Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of neuronal and hemodynamics. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. We then turn to basic 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. We will cover models of brain responses, starting with general linear models of functional magnetic resonance imaging (fMRI) data, used for classical inference about regionally specific responses. This model is successively refined until we arrive at neuronal mass models of electroencephalographic (EEG) responses. The latter models afford mechanistic inferences about how evoked responses are caused, at the level of neuronal subpopulations and the coupling among them.
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