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。这种分析使研究人员能够描述整个大脑的结构或功能组织，而不仅仅是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互连的节点集群的集合，称为模块。这些模块通常在空间上与某些认知和感觉过程相关的大脑区域相一致。与其他类型的网络数据相比，大脑网络数据具有独特的属性。第一个特性是，可以从多个主体观察到大脑网络的多个实现，这在许多社交或技术网络中是不可能的，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）或功能相关模块处跨受试者对齐。利用这些特性的一种方法是将联合收割机大脑网络数据结合起来。这使研究人员能够研究跨学科的一致网络特性。在该提案中，将开发一个方法框架，以一起分析多个网络数据。首先，通过从多个受试者建立一个群体脑网络来开发一个群体分析的框架。这可以通过在每个节点（具体目标1）或模块（具体目标2）处跨学科对齐网络来实现。除了组合受试者之间的网络外，还将开发一个框架，用于在图像模态之间组合联合收割机脑网络数据，以检查结构和功能脑网络的相似性和差异（具体目标3）。多个结构和功能的脑网络研究表明，网络结构和关键节点的位置的共性。因此，所提出的方法将提供一个非常需要的工具，这种结构-功能关系的调查。 公共卫生相关性：该项目将开发必要的工具，以了解不同的大脑区域在结构和功能方面是如何连接的。由此产生的方法可以应用于各种各样的认知过程和神经系统疾病的神经成像研究，并可以提供一个全新的视角，大脑作为一个单一的网络，而不是专注于识别大脑中最异常的区域。

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.