Institut de Mécanique des Fluides de Toulouse, UMR CNRS/INP/UPS 5502, Toulouse, France.
Neuroimage. 2011 Feb 14;54(4):2840-53. doi: 10.1016/j.neuroimage.2010.10.040. Epub 2010 Nov 1.
In a companion paper (Lorthois et al., Neuroimage, in press), we perform the first simulations of blood flow in an anatomically accurate large human intra-cortical vascular network (~10000 segments), using a 1D non-linear model taking into account the complex rheological properties of blood flow in microcirculation. This model predicts blood pressure, blood flow and hematocrit distributions, volumes of functional vascular territories, regional flow at voxel and network scales, etc. Using the same approach, we study flow reorganizations induced by global arteriolar vasodilations (an isometabolic global increase in cerebral blood flow). For small to moderate global vasodilations, the relationship between changes in volume and changes in flow is in close agreement with Grubb's law, providing a quantitative tool for studying the variations of its exponent with underlying vascular architecture. A significant correlation between blood flow and vascular structure at the voxel scale, practically unchanged with respect to baseline, is demonstrated. Furthermore, the effects of localized arteriolar vasodilations, representative of a local increase in metabolic demand, are analyzed. In particular, localized vasodilations induce flow changes, including vascular steal, in the neighboring arteriolar trunks at small distances (<300 μm), while their influence in the neighboring veins is much larger (about 1 mm), which provides an estimate of the vascular point spread function. More generally, for the first time, the hemodynamic component of various functional neuroimaging techniques has been isolated from metabolic and neuronal components, and a direct relationship with several known characteristics of the BOLD signal has been demonstrated.
在一篇相关论文中(Lorthois 等人,Neuroimage,待发表),我们首次对一个解剖结构精确的大型人类皮质内血管网络(~10000 个节段)进行血流模拟,采用考虑微循环中血流复杂流变特性的一维非线性模型。该模型可以预测血压、血流和血细胞比容分布、功能血管区域的体积、体素和网络尺度的区域流量等。使用相同的方法,我们研究了由全局小动脉扩张引起的血流重排(大脑血流的等代谢性全局增加)。对于较小到中等程度的全局血管扩张,体积变化与流量变化之间的关系与 Grubb 定律非常吻合,为研究其指数随基础血管结构的变化提供了定量工具。在体素尺度上,证明了血流与血管结构之间存在显著相关性,与基线相比几乎不变。此外,还分析了局部小动脉扩张的影响,这代表代谢需求的局部增加。特别是在小距离(<300μm)处,局部血管扩张会引起邻近小动脉主干的血流变化,包括血管窃血,而对邻近静脉的影响要大得多(约 1mm),这为血管点扩散函数提供了一个估计值。更一般地说,首次从代谢和神经元成分中分离出各种功能神经影像学技术的血液动力学成分,并证明了与几个已知的 BOLD 信号特征的直接关系。