Estimating Dynamic Connectivity States in fMRI Using Regime-Switching Factor Models

• Published in: IEEE transactions on medical imaging Vol. 37; no. 4; pp. 1011 - 1023
• Main Authors: Chee-Ming Ting, Ombao, Hernando, Samdin, S. Balqis, Salleh, Sh-Hussain
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Autoregressive processes; Brain; Brain - diagnostic imaging; Brain - physiology; Brain mapping; Brain modeling; Cluster Analysis; Clustering; Computer simulation; Connectome - methods; Covariance matrices; dynamic brain connectivity; Economic models; Estimation; factor analysis; fMRI; Functional magnetic resonance imaging; Hidden Markov models; Humans; Image Processing, Computer-Assisted - methods; large VAR models; Load modeling; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Markov chains; Models, Statistical; Neural networks; Neural Pathways - diagnostic imaging; Neuroimaging; Principal components analysis; Reactive power; Regime-switching models; Switching theory; Vector quantization; Windows (intervals)

We consider the challenges in estimating the state-related changes in brain connectivity networks with a large number of nodes. Existing studies use the sliding-window analysis or time-varying coefficient models, which are unable to capture both smooth and abrupt changes simultaneously, and rely on...