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.