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Bayesian BrainProbabilistic Approaches to Neural Coding$
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Kenji Doya, Shin Ishii, Alexandre Pouget, and Rajesh P.N. Rao

Print publication date: 2006

Print ISBN-13: 9780262042383

Published to MIT Press Scholarship Online: August 2013

DOI: 10.7551/mitpress/9780262042383.001.0001

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Bayesian Treatments of Neuroimaging Data

Bayesian Treatments of Neuroimaging Data

Chapter:
(p.92) (p.93) 5 Bayesian Treatments of Neuroimaging Data
Source:
Bayesian Brain
Author(s):

Will Penny

Karl Friston

Publisher:
The MIT Press
DOI:10.7551/mitpress/9780262042383.003.0005

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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