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L Costa, JQ Smith and TE Nichols

On the Selection of Multigression Dynamic Models of fMRI networked time series

Date: 20 March 2013

Abstract: A Multiregression Dynamic Model (MDM) is a class of multivariate time series that allows various dynamic causal processes to be represented in a graphical way. In contrast with many other Dynamic Bayesian Networks, the hypothesized relationships accommodate conditional conjugate inference. This means that it is straightforward to search over many different connectivity networks with dynamically changing intensity of transmission to find the MAP model within a class of models. In this paper we customize this conjugate search within scientific models describing the dynamic connectivity of the brain. As well as demonstrating the efficacy of our dynamic models, we illustrate 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.

Keywords: Multiregression Dynamic Model, Bayesian Network, Markov Equivalent Graph, Model Selection, Functional magnetic resonance imaging (fMRI).