Improving Human fMRI through Modeling and Imaging Microvascular Dynamics

项目摘要/摘要 所有的fmri信号都来自血管，这一直被认为是精确的一个主要限制。 血流动力学功能对比时神经元激活的时空定位，如BOLD。 然而，最近使用强大的超高分辨率光学成像技术所取得的重大发现 已经挑战了这一信念。不幸的是，这些措施需要侵入性程序，因此不能 可以在人类身上进行。我们的目标是将从这些侵入性研究中获得的知识转化为口译 人类功能磁共振成像数据，以帮助功能磁共振成像充分发挥其潜力。在这份提案中，我们计划将详细的 用高分辨率磁共振成像测量的人体宏观和中尺度血管构筑图 用清晰度辅助显微成像测量人脑标本中的微尺度血管系统。我们会 然后将这些解剖学信息与啮齿动物双光子显微镜建立的动态模型联系起来。 其中血管直径、血流量和氧合的变化可以在每个血管中直接测量 在血管层次结构的所有阶段中键入。我们假设新引入的血液和血液模型 从双光子显微镜构建的血管动力学，与详细的微观和宏观映射相联系 人体微血管解剖学，可以用来提高人类功能磁共振成像的空间和时间特异性。 为了给我们的模型提供人体血管重建，我们提出了一种双尺度方法。我们首先 先进的7特斯拉磁共振血管成像(MRA)技术成像软脑膜血管网络以及皮质内 实现大脑皮层血管和血管各层的介观模型。以形成微米级的 在毛细血管水平的血管系统模型，我们将使用Clarity技术来成像完整的血管树 (从小动脉、毛细血管到小静脉)在人类初级视皮层。 为了预测由神经元激活驱动的血管动力学变化，我们将采用一个源自 啮齿动物体内血管直径的动态双光子显微镜对人体微血管解剖的影响。至 使其适应人类微血管系统需要仔细的多阶段移植。首先，我们将测量体积 微血管直径的变化，也称为。脑血容量(CBV)，跨越多个水平的血管 并验证了该模型对CBV-fMRI信号的预测能力。使用CBV-fMRI信号是因为它 是直接反映局部神经元活动同时发生的血管直径变化的血管动力学信号吗？ (而不是随后的血流动力学变化)。执行此验证后，我们将构建一个动态 基于我们的血管重建的人脑皮质微血管树模型，并再次测量 CBV-fMRI在血管层次的多个层次上发生变化。我们将最终测试该模型的能力 目的：通过对人视皮层功能构筑的成像，提高功能磁共振成像的神经元特异性。这 模型还将使关于功能磁共振反应的可区分性的假设的形成和测试成为可能 从附近的神经元群体中产生的信息，并指导未来先进采集技术的发展。

PROJECT SUMMARY/ABSTRACT All fMRI signals have a vascular origin, and this has been believed to be a major limitation to precise spatiotemporal localization of neuronal activation when using hemodynamic functional contrast such as BOLD. However, significant recent discoveries made using powerful ultrahigh-resolution optical imaging techniques have challenged this belief. Unfortunately these measures require invasive procedures and therefore cannot be performed in humans. Our aim is to transfer knowledge gained from these invasive studies into interpreting human fMRI data in order to help fMRI reach its full potential. In this proposal we plan to combine detailed maps of human macro- and meso-scale vasculature measured with high-resolution MRI with maps of the micro-scale vasculature measured in human brain specimens with CLARITY-assisted microimaging. We will then link this anatomical information with dynamic models built from 2-photon microscopy performed in rodents where the changes in vessel diameter, blood flow and oxygenation can be measured directly in each vessel type across all stages of the vascular hierarchy. We hypothesize that newly introduced models of hemo- and vaso-dynamics built from 2-photon microscopy, linked with a detailed micro- and macroscopically mapped human microvascular anatomy, can be exploited to improve the spatial and temporal specificity of human fMRI. To supply human vasculature reconstructions to our models, we propose a two-scale approach. We first advance 7 Tesla MR Angiography (MRA) techniques to image the pial vascular network as well as intracortical vessels and vascular layers of the cerebral cortex to achieve a mesoscopic model. To form the micron-scale model of vasculature at the capillary level, we will use the CLARITY technique to image the full vascular tree (from arterioles through capillaries to venules) in human primary visual cortex. To predict vasodynamic changes driven by neuronal activation, we will adapt a model derived from dynamic in vivo 2-photon microscopy of vessel diameters in rodents to human microvascular anatomy. To adapt this to human microvasculature requires a careful multi-stage transferal. First we will measure bulk changes in microvessel diameter, a.k.a. cerebral blood volume (CBV), across multiple levels of the vascular hierarchy and confirm that the model can predict the CBV-fMRI signal. The CBV-fMRI signal is used because it is a vasodynamic signal directly reflecting vessel diameter changes occurring alongside local neuronal activity (rather than the subsequent hemodynamic changes). After performing this validation we will build a dynamic model of the microvascular tree in human cortex based on our vascular reconstruction, and again measure CBV-fMRI changes across multiple levels of the vascular hierarchy. We will finally test the ability of this model to improve the neuronal specificity of fMRI by imaging the functional architecture in human visual cortex. This model will also enable the formulation and testing of hypotheses about the discriminability of fMRI responses elicited from nearby neuronal populations, and guide development of future advanced acquisition technologies.