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Methods ARTICLE

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Parallel workflows for data-driven structural equation modeling in functional neuroimaging

Sarah Kenny1*, Michael Andric2, Steven M. Boker3, Michael C. Neale4, Michael Wilde1,6 and Steven L. Small1,2,5
1
Computation Institute, The University of Chicago, Chicago, IL, USA
2
Department of Psychology, The University of Chicago, Chicago, IL, USA
3
Department of Psychology, University of Virginia, Charlottesville, VA, USA
4
Department of Psychiatry, Virginia Commonwealth University, Richmond, VA USA
5
Department of Neurology, The University of Chicago, Chicago, IL, USA
6
Mathematics and Computer Science Division, Argonne National Laboratories, Argonne, IL, USA
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.
Keywords:
exhaustive search, OpenMx, SEM, swift, workflows
Citation:
Kenny S, Andric M, Boker SM, Neale MC, Wilde M and Small SL (2009). Parallel workflows for data-driven structural equation modeling in functional neuroimaging. Front. Neuroinform. 3:34. doi: 10.3389/neuro.11.034.2009
Received:
11 April 2009;
 Paper pending published:
10 July 2009;
Accepted:
09 September 2009;
 Published online:
20 October 2009.

Edited by:

John Van Horn, University of California at Los Angeles, USA

Reviewed by:

Shantanu Joshi, University of California at Los Angeles, USA
John Van Horn, University of California at Los Angeles, USA
Copyright:
© 2009 Kenny S, Andric M, Boker SM, Neale MC, Wilde L and Small SL. This is an open-access article subject to an exclusive license agreement between the authors and the Frontiers Research Foundation, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are credited.
*Correspondence:
Sarah Kenny, Computation Institute, University of Chicago, 5640 S Ellis Avenue, Chicago, IL 60637, USA. e-mail: skenny@uchicago.edu
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