Dynamic causal models for EEG

Abstract

Neuronally plausible, generative or forward models are essential for understanding how event-related fields (ERFs) and potentials (ERPs) are generated. In this chapter, we describe the dynamic causal modelling (DCM) of event-related responses measured with electroencephalography (EEG) or magnetoencephalography (MEG). This approach uses a biologically informed causal model to make inferences about the underlying neuronal networks generating responses. The approach can be regarded as a neurobiologically constrained source reconstruction scheme, in which the parameters of the reconstruction have an explicit neuronal interpretation. Specifically, these parameters encode, among other things, the ..