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Independent component analysis (ICA) is an unsupervised learning methodpopular in functional magnetic resonance imaging (fMRI). Group ICA has beenused to search for biomarkers in neurological disorders including autismspectrum disorder and dementia. However, current methods use a principalcomponent analysis (PCA) step that may remove low-variance features. Linearnon-Gaussian component analysis (LNGCA) enables simultaneous dimensionreduction and feature estimation including low-variance features insingle-subject fMRI. We present a group LNGCA model to extract group componentsshared by more than one subject and subject-specific components. To determinethe total number of components in each subject, we propose a parametricresampling test that samples spatially correlated Gaussian noise to match thespatial dependence observed in data. In simulations, our estimated groupcomponents achieve higher accuracy compared to group ICA. We apply our methodto a resting-state fMRI study on autism spectrum disorder in 342 children (252typically developing, 90 with autism), where the group signals includeresting-state networks. We find examples of group components that appear toexhibit different levels of temporal engagement in autism versus typicallydeveloping children, as revealed using group LNGCA. This novel approach tomatrix decomposition is a promising direction for feature detection inneuroimaging.

Group Linear non-Gaussian Component Analysis with Applications to Neuroimaging