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: 9750274
• 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/9750274
• 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; Imagery; 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 software; Writing; analysis pipeline; autism spectrum disorder; base; cloud based; cluster computing; computing resources; data acquisition; falls; human data; innovation; 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，非侵入性功能性脑成像已在 测量技术，分析方法和创新范例以捕获有关大脑的信息 在健康和患病的个体中起作用。尽管功能性MRI（fMRI）变得非常有用，但 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。磁脑摄影（MEG）和脑电图（EEG）仍然是唯一的 可用的无创技术，能够直接测量电生理活性 毫秒分辨率。在过去的八年中 软件涵盖了多种数据预处理，来源本地化，统计分析和 估计分布式大脑区域之间的功能连通性。所有算法和实用程序功能均为 与有据可查的接口以一致的方式实施，使用户能够创建M/EEG数据 分析管道通过编写Python脚本。进一步扩展我们的软件以满足不断增长的用户的需求 基础并反映了MEG/EEG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 意志：（i）创建一个包含和扩展方法的全弹药套件 在不同的MEG系统中； （ii）实施设备独立的方法来确定头部的确定和 根据在MEG会议期间记录的头部移动数据赔偿； （iii）开发用于 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 使现代分布式计算资源轻松在处理中可用并允许远程使用 可视化无需在整个网络上移动大量数据。最后，在AIM 3中，我们将 继续使用最佳编程实践来开发MNE-Python，以确保乘以乘法兼容性， 广泛的基于网络的文档，培训和论坛以及动手培训研讨会。后果 这些事态发展MNE-Python将能够有效地处理大量主题和巨大的主题 随之而来的数据和来自不同MEG/EEG系统的多站点研究。