Associations between cerebral blood transit time, amyloid‐β pathology and cognitive decline in non‐demented older adults
Bibliographic record
Abstract
Abstract INTRODUCTION The contribution of cerebrovascular hemodynamic disturbances to Alzheimer's disease (AD) remains unclear. Using time‐shift analysis of blood‐oxygenation‐level‐dependent (BOLD) signals, we explored associations between cerebral blood transit time, amyloid beta (Aβ) pathology, and cognition. METHODS We included 131 non‐demented individuals from the Alzheimer's Disease Neuroimaging Initiative. Three transit metrics – termed Lag ICA‐SSS , Lag ICA‐global , and Lag global‐SSS – were derived from BOLD time lags between the internal carotid artery (ICA), superior sagittal sinus (SSS), and global signal. Associations between transit metrics, Aβ burden, and cognition were investigated through cross‐sectional and longitudinal analyses. RESULTS At baseline, prolonged Lag ICA‐global and Lag ICA‐SSS were associated with higher Aβ burden, while their adverse effect on cognition was largely mediated by Aβ pathology. In longitudinal analyses, prolonged Lag global‐SSS and Lag ICA‐SSS predicted faster Aβ accumulation. Synergistic interactions between Lag ICA‐global , Lag global‐SSS , and Aβ burden were linked to accelerated cognitive decline. DISCUSSION Prolonged blood transit time can reflect early vascular impairment in AD. Highlights Vascular risks are associated with prolonged arterial transit time (Lag ICA‐global ) and cerebral blood transit time (Lag ICA‐SSS ). Prolonged venous blood transit time (Lag global‐SSS ) and Lag ICA‐SSS are linked to accelerated Aβ accumulation. Aβ burden mediates the association between prolonged Lag ICA‐global and Lag ICA‐SSS and cognitive impairment. Aβ Amyloid burden may interact synergistically with prolonged Lag ICA‐global and Lag global‐SSS to exacerbate cognitive decline.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".