Comparing fMRI Activity Maps from GLM and CCA at the Same Significance Level by Fast Random Permutation Tests on the GPU
2011 (English)Conference paper (Other academic)
Parametric statistical methods 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 isassumed 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. In this work it is shown how the computational power of the Graphics Processing Unit (GPU) can be used to speedup non-parametric tests, such as random permutation tests. With random permutation tests it is possible to calculate significance thresholds for any test statistics. As an example, fMRI activity maps from the General Linear Model (GLM) and Canonical Correlation Analysis (CCA) are compared at the same significance level.
Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press , 2011.
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-66206OAI: oai:DiVA.org:liu-66206DiVA: diva2:402372
SSBA Symposium on Image Analysis, March 17-18, Linköping, Sweden