CIF:Small: A Signal Processing Approach to the Analysis of Time-Varying Functional Networks of the Brain

CIF:Small：一种分析大脑时变功能网络的信号处理方法

• 批准号: 1218377
• 负责人: Selin Aviyente
• 金额: $ 25.4万
• 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: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218377&HistoricalAwards=false
• 关键词: CIF; Small; Signal; Processing; Approach

Complex network theory has proved to be a versatile framework to represent and analyze relational data in many disciplines including the social sciences, biology and information systems. One particular application that has benefited from these developments is cognitive neuroscience. Contemporary neuroimaging techniques provide neural recordings with increasing spatial and temporal resolution yielding rich multichannel datasets that can be exploited for detailed description of functional connectivity patterns in the brain. Recent research provides evidence that neural integration across various spatial and temporal scales plays an important role in a wide range of cognitive and executive processes as well as in the manifestation of neural diseases and psychopathologies. The current characterizations of functional brain networks are limited to global and static measures that quantify average activity across subjects, brain regions and time. This research addresses this problem by developing a signal processing framework to study the time-varying nature of the functional brain networks based on multichannel electroencephalogram (EEG) data for a better understanding of cognitive control. This research develops three major approaches to study the dynamic nature of functional connectivity in the brain. First, time-varying measures of synchrony along with statistical hypothesis testing are implemented to construct sparse weighted graphs across time and frequency. Second, hierarchical multiple subject clustering algorithms are developed with information theoretic criteria to identify time-varying community structure. Third, a statistical signal processing framework is developed to summarize dynamic network activity with a few representative networks and to identify transient and stationary activity. Finally, this research is applied to the study of cognitive control for identifying the pathways that control cognition and perception, and in understanding the basic network causes of psychopathologies.

复杂网络理论已被证明是一个通用的框架，表示和分析关系数据在许多学科，包括社会科学，生物学和信息系统。从这些发展中受益的一个特殊应用是认知神经科学。当代神经成像技术提供了具有越来越高的空间和时间分辨率的神经记录，产生了丰富的多通道数据集，这些数据集可以用于详细描述大脑中的功能连接模式。最近的研究提供的证据表明，跨各种空间和时间尺度的神经整合在广泛的认知和执行过程以及神经疾病和精神病理学的表现中起着重要的作用。目前对功能性大脑网络的表征仅限于量化受试者、大脑区域和时间的平均活动的全局和静态测量。本研究通过开发一个信号处理框架来解决这个问题，该框架基于多通道脑电图（EEG）数据来研究功能性大脑网络的时变性质，以更好地理解认知控制。这项研究开发了三种主要方法来研究大脑功能连接的动态性质。首先，实现同步性的时变测量沿着统计假设检验以构建跨时间和频率的稀疏加权图。 其次，提出了基于信息论准则的层次多主题聚类算法来识别时变社团结构。第三，开发了一个统计信号处理框架，以总结具有几个代表性的网络的动态网络活动，并识别瞬态和稳态活动。最后，本研究应用于认知控制的研究，以确定控制认知和知觉的途径，并在理解的基本网络的精神病理学的原因。