Dynamic Causal Modeling of Brain Responses

This chapter is about modeling-distributed brain responses and, in particular, the functional integration among neuronal systems. Inferences about the functional organization of the brain rest on models of how measurements of evoked responses are caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to mathematical models of neuronal and hemodynamics. The aim of this chapter is to introduce dynamic causal models. These models can be regarded as generalizations of the simple models employed in conventional analyses of regionally specific brain responses. In what follows, we will start with anatomical models of functional brain architectures, which motivate some of the basic principles of neuroimaging. We then review briefly statistical models (e.g., the general linear model) used for making classical and Bayesian inferences about where neuronal responses are expressed. By incorporating biophysical constraints, these basic models can be finessed and, in a dynamic setting, rendered causal. This allows us to infer how interactions among brain regions are mediated. This chapter focuses on causal models for distributed responses measured with fMRI and electroencephalography. The latter is based on neural-mass models and affords mechanistic inferences about how evoked responses are caused, at the level of neuronal subpopulations and the coupling among them.