Adaptive Constrained Independent Vector Analysis: An Effective Solution for Analysis of Large-Scale Medical Imaging Data

• Published in: IEEE journal of selected topics in signal processing Vol. 14; no. 6; pp. 1255 - 1264
• Main Authors: Bhinge, Suchita, Long, Qunfang, Calhoun, Vince D., Adali, Tulay
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Blind source separation; Constraints; Correlation; Cost function; Datasets; Feature extraction; fMRI analysis; Functional magnetic resonance imaging; Gender aspects; high dimensional; Magnetic resonance imaging; Medical imaging; Mutual information; Neuroimaging; Parameters; Reference signals; semi-blind; Vector analysis; high dimensional; Blind source separation; semi-blind; fMRI analysis

There is a growing need for flexible methods for the analysis of large-scale functional magnetic resonance imaging (fMRI) data for the estimation of global signatures that summarize the population while preserving individual-specific traits. Independent vector analysis (IVA) is a data-driven method...