Dynamic Causal Modeling

Will Penny
Wellcome Dept.of Imaging Neuroscience

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 talk 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. 

Feedforward and reciprocal DCMs of activity in the dorsal visual system
during attention to visual motion. In both models, photic stimulation
enters V1 and the motion and attention variables modulate the connection
from V1 to V5. Bayesian model comparison favours the network with
reciprocal connectivity.

Presentation (PDF File)

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