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

复杂网络理论已经被证明是一种通用的框架来表示和分析关系数据，这些数据在社会科学、信息系统、生物学和神经科学等许多学科中都有丰富的应用。直到最近，网络理论的研究主要集中在静态图上，即网络中节点之间的关系不随时间变化。然而，几乎所有真实的网络本质上都是动态的，例如具有随时间变化的连接的社交网络，或者对刺激做出快速重组的功能神经网络。目前对动态网络的研究大多集中在两个主要且相互关联的问题上：图的时间序列中的异常或变化点检测和用于识别网络时变结构的进化聚类。本研究在一个统一的框架内同时解决这两个问题，以监控和跟踪网络拓扑的变化，并对每个网络状态进行表征。有一个单一的社区结构。特别针对人脑在感知、认知和执行心理过程中动态重组的功能连接网络，研究人员提出了两种互补的方法来解决动态网络监测问题：1)时变网络中联合异常检测和网络状态识别的多尺度框架；2)共识谱聚类方法和张量分解方法用于简洁的网络状态拓扑表示。最后，将该动态网络监控框架应用于使用实验协议收集的脑电(EEG)数据，该实验协议旨在评估与情感调节和认知控制相关的众所周知的突出度和控制功能网络。

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.