A statistical model of the penetrating arterioles and venules in the human cerebral cortex
Objective 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...
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Published in | Microcirculation (New York, N.Y. 1994) Vol. 23; no. 7; pp. 580 - 590 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
United States
Blackwell Publishing Ltd
01.10.2016
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | Objective
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.
Methods
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.
Results
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.
Conclusions
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. |
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Bibliography: | EPSRC Doctoral Training Partnership studentship - No. EP/M50659X/1. ArticleID:MICC12318 ark:/67375/WNG-6L5HSWS6-C istex:CF70FA82FC7AF8A70356672F0F93998EA8DB6D04 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 1073-9688 1549-8719 |
DOI: | 10.1111/micc.12318 |