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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

基本信息

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
项目类别：
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

项目摘要

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，最广泛的无创测量全脑活动的工具是功能磁