Recent advances in functional connectivity (FC) analysis of functional magnetic resonance imaging (fMRI) data facilitate the characterization of the brain’s intrinsic functional networks (FC-fMRI). Because the fMRI signal does not provides a perfect representation of neuronal activity, the potential for FC-fMRI to identify functionally relevant networks critically depends upon separating overlapping signals from one another and from external noise. As a step in data preconditioning, researchers often band-pass filter fMRI signals to the range from 0.01 Hz to 0.1 Hz. However, coordinated network oscillations operate across multiple frequencies. Thus, it is not clear that the view of FC-fMRI networks within a single spectral range produces the fullest characterization of brain’s multiple and overlapping systems. The following study addresses this limitation by advancing a multiscale fractionation of FC-fMRI networks, as well methods for quantifying cross-spectral network similarity. These methods clearly and consistently represent group-level brains as composed of well-known functional networks.