Tools for Large-Scale Platform-Independent MEG Data Analysis

描述(申请人提供)：脑磁图(MEG)为了解构成人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，即使在有限制性模型的情况下，低信噪比环境、逆问题的不适当性以及将正在进行的脑活动和其他电生理信号与诱发的和事件相关的变化区分开来的困难导致了数据分析和解释中的独特挑战。所获得的结果取决于源估计程序中施加的约束条件的选择、正演模型、为分析选择的时间窗口和预处理算法，因此，由于脑磁图社区缺乏标准化，导致在不同地点进行的类似实验的分析和结果有很大差异。该项目将制定一个分析脑磁图数据的标准工作流程，并辅之以数据结构规范，以实现跨站点数据共享和结果比较。为了实现这一目标，我们将修改和扩展两个独立开发的现有MEG软件包：MGH的MNE和南加州大学的BrainStorm。我们将追求四个具体目标：(1)我们将为BrainStorm和MNE建立统一的工作流程、数据结构和文件格式，以实现这两个包之间在所有分析级别的数据共享。该工作流程还将促进目前和未来的应用，需要融合脑磁图和解剖核磁共振以外的成像方式的数据。(2)利用现有的脚本语言，我们将开发一种高效的机制来分析多个数据集，重点是计算个人和组的统计数据，并使用阈值来校正多次比较。这些工具将与所有主要操作系统兼容。(3)使用Mind MEG联盟人体校准研究数据，我们将建立标准程序，以验证和确认MEG逆程序的实施。(4)我们将通过麻省理工学院和南加州大学的链接网站分发开源软件，以及相关的文档、论坛和在线教程。公共卫生相关性：脑磁图(MEG)是一种完全非侵入性的脑成像工具，它提供关于人类大脑活动的空间分布和精确的时间编排的信息。因此，脑磁图可以用来了解和诊断各种神经和精神疾病的潜在异常，包括癫痫、精神分裂症、强迫症、自闭症谱系障碍和阿尔茨海默病，以及认知缺陷，如语言习得延迟。然而，由于缺乏完善的分析方法，更广泛地使用脑磁图，特别是在大量人口中，一直是有问题的。这项研究将提供经过充分记录和测试的分析工具，以促进基础神经科学和临床研究应用，将脑磁图与解剖核磁共振相结合。

DESCRIPTION (provided by applicant): Magnetoencephalography (MEG) provides a unique window on the large scale spatiotemporal neural processes that underlie human brain function. However, even with restrictive models, the low SNR environment, 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. Results obtained are dependent on factors such as the choice of the constraints imposed in the source estimation procedures, the forward model, the time windows chosen for the analyses, and preprocessing algorithms, so that the absence of standardization in the MEG community results in substantial differences in the analysis and findings from similar experiments conducted at different sites. This project will develop a standard work flow for the analysis of MEG data, coupled with specifications of data structures to enable cross site data sharing and comparison of results. To achieve this goal we will modify and extend two existing MEG software packages that have been developed independently: MNE at MGH and BrainStorm at USC. We will pursue four specific aims: (1) We will establish a unified workflow, data structures, and file formats for BrainStorm and MNE to enable sharing of data at all levels of analysis between the two packages. The workflow will also facilitate present and future applications requiring the fusion of data from imaging modalities other than MEG and anatomical MRI. (2) Using available scripting languages, we will develop an efficient mechanism for analyzing multiple data sets with an emphasis on computing individual and group statistics with thresholding to correct for multiple comparisons. These tools will be compatible with all major operating systems. (3) Using the MIND MEG Consortium human calibration study data we will establish standard procedures for verification and validation of the implementations of MEG inverse procedures. (4) We will distribute open source software through linked sites at MGH and USC, with associated documentation, discussion forums, and online tutorials. PUBLIC HEALTH RELEVANCE: Magnetoencephalography (MEG) is a totally non-invasive brain imaging tool which provides information on the spatial distribution and precise temporal orchestration of human brain activity. MEG can be thus used to understand and diagnose abnormalities underlying a wide range neurological and psychiatric illnesses including as epilepsy, schizophrenia, obsessive- compulsive disorder, autism spectrum disorders, and Alzheimer's disease, as well as cognitive deficits such as delayed acquisition of language. However, more widespread use of MEG especially in large populations has been problematic because of the lack of well-established analysis appproaches. This research will provide well-documented and tested analysis tools to promote both basic neuroscience and clinical research applications using MEG in combination with anatomical MRI.