Group linear non-Gaussian component analysis with applications to neuroimaging

DOI10.1016/J.CSDA.2022.107454OpenAlexW3119517265WikidataQ114191855 ScholiaQ114191855MaRDI QIDQ2129603

Mary Beth Nebel, Yuxuan Zhao, Benjamin B. Risk, David S. Matteson, Stewart H. Mostofsky

Publication date: 22 April 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2101.04809

zbMATH Keywords

principal component analysismatrix decompositionbig datafunctional magnetic resonance imaging (fMRI)group inferenceindependent component analysis (ICA)resting-state fMRI

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• JIVE
• neuRosim
• ICtest

Cites Work

• Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging
• Angle-based joint and individual variation explained
• Automatic dimensionality selection from the scree plot via the use of profile likelihood
• Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
• A General Probabilistic Model for Group Independent Component Analysis and Its Estimation Methods
• Projection pursuit in high dimensions
• An evaluation of independent component analyses with an application to resting‐state fMRI
• Structural learning and integrative decomposition of multi‐view data
• Linear Non-Gaussian Component Analysis Via Maximum Likelihood
• A Hierarchical Model for Probabilistic Independent Component Analysis of Multi‐Subject fMRI Studies

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