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9 April 2007 Exploratory analysis of functional MRI data using HSOM and HTMP
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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.
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Axel Saalbach, Oliver Lange, and Anke Meyer-Baese "Exploratory analysis of functional MRI data using HSOM and HTMP", Proc. SPIE 6576, Independent Component Analyses, Wavelets, Unsupervised Nano-Biomimetic Sensors, and Neural Networks V, 657608 (9 April 2007);

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