CRCNS: Computational Modeling of Microvascular Effects in Cortical Laminar fMRI

CRCNS：皮质层状功能磁共振成像微血管效应的计算模型

• 批准号: 10482354
• 负责人: Jonathan Rizzo Polimeni
• 金额: $ 16.73万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-10 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10482354
• 关键词: Address; Algorithms; Anatomy; Animal Model; Animals; Architecture; Biological; Blood; Blood Vessels; Blood flow; Brain; Cerebrum; Clinical Research; Communication; Complex; Computer Models; Cortical Column; Data; Dementia; Disease; Experimental Designs; Feedback; Fellowship; Functional Magnetic Resonance Imaging; Goals; Health; Human; Image; Imaging technology; Impaired cognition; Individual; Instruction; Investigation; Investments; Knowledge; Magnetic Resonance Imaging; Measurement; Measures; Medical; Mental disorders; Microscopy; Microvascular Dysfunction; Modeling; Modernization; Motor; Neurons; Neurosciences; Neurosciences Research; Optics; Output; Oxygen; Pattern; Performance; Principal Investigator; Reporting; Research Personnel; Resolution; STEM research; Sampling; Signal Transduction; Specificity; Techniques; Testing; Time; Translating; Trust; Visit; Work; anatomic imaging; base; biophysical model; brain circuitry; brain pathway; career; computer framework; computerized tools; contrast imaging; design; hemodynamics; improved; in vivo; insight; nervous system disorder; neural patterning; neuroimaging; programs; relating to nervous system; response; theories; tool; ultra high resolution; vascular cognitive impairment and dementia; volunteer

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

今天，最广泛使用的非侵入性测量全脑活动的工具是功能磁学。 磁共振成像(FMRI)。尽管fMRI通过测量相关的神经活动间接跟踪神经活动 血流、血容量和氧合的变化，最近的证据表明，这些活动 大脑中的血液动力学变化比之前认为的要精确得多，可能是在 功能建筑基本模块的精细空间尺度：大脑皮层的柱和层。如果 诚然，这可以使对大脑计算和电路的新研究成为可能，因为几个皮质层是很好的- 已知的输入和输出沿着大脑交流的规范前馈和反馈路径。 这个新兴的“层流功能磁共振成像”领域面临的主要挑战是如何解释复杂的 血流动力学信号，以推断潜在的神经活动模式。 受此启发，我们的总体目标是提高从不同大脑皮层测量神经活动的能力 通过对潜在的血流动力学响应进行详细的生物物理建模，使用人类功能磁共振成像进行层析。 我们将开发一种新的计算框架，以使用真实的微血管来模拟功能磁共振信号 随之而来的相关血流、容量和氧合变化的网络和动力学 神经活动。这一框架已经通过光学显微镜测量微血管得到了验证 来自小动物模型的解剖学和动力学，这里我们第一次将其扩展到人类 大脑皮层。我们将结合9.4收集的超高分辨率活体血管解剖成像数据 特斯拉使用我们经过验证的算法合成真实的微血管网络来生成人类 特定于个体志愿者的血管模型，并使用这些模型来模拟运动任务的fMRI反应 旨在激活特定的皮质层。然后我们将模拟几种形式的fMRI的响应 对比--每个人对复杂血流动力学反应的不同方面都很敏感--和 将我们的预测与高分辨率功能磁共振测量结果进行比较。最后，为了深入了解功能磁共振成像 可以正确地用来推断皮层内的神经活动，我们将量化 通过模拟跨层的各种神经活动模式，然后比较 计算出功能磁共振激活曲线。这将告诉我们可以区分哪些神经活动模式 来帮助量化层流功能磁共振成像破译人脑的能力 电路。我们解决了我们对人类功能磁共振成像局限性的认识中的一个根本差距： 功能磁共振成像可以准确地报告不同皮质层内的激活情况。我们的方法将使我们能够量化 FMRI如何通过血管反应的“过滤器”来观察神经活动，并提供对 最新可用的功能磁共振成像对比的起源。这将有助于功能磁共振成像对两种神经科学的可解释性 研究以及转化/临床研究，通过帮助消除 血管系统-将观察到的fMRI模式转换为神经活动模式，以更好地了解大脑 在健康和疾病中发挥作用。 相关性(请参阅说明)： 功能磁共振成像(Fmri)是最广泛使用的测量活动的工具。 整个大脑是非侵入性的，并产生了我们对功能组织的大部分知识 然而，人类大脑的功能磁共振成像并不测量神经元的放电--它通过测量大脑活动来检测 大脑中向神经元输送氧气的血液流动的变化。在这里，我们寻求开发一种 分析框架，使我们能够更准确地推断哪些神经元组正在根据 利用计算模型计算人体fMRI数据的血流量和血氧变化 大脑中最小的血管网络。今天，功能磁共振成像对于实验人类来说是不可或缺的 神经科学；通过提高这种技术的神经特异性，功能磁共振成像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和 翻译/临床研究，包括对神经和精神疾病的调查，并可以 还提供了对小血管疾病和其他血管贡献的更深层次的机制理解 认知障碍和痴呆症。 小灵通398号(截至2023年2月28日批准的第03/2020版)OMB编号0925-0001 第2页表单第2页 项目主任/首席调查员(最后、第一、中间)：Polimeni，Jonathan Rizzo