cases, there is a strong correlation between the mean dynamic functional with the static connectivity and that, to varying degrees, static connectivity still con- strains the shape of the standard deviation in dynamic functional connectivity (Figure S3). Our results were based on a univariate analysis of fluctuations in dynamic functional connections. Specifically, in estimating the number of connections that were unexpectedly strong or weak at any time point, we considered each connection independently. Of course, due to the correlative nature of the con- nection weights, functional connections are very often not independent of one an- other. Future work could include explicitly multivariate methods, as in Leonardi et al. (2013), where the authors identified connection-level components with spatial distributions reminiscent of known functional systems that collectively co-varied together across time. Importantly, this component-style analysis in- cluded a step in which time-averaged connectivity levels were subtracted from each connection’s time course prior to estimating each component’s temporal profile. This reveals patterns of temporal fluctuations of functional connections while discounting the baseline level of dynamic functional connectivity, which, as we and others show, is constrained by the level of static connectivity. In this report we argue that the magnitude of static functional connectivity plays an important role in constraining expectations about dynamic fluctua- tions. We cite some practical examples using fMRI BOLD times series to moti- vate our claim. It is important to note, however, that in all but the most trivial examples (i.e. correlations of ± 1), it is mathematically possible for a dynamic fluctuation to take on any possible correlation value between -1 and 1. While this may be true, we assert that the probability of observing such fluctuations is not uniform, and that the shape of such a probability distribution is determined Limitations 26