Network community structure and resilience to localized damage: Application to brain microcirculation

Q3 Engineering
F. Goirand , B. Georgeot , O. Giraud , S. Lorthois
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引用次数: 5

Abstract

In cerebrovascular networks, some vertices are more connected to each other than with the rest of the vasculature, defining a community structure. Here, we introduce a class of model networks built by rewiring Random Regular Graphs, which enables reproduction of this community structure and other topological properties of cerebrovascular networks. We use these model networks to study the global flow reduction induced by the removal of a single edge. We analytically show that this global flow reduction can be expressed as a function of the initial flow rate in the removed edge and of a topological quantity, both of which display probability distributions following Cauchy laws, i.e. with large tails. As a result, we show that the distribution of blood flow reductions is strongly influenced by the community structure. In particular, the probability of large flow reductions increases substantially when the community structure is stronger, weakening the network resilience to single capillary occlusions. We discuss the implications of these findings in the context of Alzheimers Disease, in which the importance of vascular mechanisms, including capillary occlusions, is beginning to be uncovered.

Statement of significance

“Occlusions of capillary vessels, the smallest blood vessels in the brain, are involved in major diseases, including Alzheimers Disease and ischemic stroke. To better understand their impact on cerebral blood flow, we theoretically study the vessel network response to a single occlusion. We show that the reduction of blood flow at network scale is a function of the initial blood flow in the occluded vessel and of a topological quantity, both of which have broad distributions, that is, with significant probabilities of extreme values. Using model networks built from Random Regular Graphs, we show that the presence of communities in the network (subparts more connected to each other than with the rest of the vasculature) yield a broader distribution of the topological quantity. This weakens the resilience of brain vessel networks to single capillary occlusions, which may contribute to the pathogenicity of capillary occlusions in the brain”.

网络社区结构和局部损伤复原力:在脑微循环中的应用
在脑血管网络中,一些顶点彼此之间的联系比与其他脉管系统的联系更紧密,从而定义了一个群落结构。在这里,我们介绍了一类通过重新布线随机规则图构建的模型网络,它可以再现这种社区结构和脑血管网络的其他拓扑特性。我们使用这些模型网络来研究去除单个边缘引起的全局流量减少。我们分析表明,这种全局流量减少可以表示为去除边缘的初始流量和拓扑量的函数,两者都显示遵循柯西定律的概率分布,即具有大尾。因此,我们表明血流量减少的分布受到社区结构的强烈影响。特别是,当群落结构越强时,大流量减少的可能性显著增加,削弱了网络对单个毛细血管闭塞的弹性。我们讨论这些发现在阿尔茨海默病的背景下的意义,其中血管机制的重要性,包括毛细血管闭塞,开始被发现。“毛细血管是大脑中最小的血管,它的闭塞与阿尔茨海默病和缺血性中风等重大疾病有关。为了更好地了解它们对脑血流的影响,我们从理论上研究了血管网络对单一闭塞的反应。我们表明,在网络尺度上血流量的减少是闭塞血管中初始血流量和拓扑量的函数,两者都具有广泛的分布,即具有显著的极值概率。使用随机规则图构建的模型网络,我们表明网络中社区的存在(子部分彼此之间的连接比与其他脉管系统的连接更多)产生了更广泛的拓扑数量分布。这削弱了脑血管网络对单个毛细血管闭塞的弹性,这可能有助于脑毛细血管闭塞的致病性”。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Brain multiphysics
Brain multiphysics Physics and Astronomy (General), Modelling and Simulation, Neuroscience (General), Biomedical Engineering
CiteScore
4.80
自引率
0.00%
发文量
0
审稿时长
68 days
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