Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis

DOI10.1214/16-AOAS946zbMath1454.62399OpenAlexW2567769634WikidataQ37732271 ScholiaQ37732271MaRDI QIDQ512396

Ran Shi, Ying Guo

Publication date: 24 February 2017

Published in: The Annals of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/16-aoas946

zbMATH Keywords

EM algorithmblind source separationfMRIbrain functional networkssubspace concentration

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10)

Related Items (7)

Distributional independent component analysis for diverse neuroimaging modalities ⋮ Rejoinder to discussions of “distributional independent component analysis for diverse neuroimaging modalities” ⋮ LOCUS: a regularized blind source separation method with low-rank structure for investigating brain connectivity ⋮ Template Independent Component Analysis with Spatial Priors for Accurate Subject-Level Brain Network Estimation and Inference ⋮ Bayesian Spatial Blind Source Separation via the Thresholded Gaussian Process ⋮ Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks Using Big Data Population Priors ⋮ Linear Non-Gaussian Component Analysis Via Maximum Likelihood

This page was built for publication: Investigating differences in brain functional networks using hierarchical covariate-adjusted independent component analysis