Title |
Harnessing graphics processing units for improved neuroimaging statistics
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Published in |
Cognitive, Affective, & Behavioral Neuroscience, April 2013
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DOI | 10.3758/s13415-013-0165-7 |
Pubmed ID | |
Authors |
Anders Eklund, Mattias Villani, Stephen M. LaConte |
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. |
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United Kingdom | 3 | 38% |
United States | 2 | 25% |
Australia | 1 | 13% |
Sweden | 1 | 13% |
Unknown | 1 | 13% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 4 | 50% |
Scientists | 2 | 25% |
Practitioners (doctors, other healthcare professionals) | 2 | 25% |
Mendeley readers
Geographical breakdown
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United States | 1 | 5% |
Germany | 1 | 5% |
Unknown | 20 | 91% |
Demographic breakdown
Readers by professional status | Count | As % |
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Researcher | 7 | 32% |
Student > Ph. D. Student | 5 | 23% |
Professor | 2 | 9% |
Professor > Associate Professor | 2 | 9% |
Student > Master | 1 | 5% |
Other | 3 | 14% |
Unknown | 2 | 9% |
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Neuroscience | 5 | 23% |
Agricultural and Biological Sciences | 4 | 18% |
Computer Science | 3 | 14% |
Psychology | 3 | 14% |
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Other | 2 | 9% |
Unknown | 3 | 14% |