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

Area of Science:

• Biomedical Engineering
• Computational Fluid Dynamics
• Physiology

Background:

• Cerebral autoregulation is vital for stable brain blood flow.
• Estimating in vivo haemodynamic forces in cerebral arteries is challenging.
• Vascular tone modulation is key to autoregulation.

Purpose of the Study:

• To develop a computational framework for evaluating blood flow dynamics in myogenically-active cerebral arteries.
• To quantify the impact of upstream pressure changes on cerebral arterial networks.
• To investigate the role of myogenic tone in stabilizing flow and reducing vascular stress.

Main Methods:

• Integrated a continuum mechanics model of the rat vascular wall with 1D blood flow dynamics.
• Employed a fluid-structure interaction framework with weak coupling for computational efficiency.
• Validated the model against various pressure protocols and extracellular calcium conditions.
• Assessed network robustness using different inlet signals and numerical settings in an idealized vascular network.

Main Results:

• The computational methodology accurately simulated blood flow dynamics in cerebral arterial networks.
• Myogenic tone was shown to effectively stabilize flow and redistribute pressure/flow across vessel generations.
• The study quantified the influence of upstream pressure surges on haemodynamics with and without myogenic tone.

Conclusions:

• The developed in-silico methodology provides a robust tool for studying cerebral autoregulation.
• This framework can elucidate how pressure fluctuations are managed by cerebral vasculature.
• The findings support future experimental-computational studies on cerebral blood flow regulation.