Open AccessBayesian comparison of spatially regularised general linear models
Author(s) -
Penny Will,
Flandin Guillaume,
TrujilloBarreto Nelson
Publication year - 2007
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20327
Subject(s) - bayesian probability , general linear model , computer science , noise (video) , model selection , artificial intelligence , linear model , basis (linear algebra) , variance (accounting) , selection (genetic algorithm) , pattern recognition (psychology) , algorithm , machine learning , mathematics , geometry , accounting , business , image (mathematics)
Abstract 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. Hum Brain Mapp 2007. © 2006 Wiley‐Liss, Inc.