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Clustering of fMRI Data Using Affinity Propagation

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Brain Informatics (BI 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6334))

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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.

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Author information

Authors and Affiliations

  1. International WIC Institute, Beijing University of Technology, Beijing, 100124, China

    Dazhong Liu, Wanxuan Lu & Ning Zhong

  2. School of Mathematics and Computer Science, Hebei University, Baoding, 071002, China

    Dazhong Liu

  3. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, 371-0816, Japan

    Ning Zhong

Authors
  1. Dazhong Liu
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  2. Wanxuan Lu
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  3. Ning Zhong
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Regina, S4S 0A2, Regina, SK, Canada

    Yiyu Yao

  2. Rensselaer Polytechnic Institute, NY 12180, Troy, USA

    Ron Sun

  3. McGovern Institute for Brain Research, Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Tomaso Poggio

  4. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jiming Liu

  5. Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-Cho, 371.0816, Maebashi-City, Japan

    Ning Zhong

  6. York University, Toronto, ON, Canada

    Jimmy Huang

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© 2010 Springer-Verlag Berlin Heidelberg

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Liu, D., Lu, W., Zhong, N. (2010). Clustering of fMRI Data Using Affinity Propagation. In: Yao, Y., Sun, R., Poggio, T., Liu, J., Zhong, N., Huang, J. (eds) Brain Informatics. BI 2010. Lecture Notes in Computer Science(), vol 6334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15314-3_38

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  • DOI: https://doi.org/10.1007/978-3-642-15314-3_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15313-6

  • Online ISBN: 978-3-642-15314-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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