  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. We present a group LNGCA model to extract group components
shared by more than one subject and subject-specific components. To determine
the total number of components in each subject, we propose a parametric
resampling test that samples spatially correlated Gaussian noise to match the
spatial dependence observed in data. In simulations, our estimated group
components achieve higher accuracy compared to group ICA. We apply our method
to a resting-state fMRI study on autism spectrum disorder in 342 children (252
typically developing, 90 with autism), where the group signals include
resting-state networks. We find examples of group components that appear to
exhibit different levels of temporal engagement in autism versus typically
developing children, as revealed using group LNGCA. This novel approach to
matrix decomposition is a promising direction for feature detection in
neuroimaging.
