Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA; Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, USA.
Department of Bioengineering, University of Illinois at Chicago, Chicago, IL, USA.
Comput Biol Med. 2019 Jul;110:265-275. doi: 10.1016/j.compbiomed.2019.05.004. Epub 2019 May 14.
Microcirculation plays a significant role in cerebral metabolism and blood flow control, yet explaining and predicting functional mechanisms remains elusive because it is difficult to make physiologically accurate mathematical models of the vascular network. As a precursor to the human brain, this paper presents a computational framework for synthesizing anatomically accurate network models for the cortical blood supply in mouse. It addresses two critical deficiencies in cerebrovascular modeling. At the microscopic length scale of individual capillaries, we present a novel synthesis method for building anatomically consistent capillary networks with loops and anastomoses (=microcirculatory closure). This overcomes shortcomings in existing algorithms which are unable to create closed circulatory networks. A second critical innovation allows the incorporation of detailed anatomical features from image data into vascular growth. Specifically, computed tomography and two photon laser scanning microscopy data are input into the novel synthesis algorithm to build the cortical circulation for the entire mouse brain in silico. Computer predictions of blood flow and oxygen exchange executed on synthetic large-scale network models are expected to elucidate poorly understood functional mechanisms of the cerebral circulation.
微循环在大脑代谢和血流控制中起着重要作用，但由于难以对血管网络进行生理上精确的数学建模，因此解释和预测其功能机制仍然难以捉摸。作为人类大脑的前体，本文提出了一种用于合成小鼠皮质血供的解剖学上准确的网络模型的计算框架。它解决了脑血管建模中的两个关键缺陷。在单个毛细血管的微观长度尺度上，我们提出了一种新的合成方法，用于构建具有循环和吻合（=微循环闭合）的解剖一致的毛细血管网络。这克服了现有算法无法创建封闭循环网络的缺点。第二个关键创新允许将详细的解剖特征从图像数据纳入血管生长中。具体来说，将计算机断层扫描和双光子激光扫描显微镜数据输入到新的合成算法中，以在计算机上构建整个小鼠大脑的皮质循环。对合成大规模网络模型上的血流和氧气交换的计算机预测有望阐明大脑循环中尚未完全理解的功能机制。
