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: 10531274
• 负责人: Grant Hartung
• 金额: $ 7.75万
• 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/10531274
• 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; Joints; Knowledge; Measurable; Measurement; Measures; Methods; Microscopic; Microscopy; Mission; Modeling; Modernization; Monitor; Mus; Neurons; Outcome; Pattern; Physics; Physiology; Postdoctoral Fellow; Publishing; Regulation; Reporting; Research; Research Priority; Resolution; Series; Shapes; Signal Transduction; Site; Stimulus; Techniques; Technology; Testing; Time; Tissues; Variant; Vascular System; Work; 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; neural; neuroimaging; neuronal patterning; novel; optical imaging; oxygen transport; predictive modeling; 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.

非侵入性测量人类大脑活动最广泛的工具是功能磁共振 成像(FMRI)，通常使用血氧水平来跟踪血流和氧合的变化- 依赖(粗体)信号。尽管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，然而脑血管解剖和生理学的细节是已知的 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 与神经元活动同时发生的血流调节的变化远比 以前认为，功能磁共振成像可以忠实地反映神经元在精细空间和 时间尺度。最近的生物物理模拟进一步证明了微血管网络是如何 血管对神经活动的反应，可以影响人类的fMRI信号，这表明建模可以 帮助改进功能磁共振成像解释。我们建议通过一系列生物物理模拟来扩展这项工作 我们将从参数上改变血管解剖、神经元活动和血管对神经元的反应。 然后，活动模拟由此产生的大胆反应，以表征这些对功能磁共振的影响。我们假设 血管解剖和神经元活动模式的细节都将对 FMRI信号，我们建模框架可以预测这些影响，这可以改进推断 功能性核磁共振的神经活动。这种方法现在才有可能，因为有了足够大规模的 显微数据，我们的高效计算框架，以及我们新的血管合成算法。 在这项工作中，我们将扩展我们的新的血流和氧气运输框架来模拟血管运动 对神经元活动的反应，然后结合磁共振物理学产生相应的大胆信号。我们的 建模平台提供独特的功能：合成逼真、大规模的血管网络，并完全 可控的几何、密度和拓扑；以及对比以往任何时候都大得多的血管系统的强大模拟 已尝试。这将允许以足够的比例进行准确、高效的计算，以生成有意义的粗体 可以与人类功能磁共振数据相关的反应。我们将测试血液动力学的其他方面 反应可能会更真实地代表神经元的活动。最后，我们将测试我们的模型预测 通过一个简单、高分辨率的人体功能磁共振实验来对照经验数据。这项工作跨越了四个目标。在AIM 1我们比较了四种描述血管对神经活动的反应的候选“情景”。在目标2中，我们测试 通过合成大规模的血管网络，大胆地依赖血管解剖。在目标3中，我们测试 通过模拟系统变化的时空变化研究神经元活动模式对BOLD的依赖性 神经元活动的模式。在目标4中，我们通过高分辨率人体功能磁共振成像来测试模型预测 实验测量对参数不同的神经元活动模式的大胆反应。这样做的结果是 工作将是表征fMRI信号对无法在人体内测量的因素的依赖性。