Spatio-temporal dynamics in fMRI recordings revealed with complex independent component analysis

Independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data is commonly carried out under the assumption that each source may be represented as a spatially fixed pattern of activation, which leads to the instantaneous mixing model. To allow modeling patterns of spatio-temporal dynamics, in particular, the flow of oxygenated blood, we have developed a convolutive ICA approach: spatial complex ICA applied to frequency-domain fMRI data. In several frequency-bands, we identify components pertaining to activity in primary visual cortex (V1) and blood supply vessels. One such component, obtained in the 0.10 Hz band, is analyzed in detail and found to likely reflect flow of oxygenated blood in V1.

[1]  Jean-François Cardoso,et al.  Equivariant adaptive source separation , 1996, IEEE Trans. Signal Process..

[2]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[3]  V D Calhoun,et al.  Independent component analysis of fMRI data in the complex domain , 2002, Magnetic resonance in medicine.

[4]  T. Sejnowski,et al.  Single-Trial Variability in Event-Related BOLD Signals , 2002, NeuroImage.

[5]  Birger Kollmeier,et al.  Adaptive separation of acoustic sources for anechoic conditions: A constrained frequency domain approach , 2003, Speech Commun..

[6]  Lucas C. Parra,et al.  Convolutive blind separation of non-stationary sources , 2000, IEEE Trans. Speech Audio Process..

[7]  Simone G. O. Fiori,et al.  Nonlinear Complex-Valued Extensions of Hebbian Learning: An Essay , 2005, Neural Computation.

[8]  Terrence J. Sejnowski,et al.  Complex Independent Component Analysis of Frequency-Domain Electroencephalographic Data , 2003, Neural Networks.

[9]  Jörn Anemüller,et al.  Across-frequency processing in convolutive blind source separation , 2001 .

[10]  S. Araki,et al.  A POLAR-COORDINATE BASED ACTIVATION FUNCTION FOR FREQUENCY DOMAIN BLIND SOURCE SEPARATION , 2001 .

[11]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis of Complex Valued Signals , 2000, Int. J. Neural Syst..

[12]  James V. Stone,et al.  Spatiotemporal Independent Component Analysis of Event-Related fMRI Data Using Skewed Probability Density Functions , 2002, NeuroImage.

[13]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[14]  Jörn Anemüller,et al.  ON-LINE BLIND SEPARATION OF MOVING SOUND SOURCES , 1999 .

[15]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[16]  S Makeig,et al.  Spatially independent activity patterns in functional MRI data during the stroop color-naming task. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Hiroshi Sawada,et al.  Polar coordinate based nonlinear function for frequency-domain blind source separation , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[18]  Walter Kellermann,et al.  A generalization of blind source separation algorithms for convolutive mixtures based on second-order statistics , 2005, IEEE Transactions on Speech and Audio Processing.

[19]  S Makeig,et al.  Analysis of fMRI data by blind separation into independent spatial components , 1998, Human brain mapping.