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Computational Medical Image Analysis
"... Functional magnetic resonance imaging (fMRI) is a prime example of multidisciplinary research. Without the beautiful physics of MRI, there would not be any images to look at in the first place. To obtain images of good quality, it is necessary to fully understand the concepts of the frequency domain ..."
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Functional magnetic resonance imaging (fMRI) is a prime example of multidisciplinary research. Without the beautiful physics of MRI, there would not be any images to look at in the first place. To obtain images of good quality, it is necessary to fully understand the concepts of the frequency domain. The analysis of fMRI data requires understanding of signal processing, statistics and knowledge about the anatomy and function of the human brain. The resulting brain activity maps are used by physicians, neurologists, psychologists and behaviourists, in order to plan surgery and to increase their understanding of how the brain works. This thesis presents methods for realtime fMRI and nonparametric fMRI analysis. Realtime fMRI places high demands on the signal processing, as all the calculations have to be made in realtime in complex situations. Realtime fMRI can, for example, be used for interactive brain mapping. Another possibility is to change the stimulus that is given to the subject, in realtime, such that the brain and the computer can work together to solve
doi:10.1155/2011/627947 Research Article Fast Random Permutation Tests Enable Objective Evaluation of Methods for SingleSubject fMRI Analysis
"... Copyright © 2011 Anders Eklund et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Parametric statistical methods, such as Z, t, ..."
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Copyright © 2011 Anders Eklund et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Parametric statistical methods, such as Z, t,andFvalues, are traditionally employed in functional magnetic resonance imaging (fMRI) for identifying areas in the brain that are active with a certain degree of statistical significance. These parametric methods, however, have two major drawbacks. First, it is assumed that the observed data are Gaussian distributed and independent; assumptions that generally are not valid for fMRI data. Second, the statistical test distribution can be derived theoretically only for very simple linear detection statistics. With nonparametric statistical methods, the two limitations described above can be overcome. The major drawback of nonparametric methods is the computational burden with processing times ranging from hours to days, which so far have made them impractical for routine use in singlesubject fMRI analysis. In this work, it is shown how the computational power of costefficient graphics processing units (GPUs) can be used to speed up random permutation tests. A test with 10000 permutations takes less than a minute, making statistical analysis of advanced detection methods in fMRI practically feasible. To exemplify the permutationbased approach, brain activity maps generated by the general linear model (GLM) and canonical correlation analysis (CCA) are compared at the same significance level. 1.