Geometrically reduced modelling of pulsatile flow in perivascular networks

TL;DR

This paper introduces a mathematical framework for simplified models of pulsatile cerebrospinal fluid flow in brain perivascular networks, enabling efficient large-scale simulations while maintaining accuracy in pressure and flow predictions.

Contribution

The authors develop a novel reduced modeling approach for pulsatile flow in perivascular networks, balancing computational efficiency and accuracy, applicable to complex brain fluid dynamics.

Findings

Reduced models accurately predict pulsatile flow characteristics.

Models capture pressure and flux with high fidelity compared to full models.

Framework enables large-scale in-silico studies of brain fluid transport.

Abstract

Flow of cerebrospinal fluid in perivascular spaces is a key mechanism underlying brain transport and clearance. In this paper, we present a mathematical and numerical formalism for reduced models of pulsatile viscous fluid flow in networks of generalized annular cylinders. We apply this framework to study cerebrospinal fluid flow in perivascular spaces induced by pressure differences, cardiac pulse wave-induced vascular wall motion and vasomotion. The reduced models provide approximations of the cross-section average pressure and cross-section flux, both defined over the topologically one-dimensional centerlines of the network geometry. Comparing the full and reduced model predictions, we find that the reduced models capture pulsatile flow characteristics and provide accurate pressure and flux predictions across the range of idealized and image-based scenarios investigated at a fraction of the computational cost of the corresponding full models. The framework presented thus provides a robust and effective computational approach for large scale in-silico studies of pulsatile perivascular fluid flow and transport.