Joint connectivity matrix independent component analysis: Auto-linking of structural and functional connectivities

• Type of Material: Article
• Authors: Lei Wu, Emory University; Vince Calhoun, Emory University
• Language: English
• Date: 2022-11-24
• Publisher: WILEY
• Publication Version: Final Published Version
• Copyright Statement: © 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1002/hbm.26155
• Title of Journal or Parent Work: HUMAN BRAIN MAPPING
• Volume: 44
• Issue: 4
• Start Page: 1533
• End Page: 1547
• Grant/Funding Information: Foundation for the National Institutes of Health, Grant/Award Numbers: 1R01EB005846, 1R01EB006841; National Science Foundation, Grant/Award Number: 2112455
• Keywords: structural connectivity; Science & Technology; NETWORK FUNCTION; ICA; data fusion; Neurosciences; Life Sciences & Biomedicine; Neurosciences & Neurology; fMRI; PARCELLATION; dMRI; Radiology, Nuclear Medicine & Medical Imaging; FMRI DATA; functional connectivity; Neuroimaging; BRAIN
• Research Categories: Physics, Electricity and Magnetism

Abstract

The study of human brain connectivity, including structural connectivity (SC) and functional connectivity (FC), provides insights into the neurophysiological mechanism of brain function and its relationship to human behavior and cognition. Both types of connectivity measurements provide crucial yet complementary information. However, integrating these two modalities into a single framework remains a challenge, because of the differences in their quantitative interdependencies as well as their anatomical representations due to distinctive imaging mechanisms. In this study, we introduced a new method, joint connectivity matrix independent component analysis (cmICA), which provides a data-driven parcellation and automated-linking of SC and FC information simultaneously using a joint analysis of functional magnetic resonance imaging (MRI) and diffusion-weighted MRI data. We showed that these two connectivity modalities produce common cortical segregation, though with various degrees of (dis)similarity. Moreover, we show conjoint FC networks and structural white matter tracts that directly link these cortical parcellations/sources, within one analysis. Overall, data-driven joint cmICA provides a new approach for integrating or fusing structural connectivity and FC systematically and conveniently, and provides an effective tool for connectivity-based multimodal data fusion in brain.