A statistical model of the penetrating arterioles and venules in the human cerebral cortex

El-Bouri, Wahbi K ORCID: 0000-0002-2732-5927 and Payne, Stephen J (2016) A statistical model of the penetrating arterioles and venules in the human cerebral cortex. MICROCIRCULATION, 23 (7). pp. 580-590.

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Official URL: http://dx.doi.org/10.1111/micc.12318

Abstract

<h4>Objective</h4>Models of the cerebral microvasculature are required at many different scales in order to understand the effects of microvascular topology on CBF. There are, however, no data-driven models at the arteriolar/venular scale. In this paper, we develop a data-driven algorithm based on available data to generate statistically accurate penetrating arterioles and venules.<h4>Methods</h4>A novel order-based density-filling algorithm is developed based on the statistical data including bifurcating angles, LDRs, and area ratios. Three thousand simulations are presented, and the results validated against morphological data. These are combined with a previous capillary network in order to calculate full vascular network parameters.<h4>Results</h4>Statistically accurate penetrating trees were successfully generated. All properties provided a good fit to experimental data. The k exponent had a median of 2.5 and an interquartile range of 1.75-3.7. CBF showed a standard deviation ranging from ±18% to ±34% of the mean, depending on the penetrating vessel diameter.<h4>Conclusions</h4>Small CBF variations indicate that the topology of the penetrating vessels plays only a small part in the large regional variations of CBF seen in the brain. These results open up the possibility of efficient oxygen and blood flow simulations at MRI voxel scales which can be directly validated against MRI data.

Item Type:	Article
Uncontrolled Keywords:	cerebral blood flow, microcirculation, microvasculature, Murray's law, vascular resistance
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	23 Aug 2021 15:53
Last Modified:	18 Jan 2023 21:32
DOI:	10.1111/micc.12318
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3134475