Sensitivity analysis on a network model of glymphatic flow
Sensitivity analysis on a network model of glymphatic flow
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DOI：
10.1098/rsif.2022.0257
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发表时间：
2022-06-01
影响因子：
3.9
通讯作者：
Kelley, Douglas H.
中科院分区：
文献类型：
--
作者：
Boster, Kimberly A. S.;Tithof, Jeffrey;Kelley, Douglas H.
颅内脑脊液和间质液（ISF）的流动和溶质转运具有重要的临床意义，但有限的体内进入大脑内部留下了人类理解这些神经生理学现象的性质的漏洞。模型可以解决一些差距，但只有在模型输入准确的情况下。我们进行了灵敏度分析，使用蒙特卡罗方法的集中参数网络模型的脑脊髓和ISF在血管周围和细胞外空间在小鼠大脑。考虑到输入参数的不确定性，我们对模型预测进行了限制。在穿透血管周围空间（PVS）和实质内的运输的Peclet数被分开至少两个数量级。穿透性PVS的低渗透性需要不切实际的大驱动压力和/或导致灌注不良，认为不太可能。该模型是最敏感的渗透PVS的渗透性，其值在很大程度上是未知的参数，突出了未来实验的一个重要方向。直到渗透PVS的渗透率的值被更准确地测量，包括渗透PVS中的流动的任何模型的不确定性是如此之大，以至于绝对数字几乎没有意义，并且实际应用是有限的。
Intracranial cerebrospinal and interstitial fluid (ISF) flow and solute transport have important clinical implications, but limited in vivo access to the brain interior leaves gaping holes in human understanding of the nature of these neurophysiological phenomena. Models can address some gaps, but only insofar as model inputs are accurate. We perform a sensitivity analysis using a Monte Carlo approach on a lumped-parameter network model of cerebrospinal and ISF in perivascular and extracellular spaces in the murine brain. We place bounds on model predictions given the uncertainty in input parameters. Peclet numbers for transport in penetrating perivascular spaces (PVSs) and within the parenchyma are separated by at least two orders of magnitude. Low permeability in penetrating PVSs requires unrealistically large driving pressure and/or results in poor perfusion and are deemed unlikely. The model is most sensitive to the permeability of penetrating PVSs, a parameter whose value is largely unknown, highlighting an important direction for future experiments. Until the value of the permeability of penetrating PVSs is more accurately measured, the uncertainty of any model that includes flow in penetrating PVSs is so large that absolute numbers have little meaning and practical application is limited.