Bayesian comparison of spatially regularised general linear models.
In previous work (Penny et al., [2005]: Neuroimage 24:350-362) we have developed a spatially regularised General Linear Model for the analysis of functional magnetic resonance imaging data that allows for the characterisation of regionally specific effects using Posterior Probability Maps (PPMs). In this paper we show how it also provides an approximation to the model evidence. This is important as it is the basis of Bayesian model comparison and provides a unified framework for Bayesian Analysis of Variance, Cluster of Interest analyses and the principled selection of signal and noise models. We also provide extensions that implement spatial and anatomical regularisation of noise process parameters.
Penny
Will
W
Wellcome Department of Imaging Neuroscience, University College, London WC1N 3BG, UK. wpenny@ion.ucl.ac.uk
Flandin
Guillaume
G
Trujillo-Barreto
Nelson
N
D016477:Artifacts; D001499:Bayes Theorem; D001921:Brain / Q000098:blood supply / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D002560:Cerebrovascular Circulation; D003198:Computer Simulation; D004734:Energy Metabolism; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D013028:Space Perception / Q000502:physiology*