Multifaceted Neuroimaging Data Integration via Analysis of Subspaces

Published

August 28, 2024

Author(s)

Andrew Ackerman, Zhengwu Zhang, Jan Hannig, Jack Prothero, J.S. Marron

Abstract

Neuroimaging studies, such as the Human Connectome Project (HCP), often collect multifaceted and multi-block data to study the complex human brain. However, these data are often analyzed in a pairwise fashion, which can hinder our understanding of how different brain-related measures interact with each other. In this study, we analyze the multi-block HCP data using the Data Integration via Analysis of Subspaces (DIVAS) method. We integrate structural and functional brain connectivity, substance use, cognition, and genetics in an exhaustive five-block analysis. This gives rise to the important finding that genetics is the single data modality most predictive of brain connectivity, outside of brain connectivity itself. Nearly 14% of the variation in functional connectivity (FC) and roughly 12% of the variation in structural connectivity (SC) is attributed to shared spaces with genetics. Moreover, investigations of shared space loadings provide interpretable associations between particular brain regions and drivers of variability, such as alcohol consumption in the substance-use data block. Novel Jackstraw hypothesis tests are developed for the DIVAS framework to establish statistically significant loadings. For example, in the (FC, SC, and Substance Use) shared space, these novel hypothesis tests highlight largely negative functional and structural connections suggesting the brain's role in physiological responses to increased substance use. Furthermore, our findings have been validated using a subset of genetically relevant siblings or twins not studied in the main analysis.

Citation

Psychometrika

Pub Type

Journals

Keywords

Data Integration, Human Connectome Project, Substance Use, Jackstraw Inference

Citation

Ackerman, A. , Zhang, Z. , Hannig, J. , Prothero, J. and Marron, J. (2024), Multifaceted Neuroimaging Data Integration via Analysis of Subspaces, Psychometrika (Accessed December 11, 2024)