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Bayesian comparison of spatially regularised general linear models

✍ Scribed by Will Penny; Guillaume Flandin; Nelson Trujillo-Barreto

Publisher: John Wiley and Sons
Year: 2007
Tongue: English
Weight: 619 KB
Volume: 28
Category: Article
ISSN: 1065-9471
DOI: 10.1002/hbm.20327

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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