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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Arfanakis, K., Cordes, D., Haughton, V., Moritz, C., Quigley, M., Meyerand, M.: Combining independent component analysis and correlation analysis to probe interregional connectivity in fmri task activation datasets. Magnetic Resonance Imaging 18, 921–930 (2000)
McKeown, M., Jung, T., Makeig, S., Brown, G., Jung, T., Kindermann, S., Bell, A., Sejnowski, T.: Analysis of fmri data by blind separation into independent spatial components. Human Brain Mapping 6, 160–188 (1998)
Biswal, B., Ulmer, J.: Blind source separation of multiple signal sources of fmri data sets using independent component analysis. Journal of Computer Assisted Tomography 23, 265–271 (1999)
Ziehe, A., Müller, K.: Tdsep - an efficient algorithm for blind separation using time structure. In: Proc. ICANN, vol. 2, pp. 675–680 (1998)
Cardoso, J.F., Souloumiac, A.: Blind beamforming for non gausssian signals. IEE Proceedings-F 140, 362–370 (1993)
Hyvarinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411–430 (2000)
Bach, F.R., Jordan, M.I.: Beyond independent components: Trees and clusters. Journal of Machine Learning Research 4, 1205–1233 (2003)
Hyvarinen, A., Hoyer, P.: Topographic independent component analysis. Neural Computation 13, 1527–1558 (2001)
Chow, C.K., Liu, C.N.: Approximating discrete probability distributions with dependence trees. IEEE Transaction on Information Theory 14, 462–467 (1968)
Hyvarinen, A., Hoyer, P.: Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural Computation 12, 1705–1720 (2000)
Kohonen, T.: Emergence of invariant-feature detectors in the adaptive-subspace self-organizing map. Biological Cybernetics 75, 281–291 (1996)
Cardoso, J.F.: Multidimensional independent component analysis. In: Proc. IEEE ICASSP, Seattle, vol. 4, pp. 1941–1944 (1998)
Wismüller, A., Lange, O., Dersch, D., Leinsinger, G., Hahn, K., Pütz, B., Auer, D.: Cluster analysis of biomedical image time–series. International Journal on Computer Vision 46, 102–128 (2002)
Woods, R., Cherry, S., Mazziotta, J.: Rapid automated algorithm for aligning and reslicing pet images. Journal of Computer Assisted Tomography 16, 620–633 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-540-30110-3_99
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23056-4
Online ISBN: 978-3-540-30110-3
eBook Packages: Springer Book Archive