shorter timescales (Bassett et al., 2011, 2013). In this report, we show that the strength of static functional connectivity im- poses constraints and implies statistical expectations on the range of fluctuations expressed in dynamic functional connectivity. We propose a non-parametric, model-free statistical test for identifying unexpected fluctuations in functional connectivity over time. We then apply this technique to fMRI data acquired from N = 80 individuals and identify intermittent episodes in resting brain activity when functional brain network exhibit varying levels of fluctuating dy- namics. Episodes of high modularity characterized by an over-expression of stronger/weaker than expected functional connections, are interspersed among periods of low modularity during which connection weights are within the statis- tical limits predicted by their static connectivity levels. Finally, we find that the stronger/weaker-than-expected connections show specific spatial distributions, and include disproportionately many connections within and between visual and somotomotor systems. All data analyzed in this report come from the NKI-Rockland Lifespan Sam- ple (Nooner et al., 2012). This study was approved by the NKI review board and all participants provided informed consent prior to data collection. As part of the data collection process, each participant completed one anatomi- cal scan, one diffusion structural scan and three resting-state functional MRI (rfMRI) scans that varied in terms of TR time, voxel size, and scan duration: 1) TR = 2,500 ms, voxel size = 3 mm, scan duration = 5 min; 2) TR = 1,400 ms, voxel size = 2 mm, scan duration = 10 min; and 3) TR = 645 ms, voxel size = 3 mm and duration = 10 min. We analyzed the fastest multi- 4 Image acquisition and processing Methods