1097-0193/asset/HBM_centre.gif?v=1&s=8ec180dd87bbc24c1b35fbfc48370a4e41c34713)
Research Article
Bayesian comparison of spatially regularised general linear models
Article first published online: 28 NOV 2006
DOI: 10.1002/hbm.20327
Copyright © 2006 Wiley-Liss, Inc.
Additional Information
How to Cite
Penny, W., Flandin, G. and Trujillo-Barreto, N. (2007), Bayesian comparison of spatially regularised general linear models. Human Brain Mapping, 28: 275–293. doi: 10.1002/hbm.20327
Publication History
- Issue published online: 12 MAR 2007
- Article first published online: 28 NOV 2006
- Manuscript Accepted: 20 SEP 2005
- Manuscript Revised: 29 JUL 2005
- Manuscript Received: 22 APR 2005
Funded by
- Wellcome Trust
- Abstract
- Article
- References
- Cited By
Keywords:
- fMRI;
- Bayesian;
- spatial model
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.