Biophysical modeling of the functional MRI signal through parametric variations in neuronal activation and blood vessel anatomy using realistic synthetic microvascular networks

使用真实的合成微血管网络，通过神经元激活和血管解剖的参数变化对功能性 MRI 信号进行生物物理建模

• 批准号: 10323249
• 负责人: Grant Hartung
• 金额: $ 7.62万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-01 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10323249
• 关键词: Algorithms; Anatomy; Animal Model; Animals; Area; BRAIN initiative; Back; Biophysics; Blood; Blood Vessels; Blood flow; Brain; Brain Mapping; Data; Dependence; Evolution; Experimental Designs; Fellowship; Functional Magnetic Resonance Imaging; Future; Geometry; Goals; Human; Human Volunteers; Image Analysis; Joints; Knowledge; Measurable; Measurement; Measures; Methods; Microscopic; Microscopy; Mission; Modeling; Modernization; Monitor; Mus; Neurons; Outcome; Pattern; Physics; Physiology; Publishing; Regulation; Reporting; Research; Research Priority; Resolution; Series; Shapes; Signal Transduction; Site; Stimulus; Techniques; Technology; Testing; Time; Variant; Vascular System; Work; base; biophysical model; blood oxygen level dependent; blood oxygenation level dependent response; brain tissue; computer framework; data acquisition; density; experimental study; hemodynamics; imaging study; improved; in vivo; neuroimaging; neuronal patterning; novel; optical imaging; oxygen transport; predictive modeling; relating to nervous system; response; simulation; spatiotemporal; tool

The most widespread tool for measuring brain activity noninvasively in humans is functional magnetic resonance imaging (fMRI), which typically tracks changes in blood flow and oxygenation using the blood-oxygenation-level- dependent (BOLD) signal. Although BOLD is an indirect measure of neural firing, it has been shown to be a faithful measure of brain activation, yet the details of brain vascular anatomy and physiology are known to influence all fMRI signals including BOLD. Recently, invasive optical imaging studies in animals demonstrated that the changes in blood flow regulation occurring alongside neuronal activity are far more precise than previously believed, indicating fMRI can be a faithful representation of neuronal activity at fine spatial and temporal scales. Recent biophysical simulations have further demonstrated how the microvascular network, and the vascular response to neural activity, can influence fMRI signals in humans, suggesting that modeling can help improve fMRI interpretation. We propose to extend this work through a series of biophysical simulations in which we will parametrically vary vascular anatomy, neuronal activity, and the vascular response to neuronal activity then simulate the resulting BOLD responses to characterize these influences on fMRI. We hypothesize that the specifics of the vascular anatomy and neuronal activity patterns will both have measurable effects on the fMRI signal and that our modeling framework can predict these influences—which can improve inferences of neural activity from fMRI. This approach is only now possible due to the availability of sufficiently-large-scale microscopy data, our highly efficient computational framework, and our novel vascular synthesis algorithm. For this work we will extend our new blood flow and oxygen transport framework to simulate vasomotive responses to neuronal activity, then incorporate MR physics to generate the corresponding BOLD signals. Our modeling platform provides unique capabilities: synthesis of realistic, large-scale vascular networks with fully controllable geometry, density, and topology; and robust simulations of vascular systems far larger than ever attempted. This will allow for accurate, efficient calculations at a sufficient scale to generate meaningful BOLD responses that can be related to human fMRI data. We will test whether other aspects of the hemodynamic response may provide more faithful representations of neuronal activity. Finally, we will test our model predictions against empirical data with a simple, high-resolution human fMRI experiment. This work spans four Aims. In Aim 1 we compare four candidate “scenarios” describing the vascular response to neural activity. In Aim 2 we test the dependence BOLD on vascular anatomy by synthesizing large-scale vascular networks. In Aim 3 we test dependence of patterns of neuronal activity on BOLD by simulating systematically varying spatiotemporal patterns of neuronal activity. In Aim 4 we test model predictions through a high-resolution human fMRI experiment measuring BOLD responses to parametrically varied neuronal activity patterns. The outcome of this work will be a characterization of fMRI signal dependence on factors that cannot be measured in humans in vivo.

用于无创测量人类大脑活动的最广泛的工具是功能磁共振 成像（fMRI），通常使用血氧水平跟踪血流和氧合的变化 相关（粗体）信号。虽然 BOLD 是神经放电的间接测量，但它已被证明是一种 大脑活动的忠实测量，但脑血管解剖学和生理学的细节众所周知 影响所有 fMRI 信号，包括 BOLD。最近，对动物的侵入性光学成像研究表明 与神经元活动一起发生的血流调节的变化比 以前认为，这表明功能磁共振成像可以忠实地代表精细空间和神经元活动 时间尺度。最近的生物物理模拟进一步证明了微血管网络和 血管对神经活动的反应可以影响人类的功能磁共振成像信号，这表明建模可以 帮助改善功能磁共振成像的解释。我们建议通过一系列生物物理模拟来扩展这项工作 我们将参数化地改变血管解剖结构、神经元活动以及血管对神经元的反应 然后模拟由此产生的 BOLD 反应来表征这些对功能磁共振成像的影响。我们假设 血管解剖结构和神经元活动模式的细节都会对 fMRI 信号以及我们的建模框架可以预测这些影响，从而改进推断 功能磁共振成像的神经活动。由于具有足够大规模的可用性，这种方法现在才成为可能 显微镜数据、我们的高效计算框架和我们新颖的血管合成算法。 对于这项工作，我们将扩展新的血流和氧气运输框架来模拟血管运动 对神经元活动做出反应，然后结合 MR 物理原理来生成相应的 BOLD 信号。我们的 建模平台提供独特的功能：合成真实的、大规模的血管网络 可控几何形状、密度和拓扑；以及对比以往大得多的血管系统的强大模拟 尝试过。这将允许在足够的规模上进行准确、高效的计算，以生成有意义的 BOLD 可能与人类功能磁共振成像数据相关的反应。我们将测试血流动力学的其他方面是否 反应可以提供更忠实的神经元活动表征。最后，我们将测试我们的模型预测 通过简单、高分辨率的人体功能磁共振成像实验来对照经验数据。这项工作涵盖四个目标。瞄准 在图 1 中，我们比较了描述血管对神经活动的反应的四种候选“场景”。在目标 2 中我们测试 通过合成大规模血管网络，BOLD 对血管解剖结构的依赖。在目标 3 中我们测试 通过模拟系统变化的时空来确定神经元活动模式对 BOLD 的依赖性 神经元活动模式。在目标 4 中，我们通过高分辨率人体功能磁共振成像测试模型预测 测量对参数变化的神经元活动模式的大胆反应的实验。这件事的结果 这项工作将表征功能磁共振成像信号对人体体内无法测量的因素的依赖性。