Fundamental constraints of vessels network architecture properties revealed by reconstruction of a rat brain vasculature

• Published in: Mathematical biosciences Vol. 315; p. 108237
• Main Authors: Kopylova, V.S., Boronovskiy, S.E., Nartsissov, Ya.R.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.09.2019; Elsevier Science Ltd
• Subjects: Animals; Arterial tree; Bifurcation of blood vessels; Bifurcations; Blood flow; Blood vessels; Blood volume; Brain; Brain - blood supply; Cerebral Arteries - anatomy & histology; Cerebral Arteries - physiology; Mathematical models; Modelling; Models, Biological; Models, Cardiovascular; Parameters; Rats; Stochasticity; Vascular system; Bifurcation of blood vessels; Modelling; Arterial tree; Vascular system
• ISSN: 0025-5564; 1879-3134
• DOI: 10.1016/j.mbs.2019.108237

•The vasculature with the correct main arteries spatial arrangement was constructed.•Average node density increases with increasing bifurcation exponent.•The bifurcation parameter increase leads to a more efficient tissue supply.•Bifurcation exponent below 2.7 does not provide the necessary volume of perfusion.•The magnitude of the blood flow impose a limitation on the network architecture. The studies of mammalian vasculature are an essential part of biomedical research, enabling the development of physiological understanding and forming the background of medical techniques and therapy. Despite the fact that the basic principles of vessel network description were established in the first quarter of the twentieth century, a digital model describing the vasculature in full accordance with experimental data has not yet been created. In the present study, we combine the determined structure design of basic arterial vessels with the stochastic creation of small vessel networks. By the example of rat brain arterial network model it was shown that the arterial blood volume and the magnitude of the blood flow impose a limitation on the network architecture. In particular, the bifurcation exponent (γ) should not be less than 2.7, and the optimal value of this parameter lies in the range of 2.9–3.0. Although the networks with a low γ appear as branched and complex, they do not fill out the phantom properly. Thus, the architecture of the vasculature is fundamentally determined by topological geometrical parameters.