Clustering of dependent components: A new paradigm for fMRI signal detection

Meyer-Bäse A, Hurdal MK, Lange O, Ritter H (2005)
EURASIP Journal on Advances in Signal Processing 2005(19): 3089-3102.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor/in
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Abstract / Bemerkung
Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Recently, a new paradigm in ICA emerged, that of finding “clusters” of dependent components. This intriguing idea found its implementation into two new ICA algorithms: tree-dependent and topographic ICA. For fMRI, this represents 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 by (1) task-related activation maps, (2) associated time courses, and (3) ROC study. The most important findings in this paper are that (1) both tree-dependent and topographic ICA are able to identify signal components with high correlation to the fMRI stimulus, and that (2) 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 tree-dependent ICA (KGV) using as an approximation of the mutual information the kernel generalized variance. The applicability of the new algorithm is demonstrated on experimental data.
Stichworte
fMRI; topographic ICA; tree-dependent ICA; dependent component analysis
Erscheinungsjahr
2005
Zeitschriftentitel
EURASIP Journal on Advances in Signal Processing
Band
2005
Ausgabe
19
Seite(n)
3089-3102
ISSN
1687-6172
eISSN
1687-6180
Page URI
https://pub.uni-bielefeld.de/record/1599818

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Meyer-Bäse A, Hurdal MK, Lange O, Ritter H. Clustering of dependent components: A new paradigm for fMRI signal detection. EURASIP Journal on Advances in Signal Processing. 2005;2005(19):3089-3102.
Meyer-Bäse, A., Hurdal, M. K., Lange, O., & Ritter, H. (2005). Clustering of dependent components: A new paradigm for fMRI signal detection. EURASIP Journal on Advances in Signal Processing, 2005(19), 3089-3102. doi:10.1155/ASP.2005.3089
Meyer-Bäse, A., Hurdal, M. K., Lange, O., and Ritter, H. (2005). Clustering of dependent components: A new paradigm for fMRI signal detection. EURASIP Journal on Advances in Signal Processing 2005, 3089-3102.
Meyer-Bäse, A., et al., 2005. Clustering of dependent components: A new paradigm for fMRI signal detection. EURASIP Journal on Advances in Signal Processing, 2005(19), p 3089-3102.
A. Meyer-Bäse, et al., “Clustering of dependent components: A new paradigm for fMRI signal detection”, EURASIP Journal on Advances in Signal Processing, vol. 2005, 2005, pp. 3089-3102.
Meyer-Bäse, A., Hurdal, M.K., Lange, O., Ritter, H.: Clustering of dependent components: A new paradigm for fMRI signal detection. EURASIP Journal on Advances in Signal Processing. 2005, 3089-3102 (2005).
Meyer-Bäse, Anke, Hurdal, Monica K., Lange, Oliver, and Ritter, Helge. “Clustering of dependent components: A new paradigm for fMRI signal detection”. EURASIP Journal on Advances in Signal Processing 2005.19 (2005): 3089-3102.