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A0606
Title: Estimation and inference for brain connectivity analysis Authors:  Lexin Li - University of California Berkeley (United States) [presenting]
Abstract: Brain connectivity analysis is now at the foreground of neuroscience research. A connectivity network is often characterized by a graph, where nodes represent neural elements such as neurons and brain regions, and links represent statistical dependency and interactions among those neural elements. Such a graph is commonly derived from neuroimaging data such as electroencephalography and functional magnetic resonance imaging. We discuss a number of projects addressing brain connectivity network analysis, including estimation of multiple networks across groups, hypothesis testing of inferring and comparing networks, and association modeling of networks and other biological phenotypes.