Comparing dynamic causal models.
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 concerning 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 large-scale neural networks and the neuronal interactions that mediate perception and cognition.
Penny
W D
WD
Wellcome Department of Imaging Neuroscience, University College London, London, UK. w.penny@fil.ion.ucl.acl.uk
Stephan
K E
KE
Mechelli
A
A
Friston
K J
KJ
D001288:Attention / Q000502:physiology; D001499:Bayes Theorem; D001921:Brain / Q000502:physiology*; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D009039:Motion Perception / Q000502:physiology; D009434:Neural Pathways / Q000502:physiology; D010775:Photic Stimulation