Committee:
Xiaoping Hu, PhD, GT-BME (Advisor)
Shella Keilholz, PhD, GT-BME
Lena Ting, PhD, GT-BME
Krish Sathian, PhD, Emory-Neurology, Rehabilitation Medicine and Psychology
Chin-Hui Lee, PhD, GT-ECE

Title: Spatiotemporal Modeling of Brain Dynamics Using Gaussian Hidden Markov Model

Abstract: Analyzing functional magnetic resonance imaging time courses with dynamic approaches has generated a great deal of interest because of the additional temporal features that can be extracted. To systemically model spatiotemporal patterns of the brain, a Gaussian hidden Markov model (GHMM) is adopted to model the brain state switching process. The model assumes that the brain switches among a number of different brain states as a Markov process and uses multivariate Gaussian distributions to represent the spontaneous activity patterns of brain states. This model is applied to resting-state fMRI data from 100 subjects to detect reproducible brain states and their spatiotemporal characteristics. To investigate neurobiological bases of prenatal alcohol exposure (PAE) and test the hypothesis that PAE causes changes in a wide range of brain networks and their functions, the GHMM is applied on both PAE subjects and controls with similar socioeconomic factors. Statistical tests on spatial and temporal characteristics of each brain state are conducted to demonstrate the differences between two groups of subjects.