Clustering of fMRI data using affinity propagation

Authors:
Dazhong Liu;Wanxuan Lu;Ning Zhong
Affiliations:
International WIC Institute, Beijing University of Technology, Beijing, China and School of Mathematics and Computer Science, Hebei University, Baoding, China;International WIC Institute, Beijing University of Technology, Beijing, China;International WIC Institute, Beijing University of Technology, Beijing, China and Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan
Venue:
BI'10 Proceedings of the 2010 international conference on Brain informatics
Year:
2010

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Abstract

Clustering methods are commonly used for fMRI (functional Magnetic Resonance Imaging) data analysis. Based on an effective clustering algorithm called Affinity Propagation (AP) and a new defined similarity measure, we present a method for detecting activated brain regions. In the proposed method, autocovariance function values and the Euclidean distance metric of time series are firstly calculated and combined into a new similarity measure, then the AP algorithm with the measure is carried out on all time series of data, and at last regions with which their cross-correlation coefficients are greater than a threshold are taken as activations. Without setting the number of clusters in advance, our method is especially appropriate for the analysis of fMRI data collected with a periodic experimental paradigm. The validity of the proposed method is illustrated by experiments on a simulated dataset and a benchmark dataset. It can detect all activated regions in the simulated dataset accurately, and its error rate is smaller than that of K-means. On the benchmark dataset, the result is very similar to SPM.