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.

事实证明，复杂的网络理论是代表和分析许多学科的关系数据的多功能框架，包括社会科学，生物学和信息系统。从这些发展中受益的一种特殊应用是认知神经科学。当代神经影像学技术提供了神经记录，并增加了空间和时间分辨率，从而产生了丰富的多通道数据集，可以利用这些数据集，以详细描述大脑功能连接模式。最近的研究提供了证据，表明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心理病理学的表现中起着重要作用。功能性大脑网络的当前表征仅限于全球和静态度量，这些措施量化了跨受试者，大脑区域和时间的平均活动。这项研究通过开发信号处理框架来解决此问题，以研究基于多通道脑电图（EEG）数据的功能性脑网络的随时间变化性质，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通性的动态性质。首先，实施了同步的时变量以及统计假设测试，以跨时间和频率构建稀疏的加权图。 其次，层次多主题聚类算法是使用信息理论标准来确定随时间变化的社区结构的。第三，开发了一个统计信号处理框架，以总结具有一些代表性网络的动态网络活动，并确定瞬态和固定活动。最后，这项研究应用于认知控制的研究，以识别控制认知和感知的途径，以及理解心理病理学的基本网络原因。