Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks Richard F. Betzel 1 , 2 , $^{∗}$, Makoto Fukushima $^{2}$, Ye He$^{4}$, Xi-Nian Zuo$^{4}$, Olaf Sporns 2 , 3 1 University of Pennsylvania, School of Engineering and Applied Science, Department of Bioengineering, Philadelphia PA, 19104, USA 2 Indiana University, Psychological and Brain Sciences, Bloomington IN, 47405, USA 3 Indiana University, Network Science Institute, Bloomington IN, 47405, USA 4 Key Laboratory of Behavioral Science and Magnetic Resonance Imaging Research Center, Institute of Psychology, Chinese Academy of Sciences, Beijing, China We investigate the relationship of resting-state fMRI functional connectivity estimated over long periods of time with time-varying functional connectivity estimated over shorter time intervals. We show that using Pearson’s correla- tion to estimate functional connectivity implies that the range of fluctuations of functional connections over short time scales is subject to statistical constraints imposed by their connectivity strength over longer scales. We present a method for estimating time-varying functional connectivity that is designed to mitigate this issue and allows us to identify episodes where functional connections are un- expectedly strong or weak. We apply this method to data recorded from N = 80 participants, and show that the number of unexpectedly strong/weak connec- tions fluctuates over time, and that these variations coincide with intermittent periods of high and low modularity in time-varying functional connectivity. We also find that during periods of relative quiescence regions associated with de- fault mode network tend to join communities with attentional, control, and primary sensory systems. In contrast, during periods where many connections are unexpectedly strong/weak, default mode regions dissociate and form dis- tinct modules. Finally, we go on to show that, while all functional connections can at times manifest stronger (more positively correlated) or weaker (more negatively correlated) than expected, a small number of connections, mostly within the visual and somatomotor networks, do so a disproportional number of times. Our statistical approach allows the detection of functional connections that fluctuate more or less than expected based on their long-time averages and may be of use in future studies characterizing the spatio-temporal patterns of arXiv:1511.06352v1 [q-bio.NC] 19 Nov 2015 $^{∗}$corresponding author: rbetzel@seas.upenn.edu Preprint submitted to Elsevier June 14, 2018 Abstract