Simultaneous non-Gaussian component analysis (SING) for data integration in neuroimaging

Abstract

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.

Interpretive JIVE: Connections with CCA and an application to brain connectivity

“…JIVE decompositions may result in fewer components than ICA or related non-Gaussian approaches. Simultaneous non-Gaussian component analysis (SING) of working memory task maps and functional connectivity matrices resulted in dozens of joint components that appeared to correspond to smaller regions with greater network specificity (Risk and Gaynanova, 2021). A possible limitation of JIVE is that the joint components may reflect brain connections involved in a variety of processes, including fluid intelligence, which may have less network specificity than ICA and non-Gaussian approaches.…”

“…In the HCP, gF was measured as the number of correct responses to the Penn Progressive Matrices Test. We selected this variable as it has previously been examined in Finn et al (2015), Smith et al (2015), and our prior study Risk and Gaynanova (2021), and no other behavioral variables were examined. Here, AJIVE-Scree plot results are equivalent to CJIVE-Scree plot, since both methods chose two joint components.…”

Batch-effect correction in single-cell RNA sequencing data using JIVE

“…A GAS model with a negative binomial link might be a better fit to the scRNA-seq data. Another option would be simultaneous non-Gaussian component analysis, which divides the resulting components into Gaussian and non-Gaussian components [22]. However, more research is needed to develop multi-source dimension reduction methods that can effectively account for zero-inflation.…”