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• W122291211 abstract "Inferences about brain function that are made based on 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 responses and hemodynamics. The aim of this review is to introduce the key models used in imaging neuroscience and explain how they relate to each other. We start with anatomical models of functional brain architectures, which motivate some of the theoretical foundations of neuroimaging. We then turn to basic statistical models (e.g., the general linear model) used to make classical and Bayesian inferences about where neuronal responses are expressed. When biophysical constraints are incorporated, 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 inferences about regionally specific responses. This model is successively refined until we arrive at neuronal mass models of electroencephalograph (EEG) responses.The latter models allow mechanistic inferences about how evoked responses are caused, at the level of neuronal subpopulations and the coupling among them." @default.
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• W122291211 date "2005-01-01" @default.
• W122291211 modified "2023-10-16" @default.
• W122291211 title "Modeling Brain Responses" @default.
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• W122291211 doi "https://doi.org/10.1016/s0074-7742(05)66003-5" @default.
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