  As advances in technology allow the acquisition of complementary information,
it is increasingly common for scientific studies to collect multiple datasets.
Large-scale neuroimaging studies often include multiple modalities (e.g., task
functional MRI, resting-state fMRI, diffusion MRI, and/or structural MRI), with
the aim to understand the relationships between datasets. In this study, we
seek to understand whether regions of the brain activated in a working memory
task relate to resting-state correlations. In neuroimaging, a popular approach
uses principal component analysis for dimension reduction prior to canonical
correlation analysis with joint independent component analysis, but this may
discard biological features with low variance and/or spuriously associate
structure unique to a dataset with joint structure. We introduce Simultaneous
Non-Gaussian component analysis (SING) in which dimension reduction and feature
extraction are achieved simultaneously, and shared information is captured via
subject scores. We apply our method to a working memory task and resting-state
correlations from the Human Connectome Project. We find joint structure as
evident from joint scores whose loadings highlight resting-state correlations
involving regions associated with working memory. Moreover, some of the subject
scores are related to fluid intelligence.
