Spatial-Temporal MEG and EEG Source Estimation

该项目的重点是开发，实施，验证和分发计算 分析MEG和EEC（E/MEG）数据的工具，以加深对 人类大脑的正常和病理功能。在过去四年中，我们取得了进展， 在以下领域：（i）开发用于分析解剖MR数据的工具，用于定位 E/MEG源;（ii）计算效率高和基于地图集的前向模型;（iii）参数和图像- 基于逆MEG和EEG方法;（iv）参数和基于图像的逆解的统计分析， （v）软件开发、分发和支持;（vi）用模拟数据和体内数据进行验证; 以及（vi）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- 单个受试者事件相关平均数据的SIS。统计分析仅限于成对比较， 个儿子在下一个项目期间，我们的目标是扩展我们的反向方法来研究诱发和 使用成像和参数方法诱导反应，并扩展我们的统计工具， 涉及个人和群体的多种条件的实验数据。我们还将开发工具， 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。这些方法将使用计算机模拟和体内研究进行验证， 在正常人中的线索注意范式。我们将在发展和 对这些工具的评价如下：（1）逆方法：发展线性成像方法的优化 对于最大各向同性和平移不变分辨率，皮质约束偶极子和多极拟合估计， 多个焦点源，和波束形成方法，用于同时监测多个不同的皮质， （2）皮质对齐：开发使用协变量的皮质表面配准工具 偏微分方程的基础上对齐的沟功能和最小化的薄板样条弯曲能量在 皮质表面的内在几何形状;（3）统计分析：非参数方法的发展， 对个体和群体中诱导和诱发反应的皮层图进行单变量分析;（4）开发- 基于宽带线性和非线性方法的大规模皮层相互作用模型的改进， 分别对多通道自回归模型和双谱分析进行了研究 通过继续开发头脑风暴和BrainSuite，实施目标1- 4中的方法 软件工具;（6）上述方法在实验高密度分析中的应用 EEG和MEG数据从视觉提示的注意力研究，以调查的能力， 目的1-4提取一个有意义的和一致的大脑激活和通信的图片。

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 1-4through 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.