Regional Cerebral Arterial Transit Time Hemodynamics Correlate with Vascular Risk Factors and Cognitive Function in Men with Coronary Artery Disease
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Arterial transit time is the time needed for blood to travel from large arteries to capillaries, as estimated from arterial spin-labeling MR imaging. The purpose of this study was to determine whether vascular risk factors and cognitive performance are related to regional differences in cerebral arterial transit time in patients with coronary artery disease who are at risk for cognitive decline. MATERIALS AND METHODS: Arterial transit time was estimated from multiple postlabel delay pseudocontinuous arterial spin-labeling images obtained from 29 men with coronary artery disease. Tests of memory, attention, processing speed, and executive function were administered. Principal component analysis was used to create separate models of cognition and vascular risk, which were related to brain regions through voxelwise analyses of arterial transit time maps. RESULTS: Principal component analysis identified 2 components of vascular risk: 1) "pressor" (age, systolic blood pressure, and pulse pressure) and 2) "obesity" (body fat percentage and body mass index). Obesity was inversely related to arterial transit time in the posterior cingulate, precuneus, lateral occipital cortices, middle temporal gyrus, and frontal pole (P corrected < .05), whereas pressor was not significant. Cognitive scores were factored into a single component. Poor performance was inversely related to precuneus arterial transit time (P corrected < .05). The average arterial transit time in regions identified by obesity was associated with poorer cognitive function (r(2) = 0.21, t = -2.65, P = .01). CONCLUSIONS: Altered cerebral hemodynamics, notably in nodal structures of the default mode network, may be one way that vascular risk factors impact cognition in patients with coronary artery 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 | 0.002 |
| 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".