title: Harnessing graphics processing units for improved neuroimaging statistics.
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.
authors.affiliation: Virginia Tech Carilion Research Institute, Virginia Tech, 2 Riverside Circle, Roanoke, 24016, VA, USA, andek034@gmail.com.
authors.forename: Anders
authors.initials: A
authors.lastname: Eklund
authors.forename: Mattias
authors.initials: M
authors.lastname: Villani
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Laconte
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000473:pathology* / Q000502:physiology; D001931:Brain Mapping / Q000379:methods; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods; D059906:Neuroimaging / Q000379:methods*