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

用于多站点 MEG/EEG 数据集分布式处理和可视化的可扩展软件

• 批准号: 10175064
• 负责人: MATTI HAMALAINEN
• 金额: $ 54.4万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10175064
• 关键词: Adult; Age; Algorithms; Alzheimer' s Disease; Brain; Brain imaging; Brain region; Clinical Research; Cloud Computing; Code; Cognitive deficits; Collaborations; Communities; Computer software; Data; Data Analyses; Data Scientist; Data Set; Databases; Development; Devices; Diagnosis; Disease; Documentation; Ecosystem; Educational workshop; Electroencephalography; Electrophysiology (science); Ensure; Epilepsy; Experimental Designs; Financial compensation; Functional Magnetic Resonance Imaging; Guidelines; Head; Head Movements; Hour; Human; Individual; Laboratories; Language Development; Link; Machine Learning; Magnetoencephalography; Maintenance; Maps; Measurement; Measures; Mental disorders; Methods; Modernization; Morphologic artifacts; Neurologic; Neurons; Neurosciences Research; Noise; Obsessive-Compulsive Disorder; Online Systems; Population; Process; Pythons; Reproducibility; Research; Resolution; Resources; Schizophrenia; Science; Scientist; Site; Statistical Data Interpretation; System; Techniques; Technology; Testing; Training; United States National Institutes of Health; Visualization; Visualization software; Writing; analysis pipeline; autism spectrum disorder; base; cloud based; cluster computing; computing resources; data acquisition; data analysis pipeline; data exchange; falls; human data; innovation; large datasets; millisecond; multithreading; neurovascular coupling; novel; open source; pedagogy; sensor; sensor technology; software development; source localization; symposium; temporal measurement; tool; verification and validation

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.

项目摘要 在过去的三十年中，非侵入性脑功能成像在以下方面得到了极大的发展： 测量技术、分析方法和创新范式，以获取有关大脑的信息 在健康和患病的个体中都起作用。虽然功能性磁共振成像（fMRI）已经变得非常有用， 仅通过神经血管偶联提供关于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 能够直接测量电生理活动的可用的非侵入性技术 毫秒分辨率在过去的八年里，我们在NIH的支持下开发了MNE-Python 软件，涵盖数据预处理、源定位、统计分析和 分布的大脑区域之间的功能连接的估计。所有的算法和效用函数都是 以一致的方式实现，并具有记录良好的界面，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 基于和反映在脑磁/脑电图领域的最新发展，我们将追求三个具体的目标。在目标1中， 将：（i）创建一套包罗万象的噪音消除工具，整合和扩展现有方法 （ii）实施用于头部移动确定的设备独立方法， （三）根据MEG会话期间记录的头部运动数据进行补偿;（三）制定方法， 使用机器学习方法自动标记工件。在目标2中，我们的重点是扩展软件 为了使现代分布式计算资源易于在处理中使用， 可视化，而无需在网络上移动大量数据。在目标3中，我们将 继续使用最佳编程实践开发MNE-Python，确保多平台兼容性， 广泛的网络文件、培训和论坛以及实践培训讲习班。的结果 这些发展，MNE-Python将能够有效地处理大量的主题和巨大的 在不同的MEG/EEG系统之间和谐地进行多站点研究。