POSTDOC: The Development of a 3D Parallel Finite Element Model for EEG and MEG Source Localization

大脑中信息处理的时间动态可以使用脑电图（EEG）或脑磁图（MEG）进行研究，但由于粗糙的数学模型，这些技术的空间分辨率往往很差。 通过使用有限元来模拟头部和药柱的真实几何形状和电导率，可以提高精度和分辨率。 已经开发了基于有限元方法的2D EEG源定位代码，但是将其扩展到3D逼真形状的头部模型将需要大幅提高计算速度。 3D有限元模型中的源定位需要对可能持续数秒的脑电波中的每个时间点进行数百次迭代的大型方程系统（超过100，000）的求解。 因此，为了使这种计算切实可行，3D EEG代码将被优化和并行化，以在匹兹堡超级计算中心的CRAY超级计算机之一或匹兹堡大学医学中心的80个惠普工作站的集群上运行。 在第二年，将开发和并行化3D MEG源定位代码。一旦3D EEG代码被并行化，这是一个直接且相对简单的任务，因为EEG和MEG都需要求解相同的方程组。 该奖学金将通过参加正式课程、讲习班、研讨会以及与计算机科学、数学、神经生理学和工程学领域的多学科专家进行磋商，帮助研究助理拓宽代码优化、代码并行化和神经科学方面的知识。从这个项目开发的代码将通过域提供，以帮助其他研究人员利用准确和有效的源定位模型。

The temporal dynamics of information processing in the brain can be studied using electroencephalography (EEG) or magnetoencephalpgrahpy (MEG), but the spatial resolution of these techniques is often poor due to crude mathematical models. Accuracy and resolution can be improved by using finite elements to model the realistic geometry and electrical conductivity of the head and grain. A 2D EEG source localization code based on a finite element method has been developed, but extending this to the 3D realistic-shaped head model will require a huge increase in computational speed. Source localization in a 3D finite element model requires the solution of a large system of equations (more than 100,000) for hundreds of iterations for each time point in a brainwave that may last for seconds. Therefore, to make this computations practicable the 3D EEG code will be optimized and parallelized to run on one of the CRAY supercomputers at the Pittsburgh Supercomputing Center or on a cluster of 80 Hewlett-Packard workstations at the University of Pittsburgh Medical Center. During the second year, a 3D MEG source localization code will be developed and parallelized. This is a straightforward and relatively simple task once the 3D EEG code is parallelized, because the solution of the same system of equations is needed for both EEG and MEG. The fellowship will help broaden the research Associate's knowledge in code optimization, code parallelization and the neurosciences through participation in formal courses, workshops, seminars and consultation with a group of multi-disciplinary advisops in computer science, mathematics, neurophysiology and engineering. The code developed from this project will be made available through the domain to help other researchers utilize accurate and efficient source localization models.