Determinants of human cerebral pressure–flow velocity relationships: new insights from vascular modelling and Ca<sup>2+</sup>channel blockade
Bibliographic record
Abstract
The fundamental determinants of human dynamic cerebral autoregulation are poorly understood, particularly the role of vascular compliance and the myogenic response. We sought to 1) determine whether capacitive blood flow associated with vascular compliance and driven by the rate of change in mean arterial blood pressure (dMAP/dt) is an important determinant of middle cerebral artery velocity (MCAv) dynamics and 2) characterise the impact of myogenic blockade on these cerebral pressure-flow velocity relations in humans. We measured MCAv and mean arterial pressure (MAP) during oscillatory lower body negative pressure (n =8) at 0.10 and 0.05 Hz before and after cerebral Ca²⁺ channel blockade (nimodipine). Pressure-flow velocity relationships were characterised using transfer function analysis and a regression-based Windkessel analysis that incorporates MAP and dMAP/dt as predictors of MCAv dynamics. Results show that incorporation of dMAP/dt accounted for more MCAv variance (R² 0.80-0.99) than if only MAP was considered (R2 0.05-0.90). The capacitive gain relating dMAP/dt and MCAv was strongly correlated to transfer function gain (0.05 Hz, r =0.93, P<0.01; 0.10 Hz, r =0.91, P<0.01), but not to phase or coherence. Ca²⁺ channel blockade increased the conductive gain relation between MAP and MCAv (P<0.05), and reduced phase at 0.05 Hz (P<0.01). Capacitive and transfer function gain were unaltered. The findings suggest capacitive blood flow is an important determinant of cerebral haemodynamics that bears strong relations to some metrics of dynamic cerebral autoregulation derived from transfer function analysis, and that Ca²⁺ channel blockade enhances pressure-driven resistive blood flow but does not alter capacitive blood flow. the causes and effects of cerebrovascular diseases such as stroke and dementia.
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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 | 0.001 | 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".