Dynamic Network Analysis: Analyzing the Chronnectome

动态网络分析：分析时间组

• 批准号: 1610762
• 负责人: Marc Niethammer
• 金额: $ 35.64万
• 依托单位: University of North Carolina at Chapel Hill
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1610762&HistoricalAwards=false
• 关键词: Dynamic; Network; Analysis; Analyzing; Chronnectome

Networks describe interactions between objects: they can for example describe connections between people, as in social networks; capture dependencies in biological processes such as gene regulation; or represent the interplay and connectivity of different structural and functional areas in the brain. Numerous methods for network analysis have been developed using different quantitative approaches. However, many of these methods are still predominantly available for the analysis of static networks, i.e., snapshots of a network at a particular point in time. However, to assess how networks such as those in the brain change over time, improved methods for dynamic network analysis are required. This project will develop mathematical approaches motivated by the driving biological problem of analyzing temporal change of structural and functional brain connectivity, the chronnectome. Structural connectivity includes white matter connections in the brain and hence the underlying cabling enabling information exchange. Functional brain connectivity on the other hand describes how tasks influence brain activity and how different brain areas behave similarly under tasks. Quantifying changes in structural and/or functional connectivity over time can improve understanding of brain diseases, which deviate from normality. Moreover, the approaches developed here will have general use for other dynamic networks, from other biological networks to social networks and beyond. To maximize impact, the computational methods developed here will be made available in open-source form, with a software license permitting free commercial and non-commercial use. The project also includes research training and courses for students in network science.Networks are commonly described as graphs, with nodes describing entities in a system and edges node relationships. Networks range from unstructured and rapidly changing social networks to structured slowly varying networks capturing structural brain connectivity. Network science seeks to develop methods to mine information from interaction patterns, for example, extracting tightly coupled nodes in communities. Time-dependent network data is increasingly available; however, sufficient analysis methods are still lacking as the majority of approaches have focused on static data, with ongoing development of methods for time-dependent networks having become more common only recently. The goal of this project is to advance general time-dependent network analysis, with brain chronnectome analysis as the guiding driving problem to motivate the technical development. Current approaches for the analysis of time-dependent networks lack several key properties required for chronnectome analysis: e.g., (i) the ability to analyze longitudinal network data, where networks are available for multiple subjects at multiple timepoints, (ii) the ability to include domain-specific prior information (such as prior knowledge of connectivity patterns), and (iii) the ability to deal with inhomogeneous subject groups and discontinuities in time. To address these shortcomings, this project will develop customized network analysis approaches based on (i) extensions to the stochastic block model approach of network analysis, and (ii) regression models for network-valued data.

网络描述对象之间的交互：例如，它们可以描述人与人之间的联系，如社交网络；捕捉生物过程中的依赖性，例如基因调控；或代表大脑中不同结构和功能区域的相互作用和连接。已经使用不同的定量方法开发了许多网络分析方法。然而，其中许多方法仍然主要用于静态网络的分析，即特定时间点的网络快照。然而，为了评估大脑中的网络如何随时间变化，需要改进的动态网络分析方法。该项目将开发由驱动生物学问题驱动的数学方法，分析大脑结构和功能连接的时间变化，即时间组。结构连接包括大脑中的白质连接，以及支持信息交换的底层布线。另一方面，功能性大脑连接描述了任务如何影响大脑活动以及不同大脑区域在任务下如何表现相似。量化结构和/或功能连接随时间的变化可以提高对偏离正常状态的脑部疾病的理解。此外，这里开发的方法将普遍用于其他动态网络，从其他生物网络到社交网络等等。为了最大限度地发挥影响，这里开发的计算方法将以开源形式提供，并提供允许免费商业和非商业使用的软件许可证。该项目还包括为网络科学学生提供研究培训和课程。网络通常被描述为图，其中节点描述系统中的实体和边缘节点关系。网络范围从非结构化且快速变化的社交网络到捕获结构性大脑连接的结构化缓慢变化的网络。网络科学寻求开发从交互模式中挖掘信息的方法，例如提取社区中紧密耦合的节点。与时间相关的网络数据越来越可用；然而，仍然缺乏足够的分析方法，因为大多数方法都集中在静态数据上，而持续开发的时间相关网络方法直到最近才变得更加普遍。该项目的目标是推进通用的时间相关网络分析，以脑时间组分析作为指导驱动问题来推动技术发展。当前用于分析时间相关网络的方法缺乏时间组分析所需的几个关键属性：例如，（i）分析纵向网络数据的能力，其中网络在多个时间点可用于多个受试者，（ii）包含特定领域先验信息（例如连接模式的先验知识）的能力，以及（iii）处理不均匀受试者组和时间不连续性的能力。为了解决这些缺点，该项目将基于（i）网络分析随机块模型方法的扩展，以及（ii）网络价值数据的回归模型来开发定制的网络分析方法。