A highly parallelized framework for computationally intensive MR data analysis
- 276 Downloads
The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation.
Materials and methods
Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework.
A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15.
The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.
KeywordsMagnetic resonance imaging fMRI High performance computing Statistical computing
This study was funded by the Institute for the Study of Affective Neuroscience (ISAN), by the Oesterreichische Nationalbank (OeNB P13890, OeNB P13903, OeNB P12982) and by the Austrian Science Fund (FWF) as part of the Special Research Program 35 (SFB-35). The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC).
- 1.Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, Dogonowski AM, Ernst M, Fair D, Hampson M, Hoptman MJ, Hyde JS, Kiviniemi VJ, Kötter R, Li SJ, Lin CP, Lowe MJ, Mackay C, Madden DJ, Madsen KH, Margulies DS, Mayberg HS, McMahon K, Monk CS, Mostofsky SH, Nagel BJ, Pekar JJ, Peltier SJ, Petersen SE, Riedl V, Rombouts SARB, Rypma B, Schlaggar BL, Schmidt S, Seidler RD, Siegle GJ, Sorg C, Teng GJ, Veijola J, Villringer A, Walter M, Wang L, Weng XC, Whitfield-Gabrieli S, Williamson P, Windischberger C, Zang YF, Zhang HY, Castellanos FX, Milham MP (2010) Toward discovery science of human brain function. Proc Natl Acad Sci USA 107:4734–4739PubMedCrossRefGoogle Scholar
- 4.Sladky R, Friston KJ, Tröstl J, Cunnington R, Moser E, Windischberger C (2011) Slice-timing effects and their correction in functional MRI. Neuroimage 58:588–594Google Scholar
- 6.Penny, WD, Friston, KJ, Ashburner, JT, Kiebel, SJ, Nichols, TE (eds) (2007) Statistical parametric mapping: the analysis of functional brain images. Academic Press, LondonGoogle Scholar
- 9.R Development Core Team (2011) R: a language and environment for statistical computing. ISBN 3-900051-07-0Google Scholar
- 10.Tomasi D, Volkow ND (2011) Gender differences in brain functional connectivity density. Hum Brain Mapp. doi: 10.1002/hbm.21252
- 11.Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks. Neuroimage 56:2068–2079Google Scholar
- 13.Parallel SPM. http://sourceforge.net/projects/parallelspm/
- 14.NVIDIA Corporation (2011) CUDA API Reference ManualGoogle Scholar
- 15.R Development Core Team (2011) Writing R extensions. R foundation for statistical computing. Austria, ViennaGoogle Scholar
- 22.Granert O (2010) Rniftilib: Rniftilib—R Interface to NIFTICLIB (V1.1.0). R package version 0.0-29Google Scholar
- 23.Kane MJ, Emerson JW (2010) bigmemory: manage massive matrices with shared memory and memory-mapped files. R package version 4.2.3Google Scholar
- 24.Knaus J (2010) snowfall: easier cluster computing (based on snow). R package version 1.84Google Scholar
- 25.da Silva ARF (2010) cudaBayesreg: Bayesian computation in CUDA. The R Journal 2/2:48–55Google Scholar
- 27.NVIDIA Corporation (2011) NVIDIA CUDA C Programming GuideGoogle Scholar
- 29.Yu H (2010). Interface (wrapper) to MPI (message-passing interface). Package version 0.5-9Google Scholar