Three-way parallel group independent component analysis: Fusion of spatial and spatiotemporal magnetic resonance imaging data

• Authors: Shile Qi, Nanjing University of Aeronautics and Astronautics; Rogers F. Silva, Georgia State University; Daoqiang Zhang, Nanjing University of Aeronautics and Astronautics; Sergey M. Plis, Georgia State University; Robyn Miller, Georgia State University; Victor M. Vergara, Georgia State University; Rongtao Jiang, Yale University; Dongmei Zhi, Beijing Normal University; Jing Sui, Beijing Normal University; Vince D. Calhoun, Georgia State University
• Journal Title: Human Brain Mapping
• Volume: Volume 43, Number 4
• Publisher: John Wiley & Sons | 2022-03-01, Pages 1280-1294
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1002/hbm.25720
• Subjects: Biology, Neuroscience; Computer Science; Health Sciences, Radiology; Engineering, Biomedical
• Keywords: 4D fMRI; group ICA; independent component analysis (ICA); parallel ICA; three-way multimodal fusion; unify; Adult; Brain; Female; Functional Neuroimaging; Humans; Image Processing, Computer-Assisted; Magnetic Resonance Imaging; Male; Middle Aged; Nerve Net; Schizophrenia; Spatial Analysis; Spatio-Temporal Analysis

Abstract

Advances in imaging acquisition techniques allow multiple imaging modalities to be collected from the same subject. Each individual modality offers limited yet unique views of the functional, structural, or dynamic temporal features of the brain. Multimodal fusion provides effective ways to leverage these complementary perspectives from multiple modalities. However, the majority of current multimodal fusion approaches involving functional magnetic resonance imaging (fMRI) are limited to 3D feature summaries that do not incorporate its rich temporal information. Thus, we propose a novel three-way parallel group independent component analysis (pGICA) fusion method that incorporates the first-level 4D fMRI data (temporal information included) by parallelizing group ICA into parallel ICA via a unified optimization framework. A new variability matrix was defined to capture subject-wise functional variability and then link it to the mixing matrices of the other two modalities. Simulation results show that the three-way pGICA provides highly accurate cross-modality linkage estimation under both weakly and strongly correlated conditions, as well as comparable source estimation under different noise levels. Results using real brain imaging data identified one linked functional–structural–diffusion component associated to differences between schizophrenia and controls. This was replicated in an independent cohort, and the identified components were also correlated with major cognitive domains. Functional network connectivity revealed visual–subcortical and default mode-cerebellum pairs that discriminate between schizophrenia and controls. Overall, both simulation and real data results support the use of three-way pGICA to identify multimodal spatiotemporal links and to pursue the study of brain disorders under a single unifying multimodal framework.