title: Model-free functional MRI analysis using topographic independent component analysis.
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.
authors.affiliation: Department of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32310-6046, USA. amb@eng.fsu.edu
authors.forename: Anke
authors.initials: A
authors.lastname: Meyer-Bäse
authors.forename: Oliver
authors.initials: O
authors.lastname: Lange
authors.forename: Axel
authors.initials: A
authors.lastname: Wismüller
authors.forename: Helge
authors.initials: H
authors.lastname: Ritter
mesh_terms: D000465:Algorithms; D001921:Brain / Q000098:blood supply / Q000502:physiology*; D001931:Brain Mapping; D016000:Cluster Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D016571:Neural Networks, Computer; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis / Q000379:methods*; D012372:ROC Curve; D013997:Time Factors
chemical_list: D010100:Oxygen