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

可扩展且与传感器无关的软件，用于多站点 MEG/EEG 数据集的分布式处理和可视化

• 批准号: 10442915
• 负责人: MATTI HAMALAINEN
• 金额: $ 67.13万
• 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 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10442915
• 关键词: Algorithms; Alzheimer' s Disease; Aptitude; Basic Science; Brain; Brain imaging; Brain region; Clinical; Clinical Research; Code; Cognitive deficits; Communities; Computer software; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Disease; Documentation; Educational workshop; Electrocorticogram; Electrodes; Electroencephalography; Electromagnetics; Electrophysiology (science); Ensure; Epilepsy; Frequencies; Functional Magnetic Resonance Imaging; Grant; Human; Individual; Institution; Knowledge; Laboratories; Language Development; Machine Learning; Magnetoencephalography; Manufacturer Name; Maps; Measurement; Measures; Mental disorders; Methodology; Methods; Modeling; Modernization; Neurologic; Neurosciences Research; Noise; Obsessive-Compulsive Disorder; Online Systems; Optics; Population; Positioning Attribute; Process; Pump; Pythons; Reading; Reproducibility; Research; Research Personnel; Resolution; Sampling; Scalp structure; Schizophrenia; Science; Signal Transduction; Site; Source; Statistical Data Interpretation; Stream; Structure; Surface; System; Techniques; Technology; Temperature; Testing; Training; United States National Institutes of Health; Visualization; Work; Writing; autism spectrum disorder; base; computerized data processing; data analysis pipeline; data format; data standards; design; flexibility; hemodynamics; human data; improved; innovation; light weight; magnetic field; millisecond; novel; pedagogy; reconstruction; sensor; software development; source localization; tool

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. While functional MRI (fMRI) provides a wealth of information by measuring the indirect slow hemodynamic signals. Magnetoencephalography (MEG) and electroencephalography (EEG) remain the only noninvasive techniques capable of directly measuring the electrophysiological activity directly with a millisecond resolution. During the past twelve 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 MEG/EEG as well as in invasive electrophysiological recordings. Optically Pumped Magnetometers (OPMs) are sensitive room- temperature magnetic field sensors that have begun to provide movable, flexible, lightweight, on-scalp MEG systems, and may soon provide higher signal-to-noise ratio and more complete spatial frequency sampling than SQUID-based systems. However, analysis tools optimal processing of OPM-MEG data are largely missing. Therefore, in Aim 1, we will introduce tools for High-Resolution On-Scalp OPM-MEG Data Analysis. Electrocorticography (ECoG) and subcortical EEG (sEEG) provide focal spatial measurements of the electrophysiological activity. In Aim 2, we will develop sEEG and ECoG workflows, which includes electrode localization and intracranial inverse and forward modeling. Recent methodological advances by our group and the availability of on-scalp OPM-MEG systems (Aim 1) and ECoG/sEEG (Aim 2) have expanded the possibilities for improved localization of deep (cortical and subcortical) sources in basic and clinical research applications. In Aim 3, we will introduce these methods to the repertoire of MNE-Python and will use phantom recordings, human data with known ground truth, and existing MEG databases to validate the new methods. Finally, in Aim 4, 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.

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）提供了丰富的信息， 通过测量间接的缓慢血流动力学信号。脑磁图（MEG）和 脑电图（EEG）仍然是唯一能够直接测量脑电活动的非侵入性技术。 电生理活动直接与毫秒分辨率。在过去的12年里， 在NIH的支持下开发了MNE-Python软件，该软件涵盖了多种数据预处理方法， 分布式大脑之间功能连接的源定位、统计分析和估计 地区所有算法和实用程序功能都以一致的方式实现， 接口，使用户能够通过编写Python脚本创建M/EEG数据分析管道。进一步延长 我们的软件，以满足不断增长的用户群的需求，并反映在MEG/EEG的最新发展，以及 如侵入性电生理记录。光泵磁力计（OPMs）是一种灵敏的室温磁力计。 温度磁场传感器已经开始提供可移动的，灵活的，轻便的，头皮上的MEG 系统，并可能很快提供更高的信噪比和更完整的空间频率采样 基于SQUID的系统。然而，OPM-MEG数据的分析工具优化处理在很大程度上是 失踪了因此，在目标1中，我们将介绍高分辨率头皮OPM-MEG数据分析工具。 皮质电图（ECoG）和皮质下EEG（sEEG）提供了脑电活动的局部空间测量。 电生理活动在目标2中，我们将开发sEEG和ECoG工作流程，其中包括电极 定位和颅内逆向和正向建模。我们小组最近在方法上取得的进展， 头皮OPM-MEG系统（目标1）和ECoG/sEEG（目标2）的可用性扩大了 在基础和临床研究中改进深层（皮层和皮层下）源定位的可能性 应用.在目标3中，我们将把这些方法引入MNE-Python的指令表，并将使用phantom 记录，具有已知地面真相的人类数据，以及现有的MEG数据库，以验证新方法。 最后，在目标4中，我们将继续使用最佳编程实践来开发MNE-Python，以确保 多平台兼容性、广泛的基于Web的文档、培训和论坛以及实践培训 工作坊.