Dynamic causal modelling of induced responses

Abstract

This paper describes a dynamic causal model (DCM) for induced or spectral responses as measured with the electroencephalogram (EEG) or the magnetoencephalogram (MEG). We model the time-varying power, over a range of frequencies, as the response of a distributed system of coupled electromagnetic sources to a spectral perturbation. The model parameters encode the frequency response to exogenous input and coupling among sources and different frequencies. The Bayesian inversion of this model, given data enables inferences about the parameters of a particular model and allows us to compare different models, or hypotheses. One key aspect of the model is that it differentiates between linear and non-linear coupling; which correspond to within and betweenfrequency coupling respectively. To establish the face validity of our approach, we generate synthetic data and test the identifiability of various parameters to ensure they can be estimated accurately, under different levels of noise. We then apply our model to EEG data from a faceperception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.

Functional and Effective Connectivity: A Review

“…More recent efforts have focused on DCMs for electromagnetic (EEG and MEG) data (Chen et al, 2008;Clearwater et al, 2008;David et al, 2006;Garrido et al, 2007aGarrido et al, ,b, 2008Kiebel et al, 2006Kiebel et al, , 2007, with related developments to cover local field potential recordings (Moran et al, 2007(Moran et al, , 2008. These models are more sophisticated than the neuronal models for fMRI and are based on neuralmass or mean-field models of interacting neuronal populations (see Deco et al, 2008).…”

“…This has proved particularly useful for local field potentials and has been validated using animal models and psychopharmacological constructs (Moran et al, 2008. Finally, there are DCMs for induced responses (Chen et al, 2008). Like steadystate models, these predict the spectral density of responses, but in a time-dependent fashion.…”

Source connectivity analysis with MEG and EEG

“…DCM had originally been devised for the analysis of evoked responses [Garrido et al, 2007]. Recent developments have extended the functionality of this promising technique to induced responses [Chen et al, 2008] and steady-state responses [Moran et al, 2007].…”

Model driven EEG/fMRI fusion of brain oscillations

“…The initial formulation of DCM ] did not consider noise inputs (R 5 0) and was therefore a deterministic SSM stated in terms of ordinary differential equations (ODE). More recent versions have been in terms of SDEs [Chen et al, 2008;Friston et al, 2008;Stephan et al, 2008]. We note that since the focus of this article is EEG/fMRI fusion of resting state oscillations, we shall not consider in the remainder of this article the external inputs v(t) in Eq.…”

“…(1). SSM (DCM) with external inputs v(t) are of course necessary for studying event related [ (David et al, 2006b,c;Friston, 2006]) or induced activity [Chen et al, 2008].…”