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

• Type of Material: Article
• Authors: Raphiel Murden, Emory University; Zhengwu Zhang, University of North Carolina; Ying Guo, Emory University; Benjamin Risk, Emory University
• Language: English
• Date: 2022-10-14
• Publisher: FRONTIERS MEDIA SA
• Publication Version: Final Published Version
• Copyright Statement: © 2022 Murden, Zhang, Guo and Risk.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.3389/fnins.2022.969510
• Title of Journal or Parent Work: FRONTIERS IN NEUROSCIENCE
• Volume: 16
• Start Page: 969510
• End Page: 969510
• Grant/Funding Information: This work was supported by R21 AG066970 to ZZ and BR, R01 MH105561 to YG, and R01 MH129855 to BR.
• Keywords: Neurosciences & Neurology; JOINT; joint and individual variance explained; data integration; canonical correlation analysis; functional connectivity; principal component analysis; Science & Technology; Neurosciences; structural connectivity; Human Connectome Project; INTEGRATION; PATTERNS; Life Sciences & Biomedicine
• Research Categories: Health Sciences, Public Health; Statistics; Biology, Biostatistics

Abstract

Joint and Individual Variation Explained (JIVE) is a model that decomposes multiple datasets obtained on the same subjects into shared structure, structure unique to each dataset, and noise. JIVE is an important tool for multimodal data integration in neuroimaging. The two most common algorithms are R.JIVE, an iterative approach, and AJIVE, which uses principal angle analysis. The joint structure in JIVE is defined by shared subspaces, but interpreting these subspaces can be challenging. In this paper, we reinterpret AJIVE as a canonical correlation analysis of principal component scores. This reformulation, which we call CJIVE, (1) provides an intuitive view of AJIVE; (2) uses a permutation test for the number of joint components; (3) can be used to predict subject scores for out-of-sample observations; and (4) is computationally fast. We conduct simulation studies that show CJIVE and AJIVE are accurate when the total signal ranks are correctly specified but, generally inaccurate when the total ranks are too large. CJIVE and AJIVE can still extract joint signal even when the joint signal variance is relatively small. JIVE methods are applied to integrate functional connectivity (resting-state fMRI) and structural connectivity (diffusion MRI) from the Human Connectome Project. Surprisingly, the edges with largest loadings in the joint component in functional connectivity do not coincide with the same edges in the structural connectivity, indicating more complex patterns than assumed in spatial priors. Using these loadings, we accurately predict joint subject scores in new participants. We also find joint scores are associated with fluid intelligence, highlighting the potential for JIVE to reveal important shared structure.