Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data

DOI10.1214/14-BA873zbMath1327.62507WikidataQ34721295 ScholiaQ34721295MaRDI QIDQ899050

Galin L. Jones, Susan S. Bassett, Brian S. Caffo, Kuo-Jung Lee

Publication date: 21 December 2015

Published in: Bayesian Analysis (Search for Journal in Brave)

Full work available at URL: https://projecteuclid.org/euclid.ba/1409921111

zbMATH Keywords

Markov chain Monte CarlofMRIBayesian variable selectionIsing distribution

Mathematics Subject Classification ID

Random fields; image analysis (62M40) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15)

Related Items (13)

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 ⋮ Bayesian variable selection for non‐Gaussian responses: a marginally calibrated copula approach ⋮ Bayesian Spatiotemporal Modeling on Complex-Valued fMri Signals via Kernel Convolutions ⋮ Spatial 3D Matérn priors for fast whole-brain fMRI analysis ⋮ Spatio‐temporal Bayesian model selection for disease mapping ⋮ Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models ⋮ A Bayesian Approach for Estimating Dynamic Functional Network Connectivity in fMRI Data ⋮ Component-wise Markov chain Monte Carlo: uniform and geometric ergodicity under mixing and composition ⋮ Joint Bayesian estimation of voxel activation and inter-regional connectivity in fMRI experiments ⋮ Approximate large-scale Bayesian spatial modeling with application to quantitative magnetic resonance imaging ⋮ A Bayesian Variable Selection Approach Yields Improved Detection of Brain Activation From Complex-Valued fMRI ⋮ Regularized brain reading with shrinkage and smoothing

Uses Software

• FMRISTAT

Cites Work

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