title: Resampling as a cluster validation technique in fMRI.
abstract: Exploratory, data-driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network-based techniques are complementary to hypothesis-led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, i.e., "no activation." We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation-based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions.
authors.affiliation: Institute for Biodiagnostics, National Research Council Canada, Winnipeg, Manitoba, Canada.
authors.forename: R
authors.initials: R
authors.lastname: Baumgartner
authors.forename: R
authors.initials: R
authors.lastname: Somorjai
authors.forename: R
authors.initials: R
authors.lastname: Summers
authors.forename: W
authors.initials: W
authors.lastname: Richter
authors.forename: L
authors.initials: L
authors.lastname: Ryner
authors.forename: M
authors.initials: M
authors.lastname: Jarmasz
mesh_terms: D001921:Brain / Q000033:anatomy & histology*; D001931:Brain Mapping; D016000:Cluster Analysis; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000706:statistics & numerical data; D010775:Photic Stimulation