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Understanding evoked and resting-state fMRI through multi scale imaging

通过多尺度成像了解诱发和静息态 fMRI

基本信息

批准号：
9205912
负责人：
R Todd Constable
金额：
$ 107.32万
依托单位：
YALE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-16 至 2021-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9205912
关键词：
Animal Experiments Animal Model Animals Area Autistic Disorder Bioinformatics Biological Biomedical Engineering Blood flow Brain Brain imaging Cells Communication Complement Data Data Analyses Data Set Development Dimensions Disease Ensure Equilibrium Fingers Fluorescence Functional Magnetic Resonance Imaging Gold Human Image Imaging Device Imaging Techniques Knock-out Label Lesion Link Measures Mental Depression Metabolic Methodology Methods Microscopic Modality Modeling Molecular Biology Motor Mus Names Nature Neuroglia Neurological Models Neurons Physiological Population Post-Traumatic Stress Disorders Property Resolution Rest Rodent Role Scientist Sensory Signal Transduction Source System Techniques Testing Therapeutic Intervention Time Transgenic Mice Vibrissae Work animal data base blood oxygen level dependent calcium indicator cell type cellular imaging cognitive task control theory data modeling design excitatory neuron genetic manipulation graph theory human data human subject improved inhibitory neuron insight mouse model multidisciplinary network models novel novel strategies optogenetics predictive modeling relating to nervous system research study response spatiotemporal temporal measurement theories two-photon

项目摘要

Project Summary This RFA is aimed at bringing together interdisciplinary teams to focus on novel, transformative and integrative efforts that will revolutionize our understanding of the biological and bioinformatics content of the data collected from non-invasive human functional brain imaging techniques. Our proposal does exactly this. We are a multidisciplinary team of scientists with combined expertise in optogenetics, two photon Ca2+ imaging, biomedical engineering, molecular biology, animal and human fMRI, network theory, data analysis and modeling. In this work, we will use a novel imaging device that combines mesoscopic imaging of genetically encoded Ca2+ indicators with very high (50m) spatial and high temporal (25ms) resolution across the entire cortex and simultaneous fMRI in transgenic mouse models. These animal experiments are designed to complement similar experiments in healthy human subjects. The results from the animal experiments will answer several long-standing questions about the source of the fMRI signal. Specifically, using imaging, we will quantify the contributions of different cell populations (excitatory neurons, inhibitory neurons, and glial cells) to the fMRI signal observed. We will be able to test and validate, for the first time, the application of graph theory approaches to the analysis of human fMRI data, and we will develop and test a new approach based on control theory for extracting more information from the fMRI signal. A powerful set of carefully controlled imaging experiments in mice will inform several aspects of analysis of human data. The human data will contain a test/retest component to ensure replication of the results and to allow predictive models to be built in one data set and tested in another. This work truly bridges scale and modalities and the simultaneous nature of the animal experiments will allow unprecedented clarity on the underlying source of the signal changes observed in fMRI. These animal studies are essential for providing new insights into the basis of human fMRI signals and data of this nature has not previously been available. The work in this proposal is novel in that it will directly inform measures of both evoked and spontaneous activity in terms of the underlying cell signal sources revealing the relative contributions of excitatory, inhibitory and glial cells to the fMRI signal. The implications of the work are multifaceted. This work will provide a platform for evaluating neurological models of disease. For example, mouse models of disease can be used to link to human data in diseases such as PTSD, depression, and autism, to name a few. It will also provide a firmer biological basis for understanding the node and network measures used in assessing the functional organization of the brain and will have important implications for the design of therapeutic interventions across a range of diseases.

项目总结