Cerebral Blood Flow Modeling in Primate Cortex

Abstract

We report new results on blood flow modeling over large volumes of cortical gray matter of primate brain. We propose a network method for computing the blood flow, which handles realistic boundary conditions, complex vessel shapes, and complex nonlinear blood rheology. From a detailed comparison of the available models for the blood flow rheology and the phase separation effect, we are able to derive important new results on the impact of network structure on blood pressure, hematocrit, and flow distributions. Our findings show that the network geometry (vessel shapes and diameters), the boundary conditions associated with the arterial inputs and venous outputs, and the effective viscosity of the blood are essential components in the flow distribution. In contrast, we show that the phase separation effect has a minor function in the global microvascular hemodynamic behavior. The behavior of the pressure, hematocrit, and blood flow distributions within the network are described through the depth of the primate cerebral cortex and are discussed.

On the Normalization of Cerebral Blood Flow

“…On the contrary, when rescaling the perfusion flux Q by the voxel surface S, its evaluation at different scales does not statistically differ anymore (Student's t-tests P40.75 in Figure 2C). Albeit presented here for one in vivo blood rheology model, 11 this finding is robust to any rheology models previously analyzed 10 so that this observation is a very strong property of blood perfusion spatial distribution. We also statistically tested the Q/V 1/6 normalization 6 and found that it does not provide a scale-invariant quantity for the four different voxel sizes chosen.…”

“…Figure 1A illustrates the largest cube of 8 mm 3 that could be extracted from the complete reconstruction of a cylindrical sample of primate cortex excluding the pia matter ( Figure 1B), which is essential for applying relevant boundary conditions. 10 The resulting prediction for the blood pressure and flow distribution depends on the considered blood rheological model; we already analyzed this issue in a recent study. 10 Here, we show that the CBF measurement depends on the size of the processed virtual voxel, i.e., the size of the cubic box.…”

“…10 The resulting prediction for the blood pressure and flow distribution depends on the considered blood rheological model; we already analyzed this issue in a recent study. 10 Here, we show that the CBF measurement depends on the size of the processed virtual voxel, i.e., the size of the cubic box. From evaluating CBF using different voxel sizes, we have been able to analyze its size dependence.…”

“…The blood perfusion inside this highly complex network is then computed with a network method 10 that permits the evaluation of the blood pressure and flux inside each individual vessel segment. Finally, the statistical analysis of the data discussed in the following sections uses classic Student's t-test.…”

Tomographic reconstruction of micro-vascular network in cerebral cortical samples

“…1,2 The conventional method of medical imagery is to analyze the tissues (after special pretreatment) with the use of optical-or electron microscope depending on the object of examination. Traditional microscopic methods cannot detect overlapping structures which are small relative to the thickness of the tissue.…”