Abstract
We introduce Model-free Toolbox (MFBOX), a Matlab toolbox 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 .
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References
SPM5. edn. 5 (July 2005), http://www.fil.ion.ulc.ac.uk/spm/spm5.html
Calhoun, V.D., Adali, T., Pearlson, G.D., Pekar, J.J.: Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms. Hum. Brain Map. 13, 43–53 (2001)
Cardoso, J.-F., Souloumiac, A.: Blind beamforming for non gaussian signals. IEE Proceedings-F 140(6), 362–370 (1993)
Gruber, P., Stadlthanner, K., Böhm, M., Theis, F.J., Lang, E.W., Tomé, A.M., Teixeira, A.R., Puntonet, C.G., Górriz, J.M.: Denoising using local projective subspace methods. Neurocomputing 69, 1485–1501 (2006)
Gruber, P., Theis, F.J.: Grassmann clustering. In: Proc. of EUSIPCO, Florence, Italy (2006)
Harmeling, S., Meinecke, F., Müller, K.R.: Injecting noise for analysing the stability of ICA components. Signal Processing 84, 255–266 (2004)
Himberg, J., Hyvärinen, A., Esposito, F.: Validating the independent components of neuroimaging time-series via clustering and visualization. NeuroImage 22, 1214–1222 (2004)
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis (2001)
Karvanen, J., Koivunen, V.: Blind separation methods based on pearson system and its extensions. Signal Processing 82(4), 663–673 (2002)
Karvanen, J., Theis, F.J.: Spatial ICA of fMRI data in time windows. In: Proc. MaxEnt 2004 AIP conference proceedings, Garching, Germany, vol. 735, pp. 312–319 (2004)
Keck, I.R., Lang, E.W., Nassabay, S., Puntonet, C.G.: Clustering of signals using incomplete independent component analysis. In: Cabestany, J., Prieto, A.G., Sandoval, F. (eds.) IWANN 2005. LNCS, vol. 3512, pp. 1067–1074. Springer, Heidelberg (2005)
Kohonen, T.: Self-Organizing Maps. Springer, New York, Inc., Secaucus, NJ (2001)
Lie, C.-H., Specht, K., Marshall, J.C., Fink, G.R.: Using fMRI to decompose the neural processes underlying the wisconsin card sorting test. NeuroImage 30, 1038–1049 (2006)
McKeown, M.J., Sejnowski, T.J.: Independent component analysis of FMRI data: Examining the assumptions. Human Brain Mapping 6, 368–372 (1998)
Theis, F.J., Gruber, P., Keck, I.R., Meyer-Bäse, A., Lang, E.W.: Spatiotemporal blind source separation using double-sided approximate joint diagonalization. In: Proc. of EUSIPCO, Antalya, Turkey (2005)
Theis, F.J., Meyer-Bäse, A., Lang, E.W.: Second-order blind source separation based on multi-dimensional autocovariances. In: Puntonet, C.G., Prieto, A.G. (eds.) ICA 2004. LNCS, vol. 3195, pp. 726–733. Springer, Heidelberg (2004)
Yang, B.: Projection approximation subspace tracking. IEEE Trans. on Signal Processing 43(1), 95–107 (1995)
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Gruber, P., Kohler, C., Theis, F.J. (2007). A Toolbox for Model-Free Analysis of fMRI Data. In: Davies, M.E., James, C.J., Abdallah, S.A., Plumbley, M.D. (eds) Independent Component Analysis and Signal Separation. ICA 2007. Lecture Notes in Computer Science, vol 4666. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74494-8_27
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DOI: https://doi.org/10.1007/978-3-540-74494-8_27
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