We present a computational framework suitable for a data-driven approach to structural equation modeling (SEM) and describe several workflows for modeling functional magnetic resonance imaging (fMRI) data within this framework. The Computational Neuroscience Applications Research Infrastructure (CNARI) employs a high-level scripting language called Swift, which is capable of spawning hundreds of thousands of simultaneous R processes (R Development Core Team, 2008), consisting of self-contained SEMs, on a high performance computing system (HPC). These self-contained R processing jobs are data objects generated by OpenMx, a plug-in for R, which can generate a single model object containing the matrices and algebraic information necessary to estimate parameters of the model. With such an infrastructure in place a structural modeler may begin to investigate exhaustive searches of the model space. Specific applications of the infrastructure, statistics related to model fit, and limitations are discussed in relation to exhaustive SEM. In particular, we discuss how workflow management techniques can help to solve large computational problems in neuroimaging.
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机译:我们提出了一种适用于数据驱动的结构方程模型(SEM)的计算框架,并描述了在该框架内为功能性磁共振成像(fMRI)数据建模的几种工作流程。计算神经科学应用研究基础架构(CNARI)使用一种称为Swift的高级脚本语言,该语言能够在高水平上生成由独立的SEM组成的数十万个同时进行的R进程(R Development Core Team,2008)。性能计算系统(HPC)。这些自包含的R处理作业是由OpenMx(R的插件)生成的数据对象,它可以生成单个模型对象,其中包含估计模型参数所需的矩阵和代数信息。有了这样的基础架构,结构建模者就可以开始研究模型空间的详尽搜索。有关详尽的SEM,讨论了基础结构的特定应用,与模型拟合有关的统计信息以及限制。特别是,我们讨论了工作流管理技术如何帮助解决神经成像中的大型计算问题。
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