Constrained Independent Vector Analysis With Reference for Multi-Subject fMRI Analysis

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 12; pp. 3531 - 3542
• Main Authors: Vu, Trung, Laport, Francisco, Yang, Hanlu, Calhoun, Vince D., Adal, Tulay
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adult; Algorithms; Biomedical engineering; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; constrained IVA; Constraints; Data analysis; Datasets; fMRI analysis; Functional magnetic resonance imaging; Gaussian processes; Humans; Image Processing, Computer-Assisted - methods; Independent component analysis; Independent vector analysis; Indexes; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; multivariate Gaussian distribution; Principal Component Analysis; Separation; Stacking; Statistics; Symbols; Tensors; Vector analysis; Vectors
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3432273

Objective: Independent component analysis (ICA) is now a widely used solution for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. Independent vector analysis (IVA) generalizes ICA to multiple datasets (multi-subject data). Along with higher-order statistical information in ICA, it leverages the statistical dependence across the datasets as an additional type of statistical diversity. As such, IVA preserves variability in the estimation of single-subject maps but its performance might suffer when the number of datasets increases. Constrained IVA is an effective way to bypass computational issues and improve the quality of separation by incorporating available prior information. Existing constrained IVA approaches often rely on user-defined threshold values to define the constraints. However, an improperly selected threshold can have a negative impact on the final results. This paper proposes two novel methods for constrained IVA: one using an adaptive-reverse scheme to select variable thresholds for the constraints and a second one based on a threshold-free formulation by leveraging the unique structure of IVA. Notably, the proposed algorithms do not require all components to be constrained, utilizing free components to model interferences and components that might not be in the reference set. We demonstrate that our solutions provide an attractive solution to multi-subject fMRI analysis both by simulations and through analysis of resting state fMRI data collected from 98 subjects - the highest number of subjects ever used by IVA algorithms. Our results show that both proposed approaches obtain significantly better separation quality and model match while providing computationally efficient and highly reproducible solutions.