Adaptive independent vector analysis for multi-subject complex-valued fMRI data

• Published in: Journal of neuroscience methods Vol. 281; pp. 49 - 63
• Main Authors: Kuang, Li-Dan, Lin, Qiu-Hua, Gong, Xiao-Feng, Cong, Fengyu, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.04.2017
• Subjects: Algorithms; Auditory Perception - physiology; Brain - diagnostic imaging; Brain - physiology; Brain Mapping - methods; Complex-valued fMRI data; Computer Simulation; Fingers - physiology; Humans; Independent vector analysis (IVA); Likelihood Functions; Magnetic Resonance Imaging - methods; MGGD; Models, Neurological; Motor Activity - physiology; Multivariate Analysis; Noncircularity; Nonlinear Dynamics; Post-IVA phase de-noising; Rest; Shape parameter; Subspace de-noising; MGGD; Subspace de-noising; Noncircularity; Shape parameter; Post-IVA phase de-noising; Complex-valued fMRI data; Independent vector analysis (IVA)

•An adaptive IVA algorithm was proposed for multi-subject complex-valued fMRI data.•An MGGD-based nonlinear function was exploited to match varying SCV distributions.•The MGGD shape parameter was estimated using maximum likelihood estimation.•Subspace de-noising, post-IVA phase de-noising, and nonci...