Blind processing in neuroimaging: application to FMRI

Application of "blind" processing, based on unsupervised learning in principal, for the analysis of functional brain images does not assume any a priori information on the signals generated in the brain. We describe blind processing strategies with an emphasis on Independent Component Analysis (ICA) for the analysis of functional Magnetic Resonance (fMR) images. We present a semi-blind processing technique, referred to as ICA with Reference, to overcome some limitations of pure blind-processing of fMRI data. Application of ICA and ICA-R on real fMRI datasets are presented.

[1]  Lawrence Carin,et al.  Constrained Independent Component Analysis of DNA Microarray Signals , 2004 .

[2]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[3]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

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

[5]  Shun-ichi Amari,et al.  Sequential blind signal extraction in order specified by stochastic properties , 1997 .

[6]  James V. Stone.  Blind Source Separation Using Temporal Predictability , 2001, Neural Computation.

[7]  Aapo Hyvärinen,et al.  A Fast Fixed-Point Algorithm for Independent Component Analysis , 1997, Neural Computation.

[8]  J. Rajapakse,et al.  Human Brain Mapping 6:283–300(1998) � Modeling Hemodynamic Response for Analysis of Functional MRI Time-Series , 2022 .

[9]  B. Biswal,et al.  Blind source separation of multiple signal sources of fMRI data sets using independent component analysis. , 1999, Journal of computer assisted tomography.

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

[11]  A. Cichocki,et al.  Blind separation of nonstationary sources in noisy mixtures , 2000 .

[12]  C. Büchel,et al.  Modulation of connectivity in visual pathways by attention: cortical interactions evaluated with structural equation modelling and fMRI. , 1997, Cerebral cortex.

[13]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.