Scalable and Sensor-Agnostic 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)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(EEG)仍然是唯一一种能够直接测量 直接电生理活动，分辨率为毫秒。在过去的12年里，我们有 在国家卫生研究院的支持下开发了MNE-PYTHON软件，该软件涵盖多种数据前处理方法， 分布式大脑之间的源定位、统计分析和功能连通性估计 地区。所有算法和实用函数都以一致的方式实施，并有良好的文档记录 界面，使用户能够通过编写Python脚本创建M/EEG数据分析管道。为了进一步扩展 我们的软件以满足不断增长的用户群的需求，并反映脑磁图/脑电的最新发展 就像侵入性电生理记录一样。光泵磁力计(OPM)是一种灵敏的房间- 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 而不是基于鱿鱼的系统。然而，分析工具对OPM-MEG数据的优化处理在很大程度上 失踪。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。 皮层脑电图术(ECoG)和皮质下脑电(SEEG)提供了大脑皮质的焦点空间测量 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 定位和颅内正反演模拟。我们小组最新的方法学进展和 头皮OPM-MEG系统(AIM 1)和ECoG/sEEG(AIM 2)的可用性扩大了 在基础和临床研究中改进深层(皮质和皮质下)来源定位的可能性 申请。在目标3中，我们将把这些方法介绍给MNE-Python库，并将使用幻影 录音、已知基本事实的人类数据和现有的脑磁图数据库，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开发MNE-Python，以确保 多平台兼容性、大量基于Web的文档、培训和论坛以及实践培训 研讨会。

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.