Bayesian Treatments of Neuroimaging Data
Bayesian Treatments of Neuroimaging Data
This chapter describes the application of Bayesian methods to neuroimaging data. First, it introduces a functional magnetic resonance imaging (fMRI) data set that is analysed using posterior probability maps (PPMs) and dynamic causal modeling (DCM) approaches. The chapter then discusses the general linear model (GLM) and the Bayesian approaches for estimating the parameters of GLMs and nonlinear models, and also describes PPMs for making inferences about functional specialization and DCM approaches for making inferences about functional integration.
Keywords: neuroimaging, fMRI, posterior probability maps, PPMs, dynamic causal modeling, DCM, general linear model, nonlinear model, functional specialization, functional integration
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