Signal Processing for Quantifying the Functional Integration in the Brain

用于量化大脑功能整合的信号处理

• 批准号: 0728984
• 负责人: Selin Aviyente
• 金额: $ 15.55万
• 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: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2011-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0728984&HistoricalAwards=false
• 关键词: Signal; Processing; Quantifying; Functional; Integration

The increasingly sophisticated methods for measuring and visualizing brain activity have opened up important areas of basic research in psychiatry, psychology, and neurology. These new neuroimaging modalities pose new opportunities and challenges for the signal processing community. One such challenge is the quantification of the interactions between signals recorded at different sites, also known as the functional integration in the brain. The current imaging modalities do not provide a measure of the functional interaction between electrical or magnetic activity recorded at different sites. In order to gain a better understanding of how the brain processes information, it is crucial to quantify these interactions between its subsystems. This research involves developing two complementary signal processing methods for quantifying the connectivity patterns in the brain based on the electroencephalogram (EEG) recordings.The investigator develops two types of time-varying measures of connectivity to quantify the functional integration in the brain: 1) Time-varying measures of coherence that separate the effects of amplitude correlation and phase synchrony from each other and quantify the synchrony between pairs of signals; 2) Information-theoretic measures on the time-frequency plane that quantify the complexity of individual signals, as well as the interdependence between pairs of signals. These measures are applied to EEG data sets, in particular to the study of psychopathologies such as schizophrenia. The investigator's approach offers significant improvements over the conventional EEG processing techniques since a number of factors crucial to understanding the dynamics of brain signaling, i.e. time, frequency, phase, space and information flow, are integrated into one joint representation.

测量和可视化大脑活动的日益复杂的方法开辟了精神病学、心理学和神经学基础研究的重要领域。这些新的神经成像模式为信号处理领域带来了新的机遇和挑战。其中一个挑战是量化记录在不同位置的信号之间的相互作用，也被称为大脑的功能整合。目前的成像方式不能提供在不同部位记录的电或磁活动之间的功能相互作用的测量。为了更好地理解大脑如何处理信息，量化其子系统之间的相互作用是至关重要的。本研究涉及开发两种互补的信号处理方法，用于量化基于脑电图（EEG）记录的大脑连接模式。研究者开发了两种类型的时变连接测量来量化大脑的功能整合：1)时变相干测量，将振幅相关和相位同步的影响分开，量化信号对之间的同步性；2)在时频平面上量化单个信号的复杂性以及信号对之间的相互依赖性的信息理论度量。这些措施适用于脑电图数据集，特别是精神分裂症等精神病理学的研究。研究者的方法比传统的脑电图处理技术提供了显著的改进，因为许多对理解大脑信号动力学至关重要的因素，即时间、频率、相位、空间和信息流，被集成到一个联合表示中。