Abstract:  

The human brain is a high-dimensional directed network system consisting of many regions as network nodes that exert influence on each other. The directed influence from one region to another is called directed connectivity and corresponds to one directed edge in the directed brain network. To understand how brain regions interact with each other and form different brain network patterns when performing different functions, we develop statistical modeling approaches to reveal high-dimensional directed brain networks using brain data. In this talk, I will present two models. (1) The first model is for studying normal and abnormal directed brain networks of patients with epilepsy using their intracranial electroencephalography (EEG) data. Epilepsy is a directed network disorder, as epileptic activity spreads from a seizure onset zone (SOZ) to many other regions after seizure onset. Intracranial EEG data are multivariate time series recordings of many brain regions. With our proposed model, we revealed the evolution of brain networks from normal to abnormal states and uncovered unique directed connectivity properties of the SOZ during seizure development. (2) The second model characterizes whole-brain directed networks of the population of healthy subjects based on functional magnetic resonance imaging (fMRI) data. We also propose a computationally efficient algorithm to address the challenge of analyzing thousands of subjects’ fMRI data. Using our new model and algorithm, we analyzed the resting-state fMRI data of around one thousand subjects from the Human Connectome Project (HCP). We revealed both population-mean and subject-specific whole-brain directed networks. Finally, I will introduce my future research.