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Harnessing graphics processing units for improved neuroimaging statistics
Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA.
Linköping University, Department of Computer and Information Science, Statistics. Linköping University, Faculty of Arts and Sciences.
Virginia Tech Carilion Research Institute, Virginia Tech, Roanoke, USA / School of Biomedical Engineering & Sciences, Virginia Tech-Wake Forest University, Blacksburg, USA.
2013 (English)In: Cognitive, Affective, & Behavioral Neuroscience, ISSN 1530-7026, Vol. 13, no 3, 587-597 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2013. Vol. 13, no 3, 587-597 p.
Keyword [en]
Non-parametric statistics, Neuroimaging, Bayesian statistics, Graphics processing units, Spatial normalization, fMRI, VBM, DTI
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-94635DOI: 10.3758/s13415-013-0165-7ISI: 000324557900015OAI: diva2:633790
Available from: 2013-06-27 Created: 2013-06-27 Last updated: 2013-10-17Bibliographically approved

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