CIF: Small: A comprehensive framework for dynamic network tracking and clustering with applications to functional brain connectivity

CIF：小型：动态网络跟踪和聚类的综合框架，应用于功能性大脑连接

• 批准号: 1422262
• 负责人: Selin Aviyente
• 金额: $ 38万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422262&HistoricalAwards=false
• 关键词: CIF; Small; comprehensive; framework; dynamic

Complex network theory has proved to be a versatile framework to represent and analyze relational data that is abundant in many disciplines including the social sciences, information systems, biology and neuroscience. Until recently, the research on network theory has mainly focused on static graphs, i.e. the relationships between nodes of the network do not change with time. However, almost all real networks are dynamic in nature such as social networks with connections that change over time or functional neural networks that reorganize rapidly in response to stimuli. Most of the current studies of dynamic networks focuses on two major and interrelated problems: anomaly or change point detection in a time series of graphs and evolutionary clustering to identify the time-varying structure of networks. This research addresses these two problems simultaneously in a unified framework to monitor and track the changes in network topology and to characterize each ?network state? with a single community structure. In particular, this research focuses on the functional connectivity networks (FCNs) of the human brain that reorganize themselves dynamically during perception, cognition and execution of mental processes.The investigator develops two complementary approaches to address dynamic network monitoring problem: 1) A multi-scale framework for joint anomaly detection and network state identification in time-varying networks; and, 2) Consensus spectral clustering methods along with tensor decomposition for succinct topographic representation of network states. Finally, this dynamic network monitoring framework is applied to electroencephalogram (EEG) data collected using an experimental protocol designed to assess well-known salience and control functional networks associated with affective regulation and cognitive control.

事实证明，复杂的网络理论是代表和分析关系数据的多功能框架，这些数据在许多学科中都很丰富，包括社会科学，信息系统，生物学和神经科学。直到最近，对网络理论的研究主要集中在静态图上，即网络节点之间的关系不会随时间变化。但是，几乎所有真实的网络本质上都是动态的，例如随着时间的流逝而变化的连接或功能性神经网络，这些网络会迅速反应刺激。当前的动态网络研究的大多数研究都集中在两个主要和相互关联的问题上：时间序列中的异常或变化点检测和进化聚类，以识别网络的时间变化结构。这项研究在统一的框架中同时解决了这两个问题，以监视和跟踪网络拓扑的变化并表征每个网络状态？具有单一的社区结构。特别是，这项研究的重点是人脑的功能连接网络（FCN），这些网络在感知，认知和执行心理过程中动态重组。 2）共识光谱聚类方法以及网络状态简洁地形表示的张量分解。最后，该动态网络监控框架应用于使用实验协议收集的脑电图（EEG）数据，旨在评估众所周知的显着性和与情感调节和认知控制相关的控制功能网络。