A new computational model for quantifying blood flow dynamics across myogenically-active cerebral arterial networks

Area of Science:

• Physiology
• Biomedical Engineering
• Computational Fluid Dynamics

Background:

• Cerebral autoregulation is vital for stable brain blood flow, but in-vivo hemodynamic force quantification is challenging.
• Myogenically active cerebral arteries modulate tone to stabilize flow and limit vascular stress.

Purpose of the Study:

• To develop and validate a computational framework for evaluating blood flow dynamics in myogenically active cerebral artery networks.
• To investigate the time-dependent vascular wall response to pressure changes at single vessel and network levels.

Main Methods:

• A novel computational framework coupling contractile vascular wall mechanics and blood flow dynamics.
• Numerical simulations on an idealized vascular network (middle cerebral artery and three generations).
• Assessment of weak vs. strong coupling for computational efficiency and accuracy.

Main Results:

• Weak coupling provided accurate results with lower computational cost for the studied conditions.
• The model quantitatively illustrated pressure and flow redistribution across arterial generations following an upstream pressure surge.
• Demonstrated the framework's robustness with varied inlet signals and numerical settings.

Conclusions:

• The proposed computational framework effectively models cerebral autoregulation dynamics.
• Provides a quantitative understanding of hemodynamic responses within cerebral arterial networks.
• Facilitates future experimental-computational studies to further elucidate cerebral autoregulation mechanisms.