Computational modeling of multiscale collateral blood supply in a whole-brain-scale arterial network.

IF 4.3 2区 生物学 PLoS Computational Biology Pub Date : 2023-09-08 eCollection Date: 2023-09-01 DOI:10.1371/journal.pcbi.1011452
Tomohiro Otani, Nozomi Nishimura, Hiroshi Yamashita, Satoshi Ii, Shigeki Yamada, Yoshiyuki Watanabe, Marie Oshima, Shigeo Wada
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Abstract

The cerebral arterial network covering the brain cortex has multiscale anastomosis structures with sparse intermediate anastomoses (O[102] μm in diameter) and dense pial networks (O[101] μm in diameter). Recent studies indicate that collateral blood supply by cerebral arterial anastomoses has an essential role in the prognosis of acute ischemic stroke caused by large vessel occlusion. However, the physiological importance of these multiscale morphological properties-and especially of intermediate anastomoses-is poorly understood because of innate structural complexities. In this study, a computational model of multiscale anastomoses in whole-brain-scale cerebral arterial networks was developed and used to evaluate collateral blood supply by anastomoses during middle cerebral artery occlusion. Morphologically validated cerebral arterial networks were constructed by combining medical imaging data and mathematical modeling. Sparse intermediate anastomoses were assigned between adjacent main arterial branches; the pial arterial network was modeled as a dense network structure. Blood flow distributions in the arterial network during middle cerebral artery occlusion simulations were computed. Collateral blood supply by intermediate anastomoses increased sharply with increasing numbers of anastomoses and provided one-order-higher flow recoveries to the occluded region (15%-30%) compared with simulations using a pial network only, even with a small number of intermediate anastomoses (≤10). These findings demonstrate the importance of sparse intermediate anastomoses, which are generally considered redundant structures in cerebral infarction, and provide insights into the physiological significance of the multiscale properties of arterial anastomoses.

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全脑动脉网络中多尺度侧支供血的计算建模。
覆盖大脑皮层的脑动脉网络具有多尺度吻合结构,具有稀疏的中间吻合(直径为O[102]μm)和密集的软脑膜网络(直径为O[101]μm)。最近的研究表明,脑动脉吻合的侧支供血在大血管闭塞引起的急性缺血性脑卒中的预后中起着重要作用。然而,由于固有的结构复杂性,人们对这些多尺度形态特性的生理重要性,尤其是中间吻合的生理重要性知之甚少。在本研究中,建立了全脑尺度脑动脉网络中多尺度吻合的计算模型,并用于评估大脑中动脉闭塞期间吻合的侧支供血。通过结合医学成像数据和数学建模,构建了经形态学验证的脑动脉网络。稀疏的中间吻合被分配在相邻的主动脉分支之间;pial动脉网络被建模为密集的网络结构。计算大脑中动脉闭塞模拟过程中动脉网络中的血流分布。中间吻合的侧支血液供应随着吻合次数的增加而急剧增加,与仅使用pial网络的模拟相比,即使只有少量中间吻合(≤10),也能为闭塞区域提供一个数量级的流量恢复率(15%-30%)。这些发现证明了稀疏中间吻合的重要性,稀疏中间吻合通常被认为是脑梗死中的冗余结构,并为动脉吻合的多尺度特性的生理意义提供了见解。
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PLoS Computational Biology
PLoS Computational Biology 生物-生化研究方法
CiteScore
7.10
自引率
4.70%
发文量
820
期刊介绍: PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.
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