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Bayesian model selection maps for group studies

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JournalNeuroimage
DatePublished - 1 Jan 2010
Issue number1
Volume49
Number of pages8
Pages (from-to)217-224
Original languageEnglish

Abstract

This technical note describes the construction of posterior probability maps (PPMs) for Bayesian model selection (BMS) at the group level. This technique allows neuroimagers to make inferences about regionally specific effects using imaging data from a group of Subjects. These effects are characterised using Bayesian model comparisons that are analogous to the F-tests used in statistical parametric mapping, with the advantage that the models to be compared do not need to be nested. Additionally, an arbitrary number of models can be compared together. This note describes the integration of the Bayesian mapping approach with a random effects analysis model for BMS using group data. We illustrate the method using fMRI data from a group Of Subjects performing a target detection task. (c) 2009 Elsevier Inc. All rights reserved.

    Research areas

  • FMRI TIME-SERIES, SPATIAL PRIORS, INFERENCE, UNCERTAINTY, INFORMATION, SURPRISE

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