Tools for Large-Scale Platform-Independent MEG Data Analysis

描述（由申请人提供）：脑磁图（MEG）和脑电图（EEG）为人类大脑功能的大规模时空神经过程提供了一个独特的窗口。然而，即使使用限制性模型，低SNR、逆问题的不适定性以及区分正在进行的大脑活动和其他电生理信号与诱导的和事件相关的变化的困难也导致数据分析和解释中的独特挑战。这些挑战包括考虑伪影和噪声，从大脑源到传感器空间的精确正向建模，定义适当的源模型，计算逆解，以及检测和量化相互作用。在这笔赠款中，我们将继续开发我们链接的MNE-Python和Brainstorm软件包。当前软件的重点是数据预处理、反解的形成和统计分析，以及这些解的高级交互式显示和解释。我们已经为这些程序建立了MNE和Brainstorm中的皮质电流密度标测、时频分析和统计测试的标准工作流程。在目标1的下一个项目期间，我们将在这些程序的基础上为目标3中描述的交互措施的数据工作流添加新的维度。在这一目标下，我们还将继续为用户开展一般软件开发（包括自动化测试和文档编制）、支持和传播活动。在目标2中，我们将扩展基于Python的脚本的使用，以促进来自广泛实验研究的多个主题和/或条件的大规模批处理。我们还将添加导入颅内EEG传感器（深度电极和皮层网格）位置的功能，以使用Aim 3中的方法进行显示和交互分析，并对MEG/EEG非侵入性源模型进行交叉验证。为了充分发挥EEG/MEG的潜力，阐明人类感知，认知和行动的时空网络，我们还将开发工具来研究皮层神经元群体之间的相互作用。这些工具应该考虑到这些网络的动态性质，神经群体之间因果和频率间相互作用的固有复杂性，以及多个大脑区域之间可能发生相互作用的事实。由于没有一个简单的模型可以解释所有这些相互作用，该基金的目标3将开发一套交互建模和强大的可视化工具，供神经科学和临床研究人员使用。

DESCRIPTION (provided by applicant): Magnetoencephalography (MEG) and electroencephalography (EEG) provide a unique window to the large scale spatiotemporal neural processes that underlie human brain function. However, even with restrictive models, the low SNR, ill-posedness of the inverse problem, and difficulty of differentiating ongoing brain activity and other electrophysiological signals from induced and event-related changes result in unique challenges in data analysis and interpretation. These challenges include accounting for artifacts and noise, accurately forward modeling from cerebral sources to sensor space, defining appropriate source models, computing inverse solutions, and detecting and quantifying interactions. In this grant we will continue the development of our linked MNE-Python and Brainstorm software packages. Emphasis in the current software is on data preprocessing, the formation and statistical analysis of inverse solutions, and advanced, interactive display and interpretation of these solutions. We have established standard workflows for cortical current density mapping, time-frequency analysis, and statistical testing in both MNE and Brainstorm for these procedures. In the next project period in Aim 1 we will build on these procedures adding new dimensions to the data workflows for the interaction measures described in Aim 3. Under this aim we will also continue general software development (including automated testing and documentation), support and dissemination activities for users. In Aim 2, we will expand the use of Python-based scripting to facilitate large-scale batch processing of multiple subjects and/or conditions from an extensive experimental study. We will also add the ability to import locations of intracranial EEG sensors (depth electrodes and cortical grids) for display and interaction analysis using methods from Aim 3 and for cross-validation of MEG/EEG non-invasive source models. To fully realize the potential of EEG/MEG to elucidate the spatio-temporal networks that underlie human perception, cognition, and action, we will also develop tools to investigate the interactions between cortical neuronal populations. These tools should take into account the dynamically nature of these networks, the inherent complexity of causal and inter-frequency interactions amongst neural populations, and the fact that interactions can occur between multiple brain regions. Since no single parsimonious model can account for all such interactions, Aim 3 of this grant will develop a suite of interaction modeling and powerful visualization tools for use by neuroscience and clinical researchers.