Modelling functional integration:a comparison of structural equation and dynamic causal models

Penny, W D, Stephan, K E, Mechelli, A and Friston, K J (2004) Modelling functional integration:a comparison of structural equation and dynamic causal models. NeuroImage, 23 (Suppl 1). S264-S274. ISSN 1053-8119

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Abstract

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.

Item Type: Article
Uncontrolled Keywords: algorithms,attention,bayes theorem,brain mapping,humans,magnetic resonance imaging,neurological models,statistical models,oxygen,principal component analysis,visual perception
Faculty \ School: Faculty of Social Sciences > School of Psychology
Depositing User: Pure Connector
Date Deposited: 22 Aug 2017 06:35
Last Modified: 17 Mar 2020 23:53
URI: https://ueaeprints.uea.ac.uk/id/eprint/64621
DOI: 10.1016/j.neuroimage.2004.07.041

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