CAREER: Integrated Research and Education in Functional Brain Networks

职业：功能性大脑网络的综合研究和教育

• 批准号: 0746971
• 负责人: Selin Aviyente
• 金额: $ 40万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0746971&HistoricalAwards=false
• 关键词: CAREER; Integrated; Research; Education; Functional

The advance in the neuroimaging technology has brought with itself many challenges. One key challenge is the identification of dynamic functional networks underlying the observed neural activity. The current imaging modalities reflect solely the local neural activity, and do not provide a measure of the 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 across the brain. This research addresses this problem by developing a comprehensive signal processing framework to study the functionality of the brain as a complex system based on electroencephalogram (EEG) data for a better understanding of psychopathologies.This research develops three major signal processing methods to improve the current understanding of the brain as an integrated information processing system. First, data reduction and source separation methods that use the sparsity of the EEG signals in the time-frequency plane are developed to reduce large amounts of data into a few neuronal sources.Second, synchrony and information based functional connectivity measures are developed to quantify the time-varying dependence between different neuronal sources. Third, methods from graph theory are adopted to build network models based on the functional connectivity measures and to identify the neuronal pathways and modules. Finally, this framework is applied to the study of the brain related to different psychopathologies including schizophrenia and impulse control problems. Several educational programs are tightly integrated with these research activities to emphasize the societal benefits of engineering. In particular, the investigator is designing outreach activities for K-12 women students and developing an undergraduate signal processing course with a focus on neuroscience applications.

神经成像技术的进步也带来了许多挑战。一个关键的挑战是识别作为观察到的神经活动的基础的动态功能网络。目前的成像模式仅反映局部神经活动，而不提供在不同部位记录的电或磁活动之间的相互作用的测量。为了更好地了解大脑是如何处理信息的，量化整个大脑中的这些相互作用是至关重要的。为了更好地理解精神病理学，本研究开发了一个全面的信号处理框架来研究作为复杂系统的大脑的功能，以更好地理解精神病理学。本研究开发了三种主要的信号处理方法，以提高当前对作为综合信息处理系统的大脑的理解。首先，利用脑电信号在时频平面上的稀疏性，提出了数据约简和源分离方法，将海量数据简化为几个神经元来源；其次，提出了同步和基于信息的功能连通性度量方法，量化了不同神经元来源之间的时变依赖关系。第三，采用图论中的方法，建立基于功能连通性度量的网络模型，识别神经元的通路和模块。最后，这个框架被应用于与不同精神病理相关的大脑的研究，包括精神分裂症和冲动控制问题。几个教育项目与这些研究活动紧密结合在一起，以强调工程的社会效益。特别是，研究人员正在为K-12女学生设计外展活动，并开发一门侧重于神经科学应用的本科生信号处理课程。