Bayesian model selection maps for group studies

M. J. Rosa; S. Bestmann; L. Harrison; W. Penny

doi:10.1016/j.neuroimage.2009.08.051

Bayesian model selection maps for group studies

M. J. Rosa, S. Bestmann, L. Harrison, W. Penny

The Hull York Medical School

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	217-224
Number of pages	8
Journal	Neuroimage
Volume	49
Issue number	1
DOIs	https://doi.org/10.1016/j.neuroimage.2009.08.051
Publication status	Published - 1 Jan 2010

Keywords

FMRI TIME-SERIES
SPATIAL PRIORS
INFERENCE
UNCERTAINTY
INFORMATION
SURPRISE

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10.1016/j.neuroimage.2009.08.051

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KW - SPATIAL PRIORS

KW - INFERENCE

KW - UNCERTAINTY

KW - INFORMATION

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Bayesian model selection maps for group studies

Abstract

Keywords

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