Connecting Brain Networks Across Subjects and Across Modalities

描述（由申请人提供）：近年来，脑成像数据的网络分析越来越受欢迎。这样的分析使研究人员能够从整体上描述大脑的结构或功能组织，而不是只关注信号最强的区域。网络分析允许将网络作为一个整体进行表征，以及每个网络节点如何对网络做出贡献。此外，大脑网络的分层组织可以被描述为紧密连接的节点集群的集合，称为模块。这些模块通常在空间上与与某些认知和感觉过程相关的大脑区域重合。与其他类型的网络数据相比，脑网络数据具有独特的性质。第一个属性是，大脑网络的多重实现可以从多个对象中观察到，这在许多社会或技术网络中是根本不可能的，因为只有一个感兴趣的网络。其次，大脑网络可以在每个节点（可以是解剖区域或体素）或功能相关模块上跨主题对齐。利用这些特性的一种方法是结合大脑网络数据。这使研究人员能够研究跨主题的一致网络特性。在本建议中，将开发一个方法框架来共同分析多个网络数据。首先，将通过从多个学科建立一个群体大脑网络来开发群体分析的框架。这可以通过在每个节点（具体目标1）或模块（具体目标2）对齐跨主题的网络来实现。除了结合跨主题的网络外，还将开发一个跨图像模式结合脑网络数据的框架，以检查结构和功能脑网络的异同（具体目标3）。多种脑网络结构和功能研究表明，网络结构和关键节点的位置具有共性。因此，所提出的方法将为研究这种结构-功能关系提供一个非常需要的工具。

DESCRIPTION (provided by applicant): In recent years, network analyses of brain imaging data have increased in popularity. Such analyses allow investigators to describe the structural or functional organization of the brain as a whole, rather than focusing only on the areas with the strongest signal. Network analyses allow characterization of a network as a whole, as well as how each network node contributes to the network. Furthermore, hierarchical organization of brain networks can be described as a collection of tightly interconnected clusters of nodes, known as modules. Such modules often spatially coincide with brain areas relevant to certain cognitive and sensory processes. Compared to other types of network data, brain network data have unique properties. The first property is that multiple realizations of the brain network can be observed from multiple subjects, which is simply impossible in many social or technological networks since there is only one network of interest. Secondly, brain networks can be aligned across subjects at each node (which can be an anatomical area or a voxel) or at functionally relevant modules. One way to take advantage of these properties is to combine brain network data. This enables researchers to investigate consistent network properties across subjects. In this proposal, a methodological framework will be developed to analyze multiple network data together. At first, a framework for a group analysis will be developed by building a group brain network from multiple subjects. This can be accomplished by aligning networks across subjects at each node (Specific Aim 1) or module (Specific Aim 2). In addition to combining networks across subjects, a framework to combine brain network data across image modalities will be developed in order to examine similarities and differences in structural and functional brain networks (Specific Aim 3). Multiple structural and functional brain network studies have indicated commonalities in the network structures and the location of key nodes. Thus, the proposed methods will provide a highly needed tool for investigations of such structure-function relationships. PUBLIC HEALTH RELEVANCE: The proposed project will develop tools necessary to understand how different brain areas are connected in terms of structure and function. The resulting methods can be applied to a wide variety of neuroimaging studies on cognitive processes and neurological disorders, and can provide a completely new perspective of the brain as a single network, rather than focusing on identifying the most abnormal areas in the brain.