Dissecting the vascular-cognitive nexus: energetic vs. conventional hemodynamic parameters
Bibliographic record
Abstract
Blood pressure or flow measurements have been associated with vascular health and cognitive function. We proposed that energetic hemodynamic parameters may provide a more nuanced understanding and stronger correlation with cognitive function, in comparisons with conventional aortic and carotid pressure and flow parameters. The study comprised 1858 participants, in whom we assessed cognitive function via MoCA method, and measured central aortic and carotid pressure and flow waveforms. In addition to various pressure and flow parameters, we calculated energetic hemodynamic parameters through integration of pressure multiplying flow with respect to time. Energetic hemodynamic parameters, particularly aortic and carotid mean and pulsatile energy and pulsatility index (PI), were significantly associated with MoCA score more than any aortic and carotid pressure and flow parameters, after adjusting for age, sex, education, depression score, heart rate, BMI, HDL-cholesterol, and glucose levels. MoCA exhibited a strong positive relationship with carotid mean energy (standardized beta = 0.053, P = 0.0253) and a negative relationship with carotid energy PI (standardized beta = -0.093, P = 0.0002), exceeding the association with all traditional pressure- or flow-based parameters. Aortic pressure reflection coefficient at the aorto-carotid junction was positively correlated with mean carotid energy and negatively correlated with PI. Aortic characteristic impedance positively correlated with carotid energy PI but not mean energy. Our research indicates that energetic hemodynamic parameters, particularly carotid mean energy and carotid energy PI, have a stronger association with MoCA scores than traditional pressure- or flow-based metrics. This correlation with cognitive function is notably influenced by the properties of the aorto-carotid interface.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.001 | 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".