Group linear non-Gaussian component analysis with applications to neuroimaging.

ABSTRACT: Independent component analysis (ICA) is an unsupervised learning method popular in functional magnetic resonance imaging (fMRI). Group ICA has been used to search for biomarkers in neurological disorders including autism spectrum disorder and dementia. However, current methods use a principal component analysis (PCA) step that may remove low-variance features. Linear non-Gaussian component analysis (LNGCA) enables simultaneous dimension reduction and feature estimation including low-variance features in single-subject fMRI. A group LNGCA model is proposed to extract group components shared by more than one subject. Unlike group ICA methods, this novel approach also estimates individual (subject-specific) components orthogonal to the group components. To determine the total number of compon

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC9390952 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature