Model-free functional MRI analysis using topographic independent component analysis.

Anke Meyer-Bäse; Oliver Lange; Axel Wismüller; Helge Ritter

doi:10.1142/s0129065704002017

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Model-free functional MRI analysis using topographic independent component analysis.

Anke Meyer-Bäse·Oliver Lange·Axel Wismüller·Helge Ritter

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Abstract

Data-driven fMRI analysis techniques include independent component analysis (ICA) and different types of clustering in the temporal domain. Since each of these methods has its particular strengths, it is natural to look for an approach that unifies Kohonen's self-organizing map and ICA. This is given by the topographic independent component analysis. While achieved by a slight modification of the ICA model, it can be at the same time used to define a topographic order (clusters) between the components, and thus has the usual computational advantages associated with topographic maps. In this contribution, we can show that when applied to fMRI analysis it outperforms FastICA.

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Algorithms Brain Brain Mapping Cluster Analysis Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Models, Neurological Neural Networks, Computer Oxygen Principal Component Analysis ROC Curve Time Factors Comparative Study Journal Article

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Anke Meyer-Bäse, Oliver Lange, Axel Wismüller, Helge Ritter. 2004. Model-free functional MRI analysis using topographic independent component analysis.. https://doi.org/10.1142/s0129065704002017

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