Cargando…
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...
Autores principales: | , , , |
---|---|
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 |
_version_ | 1782175186069487616 |
---|---|
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. |
format | Text |
id | pubmed-2791519 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2010 |
publisher | Academic Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT rosamj bayesianmodelselectionmapsforgroupstudies AT bestmanns bayesianmodelselectionmapsforgroupstudies AT harrisonl bayesianmodelselectionmapsforgroupstudies AT pennyw bayesianmodelselectionmapsforgroupstudies |