Validating the independent components of neuroimaging time series via clustering and visualization

被引:1008
作者
Himberg, J
Hyvärinen, A
Esposito, F
机构
[1] Univ Helsinki, Dept Comp Sci, Helsinki Inst Informat Technol, BRU, FIN-00014 Helsinki, Finland
[2] Aalto Univ, Neural Networks Res Ctr, Helsinki, Finland
[3] Univ Naples Federico II, Div Neurol 2, Naples, Italy
[4] Univ Naples Federico II, Dept Neurol Sci, Naples, Italy
基金
芬兰科学院;
关键词
clustering; visualization; neuromiaging time series;
D O I
10.1016/j.neuroimage.2004.03.027
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Recently, independent component analysis (ICA) has been widely used in the analysis of brain imaging data. An important problem with most ICA algorithms is, however, that they are stochastic; that is, their results may be somewhat different in different runs of the algorithm. Thus, the outputs of a single run of an ICA algorithm should be interpreted with some reserve, and further analysis of the algorithmic reliability of the components is needed. Moreover, as with any statistical method, the results are affected by the random sampling of the data, and some analysis of the statistical significance or reliability should be done as well. Here we present a method for assessing both the algorithmic and statistical reliability of estimated independent components. The method is based on running the ICA algorithm many times with slightly different conditions and visualizing the clustering structure of the obtained components in the signal space. In experiments with magneto-encephalographic (MEG) and functional magnetic resonance imaging (fMRI) data, the method was able to show that expected components are reliable; furthermore, it pointed out components whose interpretation was not obvious but whose reliability should incite the experimenter to investigate the underlying technical or physical phenomena. The method is implemented in a software package called Icasso. (C) 2004 Elsevier Inc. All rights reserved.
引用
收藏
页码:1214 / 1222
页数:9
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