  A Multiregression Dynamic Model (MDM) is a class of multivariate time series
that represents various dynamic causal processes in a graphical way. One of the
advantages of this class is that, in contrast to many other Dynamic Bayesian
Networks, the hypothesised relationships accommodate conditional conjugate
inference. We demonstrate for the first time how straightforward it is to
search over all possible connectivity networks with dynamically changing
intensity of transmission to find the Maximum a Posteriori Probability (MAP)
model within this class. This search method is made feasible by using a novel
application of an Integer Programming algorithm. The efficacy of applying this
particular class of dynamic models to this domain is shown and more
specifically the computational efficiency of a corresponding search of 11-node
Directed Acyclic Graph (DAG) model space. We proceed to show how diagnostic
methods, analogous to those defined for static Bayesian Networks, can be used
to suggest embellishment of the model class to extend the process of model
selection. All methods are illustrated using simulated and real resting-state
functional Magnetic Resonance Imaging (fMRI) data.
