A highly parallelized framework for computationally intensive MR data analysis

  • Roland N. Boubela
  • Wolfgang Huf
  • Klaudius Kalcher
  • Ronald Sladky
  • Peter Filzmoser
  • Lukas Pezawas
  • Siegfried Kasper
  • Christian Windischberger
  • Ewald Moser
Research Article

Abstract

Object

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.

Results

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.

Conclusion

The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.

Keywords

Magnetic resonance imaging fMRI High performance computing Statistical computing 

Notes

Acknowledgments

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).

References

  1. 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
  2. 2.
    van den Heuvel MP, Pol HEH (2010) Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol 20:519–534PubMedCrossRefGoogle Scholar
  3. 3.
    Weissenbacher A, Kasess C, Gerstl F, Lanzenberger R, Moser E, Windischberger C (2009) Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies. Neuroimage 47:1408–1416PubMedCrossRefGoogle Scholar
  4. 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
  5. 5.
    Cox RW (1996) AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput Biomed Res 29:162–173PubMedCrossRefGoogle Scholar
  6. 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
  7. 7.
    Beckmann CF, Smith SM (2004) Probabilistic independent component analysis for functional magnetic resonance imaging. IEEE Trans Med Imaging 23:137–152PubMedCrossRefGoogle Scholar
  8. 8.
    Beckmann CF, DeLuca M, Devlin JT, Smith SM (2005) Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci 360:1001–1013PubMedCrossRefGoogle Scholar
  9. 9.
    R Development Core Team (2011) R: a language and environment for statistical computing. ISBN 3-900051-07-0Google Scholar
  10. 10.
    Tomasi D, Volkow ND (2011) Gender differences in brain functional connectivity density. Hum Brain Mapp. doi: 10.1002/hbm.21252
  11. 11.
    Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks. Neuroimage 56:2068–2079Google Scholar
  12. 12.
    Tomasi D, Volkow ND (2010) Functional connectivity density mapping. Proc Natl Acad Sci USA 107:9885–9890PubMedCrossRefGoogle Scholar
  13. 13.
  14. 14.
    NVIDIA Corporation (2011) CUDA API Reference ManualGoogle Scholar
  15. 15.
    R Development Core Team (2011) Writing R extensions. R foundation for statistical computing. Austria, ViennaGoogle Scholar
  16. 16.
    Lowe MJ (2010) A historical perspective on the evolution of resting-state functional connectivity with MRI. Magn Reson Mater Phy 23:279–288CrossRefGoogle Scholar
  17. 17.
    Moser E, Ranjeva JP (2010) In vivo MR imaging of brain networks: illusion or revolution?. Magn Reson Mater Phy 23:275–277CrossRefGoogle Scholar
  18. 18.
    Damoiseaux JS, Rombouts SARB, Barkhof F, Scheltens P, Stam CJ, Smith SM, Beckmann CF (2006) Consistent resting-state networks across healthy subjects. Proc Natl Acad Sci USA 103:13848–13853PubMedCrossRefGoogle Scholar
  19. 19.
    Fox MD, Snyder AZ, Vincent JL, Corbetta M, Essen DCV, Raichle ME (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci USA 102:9673–9678PubMedCrossRefGoogle Scholar
  20. 20.
    Robinson S, Basso G, Soldati N, Sailer U, Jovicich J, Bruzzone L, Kryspin-Exner I, Bauer H, Moser E (2009) A resting state network in the motor control circuit of the basal ganglia. BMC Neurosci 10:137PubMedCrossRefGoogle Scholar
  21. 21.
    Schöpf V, Kasess CH, Lanzenberger R, Fischmeister F, Windischberger C, Moser E (2010) Fully exploratory network ICA (FENICA) on resting-state fMRI data. J Neurosci Methods 192:207–213PubMedCrossRefGoogle Scholar
  22. 22.
    Granert O (2010) Rniftilib: Rniftilib—R Interface to NIFTICLIB (V1.1.0). R package version 0.0-29Google Scholar
  23. 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. 24.
    Knaus J (2010) snowfall: easier cluster computing (based on snow). R package version 1.84Google Scholar
  25. 25.
    da Silva ARF (2010) cudaBayesreg: Bayesian computation in CUDA. The R Journal 2/2:48–55Google Scholar
  26. 26.
    da Silva ARF (2011) A Bayesian multilevel model for fMRI data analysis. Comput Methods Programs Biomed 102:238–252CrossRefGoogle Scholar
  27. 27.
    NVIDIA Corporation (2011) NVIDIA CUDA C Programming GuideGoogle Scholar
  28. 28.
    Schöpf V, Windischberger C, Robinson S, Kasess CH, Fischmeister FPhS, Lanzenberger R, Albrecht J, Kleemann AM, Kopietz R, Wiesmann M, Moser E (2011) Model-free fMRI group analysis using FENICA. Neuroimage 55:185–193PubMedCrossRefGoogle Scholar
  29. 29.
    Yu H (2010). Interface (wrapper) to MPI (message-passing interface). Package version 0.5-9Google Scholar
  30. 30.
    Eidelberg D (2009) Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci 32:548–557PubMedCrossRefGoogle Scholar
  31. 31.
    Huf W, Kalcher K, Pail G, Friedrich ME, Filzmoser P, Kasper S (2011) Meta-analysis: fact or fiction? How to interpret meta-analyses. World J Biol Psychiatry 12:188–200PubMedCrossRefGoogle Scholar
  32. 32.
    Provencher SW (1993) Estimation of metabolite concentrations from localized in vivo proton NMR spectra. Magn Reson Med 30:672–679PubMedCrossRefGoogle Scholar
  33. 33.
    Damoiseaux JS, Greicius MD (2009) Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct Funct 213:525–533PubMedCrossRefGoogle Scholar

Copyright information

© ESMRMB 2011

Authors and Affiliations

  • Roland N. Boubela
    • 1
    • 2
    • 3
  • Wolfgang Huf
    • 1
    • 2
    • 3
  • Klaudius Kalcher
    • 1
    • 2
    • 3
  • Ronald Sladky
    • 1
  • Peter Filzmoser
    • 3
  • Lukas Pezawas
    • 2
  • Siegfried Kasper
    • 2
  • Christian Windischberger
    • 1
  • Ewald Moser
    • 1
  1. 1.Center for Medical Physics and Biomedical EngineeringMedical University of ViennaViennaAustria
  2. 2.Department of Psychiatry and PsychotherapyMedical University of ViennaViennaAustria
  3. 3.Department of Statistics and Probability TheoryVienna University of TechnologyViennaAustria

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