Scalable Software for Distributed Processing and Visualization of Multi-Site MEG/EEG Datasets

项目摘要 在过去的三十年里，非侵入性脑功能成像在以下方面取得了巨大的发展 测量技术、分析方法和获取大脑信息的创新范例 在健康和患病的人身上都有作用。虽然功能磁共振成像(FMRI)已经变得非常有用，但它 仅通过有限的神经血管耦合提供有关神经元活动的间接信息 时间分辨率。脑磁图(MEG)和脑电图(EEG)仍然是唯一的 现有的非侵入性技术能够直接测量电生理活动 毫秒级分辨率。在过去的八年里，在NIH的支持下，我们开发了MNE-Python 软件，该软件涵盖多种数据预处理、来源定位、统计分析和 估计分散的大脑区域之间的功能连通性。所有算法和效用函数都是 使用记录良好的界面以一致的方式实施，使用户能够创建M/EEG数据 通过编写Python脚本来分析管道。进一步扩展我们的软件以满足不断增长的用户的需求 根据并反映脑磁图/脑电领域的最新发展，我们将追求三个具体目标。在目标1中，我们 将：(I)创建一套包罗万象的噪音消除工具，纳入和扩展现有方法 在不同的脑磁图系统中；(Ii)实施与设备无关的确定头部运动的方法和 根据脑磁图会议期间记录的头部运动数据进行补偿；(3)制定 使用机器学习方法对人工产物进行自动标记。在目标2中，我们的重点是扩展软件 使现代分布式计算资源在处理过程中易于使用，并允许远程 无需通过网络移动大量数据即可实现可视化。最后，在目标3中，我们将 继续使用确保多平台兼容性的最佳编程实践开发MNE-Python， 广泛的基于网络的文件、培训和论坛，以及实践培训讲习班。由于…… 通过这些改进，MNE-Python将能够有效地处理大量的主题和巨大的 在不同的脑磁图/脑电系统中和谐地从多个地点研究中获得大量数据。

Project Summary During the past three decades non-invasive functional brain imaging has developed immensely in terms of measurement technologies, analysis methods, and innovative paradigms to capture information about brain function both in healthy and diseased individuals. Although functional MRI (fMRI) has become very useful, it only provides indirect information about neuronal activity through the neurovascular coupling with a limited temporal resolution. Magnetoencephalography (MEG) and electroencephalography (EEG) remain the only available noninvasive techniques capable of directly measuring the electrophysiological activity with a millisecond resolution. During the past eight years we have developed, with NIH support, the MNE-Python software, which covers multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. To further extend our software to meet the needs of a growing user base and reflect recent developments in the MEG/EEG field we will pursue three specific Aims. In Aim 1 we will: (i) Create an all-embracing suite of noise cancellation tools incorporating and extending methods present in different MEG systems; (ii) Implement device independent methods for head-movement determination and compensation on the basis of head movement data recorded during a MEG session; (iii) Develop methods for automatic tagging of artifacts using machine learning approaches. In Aim 2 our focus is to extend the software to make modern distributed computing resources easily usable in processing and to allow for remote visualization without the need to move large amounts of data across the network. Finally, in Aim 3, we will continue to develop MNE-Python using best programming practices ensuring multiplatform compatibility, extensive web-based documentation, training and forums, and hands-on training workshops. As a result of these developments the MNE-Python will be able to effectively process large number of subjects and huge amounts data ensuing and from multi-site studies harmoniously across different MEG/EEG systems.