Department of Bioengineering, University of Illinois at Chicago, Chicago, Illinois, United States of America.
Polytechnique Montréal, Department of Electrical Engineering, Montreal, Canada.
PLoS Comput Biol. 2021 Jan 28;17(1):e1008584. doi: 10.1371/journal.pcbi.1008584. eCollection 2021 Jan.
Departures of normal blood flow and metabolite distribution from the cerebral microvasculature into neuronal tissue have been implicated with age-related neurodegeneration. Mathematical models informed by spatially and temporally distributed neuroimage data are becoming instrumental for reconstructing a coherent picture of normal and pathological oxygen delivery throughout the brain. Unfortunately, current mathematical models of cerebral blood flow and oxygen exchange become excessively large in size. They further suffer from boundary effects due to incomplete or physiologically inaccurate computational domains, numerical instabilities due to enormous length scale differences, and convergence problems associated with condition number deterioration at fine mesh resolutions. Our proposed simple finite volume discretization scheme for blood and oxygen microperfusion simulations does not require expensive mesh generation leading to the critical benefit that it drastically reduces matrix size and bandwidth of the coupled oxygen transfer problem. The compact problem formulation yields rapid and stable convergence. Moreover, boundary effects can effectively be suppressed by generating very large replica of the cortical microcirculation in silico using an image-based cerebrovascular network synthesis algorithm, so that boundaries of the perfusion simulations are far removed from the regions of interest. Massive simulations over sizeable portions of the cortex with feature resolution down to the micron scale become tractable with even modest computer resources. The feasibility and accuracy of the novel method is demonstrated and validated with in vivo oxygen perfusion data in cohorts of young and aged mice. Our oxygen exchange simulations quantify steep gradients near penetrating blood vessels and point towards pathological changes that might cause neurodegeneration in aged brains. This research aims to explain mechanistic interactions between anatomical structures and how they might change in diseases or with age. Rigorous quantification of age-related changes is of significant interest because it might aide in the search for imaging biomarkers for dementia and Alzheimer's disease.
正常血流和代谢物分布从脑微血管进入神经元组织的偏离与年龄相关的神经退行性变有关。受时空分布神经影像学数据启发的数学模型，对于重建整个大脑正常和病理氧输送的连贯图景变得至关重要。不幸的是，目前的脑血流和氧交换数学模型的规模过大。由于计算域不完整或生理上不准确、由于巨大的长度尺度差异导致数值不稳定性以及与精细网格分辨率相关的条件数恶化导致的收敛问题，它们还存在边界效应。我们提出的用于血液和氧气微循环模拟的简单有限体积离散化方案不需要昂贵的网格生成，这带来了一个关键的好处，即它可以大大减小耦合氧气传输问题的矩阵大小和带宽。紧凑的问题公式可以快速稳定地收敛。此外，可以通过使用基于图像的脑血管网络综合算法在计算机中生成皮质微循环的非常大的复制品，有效地抑制边界效应，从而使灌注模拟的边界远离感兴趣的区域。使用特征分辨率达到微米级的较大皮质部分进行大规模模拟，即使使用适度的计算机资源也变得可行。该新方法的可行性和准确性通过年轻和老年小鼠的体内氧灌注数据进行了验证和验证。我们的氧气交换模拟量化了穿透血管附近的陡峭梯度，并指出了可能导致老年大脑神经退行性变的病理变化。这项研究旨在解释解剖结构之间的机械相互作用，以及它们在疾病或年龄变化中可能发生的变化。对与年龄相关的变化进行严格量化非常重要，因为它可能有助于寻找痴呆症和阿尔茨海默病的成像生物标志物。
