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Updated: Mar 23, 2026
Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
Published on: June 30, 2018
Spatiotemporal Modeling of Brain Dynamics Using Resting-State Functional Magnetic Resonance Imaging with Gaussian
Shiyang Chen1, Jason Langley1, Xiangchuan Chen1
1Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University , Atlanta, Georgia .
Abstract:
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.
