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Resampling as a cluster validation technique in fMRI
Author(s) -
Baumgartner R.,
Somorjai R.,
Summers R.,
Richter W.,
Ryner L.,
Jarmasz M.
Publication year - 2000
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/(sici)1522-2586(200002)11:2<228::aid-jmri23>3.0.co;2-z
Subject(s) - resampling , principal component analysis , computer science , voxel , statistical hypothesis testing , artificial intelligence , pattern recognition (psychology) , permutation (music) , cluster analysis , nonparametric statistics , null hypothesis , data mining , cluster (spacecraft) , undersampling , functional magnetic resonance imaging , hierarchical clustering , exploratory data analysis , univariate , statistics , machine learning , mathematics , psychology , multivariate statistics , neuroscience , physics , acoustics , programming language
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, ie, “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. J. Magn. Reson. Imaging 2000;11:228–231. © 2000 Wiley‐Liss, Inc.