BOLD-Related EEG Signal Estimation Software

BOLD相关脑电图信号估计软件

• 批准号: 8058935
• 负责人: Mark E Pflieger
• 金额: $ 15万
• 依托单位: SOURCE SIGNAL IMAGING, INC.
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-03-15 至 2012-09-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8058935
• 关键词: Algorithms; Attention deficit hyperactivity disorder; Autistic Disorder; Behavioral; Brain; Brain region; Clinical; Cognitive; Computer software; Coupling; Data; Data Set; Detection; Electroencephalography; Epilepsy; Frequencies; Functional Magnetic Resonance Imaging; Heart; Human; Image; Image Analysis; Investigation; Knowledge; Late-Onset Disorder; Light; Link; Mediating; Mental disorders; Modality; Modeling; Neurodevelopmental Disorder; Neurons; Neurosciences; Noise; Phase; Positioning Attribute; Process; Protocols documentation; Provider; Pythons; Radial; Research; Research Personnel; Resolution; Scalp structure; Scanning; Schizophrenia; Scientist; Secondary to; Services; Signal Transduction; Simulate; Small Business Innovation Research Grant; Software Tools; Specificity; Technology; Testing; Work; base; blood oxygen level dependent; brain behavior; brain research; cognitive neuroscience; computerized data processing; design; flexibility; hemodynamics; human data; image processing; innovation; interest; late disease onset; lens; nervous system disorder; neuroimaging; neurophysiology; open source; prototype; research and development; sensor; simulation; social; social neuroscience; software development; spatiotemporal; tool

DESCRIPTION (provided by applicant): Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the dominant noninvasive modality for studying human brain functional localization in basic and clinical neurosciences. Although the three-dimensional spatial resolution of scalp-recorded electroencephalography (EEG) is ambiguous, its temporal resolution is roughly three orders of magnitude better than fMRI. Consequently, a key issue in imaging neuroscience is how to integrate EEG with fMRI. The absence of a reliable computational bridge linking EEG to fMRI is a critical barrier to an integrated spatiotemporal experimental investigation of human brain function. We propose to develop a data-driven approach to integration-detection and estimation of regional BOLD- related EEG (rBRE) signals-which is constrained by a simple (though extensible) functional model of neuroelectric-hemodynamic coupling. An rBRE signal is a spatially and temporally filtered EEG signal which, when transformed via the functional model, demonstrates statistically significant coupling strength and regional specificity. Concurrent EEG-fMRI datasets are used to tune spatial and temporal filters which maximize EEG-BOLD coupling based on a particular form of conditional mutual information. After detection, an rBRE signal may be estimated at the temporal resolution of EEG. After successful completion of Phase I, we will have implemented rBRE signal detection algorithms in prototype software, verified their correct implementation using quasi-realistic simulations, and studied the effects of initialization errors, SNR, and region size. In particular, we will have shown that it is feasible to detect regional BOLD-related EEG signals reliably in human data. PUBLIC HEALTH RELEVANCE: How to integrate EEG with fMRI data is an important issue faced by many cognitive, behavioral, and social neuroscientists who are practitioners of both modalities. If successful, the research and development efforts described in this proposal will position SSI as a leading, innovative provider of software for integrated EEG-fMRI analysis. In addition to supporting concurrent EEG-fMRI capabilities, the developed software will be useful to neurophysiology labs which have access primarily to EEG apart from fMRI. Scientists working with us are interested in this software for the study of neurological, neurodevelopmental and psychiatric disorders, as well as basic brain research.

描述（由申请人提供）：血氧水平依赖性（BOLD）功能磁共振成像（fMRI）是基础和临床神经科学中研究人脑功能定位的主要无创模式。虽然头皮记录脑电图（EEG）的三维空间分辨率是模糊的，其时间分辨率大约是三个数量级优于功能磁共振成像。因此，脑电与功能磁共振成像的结合是影像神经科学的一个关键问题。缺乏一个可靠的计算桥梁连接脑电图功能磁共振成像是一个关键的障碍，整合的时空人脑功能的实验研究。我们建议开发一种数据驱动的方法来集成区域BOLD相关EEG（rBRE）信号的检测和估计，该信号受神经电-血流动力学耦合的简单（但可扩展）功能模型的约束。rBRE信号是经空间和时间滤波的EEG信号，当通过功能模型进行变换时，其显示出统计上显著的耦合强度和区域特异性。并发EEG-fMRI数据集用于调整空间和时间滤波器，其基于特定形式的条件互信息来最大化EEG-BOLD耦合。在检测之后，可以在EEG的时间分辨率下估计rBRE信号。在第一阶段成功完成后，我们将在原型软件中实现rBRE信号检测算法，使用准现实模拟验证其正确实现，并研究初始化误差，SNR和区域大小的影响。特别是，我们将证明，它是可行的，以检测区域BOLD相关的EEG信号可靠的人类数据。 公共卫生关系：如何整合EEG和fMRI数据是许多认知、行为和社会神经科学家所面临的重要问题。如果成功，本提案中描述的研究和开发工作将使SSI成为集成EEG-fMRI分析软件的领先创新供应商。除了支持并发EEG-fMRI功能，开发的软件将是有用的神经生理学实验室，主要访问EEG除了fMRI。与我们合作的科学家对这款用于神经系统、神经发育和精神疾病研究以及基础大脑研究的软件感兴趣。