Paper Info
Title
Parallel workflows for data-driven structural equation modeling in functional neuroimaging.
Abstract
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.
Year
DOI
Venue
2009
10.3389/neuro.11.034.2009
Front. Neuroinform.
Keywords
Field
DocType
exhaustive search,workflows,sem,swift,openmx,structural equation model,functional neuroimaging,bioinformatics,biomedical research
Data mining,Computational problem,Data-driven,Structural equation modeling,Supercomputer,Brute-force search,Computer science,OpenMx,Artificial intelligence,Workflow,Machine learning,Scripting language
Journal
Volume
ISSN
Citations 
3
1662-5196
4
PageRank 
References 
Authors
0.86
4
6
Name
Order
Citations
PageRank
Sarah Kenny141.20
Michael Andric2162.56
Steven M Boker340.86
Michael C Neale440.86
Michael Wilde522827.80
Steven L. Small615822.15