Spectral analysis of brain activation in different regions, either in the form of network time-courses or regions of interest (ROI) time-series, has been a topic of interest in recent studies. Such studies hypothesize that observed brain fluctuations are due to different underlying sources of neurophysiological activation. Among these studies, brain fluctuations during the resting-state, as an unconstrained condition, have been a subject of interest. Some clinical studies have employed spectral analysis to locate differences between diagnostic groups such as schizophrenia and bipolar disorder. Other studies have argued that resting-state brain fluctuations are in fact dynamic, and that activation and connectivity of brain regions develops and evolves spontaneously. In this study, we combine both approaches and focus on capturing dynamics of the spectral properties of network time-courses estimated from independent components analysis (ICA) and categorizing spontaneous frequency profiles of network time-courses into three major profiles, which we call «frequency modes». We show that brain networks have distinct time-varying frequency domain characteristics, differing from one another in their occupancy rates of the frequency modes. Additionally, we identify some networks in which the occurrence rates of the different modes are significantly different based on the gender of the subjects.