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 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.
Rosa
M J
MJ
Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, 12 Queen Square, London, UK. mjoao@fil.ion.ucl.ac.uk
Bestmann
S
S
Harrison
L
L
Penny
W
W
D000465:Algorithms; D001499:Bayes Theorem; D001921:Brain / Q000033:anatomy & histology; D017352:Echo-Planar Imaging / Q000706:statistics & numerical data; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data; D015233:Models, Statistical; D010100:Oxygen / Q000097:blood; D011338:Probability Theory; D015203:Reproducibility of Results
D010100:Oxygen