In order to identify and characterize cortical responses to the stimuli, traditional fMRI data-analysis methods require knowledge of stimulus timing, or of a region of interest (Friston et al. 1995). In the perspective of EEG–fMRI integration, we aimed at developing a method for the fusion of information from electrophysiological and hemodynamic measures of ongoing cerebral processes. To this purpose, we analyzed fMRI images by means of ICA, a signal processing method able to separate independent spatio-temporal patterns of brain activity (Hyvärinen and Oja, 2000; James and Hesse, 2005). ICA allowed the decomposition of observations into independent patterns, without any prior knowledge about their activity waveforms or locations (McKeown et al., 1998). Due to its characteristics, ICA demonstrated to be a powerful tool for the extraction of functional connectivity patterns of synchronized neural activity, and in particular for the retrieval of functionally distinct cerebral networks (Bartels and Zeki, 2005; Beckmann et al., 2005).
