Model-free functional MRI analysis using topographic independent component analysis

International Journal of Neural Systems
Anke Meyer-BäseHelge Ritter

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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Citations

Jun 7, 2005·Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences·Andreas Bartels, Semir Zeki
Dec 4, 2009·Journal of Neurology, Neurosurgery, and Psychiatry·F CaudaG Geminiani
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Mar 11, 2015·International Journal of Neural Systems·Darya ChyzhykAnn K Shinn
Feb 21, 2014·International Journal of Neural Systems·Rongrong Fu, Hong Wang
Aug 31, 2020·Journal of Magnetic Resonance Imaging : JMRI·Anke Meyer-BaseKatja Pinker

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