Fusion of Electromagnetic Brain Imaging and fMRI

描述(申请人提供)：多模式非侵入性脑功能成像在改善我们对人类行为的神经关联的理解方面产生了巨大的影响，现在是系统和认知神经科学家不可或缺的工具。我们建议开发最先进的多模式功能成像融合算法，以准确地可视化大脑的动态活动和高空间和时间分辨率。我们提出了结合高空间分辨率的功能磁共振成像(FMRI)和高时间分辨率的脑磁图(MEG)和脑电(EEG)数据来高保真地重建大脑活动的算法。近年来，我们的研究小组开发了一套新颖而强大的MEG/EEG成像算法，优于现有的基准算法，并将这些结果与皮层脑电成像(ECOG)进行了比较。具体地说，我们的算法可以解决许多脑源，包括远离传感器的源，在存在来自不相关脑源的大干扰的情况下，使用快速和稳健的概率推理技术。在这里，我们建议将M/EEG逆算法的这一成功扩展到多模式成像数据融合领域。我们的总体目标是最终使用最先进的机器学习算法，从嘈杂的脑磁图/脑电和功能磁共振数据中，以亚毫米和亚毫秒的分辨率制作出与事件相关的大脑激活的健壮、高保真视频。具体地说，我们建议将我们最近开发的一种名为Chample的强大新算法扩展为两种新的融合算法，这两种算法以不同的方式结合了fMRI、MEG和EEG数据。这两种算法的性能将首先在模拟中进行严格评估，包括与现有基准融合算法的性能比较。然后，算法将在来自健康对照组的四个fMRI-MEG+EEG数据集上进行一致性测试，这些数据集来自相同的范式(听觉、运动、图片命名和动词生成)和两个fMRI-EEG数据集(面部和运动感知)。还将对从癫痫患者获得的fMRI-MEG/EEG数据集进行其他验证研究，并将其与皮层脑电图术(ECoG)进行比较。在成功测试和评估之后，本赠款提案中开发的所有算法以及示例验证数据集将使用我们开发的开源软件工具箱nutmeg(nutme.berkeley.edu)分发。 公共卫生相关性：多模式非侵入性脑功能成像在改善我们对人类行为的神经关联的理解方面产生了巨大影响，现在是系统和认知神经科学家不可或缺的工具。随着适当分析工具的开发，多模式脑功能成像正在彻底改变各种神经和精神疾病的诊断和治疗，如自闭症、精神分裂症、痴呆症和癫痫，这些疾病影响着数千万美国人。

DESCRIPTION (provided by applicant): Multimodal non-invasive functional brain imaging has made a tremendous impact in improving our understanding of the neural correlates of human behavior, and is now an indispensable tool for systems and cognitive neuroscientists. We propose to develop state-of-the-art multimodal functional imaging fusion algorithms for accurate visualization of the brain's dynamic activity and high spatial and temporal resolution. We propose to develop algorithms that combine complementary high spatial resolution of functional MRI (fMRI) and high-temporal resolution of magnetoencephalography (MEG) and electroencephalography (EEG) data for high-fidelity reconstruction of brain activity. In recent years, our research group has developed a suite of novel and powerful algorithms for MEG/EEG imaging superior to existing benchmark algorithms, and we have compared these results with electrocorticography (ECOG). Specifically, our algorithms can solve for many brain sources, including sources located far from the sensors, in the presence of large interference from unrelated brain sources using fast and robust probabilistic inference techniques. Here, we propose to extend this success in M/EEG inverse algorithms into the domain of multimodal imaging data fusion. Our overall goal here is to ultimately produce robust, high fidelity videos of event-related brain activation at a sub-millimeter and sub-millisecond resolution from noisy MEG/EEG and fMRI data using state-of-the-art machine learning algorithms. Specifically, we propose to extend a powerful new algorithm that we have recently developed, called Champagne, into two new fusion algorithms that combine fMRI, MEG and EEG data in different ways. Performance of both algorithms will first be rigorously evaluated in simulations, including performance comparisons with existing benchmark fusion algorithms. Algorithms will then tested for consistency on four fMRI-MEG+EEG datasets from healthy controls obtained for identical paradigms (auditory, motor, picture naming and verb-generation) and two fMRI-EEG datasets (face and motion perception). Additional validation studies will also be performed on fMRI-MEG/EEG datasets obtained from epilepsy patients and compared to electrocorticography (ECoG). Following successful testing and evaluation, all algorithms developed in this grant proposal, as well as example validation datasets, will be distributed using NUTMEG (nutmeg.berkeley.edu), an open-source software toolbox that we have developed. PUBLIC HEALTH RELEVANCE: Multimodal non-invasive functional brain imaging has made a tremendous impact in improving our understanding of the neural correlates of human behavior, and is now an indispensable tool for systems and cognitive neuroscientists. With the development of appropriate analytical tools, multimodal functional brain imaging is in the process of revolutionizing the diagnosis and treatment of a variety of neurological and psychiatric disorders such as autism, schizophrenia, dementia, and epilepsy that affect tens of millions of Americans.