BOLD-Related EEG Signal Estimation Software

描述（由申请人提供）：血氧水平依赖（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方式。虽然头皮记录脑电图（EEG）的三维空间分辨率是模糊的，但其时间分辨率大约比fMRI好三个数量级。因此，如何将EEG与fMRI相结合是成像神经科学的一个关键问题。缺乏连接EEG和fMRI的可靠计算桥梁是对人脑功能进行综合时空实验研究的关键障碍。我们建议开发一种数据驱动的方法来整合检测和估计区域BOLD相关的脑电图（rBRE）信号，该信号受神经电-血流动力学耦合的简单（尽管可扩展）功能模型的约束。rBRE信号是经过空间和时间过滤的脑电信号，通过功能模型进行变换后，显示出统计上显著的耦合强度和区域特异性。并发EEG-fMRI数据集用于调整基于特定形式的条件互信息的最大化EEG-BOLD耦合的空间和时间滤波器。检测后的rBRE信号可以在脑电图的时间分辨率下估计出来。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，通过准逼真的仿真验证其正确实现，并研究初始化误差、信噪比和区域大小的影响。特别是，我们将证明在人类数据中可靠地检测与bold相关的区域脑电图信号是可行的。

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.