Bayesian comparison of spatially regularised general linear models.

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.

journal_name

Hum Brain Mapp

journal_title

Human brain mapping

authors

Penny W,Flandin G,Trujillo-Barreto N

doi

10.1002/hbm.20327

subject

Has Abstract

pub_date

2007-04-01 00:00:00

pages

275-93

issue

4

eissn

1065-9471

issn

1097-0193

journal_volume

28

pub_type

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