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越来越复杂的方法开辟了精神病学，心理学和神经病学基础研究的重要领域。这些新的神经成像方式为信号处理社区带来了新的机会和挑战。一个挑战是定量在不同位点记录的信号之间的相互作用，也称为大脑中的功能整合。当前的成像模式不能提供在不同位点记录的电气或磁性活性之间的功能相互作用的量度。为了更好地了解大脑如何处理信息，量化其子系统之间的这些相互作用至关重要。这项研究涉及开发两种互补的信号处理方法，以根据脑电图记录（EEG）记录来量化大脑中的连通性模式。研究者开发了两种类型的时间变化的连通性测量，以量化大脑中的功能整合：1）与汇总的效果之间的量化量相关的量相关的量相关的效果，这些量子是分离层次的效果。信号； 2）量化单个信号复杂性的时频平面上的信息理论度量，以及信号对之间的相互依赖性。这些措施应用于脑电图数据集，特别是精神分裂症等心理病理学的研究。研究者的方法对常规脑电图处理技术进行了重大改进，因为许多因素对于理解大脑信号的动态至关重要，即时间，频率，相位，空间和信息流被整合到一个联合表示中。