title: 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 between-frequency 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 face-perception experiment, to ask whether there is evidence for non-linear coupling between early visual cortex and fusiform areas.
authors.affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, UK. c.chen@fil.ion.ucl.ac.uk
authors.forename: C C
authors.initials: CC
authors.lastname: Chen
authors.forename: S J
authors.initials: SJ
authors.lastname: Kiebel
authors.forename: K J
authors.initials: KJ
authors.lastname: Friston
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology / Q000502:physiology; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D006801:Humans; D015225:Magnetoencephalography / Q000706:statistics & numerical data*; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D013569:Synapses / Q000502:physiology