Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network: Part I: Methodology and baseline flow

Abstract

OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in: http://oatao. For this purpose, we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking account of the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, as well as volumes of functional vascular territories, and regional flow at voxel and network scales. First, the influence of the prescribed boundary conditions (BCs) on the baseline flow structure is investigated, highlighting relevant lower-and upper-bound BCs. Independent of these BCs, large heterogeneities of baseline flow from vessel to vessel and from voxel to voxel, are demonstrated. These heterogeneities are controlled by the architecture of the intra-cortical vascular network. In particular, a correlation between the blood flow and the proportion of vascular volume occupied by arterioles or venules, at voxel scale, is highlighted. Then, the extent of venous contamination downstream to the sites of neuronal activation is investigated, demonstrating a linear relationship between the catchment surface of the activated area and the diameter of the intra-cortical draining vein.

Going beyond 20 μm-sized channels for studying red blood cell phase separation in microfluidic bifurcations

“…This dependence is plotted in Figure 7, allowing to characterize the so-called Fåhraeus effect. Our experimental data for both 10 lm Â10 lm and 20 lm  20 lm are in quantitative agreement with empirical correlations obtained by Pries et al 36 for capillary tubes with circular cross-sections, using the hydraulic diameter D h instead of the tube diameter (for the square cross-section channels considered here, D h ¼ W) and dividing the coefficients by a factor of 0.86 in order to account for the difference in characteristic size between human and rat RBCs, as proposed by Lorthois et al 21 This strongly supports the assumptions made about the suspending fluid velocity profile in the present study. By contrast, matching the suspending fluid and RBC velocity profiles is not consistent with the observed RBC slip velocity at walls and would result in H t ¼ H d , i.e., no Fåhraeus effect.…”

“…12,13,[21][22][23]27,36,38,41,42 In these works, the complex rheological properties of blood flow in microcirculation, especially the phase separation effect, are either described by empirical laws obtained by least-square adjustment to experimental results (e.g., Refs. 21 and 36), theoretically derived assuming a given RBC flow regime (e.g., single file RBC flows in narrow capillaries 27 ), or modeled in an ad hoc fashion.…”

Alzheimer’s disease syndrome – Recognizing the complexity of dementia

“…Cardiovascular system disease or natural aging of the heart and vessels, and brain microcirculation may result in regional or global brain perfusion insufficiency with consequent negative effects on cognitive capacities [132]. The brain consumes disproportionate amounts of oxygen and has no energy reserves making an adequate blood supply crucial to proper function [133]. Regional brain activity levels may be inferred by detecting rapid changes in blood oxygenation levels (BOLD) which are presumed to reflect neuronal and associated tissue metabolic responses to stimulation [134].…”

Multimodal reconstruction of microvascular-flow distributions using combined two-photon microscopy and Doppler optical coherence tomography

“…3 Several groups have performed the computation of μCBF in realistic vascular networks. [4][5][6][7][8][9][10][11][12][13][14] Several difficulties arise when the computation takes place over a real vascular network measured in vivo. This includes vessel diameter estimation and, therefore, estimation of resistances.…”

“…Moreover, the truncation of the network at the edges of the imaging volume inevitably increases the number of boundary conditions required for the computation of μCBF. 7,12 All of these issues increase the uncertainty of the microvascular flow distributions computed with these models and limit their application.…”