Data partitioning and independent component analysis techniques applied to fMRI

2004 ◽  
Author(s):  
Axel Wismueller ◽  
Anke Meyer-Base ◽  
Oliver Lange ◽  
Thomas D. Otto ◽  
Dorothee Auer
Keyword(s):  
Independent Component Analysis ◽  
Component Analysis ◽  
Data Partitioning ◽  
Independent Component ◽  
Analysis Techniques

MODEL-FREE FUNCTIONAL MRI ANALYSIS USING TOPOGRAPHIC INDEPENDENT COMPONENT ANALYSIS

2004 ◽  
Vol 14 (04) ◽  
pp. 217-228 ◽  
Author(s):  
ANKE MEYER-BÄSE ◽  
OLIVER LANGE ◽  
AXEL WISMÜLLER ◽  
HELGE RITTER
Keyword(s):  
Independent Component Analysis ◽  
Slight Modification ◽  
Component Analysis ◽  
Independent Component ◽  
Self Organizing Map ◽  
Analysis Techniques ◽  
Model Free ◽  
Temporal Domain ◽  
Different Types ◽  
Fmri Analysis

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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