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Tree-Dependent and Topographic Independent Component Analysis for fMRI Analysis

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Independent Component Analysis and Blind Signal Separation (ICA 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3195))

Abstract

Recently, a new paradigm in ICA emerged, that of finding “clusters” of dependent components. This striking philosophy found its implementation in two new ICA algorithms: tree–dependent and topographic ICA. Applied to fMRI, this leads to the unifying paradigm of combining two powerful exploratory data analysis methods, ICA and unsupervised clustering techniques. For the fMRI data, a comparative quantitative evaluation between the two methods, tree–dependent and topographic ICA was performed. The comparative results were evaluated based on (1) correlation and associated time–courses and (2) ROC study. It can be seen that topographic ICA outperforms all other ICA methods including tree–dependent ICA for 8 and 9 ICs. However, for 16 ICs topographic ICA is outperformed by both FastICA and tree–dependent ICA (KGV) using as an approximation of the mutual information the kernel generalized variance.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Florida State University, Tallahassee, Florida, 32310-6046, USA

    Anke Meyer-Bäse, Fabian J. Theis & Oliver Lange

  2. Institute of Biophysics, University of Regensburg, D-93040, Regensburg, Germany

    Fabian J. Theis

  3. Department of Clinical Radiology, Ludwig–Maximilians University, Munich, 80336, Germany

    Oliver Lange

  4. Department of Architecture and Computer Technology, University of Granada, E-18071, Spain

    Carlos G. Puntonet

Authors
  1. Anke Meyer-Bäse
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  2. Fabian J. Theis
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  3. Oliver Lange
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  4. Carlos G. Puntonet
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Editor information

Editors and Affiliations

  1. Dept. of Architecture and Computer Technology, University of Granada, Spain

    Carlos G. Puntonet  & Alberto Prieto  & 

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© 2004 Springer-Verlag Berlin Heidelberg

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Meyer-Bäse, A., Theis, F.J., Lange, O., Puntonet, C.G. (2004). Tree-Dependent and Topographic Independent Component Analysis for fMRI Analysis. In: Puntonet, C.G., Prieto, A. (eds) Independent Component Analysis and Blind Signal Separation. ICA 2004. Lecture Notes in Computer Science, vol 3195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30110-3_99

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  • DOI: https://doi.org/10.1007/978-3-540-30110-3_99

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-23056-4

  • Online ISBN: 978-3-540-30110-3

  • eBook Packages: Springer Book Archive

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