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.

Journal Article | Published | English

No fulltext has been uploaded

Author
; ; ;
Abstract
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.
Publishing Year
ISSN
eISSN
PUB-ID

Cite this

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.
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.
This data publication is cited in the following publications:
This publication cites the following data publications:

Export

0 Marked Publications

Open Data PUB

Web of Science

View record in Web of Science®

Search this title in

Google Scholar