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

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 informat...