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Bayesian model selection maps for group studies

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 characteri...

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Detalles Bibliográficos
Autores principales: Rosa, M.J., Bestmann, S., Harrison, L., Penny, W.
Formato: Texto
Lenguaje:English
Publicado: Academic Press 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2791519/
https://www.ncbi.nlm.nih.gov/pubmed/19732837
http://dx.doi.org/10.1016/j.neuroimage.2009.08.051
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author Rosa, M.J.
Bestmann, S.
Harrison, L.
Penny, W.
author_facet Rosa, M.J.
Bestmann, S.
Harrison, L.
Penny, W.
author_sort Rosa, M.J.
collection PubMed
description 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.
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spelling pubmed-27915192009-12-22 Bayesian model selection maps for group studies Rosa, M.J. Bestmann, S. Harrison, L. Penny, W. Neuroimage Technical Note 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. Academic Press 2010-01-01 /pmc/articles/PMC2791519/ /pubmed/19732837 http://dx.doi.org/10.1016/j.neuroimage.2009.08.051 Text en © 2010 Elsevier Inc. https://creativecommons.org/licenses/by/3.0/ Open Access under CC BY 3.0 (https://creativecommons.org/licenses/by/3.0/) license
spellingShingle Technical Note
Rosa, M.J.
Bestmann, S.
Harrison, L.
Penny, W.
Bayesian model selection maps for group studies
title Bayesian model selection maps for group studies
title_full Bayesian model selection maps for group studies
title_fullStr Bayesian model selection maps for group studies
title_full_unstemmed Bayesian model selection maps for group studies
title_short Bayesian model selection maps for group studies
title_sort bayesian model selection maps for group studies
topic Technical Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2791519/
https://www.ncbi.nlm.nih.gov/pubmed/19732837
http://dx.doi.org/10.1016/j.neuroimage.2009.08.051
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