Tools for Large-Scale Platform-Independent MEG Data Analysis

描述（由申请人提供）：脑磁图（MEG）提供了一个独特的窗口，大规模时空神经过程的基础人脑功能。然而，即使使用限制性模型，低SNR环境，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，导致数据分析和解释的独特挑战。获得的结果是依赖于因素，如选择的约束施加在源估计程序，正演模型，选择的时间窗口的分析，和预处理算法，使在MEG社区的标准化的情况下，结果在实质性的差异，在不同地点进行的类似实验的分析和结果。该项目将为MEG数据的分析制定一个标准工作流程，加上数据结构的规范，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现有的MEG软件包，已独立开发：MNE在MGH和BrainStorm在南加州大学。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，以便在两个软件包之间共享所有分析级别的数据。该工作流程还将促进当前和未来需要融合来自MEG和解剖MRI以外的成像模式的数据的应用。(2)使用可用的脚本语言，我们将开发一种有效的机制，用于分析多个数据集，重点是计算个人和组的统计数据与阈值，以纠正多重比较。这些工具将与所有主要操作系统兼容。(3)使用MIND MEG Consortium人体校准研究数据，我们将建立标准程序，用于验证和确认MEG逆程序的实施。(4)我们将通过MGH和USC的链接站点分发开源软件，并提供相关文档、论坛和在线教程。公共卫生相关性：脑磁图（MEG）是一种完全无创的脑成像工具，它提供了人类大脑活动的空间分布和精确的时间编排信息。因此，MEG可以用于理解和诊断广泛的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，MEG的更广泛使用，特别是在大人群中，一直存在问题。这项研究将提供有据可查和经过测试的分析工具，以促进MEG与解剖MRI相结合的基础神经科学和临床研究应用。

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.