Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/23875 
Year of Publication: 
2003
Series/Report no.: 
Discussion Paper No. 317
Publisher: 
Ludwig-Maximilians-Universität München, Sonderforschungsbereich 386 - Statistische Analyse diskreter Strukturen, München
Abstract: 
Human brain mapping, i.e. the detection of functional regions and their connections, has experienced enormous progress through the use of functional magnetic resonance imaging (fMRI). The massive spatio-temporal data sets generated by this imaging technique impose challenging problems for statistical analysis. Many approaches focus on adequate modeling of the temporal component. Spatial aspects are often considered only in a separate postprocessing step, if at all, or modeling is based on Gaussian random fields. A weakness of Gaussian spatial smoothing is possible underestimation of activation peaks or blurring of sharp transitions between activated and non-activated regions. In this paper we suggest Bayesian spatio-temporal models, where spatial adaptivity is improved through inhomogeneous or compound Markov random field priors. Inference is based on an approximate MCMC technique. Performance of our approach is investigated through a simulation study, including a comparison to models based on Gaussian as well as more robust spatial priors in terms of pixelwise and global MSEs. Finally we demonstrate its use by an application to fMRI data from a visual stimulation experiment for assessing activation in visual cortical areas.
Subjects: 
Adaptive smoothing
Bayesian inference
human brain mapping
inhomogeneous Markov random fields
MCMC
spatio-temporal modeling
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

Files in This Item:
File
Size
7.04 MB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.