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.

项目概要 在过去的三十年中，非侵入性功能性脑成像在以下方面取得了巨大发展： 捕获大脑信息的测量技术、分析方法和创新范式 在健康和患病个体中均发挥作用。虽然功能性 MRI (fMRI) 提供了丰富的信息 通过测量间接慢血流动力学信号。脑磁图（MEG）和 脑电图（EEG）仍然是唯一能够直接测量脑电图的无创技术 直接以毫秒分辨率进行电生理活动。在过去的十二年里，我们 在 NIH 的支持下开发了 MNE-Python 软件，涵盖多种数据预处理方法， 源定位、统计分析和分布式大脑之间功能连接的估计 地区。所有算法和实用函数均以一致的方式实现，并有详细记录 接口，使用户能够通过编写Python脚本来创建M/EEG数据分析管道。为进一步延伸 我们的软件可以满足不断增长的用户群的需求，并反映 MEG/EEG 的最新发展 如侵入性电生理记录。光泵磁力计 (OPM) 是灵敏的室内 温度磁场传感器已开始提供可移动、灵活、轻便、头皮 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 比基于 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 丢失的。因此，在目标 1 中，我们将介绍用于高分辨率头皮 OPM-MEG 数据分析的工具。 皮质电图 (ECoG) 和皮质下脑电图 (sEEG) 提供大脑皮层的焦点空间测量 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。我们小组最近的方法论进展 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善深层（皮质和皮质下）源定位的可能性 应用程序。在目标 3 中，我们将把这些方法引入 MNE-Python 的库中，并使用 phantom 录音、具有已知地面事实的人类数据以及现有的 MEG 数据库来验证新方法。 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，确保 多平台兼容性、广泛的基于网络的文档、培训和论坛以及实践培训 研讨会。