Modelling functional integration: a comparison of structural equation and dynamic causal models.
The brain appears to adhere to two fundamental principles of functional organisation, functional integration and functional specialisation, where the integration within and among specialised areas is mediated by effective connectivity. In this paper, we review two different approaches to modelling effective connectivity from fMRI data, structural equation models (SEMs) and dynamic causal models (DCMs). In common to both approaches are model comparison frameworks in which inferences can be made about effective connectivity per se and about how that connectivity can be changed by perceptual or cognitive set. Underlying the two approaches, however, are two very different generative models. In DCM, a distinction is made between the 'neuronal level' and the 'hemodynamic level'. Experimental inputs cause changes in effective connectivity expressed at the level of neurodynamics, which in turn cause changes in the observed hemodynamics. In SEM, changes in effective connectivity lead directly to changes in the covariance structure of the observed hemodynamics. Because changes in effective connectivity in the brain occur at a neuronal level DCM is the preferred model for fMRI data. This review focuses on the underlying assumptions and limitations of each model and demonstrates their application to data from a study of attention to visual motion.
Penny
W D
WD
Wellcome Department of Imaging Neuroscience, University College London, London, United Kingdom. w.penny@fil.ion.ucl.ac.uk
Stephan
K E
KE
Mechelli
A
A
Friston
K J
KJ
D000465:Algorithms; D001288:Attention / Q000502:physiology; D001499:Bayes Theorem; D001931:Brain Mapping; D006801:Humans; D008279:Magnetic Resonance Imaging; D008959:Models, Neurological; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood; D025341:Principal Component Analysis; D014796:Visual Perception / Q000502:physiology
D010100:Oxygen