Understanding evoked and resting-state fMRI through multi scale imaging

项目摘要 该RFA旨在将跨学科团队聚集在一起，专注于新颖性、变革性和综合性 这些努力将彻底改变我们对所收集数据的生物和生物信息学内容的理解 来自非侵入性的人类功能脑成像技术。我们的提案正是做到了这一点。我们是一家 多学科科学家团队，拥有光遗传学、双光子钙离子成像、 生物医学工程、分子生物学、动物和人类功能磁共振成像、网络理论、数据分析和 模特儿。在这项工作中，我们将使用一种新型的成像设备，它结合了基因的介观成像 编码的钙离子指示器具有非常高的空间(50m)和高时间(25ms)分辨率 转基因小鼠模型的皮层和同步功能磁共振成像。这些动物实验旨在 补充健康受试者的类似实验。动物实验的结果将会 回答关于功能磁共振信号来源的几个长期存在的问题。具体地说，使用成像，我们 将量化不同细胞群(兴奋性神经元、抑制性神经元和神经胶质细胞)的贡献 与观察到的fMRI信号相吻合。我们将能够第一次测试和验证图形的应用 分析人体功能磁共振数据的理论方法，我们将开发和测试一种基于 从fMRI信号中提取更多信息的控制理论。一套强大的精心控制的 老鼠的成像实验将为人类数据分析的几个方面提供信息。人类数据将 包含测试/重新测试组件，以确保复制结果并允许内置预测模型 一个数据集，并在另一个数据集上进行测试。这项工作真正架起了规模和模式以及 动物实验将使信号变化的潜在来源得到前所未有的澄清 在功能磁共振成像中观察。这些动物研究对于为人类功能磁共振成像的基础提供新的见解是必不可少的。 这种性质的信号和数据以前从未出现过。这项提案中的工作是新颖的，因为它 将根据潜在的细胞信号直接通知诱发和自发活动的测量 来源揭示了兴奋性、抑制性和神经胶质细胞对fMRI信号的相对贡献。这个 这项工作的影响是多方面的。这项工作将为评估神经学模型提供一个平台 疾病的威胁。例如，疾病的老鼠模型可以用来链接到人类在疾病中的数据，例如 创伤后应激障碍、抑郁症和自闭症，仅举几例。它还将为理解 用于评估大脑功能组织的节点和网络测量，将具有 对一系列疾病的治疗干预措施的设计具有重要意义。

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.