Spatial-Temporal MEG and EEG Source Estimation

描述（由申请人提供）：本项目专注于开发，实施，验证和分发用于分析MEG和EEC （E/MEG）数据的计算工具，以提高对人脑正常和病理功能的理解。在过去的四年中，我们在以下领域取得了进展：(i)开发了用于定位E/MEG来源的解剖MR数据分析工具；（ii）计算效率高和基于地图集的正演模型；（iii）参数化和基于图像的逆MEG和EEG方法；（iv）参数反解和基于图像反解的统计分析；(v)软件开发、分发和支持；（vi）模拟和体内数据的验证；（六）癫痫和认知神经科学数据的应用。这项工作主要集中在分析单个受试者中与事件相关的平均数据。统计分析仅限于两两比较。在下一个项目期间，我们的目标是扩展我们的反向方法，使用成像和参数方法来调查诱发和诱导反应，并扩展我们的统计工具，以允许分析涉及个人和群体多种条件的实验数据。我们还将开发工具来研究大脑区域之间的相互作用，并再次开发统计工具来评估这些模型的重要性。这些方法将通过计算机模拟和涉及正常受试者的视觉提示注意范式的体内研究来验证。在开发和评估这些工具时，我们将解决的具体目标如下：(1)逆方法：开发线性成像方法，以最大限度地优化各向同性和移不变分辨率，皮质约束偶极子和多极子拟合用于估计多个焦点源，以及用于同时监测多个不同皮质区域的波束形成方法；(2)皮质对齐：基于沟槽特征对齐和皮质表面固有几何中薄板样条弯曲能量最小化的协变偏微分方程，开发用于皮质表面共配准的工具；(3)统计分析：发展非参数方法，对个体和群体中诱发和诱发反应的皮质图进行单变量分析；(4)分别基于多通道自回归模型和双谱分析，利用宽带线性和非线性方法建立大尺度皮层相互作用模型；(5)通过继续开发头脑风暴和BrainSuite软件工具，分发目标1-4中方法的软件实现；(6)将上述所有方法应用于一项视觉提示注意力研究的实验高密度脑电图和脑磁图数据分析，以研究目标1-4中开发的方法提取有意义且一致的大脑激活和交流图像的能力。

DESCRIPTION (provided by applicant): This project is focused on the development, implementation, validation and distribution of computational tools for the analysis of MEG and EEC (E/MEG) data for the purpose of developing an increased understand- ing of normal and pathological function in the human brain. Over the past four years we have made progress in the following areas: (i) the development of tools for the analysis of anatomical MR data for use in localizing E/MEG sources; (ii) computationally efficient and atlas-based forward models; (iii) parametric and image- based inverse MEG and EEG methods; (iv) statistical analysis of parametric and image-based inverse solu- tions; (v) software development, distribution and support; (vi) validation with both simulated and in vivo data; and (vi) applications to both epilepsy and cognitive neuroscientific data. This work focused primarily on analy- sis of event-related averaged data in single subjects. Statistical analysis was restricted to pair-wise compari- sons. For the next project period our goal is to extend our inverse approaches to investigate both evoked and induced responses using imaging and parametric methods and to extend our statistical tools to allow analysis of experimental data involving multiple conditions in individuals and groups. We will also develop tools to investigate interactions between brain regions and again develop statistical tools to assess the significance of these models. These methods will be validated using computer simulation and in vivo studies involving visu- ally cued attention paradigms in normal subjects. The specific aims that we will address in developing and evaluating these tools are as follows: (1) Inverse Methods: development of linear imaging methods optimized for maximally isotropic and shift invariant resolution, cortically constrained dipole and multipole fits for estima- tion of multiple focal sources, and beamforming methods for simultaneous monitoring of multiple distinct corti- cal regions; (2) Cortical Alignment: development of tools for coregistering cortical surfaces using covariant PDEs based on alignment of sulcal features and minimization of a thin plate spline bending energy in the intrinsic geometry of the cortical surface; (3) Statistical Analysis: development of nonparametric methods for univariate analysis of cortical maps of induced and evoked responses in individuals and groups; (4) develop- ment of models of large scale cortical interactions using broadband linear and nonlinear methods based, respectively, on the multichannel autoregressive model and bispectral analysis; (5) distribution of software implementations of the methods in Aims 1-4 through continued development of the Brainstorm and BrainSuite software tools; and (6) application of all of the above methods to the analysis of experimental high density EEG and MEG data from a visually cued attention study to investigate the ability of the methods developed in Aims 1-4 to extract a meaningful and consistent picture of brain activation and communication.