Improving Human fMRI through Modeling and Imaging Microvascular Dynamics

项目总结/摘要 所有的fMRI信号都有血管起源，这被认为是精确测量的主要限制。 当使用血液动力学功能对比如BOLD时，神经元激活的时空定位。 然而，最近使用强大的超高分辨率光学成像技术取得的重大发现 挑战了这种信念。不幸的是，这些措施需要侵入性程序，因此不能 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移到口译中 人类功能磁共振成像数据，以帮助功能磁共振成像发挥其全部潜力。在本提案中，我们计划将联合收割机的详细内容 用高分辨率MRI测量的人体宏观和中尺度血管图， 微尺度脉管系统测量人脑标本与磁共振辅助显微成像。我们将 然后将这些解剖信息与啮齿动物双光子显微镜建立的动态模型联系起来， 其中可以直接在每个血管中测量血管直径、血流和氧合的变化 在血管层次的各个阶段都有。我们假设，新引入的模型， 血管动力学建立从2光子显微镜，与详细的微观和宏观映射 人类微血管解剖学，可以利用，以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特斯拉磁共振血管造影（MRA）技术，以成像软脑膜血管网络以及皮质内 血管和大脑皮层的血管层，以实现介观模型。形成微米尺度的 在毛细血管水平的脉管系统模型中，我们将使用PERFORITY技术对完整的血管树进行成像 (from微动脉通过毛细血管到微静脉）。 为了预测由神经元激活驱动的血管动力学变化，我们将采用来自以下的模型 啮齿类动物血管直径的动态体内双光子显微镜检查与人类微血管解剖学。到 使其适应人类微脉管系统需要仔细的多阶段转移。首先我们测量体积 微血管直径的变化，也称为脑血容量（CBV），跨血管的多个水平 并证实该模型可以预测CBV-fMRI信号。使用CBV-fMRI信号是因为它 是直接反映伴随局部神经元活动发生的血管直径变化的血管动力学信号 （而不是随后的血液动力学变化）。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类皮层微血管树模型，并再次测量 CBV-fMRI在血管层次的多个水平上发生变化。我们将最终测试这个模型的能力 通过对人类视觉皮层的功能结构成像，提高fMRI的神经元特异性。这 该模型还将使有关功能磁共振成像反应的可辨别性的假设的形成和检验成为可能 从附近的神经元群体引出，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.