverse samples and among those with greater vascular risk burden.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".