Doctoral Dissertation Research: A Realtime Statistical Approach To The Inverse Problem In Magnetoencephalography By CUDA Computing

在过去的几十年里，脑成像技术一直在发展。科学家们现在能够用脑磁图（MEG）在1毫秒的时间分辨率下测量大脑活动（磁场），但大脑源的定位问题并没有得到满意的解决。本博士论文研究项目将进一步发展脑磁图中的源定位算法来估计时变脑源。脑源的时间依赖性，以及空间信息，将通过贝叶斯模型进行研究。每一时刻可能的脑源的空间分布将被呈现。为了实现分析，将使用最新的计算统一设备架构（CUDA）图形处理器单元（GPU）计算方案来获得实时脑成像。现有的并行虚拟机程序将被重写为CUDA程序。通过GPU上的大规模并行计算方案，可以利用MEG提供的高时间分辨率，从而允许以非常低的成本在个人超级计算机上实时调查大脑活动。除了对脑成像的贡献外，最先进的并行计算环境将用于开发脑成像算法，在实时分析其他大规模问题方面具有许多优势。在许多科学领域，计算算法目前落后于理论发展，特别是数据收集能力。在GPU中设计良好的程序可以加速科学计算中的操作，例如应用于超大数据集的三维傅里叶变换或寻找大量微分方程集的解。此外，CUDA提供了一个非常实惠的软件包，可以在桌面计算机上以高度并行的方式工作。因此，实验者不必离开实验室就可以在实验前测试他们的实验设计。这个项目的结果，除了为对脑成像感兴趣的科学家提供了可用的工具之外，还可能有助于刺激复杂科学实验运行方式的改变。CUDA计划将在奖励期结束时在合作研究者的网站上提供。作为博士论文研究改进奖，提供支持，使有前途的学生建立一个强大的，独立的研究生涯。

Brain imaging techniques have been developing over the past few decades. Scientists now are able to measure brain activity (magnetic fields) under a temporal resolution of 1 millisecond by magnetoencephalography (MEG), but the problem of localizing the brain source is not satisfactorily solved. This doctoral dissertation research project will further develop a source localization algorithm in MEG to estimate time varying brain sources. The temporal dependence of brain sources, as well as spatial information, will be investigated by Bayesian modeling. A spatial distribution of possible brain source at each time will be presented. To implement the analysis, the newest compute unified device architecture (CUDA) graphic processor unit (GPU) computing scheme will be utilized to obtain real-time brain imaging. An existing parallel virtual machine program will be rewritten into a CUDA program. Through a massive parallel computing scheme on GPU, it becomes possible to take advantage of the high temporal resolution that MEG offers, thus permitting real-time investigation of brain activity on a personal supercomputer at a very low cost.In addition to its contribution to brain imaging, the state-of-the-art parallel computing environment that will be used to develop the brain-imaging algorithm has many advantages in the real-time analysis of other large-scale problems. In many scientific areas, computational algorithms currently lag behind theoretical developments and especially data collection capability. A well-designed program in GPU can speed up operations in scientific computing, such as three-dimensional Fourier transformations applied to extremely large datasets or finding solutions of massive sets of differential equations. In addition, CUDA provides a very affordable package that works in a high degree of parallelism on desktop computers. It therefore becomes possible for experimenters to test their experimental designs in advance of experimentation without having to leave their laboratories. The results of this project, besides adding to tools available to scientists interested in brain imaging, may help stimulate a change in how complicated scientific experiments are run. The CUDA program will be available on the co-investigator's website at the end of the award period. As a Doctoral Dissertation Research Improvement award, support is provided to enable a promising student to establish a strong, independent research career.