皮层小动脉网络的实验约束电路模型，用于理解阻塞和神经激活引起的血流再分配。
Experimentally constrained circuit model of cortical arteriole networks for understanding flow redistribution due to occlusion and neural activation.
机构信息
1 Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA.
2 School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA.
出版信息
J Cereb Blood Flow Metab. 2018 Jan;38(1):38-44. doi: 10.1177/0271678X17741086. Epub 2017 Nov 13.
Abstract
Computations are described which estimate flows in all branches of the cortical surface arteriole network from two-photon excited fluorescence (2PEF) microscopy images which provide the network topology and, in selected branches red blood cell (RBC) speeds and lumen diameters. Validation is done by comparing the flow predicted by the model with experimentally measured flows and by comparing the predicted flow redistribution in the network due to single-vessel strokes with experimental observations. The model predicts that tissue is protected from RBC flow decreases caused by multiple occlusions of surface arterioles but not penetrating arterioles. The model can also be used to study flow rerouting due to vessel dilations and constrictions.
摘要
描述了一种计算方法，该方法可以根据双光子激发荧光（2PEF）显微镜图像估算皮质表面微动脉网络中所有分支的流量，这些图像提供了网络拓扑结构，以及在选定分支中的红细胞（RBC）速度和管腔直径。通过将模型预测的流量与实验测量的流量进行比较，以及将模型预测的由于单个血管阻塞引起的网络中流量再分配与实验观察进行比较来进行验证。该模型预测，组织可以免受由表面微动脉多次阻塞引起的 RBC 流量减少的影响，但不能免受穿透性微动脉阻塞的影响。该模型还可用于研究由于血管扩张和收缩引起的流量改道。
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