Extracting activated regions of fMRI data using unsupervised learning

Authors:
Heydar Davoudi;Ali Taalimi;Emad Fatemizadeh
Affiliations:
Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran;Biomedical Signal and Image Processing Laboratory, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran
Venue:
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Year:
2009

Citing 3
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Abstract

Clustering approaches are going to efficiently define the activated regions of the brain in fMRI studies. However, choosing appropriate clustering algorithms and defining optimal number of clusters are still key problems of these methods. In this paper, we apply an improved version of Growing Neural Gas algorithm, which automatically operates on the optimal number of clusters. The decision criterion for creating new clusters at the heart of this online clustering is taken from MB cluster validity index. Comparison with other so-called clustering methods for fMRI data analysis shows that the proposed algorithm outperforms them in both artificial and real datasets.