Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference

DOI10.1080/10618600.2022.2104289arXiv2005.13388MaRDI QIDQ6047653

Yu Ryan Yue, David Bolin, Amanda F. Mejia, Brian S. Caffo, Unnamed Author, Mary Beth Nebel

Publication date: 9 October 2023

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2005.13388

zbMATH Keywords

spatial analysisBayesian methodsexpectation-maximizationneuroimagingcomputationally intensive methods

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• An Explicit Link between Gaussian Fields and Gaussian Markov Random Fields: The Stochastic Partial Differential Equation Approach
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• Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
• Efficient independent component analysis
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• Gaussian Markov Random Fields
• Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors
• A Bayesian General Linear Modeling Approach to Cortical Surface fMRI Data Analysis
• Linear Non-Gaussian Component Analysis Via Maximum Likelihood
• Excursion and Contour Uncertainty Regions for Latent Gaussian Models
• A Hierarchical Model for Probabilistic Independent Component Analysis of Multi‐Subject fMRI Studies
• Spatial 3D Matérn priors for fast whole-brain fMRI analysis

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