TY - GEN
T1 - A toolbox for model-free analysis of fMRI data
AU - Gruber, P.
AU - Kohler, C.
AU - Theis, F. J.
PY - 2007
Y1 - 2007
N2 - We introduce Model-free Toolbox (MFBOX), a Matlab tool-box for analyzing multivariate data sets in an explorative fashion. Its main focus lies on the analysis of functional Nuclear Magnetic Resonance Imaging (fMRI) data sets with various model-free or data-driven techniques. In this context, it can also be used as plugin for SPM5, a popular tool in regression-based fMRI analysis. The toolbox includes BSS algorithms based on various source models including ICA, spatiotemporal ICA, autodecorrelation and NMF. They can all be easily combined with higher-level analysis methods such as reliability analysis using projective clustering of the components, sliding time window analysis or hierarchical decomposition. As an example, we use MFBOX for the analysis of an fMRI experiment and present short comparisons with the SPM results. The MFBOX is freely available for download at http://mfbox.sf.net.
AB - We introduce Model-free Toolbox (MFBOX), a Matlab tool-box for analyzing multivariate data sets in an explorative fashion. Its main focus lies on the analysis of functional Nuclear Magnetic Resonance Imaging (fMRI) data sets with various model-free or data-driven techniques. In this context, it can also be used as plugin for SPM5, a popular tool in regression-based fMRI analysis. The toolbox includes BSS algorithms based on various source models including ICA, spatiotemporal ICA, autodecorrelation and NMF. They can all be easily combined with higher-level analysis methods such as reliability analysis using projective clustering of the components, sliding time window analysis or hierarchical decomposition. As an example, we use MFBOX for the analysis of an fMRI experiment and present short comparisons with the SPM results. The MFBOX is freely available for download at http://mfbox.sf.net.
UR - http://www.scopus.com/inward/record.url?scp=38149127273&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74494-8_27
DO - 10.1007/978-3-540-74494-8_27
M3 - Conference contribution
AN - SCOPUS:38149127273
SN - 9783540744931
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 209
EP - 217
BT - Independent Component Analysis and Signal Separation - 7th International Conference, ICA 2007, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Independent Component Analysis (ICA) and Source Separation, ICA 2007
Y2 - 9 September 2007 through 12 September 2007
ER -