A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis

DOI10.1080/01621459.2019.1611582zbMath1445.62283arXiv1706.00959OpenAlexW2624503905WikidataQ100636416 ScholiaQ100636416MaRDI QIDQ5130592

David Bolin, Finn Lindgren, Amanda F. Mejia, Yu Ryan Yue, Martin A. Lindquist

Publication date: 28 October 2020

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1706.00959

zbMATH Keywords

spatial statisticsstochastic partial differential equationBayesian smoothingbrain imagingintegrated nested Laplace approximation

Mathematics Subject Classification ID

Directional data; spatial statistics (62H11) Applications of statistics to biology and medical sciences; meta analysis (62P10) Image analysis in multivariate analysis (62H35)

Related Items (10)

BayesfMRI ⋮ Parallelized integrated nested Laplace approximations for fast Bayesian inference ⋮ A new avenue for Bayesian inference with INLA ⋮ Spatial 3D Matérn priors for fast whole-brain fMRI analysis ⋮ Score-Driven Modeling of Spatio-Temporal Data ⋮ Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference ⋮ Stochastic modeling of geometrical uncertainties on complex domains, with application to additive manufacturing and brain interface geometries ⋮ Estimating high-resolution red sea surface temperature hotspots, using a low-rank semiparametric spatial model ⋮ Stochastic modeling and identification of material parameters on structures produced by additive manufacturing ⋮ Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

Uses Software

• R-INLA
• FreeSurfer
• FMRISTAT
• PRMLT

Cites Work

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