Applied Soft Computing
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Clustering of fMRI data using affinity propagation
BI'10 Proceedings of the 2010 international conference on Brain informatics
Computer Science - Research and Development
Stimulus related data analysis by structured neural networks
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Extraction of fuzzy features for detecting brain activation from functional MR time-series
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Exploring functional connectivity networks in fMRI data using clustering analysis
BI'11 Proceedings of the 2011 international conference on Brain informatics
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Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, "neural gas" network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.