Enhancing Understanding and Clinical Applications of Cerebral Autoregulation: A Novel Integrated Numerical Framework

Area of Science:

• Biomedical Engineering
• Computational Physiology
• Neuroscience

Background:

• Cerebral autoregulation (CA) is crucial for maintaining stable cerebral blood flow (CBF) by adjusting cerebrovascular resistance.
• Limited clinical understanding of CA stems from complex cerebral vasculature and challenges in quantifying key hemodynamic and physiological parameters.

Purpose of the Study:

• To develop and validate a novel numerical algorithm for modeling CA.
• To accurately quantify factors influencing CA, including arterial pressure, oxygen, and carbon dioxide levels within the cerebral vasculature.

Main Methods:

• A novel algorithm using three partial differential equations and one ordinary differential equation was developed.
• The algorithm models spatial and temporal distributions of arterial pressure (P), partial pressures of oxygen (PO2), and carbon dioxide (PCO2).
• A Windkessel model was integrated to regulate CBF based on calculated P, PO2, and PCO2, coupled with a personalized 0D-1D multi-dimensional model.

Main Results:

• The integrated framework was validated using two independent datasets.
• The model demonstrated high reliability and accuracy in capturing CA's regulatory effects on CBF.
• The framework accurately reflects CA across various physiological conditions.

Conclusions:

• This research significantly advances the understanding of cerebral autoregulation.
• The validated numerical framework provides a foundation for developing hemodynamic-based therapeutic strategies.
• The study offers potential for improving clinical outcomes in patients with cerebrovascular disorders.