nection’s sFC. Observing a strong static correlation coefficient makes it highly unlikely, if not physically impossible, for the corresponding dFC to fluctuate over a wide range. Instead, observing a weak static correlation coefficient is compatible with much more pronounced fluctuations, including temporary ex- pression of strong positive or strong negative dFC. Hence, the interpretation of fluctuations in dFC will vary depending on whether the corresponding sFC is weak or strong. A corollary of this approach is that fluctuations in dynamic functional con- nectivity may be unsurprising, in a statistical sense, unless they violate expec- tations conditioned by the level of sFC. We argue that the interpretation of dFC should take into account whether observed features of dFC are expected or unexpected. Doing so allows one to uncover fluctuations in dynamic func- tional connectivity between regions that are unexpectedly strong or weak given those regions’ static connectivity. To characterize such fluctuations, we propose a measure that we term conditional dynamic functional connectivity (or cdFC), which is defined as the probability of observing a dynamic functional connec- tion of a particular magnitude given the magnitude of static connectivity for the same pair of brain regions. To estimate the cdFC for any dynamic functional connection, we first char- acterize the distribution of expected dynamic fluctuations given a connection’s static connectivity. There are many ways to approximate this distribution; the approach we use is to generate surrogate BOLD time series using the ampli- tude adjusted phase randomization procedure. Briefly, this procedure works by generating a normally-distributed surrogate time series that is rank-matched to the original time series. The surrogate time series is then transformed into the frequency domain via a discrete Fourier transform. Random phase (between [0 , 2 π ]) is then added to each frequency bin. If this procedure is being performed 8