Estimation of fluid flow velocities in cortical brain tissue driven by the microvasculature.

Abstract

We present a modelling framework for describing bulk fluid flow in brain tissue. Within this framework, using computational simulation, we estimate bulk flow velocities in the grey matter parenchyma due to static or slowly varying water potential gradients-hydrostatic pressure gradients and osmotic pressure gradients. Working with the situation that experimental evidence and some model parameter estimates, as we point out, are presently insufficient to estimate velocities precisely, we explore feasible parameter ranges resulting in a range of estimates. We consider the effect of realistic microvascular architecture (extracted from mouse cortical grey matter). Although the estimated velocities are small in magnitude (e.g. in comparison to blood flow velocities), the passive transport of solutes with the bulk fluid can be a relevant process when considering larger molecules transported over larger distances. We compare velocity magnitudes resulting from filtration and pulsations. Filtration can lead to continuous directed fluid flow in the parenchyma, while pulsation-driven flow is (at least partly) reversible. For the first time, we consider the effect of the vascular architecture on the velocity distribution in a tissue sample of ca 1 mm³ cortical grey matter tissue. We conclude that both filtration and pulsations are potentially potent drivers for fluid flow.