SPATIO/TEMPORAL MEG AND EEG SOURCE ESTIMATION

脑磁图(MEG)和脑电(EEG)提供 对人脑动态行为的独特见解 能够在毫秒的时间尺度上跟踪神经活动的变化。 相比之下，其他功能成像方式(正电子 体层摄影术(PET)和功能磁共振成像(FMRI)) 时间分辨率被限制在时间尺度上，充其量， 考虑到生理和信噪比，一秒钟。目标是 该项目的主要目的是开发和评估计算技术 估计海流的位置、范围和动态行为 产生观察到的脑磁图和脑电波的来源。在最初的 在项目期间，我们已经开发了一套方法和软件来 头部建模、震源定位和成像。我们还建造了 测试了一个基于头骨的体模，并开发了计算工具 不同模型和反演法的性能比较与量化 方法：研究方法。我们计划在拟议的项目期内继续开展这项工作， 通过集中使用和扩展我们开发的方法 到目前为止，以解决与双方都相关的几个基本问题 脑电/脑磁图研究人员和整个脑成像社区：(I)如何 E/MEG能否可靠地找到多个电流源的位置 大脑？(2)E/MEG在多大程度上可以确定 分布式电流源？(三)时间走得多准？ 这些来源的序列或激活序列？(四)我们如何做到最好 处理来自认知研究的数据，这些数据涉及到 条件呢？(V)E/MEG数据如何最好地与功能性MR或 宠物激活数据？已开发的方法的扩展 在最初的项目期间，将包括相关技术 大脑中神经活动的偶极和多极估计 以及将这些估计与功能磁共振数据相结合的方法。我们 还将开发用于处理的源代码本地化方法 允许识别和移除信号的认知数据 两个不同测试条件通用的组件。贝叶斯成像 在项目初期开发的方法将扩展到 利用之前的fMRI数据。对于我们的多极和成像方法， 我们还将研究不同头部模型对计算的影响 成本和准确性。将使用以下方法评估所有方法的性能 一系列计算数据、模体数据和人体数据。计算工具 包括Cramer-Rao下界、蒙特卡罗方法和 子空间相关性。虚拟数据将使用 逼真的32偶极人体头骨模型，建造于 最初的项目期。人类数据将基于简单的电机 文献中有很好的范例记录，对于这些范例，脑磁图、脑电和功能磁共振成像 将收集数据。来自该项目的软件和数据将是 通过互联网提供给研究人员。

The magnetoencephalogram (MEG) and electroencephalogram (EEG) provide unique insights into the dynamic behavior of the human brain as they are able to follow changes in neural activity on a millisecond timescale. In comparison, the other functional imaging modalities (positron tomography (PET) and functional magnetic resonance imaging (fMRI)) are limited in temporal resolution to time scales on the order of, at best, one second by physiological and signal-to-noise consideration. The goal of this project is to develop and evaluate computational techniques for estimating the location, extent and dynamic behavior of the current sources that produce the observed MEG and EEG. During the initial project period, we have developed a suite of methods and software for head modeling, source localization and imaging. We have also built and tested a skull based phantom and developed computational tools for comparing and quantifying performance of different models and inverse methods. We plan to build on this work in the proposed project period, by concentrating on using and extending the methods we have developed to date to address several fundamental questions of relevance to both EEG/MEG researchers and the brain imaging community as a whole: (i) How reliably can E/MEG find the locations of multiple current sources in the brain? (ii) To what extent can E/MEG determine the spatial extent of distributed current sources? (iii) How accurately can we find the time series or activation sequence of these sources? (iv) How do we best process data from cognitive studies involving the differences between conditions? (v) How is E/MEG data best combined with functional MR or PET activation data? Extensions of the methods that have been developed during the initial project period will include techniques for relating dipolar and multipolar estimates to neural activity in the cerebral cortex and methods for combining these estimates with fMRI data. We will also develop a source localization methodology for processing cognitive data which allows identification and removal of the signal components common to two different test conditions. The Bayesian imaging method developed during the initial project period will be extended to utilize fMRI data in the prior. For our multipolar and imaging methods, we will also examine the impact of different head models on computation cost and accuracy. Performance of all methods will be evaluated using a range of computational, phantom and human data. Computational tools include Cramer-Rao lower bounds, Monte-Carlo methods and the use of subspace correlations. Phantom data will be generated using the realistic 32-dipole human skull phantom that was constructed during the initial project period. Human data will be based on simple motor paradigms well documented in the literature, for which MEG, EEG and fMRI data will be collected. Software and data from this project will be made available to researchers via the Internet.