Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging

DOI10.1111/j.0006-341X.2001.00554.xzbMath1209.62032WikidataQ48837381 ScholiaQ48837381MaRDI QIDQ3078776

C. Gössl, Ludwig Fahrmeir, Dorothea P. Auer

Publication date: 1 March 2011

Published in: Biometrics (Search for Journal in Brave)

zbMATH Keywords

semiparametric modelsMCMCfunctional magnetic resonance imaginghuman brain mappingspatiotemporal models

Mathematics Subject Classification ID

Bayesian inference (62F15) Neural biology (92C20) Biomedical imaging and signal processing (92C55)

Related Items (13)

Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) ⋮ Fitting large-scale structured additive regression models using Krylov subspace methods ⋮ Bayesian tensor response regression with an application to brain activation studies ⋮ Spatial 3D Matérn priors for fast whole-brain fMRI analysis ⋮ Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data ⋮ The statistical analysis of fMRI data ⋮ A nonstationary nonparametric Bayesian approach to dynamically modeling effective connectivity in functional magnetic resonance imaging experiments ⋮ Bayesian Spatial Transformation Models with Applications in Neuroimaging Data ⋮ Modeling the Spatial and Temporal Dependence in fMRI Data ⋮ The analysis of marked point patterns evolving through space and time ⋮ Spatial Bayesian variable selection and grouping for high-dimensional scalar-on-image regression ⋮ P-spline ANOVA-type interaction models for spatio-temporal smoothing ⋮ Semiparametric Bayesian models for human brain mapping

Uses Software

• Fahrmeir

Cites Work

• Bayesian image restoration, with two applications in spatial statistics (with discussion)
• Multivariate statistical modelling based on generalized linear models. With contributions by Wolfgang Hennevogl
• Robust priors for smoothing and image restoration
• Bayesian modelling of spatial heterogeneity in disease maps with application to German cancer mortality data
• Markov chains for exploring posterior distributions. (With discussion)
• Classes of Nonseparable, Spatio-Temporal Stationary Covariance Functions

This page was built for publication: Bayesian Spatiotemporal Inference in Functional Magnetic Resonance Imaging