Boas David A, Jones Stephanie R, Devor Anna, Huppert Theodore J, Dale Anders M
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
Neuroimage. 2008 Apr 15;40(3):1116-29. doi: 10.1016/j.neuroimage.2007.12.061. Epub 2008 Jan 15.
Neuronal activity-induced changes in vascular tone and oxygen consumption result in a dynamic evolution of blood flow, volume, and oxygenation. Functional neuroimaging techniques, such as functional magnetic resonance imaging, optical imaging, and PET, provide indirect measures of the neural-induced vascular dynamics driving the blood parameters. Models connecting changes in vascular tone and oxygen consumption to observed changes in the blood parameters are needed to guide more quantitative physiological interpretation of these functional neuroimaging modalities. Effective lumped-parameter vascular balloon and Windkessel models have been developed for this purpose, but the lumping of the complex vascular network into a series of arterioles, capillaries, and venules allows only qualitative interpretation. We have therefore developed a parallel vascular anatomical network (VAN) model based on microscopically measurable properties to improve quantitative interpretation of the vascular response. The model, derived from measured physical properties, predicts baseline blood pressure and oxygen saturation distributions and dynamic responses consistent with literature. Furthermore, the VAN model allows investigation of spatial features of the dynamic vascular and oxygen response to neuronal activity. We find that a passive surround negative vascular response ("negative BOLD") is predicted, but that it underestimates recently observed surround negativity suggesting that additional active surround vasoconstriction is required to explain the experimental data.
神经元活动引起的血管张力和氧消耗变化导致血流、血容量和氧合的动态演变。功能神经成像技术，如功能磁共振成像、光学成像和正电子发射断层扫描，提供了驱动血液参数的神经诱导血管动力学的间接测量方法。需要将血管张力和氧消耗的变化与观察到的血液参数变化联系起来的模型，以指导对这些功能神经成像模式进行更定量的生理学解释。为此已经开发了有效的集总参数血管球囊模型和Windkessel模型，但将复杂的血管网络集总为一系列小动脉、毛细血管和小静脉只能进行定性解释。因此，我们基于微观可测量特性开发了一种并行血管解剖网络（VAN）模型，以改进对血管反应的定量解释。该模型源自测量的物理特性，预测的基线血压和氧饱和度分布以及动态反应与文献一致。此外，VAN模型允许研究动态血管和氧对神经元活动反应的空间特征。我们发现预测了一种被动的周围负性血管反应（“负性BOLD”），但它低估了最近观察到的周围负性，这表明需要额外的主动周围血管收缩来解释实验数据。
