Since the middle of the 1990s, studies of resting-state fMRI/BOLD data have explored the correlation patterns of activity across
the whole brain, which is referred to as functional connectivity (FC). Among the many methods that have been developed to
interpret FC, a recently proposed model-based approach describes the propagation of fluctuating BOLD activity within the
recurrently connected brain network by inferring the effective connectivity (EC). In this model, EC quantifies the strengths
of directional interactions between brain regions, viewed from the proxy of BOLD activity. In addition, the tuning procedure
for the model provides estimates for the local variability (input variances) to explain how the observed FC is generated.
Generalizing, the network dynamics can be studied in the context of an input–output mapping—determined by EC—for
the second-order statistics of fluctuating nodal activities. The present paper focuses on the following detection paradigm:
observing output covariances, how discriminative is the (estimated) network model with respect to various input covariance
patterns? An application with the model fitted to experimental fMRI data—movie viewing versus resting state—illustrates
that changes in local variability and changes in brain coordination go hand in hand.

• Biological Cybernetics