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Proceedings Paper

Exploratory analysis of functional MRI data using HSOM and HTMP
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Paper Abstract

As a complement to model-based approaches for the analysis of functional magnetic resonance imaging (fMRI) data, methods of exploratory analysis offer interesting options. While unsupervised clustering techniques can be employed for the extraction of signal patterns and segmentation purposes, topographic mapping techniques such as the Self-Organizing Map (SOM) and the Topographic Mapping for Proximity Data (TMP) provide additionally a structured representation of the data. In this contribution we investigate the applicability of two recently proposed variants of these algorithms which make use of concepts from non-Euclidean geometry for the analysis of fMRI data. Compared to standard methods, both approaches provide more freedom for the representation of complex relationships in low-dimensional mappings while they offer a convenient interface for the visualization and exploration of high-dimensional data sets. Based on data from fMRI experiments, the application of these techniques is discussed and the results are quantitatively evaluated by means of ROC statistics.

Paper Details

Date Published: 9 April 2007
PDF: 8 pages
Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657608 (9 April 2007); doi: 10.1117/12.720730
Show Author Affiliations
Axel Saalbach, Florida State Univ. (United States)
Bielefeld Univ. (Germany)
Oliver Lange, Florida State Univ. (United States)
Anke Meyer-Baese, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 6576:
Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V
Harold H. Szu; Jack Agee, Editor(s)

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