![]() ![]() 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. We start with anatomical models of functional brain architectures, which motivate some of the fundaments of neuroimaging. The aim of this review is to introduce the key models used in imaging neuroscience and how they relate to each other. This consistency encompasses many levels of description and places constraints on the statistical models, adopted for data analysis, and the experimental designs they embody. However, they all have to be internally consistent because they model the same thing. These models can be quite diverse, ranging from conceptual models of functional anatomy to nonlinear mathematical models of hemodynamics. Inferences about brain function, using neuroimaging data, rest on models of how the data were caused. ![]()
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