A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data

DOI10.1214/16-AOAS926zbMath1400.62299MaRDI QIDQ312903

Francesco Versace, Jeffrey M. Engelmann, Michele Guindani, Marina Vannucci, Lin-Lin Zhang

Publication date: 9 September 2016

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.aoas/1469199888

zbMATH Keywords

variational Bayesmulti-subject fMRIspatiotemporal linear regressionvariable selection priors

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Nonparametric regression and quantile regression (62G08) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Biomedical imaging and signal processing (92C55)

Related Items (15)

Bayesian spatiotemporal modeling using hierarchical spatial priors, with applications to functional magnetic resonance imaging (with discussion) ⋮ A spatiotemporal nonparametric Bayesian model of multi-subject fMRI data ⋮ Spatial Shrinkage Via the Product Independent Gaussian Process Prior ⋮ More nonparametric Bayesian inference in applications ⋮ Integrated nested Laplace approximation method for hierarchical Bayesian inference of spatial model with application to functional magnetic resonance imaging data ⋮ Estimating causal effects in studies of human brain function: new models, methods and estimands ⋮ Bayesian Spatiotemporal Modeling on Complex-Valued fMri Signals via Kernel Convolutions ⋮ Bayesian Nonparametric Panel Markov-Switching GARCH Models ⋮ Score-Driven Modeling of Spatio-Temporal Data ⋮ Sampling hierarchies of discrete random structures ⋮ Assessing dynamic effects on a Bayesian matrix-variate dynamic linear model: an application to task-based fMRI data analysis ⋮ Network classification with applications to brain connectomics ⋮ A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data ⋮ Joint Bayesian estimation of voxel activation and inter-regional connectivity in fMRI experiments ⋮ A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI

Uses Software

• spBayes
• PRMLT

Cites Work

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