Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors

DOI10.1080/01621459.2019.1679638zbMath1441.62438arXiv1906.07294OpenAlexW2981635040WikidataQ100636427 ScholiaQ100636427MaRDI QIDQ5120655

Mary Beth Nebel, Ying Guo, Yikai Wang, Amanda F. Mejia, Brian S. Caffo

Publication date: 15 September 2020

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

Full work available at URL: https://arxiv.org/abs/1906.07294

zbMATH Keywords

Bayesian methodsexpectation-maximizationneuroimagingcomputationally intensive methodsapplications and case studies

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Image analysis in multivariate analysis (62H35) Computing methodologies for image processing (68U10) Biomedical imaging and signal processing (92C55)

Related Items (4)

Extracting brain disease‐related connectome subgraphs by adaptive dense subgraph discovery ⋮ LOCUS: a regularized blind source separation method with low-rank structure for investigating brain connectivity ⋮ A variational Bayesian approach to identifying whole-brain directed networks with fMRI data ⋮ Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
• Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis
• A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods
• Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations
• A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
• A Hierarchical Model for Probabilistic Independent Component Analysis of Multi‐Subject fMRI Studies

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