Spatiotemporal Modeling of Brain Dynamics Using Resting-State Functional Magnetic Resonance Imaging with Gaussian Hidden Markov Model

• Published in: Brain connectivity Vol. 6; no. 4; pp. 326 - 334
• Main Authors: Chen, Shiyang, Langley, Jason, Chen, Xiangchuan, Hu, Xiaoping
• Format: Journal Article
• Language: English
• Published: United States Mary Ann Liebert, Inc 01.05.2016
• Subjects: Brain - physiology; Computer Simulation; Connectome - methods; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Markov Chains; Neural Pathways - physiology; Normal Distribution; Rest - physiology; Spatio-Temporal Analysis; brain model; dynamics; modeling; functional magnetic resonance imaging; resting-state
• ISSN: 2158-0014; 2158-0022
• DOI: 10.1089/brain.2015.0398

Analyzing functional magnetic resonance imaging (fMRI) time courses with dynamic approaches has generated a great deal of interest because of the additional temporal features that can be extracted. In this work, to systemically model spatiotemporal patterns of the brain, a Gaussian hidden Markov model (GHMM) was adopted to model the brain state switching process. We assumed that the brain switches among a number of different brain states as a Markov process and used multivariate Gaussian distributions to represent the spontaneous activity patterns of brain states. This model was applied to resting-state fMRI data from 100 subjects in the Human Connectome Project and detected nine highly reproducible brain states and their temporal and transition characteristics. Our results indicate that the GHMM can unveil brain dynamics that may provide additional insights regarding the brain at resting state.