Dissecting the Vascular-Cognitive Nexus: Energetic vs. Conventional Hemodynamic Parameters
Bibliographic record
Abstract
ABSTRACT Background Blood flow measurements are being studied in relation to vascular health and cognitive function, but their role is unclear. Objective We investigated whether energetic hemodynamic parameters, such as aortic and carotid mean and pulsatile energy, and energy pulsatility index (PI), provide a more nuanced understanding of the vascular-cognitive link, as assessed by the Montreal Cognitive Assessment (MoCA), than conventional flow and flow PI. Methods Cognitive evaluation and hemodynamic measurements, including aortic and carotid pressure and flow waves, were performed on 1858 MoCA participants. Energy was calculated by integrating pressure time flow. An asymmetric bifurcation model was used to calculate aortic and carotid mean, pulsatile energy, and hemodynamic parameters across the interface. Results After adjusting for age, sex, education, depression score, heart rate, BMI, HDL-cholesterol, and glucose levels, energetic hemodynamic parameters were more associated with MoCA score than aortic and carotid flow and flow PI. In particular, carotid mean energy was most significantly positively associated with MoCA (standardized beta = 0.053, P = 0.0253) and energy PI was most significantly negatively associated (standardized beta = -0.093, P = 0.0002), surpassing conventional metrics like carotid PI. Aortic pressure reflection coefficient at the aorta-carotid bifurcation was positively correlated with mean carotid energy and weakly negatively correlated with PI. Aortic characteristic impedance positively correlated with carotid energy PI but not mean energy. Conclusion Our study shows that energetic hemodynamic parameters, particularly carotid mean energy and energy PI, better explain the vascular-cognitive nexus than conventional measures.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| 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".