The estimated functional connectivity at time t was then calculated as W$_{ij}$ ( t ) = σ$_{ij}$ ( t ) / ( σ$_{i}$ ( t ) σ$_{j}$ ( t )). From W , we can estimate the variability in the fluctuations of the functional connection between nodes i and j over time as: where m$_{ij}$ = 1 T − L +1 ∑ T − L +1 t =1 W$_{ij}$ ( t ) is the mean dynamic functional connectiv- ity over time. It is important to recognize that the strength of static functional connec- tivity observed over a long time period imposes constraints on the extent to which dynamic functional connectivity is expected to fluctuate over shorter time scales within that same period (Thompson and Fransson, 2015). We can think of these constraints in the following way. Suppose we have two BOLD time series: x = { x (1) . . . x ( T ) } and y = { y (1) . . . y ( T ) } that are correlated with magnitude ρ$_{xy}$ . Now, suppose we were to divide both x and y into sub-intervals and calculate each sub-interval’s correlation coefficient. It turns out that the magnitude ρ$_{xy}$ plays an important role in constraining the distribution of sub- interval correlation coefficients (Figure 1). Because of these constraints, we can make predictions about the expected range of dynamic fluctuations given a con- 7 Conditional dynamic functional connectivity s$_{ij}$ = √ √ 1 T − L T − L +1 ∑ t =1 ( W$_{ij}$ ( t ) − m$_{ij}$ ) (5) σ$_{ij}$ ( t ) = [ L ∑ τ =1 w ( τ )( z$_{i}$ ( t − N + τ ) − ¯ z$_{i}$ ( t )) · ( z$_{j}$ ( t − N + τ ) − ¯ z$_{j}$ ( t )) ] 1 / 2 (4) σ$_{i}$ ( t ) = [ L ∑ τ =1 w ( τ )( z$_{i}$ ( t − N + τ ) − ¯ z$_{i}$ ( t )) ] 1 / 2 (3)