Estimation and Inference for Brain Connectivity Analysis

Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is characterized by a graph, where nodes represent neural elements such as neurons and brain regions, and links represent statistical dependences that are often encoded in terms of partial correlations. Such a graph is inferred from neuroimaging data such as electroencephalography and functional magnetic resonance imaging. In this talk, we discuss a number of projects addressing estimation and inference of brain connectivity network through partial correlation matrix.Non UBCUnreviewedAuthor affiliation: University of California-BerkeleyFacult