There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. However, directed relationships in networks are difficult to estimate, and only very few methods are available. Here, we consider a method for dynamic, directed functional connectivity: Dynamic graphical models (DGM) are a multivariate graphical model with dynamic, time-varying coefficients that describe instantaneous directed relationships between nodes.Parameter estimation and model selection are implemented by using Bayesian methods. A further benefit of DGM is that networks may contain loops. We use network simulations, human resting-state-fMRI ( N = 500) and mouse fMRI ( N = 16) to investigate the validity and reliability of the estimated networks. We simulated systematic lags of the hemodynamic response at different brain regions to investigate how these potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 seconds offset between connected nodes), our method demonstrated sensitivity of 72%-77% and a specificity of 67% to detect the true direction. Stronger lag confounds reduced sensitivity, but did not increase false positives (i.e., directionality estimates of the opposite direction). On real data, we investigated the reliability of network estimates across and within subjects and found the DMN having inputs to cerebellar and limbic networks, as well as the reciprocal relationship between the visual medial and visual lateral network to be the most consistent relationships. Finally, we tested the method in mouse fMRI data and discovered a highly plausible relationship between areas in the hippocampus feeding to the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package ("multdyn") for R.