Cognitive, Affective, & Behavioral Neuroscience

, Volume 13, Issue 3, pp 587–597 | Cite as

Harnessing graphics processing units for improved neuroimaging statistics

  • Anders Eklund
  • Mattias Villani
  • Stephen M. LaConte
Article

Abstract

Simple models and algorithms based on restrictive assumptions are often used in the field of neuroimaging for studies involving functional magnetic resonance imaging, voxel based morphometry, and diffusion tensor imaging. Nonparametric statistical methods or flexible Bayesian models can be applied rather easily to yield more trustworthy results. The spatial normalization step required for multisubject studies can also be improved by taking advantage of more robust algorithms for image registration. A common drawback of algorithms based on weaker assumptions, however, is the increase in computational complexity. In this short overview, we will therefore present some examples of how inexpensive PC graphics hardware, normally used for demanding computer games, can be used to enable practical use of more realistic models and accurate algorithms, such that the outcome of neuroimaging studies really can be trusted.

Keywords

Non-parametric statistics Neuroimaging Bayesian statistics Graphics processing units Spatial normalization fMRI VBM DTI 

Notes

Acknowledgments

Anders Eklund owns the company Wanderine Consulting, which has done consulting work for the company Accelereyes (the creators of the MATLAB GPU inferface Jacket). The authors would like to thank Gerdien van Eersel for making us aware of the special issue on improved reliability and validity of neuroimaging findings.

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Copyright information

© Psychonomic Society, Inc. 2013

Authors and Affiliations

  • Anders Eklund
    • 1
  • Mattias Villani
    • 2
  • Stephen M. LaConte
    • 1
    • 3
  1. 1.Virginia Tech Carilion Research Institute, Virginia TechRoanokeUSA
  2. 2.Division of Statistics, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden
  3. 3.School of Biomedical Engineering & SciencesVirginia Tech-Wake Forest UniversityBlacksburgUSA

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