Results 1 
2 of
2
Comparing Dynamic Causal Models
 NEUROIMAGE
, 2004
"... This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, ..."
Abstract

Cited by 81 (34 self)
 Add to MetaCart
This article describes the use of Bayes factors for comparing Dynamic Causal Models (DCMs). DCMs are used to make inferences about effective connectivity from functional Magnetic Resonance Imaging (fMRI) data. These inferences, however, are contingent upon assumptions about model structure, that is, the connectivity pattern between the regions included in the model. Given the current lack of detailed knowledge on anatomical connectivity in the human brain, there are often considerable degrees of freedom when defining the connectional structure of DCMs. In addition, many plausible scientific hypotheses may exist about which connections are changed by experimental manipulation, and a formal procedure for directly comparing these competing hypotheses is highly desirable. In this article, we show how Bayes factors can be used to guide choices about model structure, both with regard to the intrinsic connectivity pattern and the contextual modulation of individual connections. The combined use of Bayes factors and DCM thus allows one to evaluate competing scientific theories about the architecture of largescale neural networks and the neuronal interactions that mediate perception and cognition.
Chapter 35: Bayesian model selection and averaging
, 2006
"... In Chapter 11 we described how Bayesian inference can be applied to hierarchical models. In this chapter we show how the members of a model class, indexed by m, can also be considered as part of a hierarchy. Model classes might be GLMs where m indexes different choices for the design matrix, DCMs wh ..."
Abstract
 Add to MetaCart
In Chapter 11 we described how Bayesian inference can be applied to hierarchical models. In this chapter we show how the members of a model class, indexed by m, can also be considered as part of a hierarchy. Model classes might be GLMs where m indexes different choices for the design matrix, DCMs where m indexes