Su S.-W.Catherall M.Payne S.STEPHEN JOHN PAYNE2022-05-242022-05-242012https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856866436&doi=10.1111%2fj.1549-8719.2011.00148.x&partnerID=40&md5=0a963df97343679458a56a614c795509https://scholars.lib.ntu.edu.tw/handle/123456789/611811In this article, we explore how the structural properties of miniature networks influence the transport of blood through the human cerebral microvasculature. We propose four methods for generating such networks, and investigate both how the resulting network properties match available experimental data from the human cortex and how these properties affect the flow of blood through the networks. As the nature of such microvascular flow patterns is inherently random, we run multiple simulations. We find that the modified spanning tree method produces artificial networks having characteristics closest to those of the microvasculature in human brain, and also allows for high network flow passage per unit material cost, being statistically significantly better than three other methods considered here. Such results are potentially extremely valuable in interpreting experimental data acquired from humans and in improving our understanding of cerebral blood flow at this very small length scale. This could have a significant impact on improving clinical outcomes for vascular brain diseases, particularly vascular dementia, where localized flow patterns are very important. ? 2012 John Wiley & Sons Ltd.articlebrainbrain blood flowbrain blood vesselcerebrovascular diseasehemodynamicshumanmicrocirculationmicrovasculaturemultiinfarct dementiaoutcomes researchpriority journalBlood Flow VelocityBrainCerebrovascular CirculationHumansMicrocirculationModels, Cardiovascular[SDGs]SDG3journal article10.1111/j.1549-8719.2011.00148.x2-s2.0-84856866436