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

@article{citeulike:4299247, 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.}, author = {Meyer-B\"{a}se, A. and Lange, O. and Wism\"{u}ller, A. and Ritter, H.}, citeulike-article-id = {4299247}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/15372699}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=15372699}, issn = {0129-0657}, journal = {International journal of neural systems}, keywords = {components, connectionism, fmri, methods}, month = {August}, number = {4}, pages = {217--228}, posted-at = {2009-04-11 02:11:57}, priority = {2}, title = {Model-free functional MRI analysis using topographic independent component analysis.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/15372699}, volume = {14}, year = {2004} }

TY - JOUR ID - citeulike:4299247 L3 - citeulike-article-id:4299247 N2 - 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. IS - 4 JF - International journal of neural systems SN - 0129-0657 EP - 228 TI - Model-free functional MRI analysis using topographic independent component analysis. VL - 14 SP - 217 KW - components KW - connectionism KW - fmri KW - methods AU - Meyer-Bäse, A AU - Lange, O AU - Wismüller, A AU - Ritter, H PY - 2004/08// UR - http://view.ncbi.nlm.nih.gov/pubmed/15372699 ER -