CRCNS: Computational Modeling of Microvascular Effects in Cortical Laminar fMRI

今天，非侵入性地测量全脑活动的最广泛的工具是功能性磁共振成像。 共振成像（fMRI）。虽然功能磁共振成像通过测量相关的神经活动间接跟踪神经活动， 血流量、血容量和氧合的变化，最近的证据表明， 大脑中的血液动力学变化比以前认为的要精确得多，也许在 功能结构的基本模块的精细空间尺度：大脑皮质柱和层。如果 的确，这可以使大脑计算和电路的新研究成为可能，因为几个皮层是很好的， 已知的输入和输出沿着大脑通信的规范前馈和反馈路径。 这一新兴领域的"层状功能磁共振成像"面临的主要挑战是如何解释复杂的 血液动力学信号来推断神经活动的潜在模式。 受此启发，我们的总体目标是提高我们测量不同皮层神经活动的能力， 层与人类功能磁共振成像通过详细的生物物理模型的基础血流动力学反应。 我们将开发一个新的计算框架来模拟fMRI信号使用真实的微血管 伴随的相关血流、容量和氧合变化的网络和动力学 神经活动这一框架已得到验证，使用光学显微镜测量的微血管 解剖学和动力学从小动物模型，在这里，我们第一次将其扩展到人类 皮层我们将结合联合收割机超高分辨率的体内血管解剖成像数据收集在9.4 特斯拉与我们的验证算法合成现实的微血管网络，以生成人类 血管模型，具体到个人志愿者，并使用这些来模拟功能磁共振成像反应的运动任务 用来激活特定的皮层然后，我们将模拟几种形式的fMRI的反应， 对比度-每种都对复杂血流动力学反应的不同方面敏感-以及 将我们的预测与高分辨率的功能磁共振成像测量结果进行比较。最后，为了深入了解功能磁共振成像是否 可以正确地用来推断皮质层内的神经活动，我们将量化 通过模拟不同层次的神经活动模式，然后比较 计算的功能磁共振成像激活曲线这将告诉我们哪些神经活动模式可以从 这有助于量化层状功能磁共振成像破译人脑的能力， 电路我们解决了一个根本的差距，我们的知识，关于人类功能磁共振成像的限制： 功能磁共振成像可以准确地报告不同皮质层内的激活。我们的方法可以量化 功能磁共振成像如何通过血管反应的"过滤器"来观察神经活动， 最新功能磁共振成像对比的起源这将有助于解释功能磁共振成像的神经科学和 研究以及转化/临床研究，帮助消除不必要的影响， 血管-将观察到的功能磁共振成像模式转化为神经活动模式，以更好地了解大脑 在健康和疾病中发挥作用。 相关性（参见说明）： 功能性磁共振成像（fMRI）是最广泛的工具，用于测量活动， 整个大脑的非侵入性，并产生了我们的大部分知识的功能组织， 然而，功能磁共振成像并不测量神经元的放电，它通过测量大脑的活动来检测大脑的活动。 大脑中向神经元输送氧气的血流发生变化。在这里，我们寻求发展一个 分析框架，这将使我们能够更准确地推断出哪些神经元组正在放电， 人类功能磁共振成像数据，通过使用血流和血氧变化的计算建模， 大脑中最小的血管网络。如今，功能磁共振成像是实验人类不可或缺的 通过提高这项技术的神经特异性，fMRI可以成为一种更可靠的工具 用于测量健康和疾病中的大脑功能，扩大其在基础神经科学中的效用， 转化/临床研究，包括对神经和精神疾病的研究， 还提供了对小血管疾病和其他血管贡献的更深入的机制理解 认知障碍和痴呆症。 PHS 398（修订版03/2020批准至02/28/2023）OMB编号0925 - 0001 第2页：第2页 项目主任/主要研究者（最后，第一，中间）：Polimeni，Jonathan Rizzo

Today, the most widespread tool for measuring whole-brain activity noninvasively is functional magnetic resonance imaging (fMRI). Although fMRI tracks neural activity indirectly through measuring the associated changes in blood flow, volume and oxygenation, recent evidence has suggested that these active hemodynamic changes in the brain are far more precisely coordinated than previously believed, perhaps at the fine spatial scale of the basic modules of functional architecture: cerebral cortical columns and layers. If true, this could enable new studies of brain computation and circuitry as several cortical layers are the well- known inputs and outputs along canonical feedforward and feedback pathways of brain communication. The main challenge faced by this emerging field of “laminar fMRI” is how to interpret the complex hemodynamic signals to infer the underlying patterns of neural activity. Motivated by this, our overall goal is to improve our ability to measure neural activity from distinct cortical layers with human fMRI through detailed biophysical modeling of the underlying hemodynamic response. We will develop a new computational framework to simulate the fMRI signals using realistic microvascular networks and dynamics of associated blood flow, volume, and oxygenation changes that accompany neural activity. This framework has been validated using optical microscopy measures of the microvascular anatomy and dynamics from small animal models, and here we extend it for the first time to the human cortex. We will combine ultra-high-resolution in vivo vascular anatomical imaging data collected at 9.4 Tesla with our validated algorithm for synthesizing realistic microvascular networks to generate human vascular models specific to individual volunteers, and use these to simulate fMRI responses to motor tasks designed to activate specific cortical layers. We will then simulate responses of several forms of fMRI contrast—that are each sensitive to different aspects of the complex hemodynamic response—and compare our predictions to high-resolution fMRI measurements. Finally, to gain insight into whether fMRI can be used correctly to infer neural activity within cortical layers, we will quantify the discriminability of laminar fMRI by simulating various patterns of neural activity across layers and then comparing the computed fMRI activation profiles. This will tell us which neural activity patterns can be distinguished from one another, and which cannot, to help quantify the ability of laminar fMRI to decipher human brain circuitry. We address a fundamental gap in our knowledge regarding the limits of human fMRI: whether fMRI can accurately report on activation within distinct cortical layers. Our approach will allow us to quantify how fMRI sees the neural activity through the “filter” of the vascular response, and provide insight into the origins of newly-available fMRI contrasts. This will aid in the interpretability of fMRI for both neuroscience research as well as for translational/clinical research by helping to remove unwanted effects of the vasculature—to translate the observed fMRI patterns into neural activity patterns to better understand brain function in health and disease. RELEVANCE (See instructions): Functional magnetic resonance imaging (fMRI) is the most widespread tool for measuring activity across the entire brain noninvasively and has produced much of our knowledge of the functional organization in the human brain, however fMRI does not measure neuron firing—it detects brain activity by measuring changes in blood flow in the brain that delivers oxygen to the neurons. Here we seek to develop an analysis framework that will allow us to more accurately infer which groups of neurons are firing based on human fMRI data by using computational modeling of blood flow and blood oxygenation changes through networks of the smallest blood vessels in the brain. Today fMRI is indispensable for experimental human neuroscience; by improving the neural specificity of this technique, fMRI can become a more reliable tool for measuring brain function in health and disease, expanding its utility in basic neuroscience and translational/clinical research including investigations into neurological and psychiatric disease, and may also provide deeper mechanistic understanding into small vessel disease and other vascular contributions to cognitive impairment and dementia. PHS 398 (Rev. 03/2020 Approved Through 02/28/2023) OMB No. 0925-0001 Page 2 Form Page 2 Program Director/Principal Investigator (Last, First, Middle): Polimeni, Jonathan Rizzo