In this paper, we propose a generic framework for the analysis of steady-state fMRI datasets, applied here to resting-state datasets. Our approach avoids the introduction of user-defined seed regions for the study of spontaneous activity. Unlike existing techniques, it yields a sparse representation of resting-state activity networks which can be characterized and investigated fairly easily in a semi-interactive fashion. We proceed in several steps, based on the idea that spectral coherence of the fMRI time courses in the low frequency band carries the information of interest. In particular, we address the question of building adapted representations of the data from the spectral coherence matrix. We analyze nine datasets taken from three subjects and show resting-state networks validated by EEG-fMRI simultaneous acquisition literature, with low intra-subject variability; we also discuss the merits of different (rapid/slow) fMRI acquisition schemes.