Modelling Effective Connectivity with Dynamic Causal Models

Abstract

Analyses of effective connectivity provide neuropsychiatric studies with new endophenotypes that are expressed in psychiatric disorders. Dynamic causal modelling (DCM) is a framework for the identification of neural networks in the brain that treats the networks as nonlinear input-state-output systems. In setting up a DCM, one can estimate (1) parameters that mediate the driving influence of exogenous or experimental inputs on brain states, (2) parameters that mediate endogenous coupling among neuronal states and (3) parameters that allow the inputs to modulate that coupling. Issues concerning selection among alternative models naturally arise in DCM analyses. Bayesian model selection (BMS) is a statistical procedure for computing how probable one model is in relation to another. This chapter presents the motivation and procedures for DCM of evoked brain responses – as well as the theoretical and operational details on which BMS rests. We describe procedures for parameter-, model- and family-level inference in the context of analysis of data from a group of subjects, and close with examples of how these procedures have been used in psychiatric neuroimaging.