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Title: Graph-theoretic modeling of brain functional connectivity Authors:  Ani Eloyan - Brown University (United States) [presenting]
Abstract: Functional connectivity (FC) has been used to study functional associations among pairs of brain regions and identify temporal correlations between neurophysiological events. FC is estimated using data collected by functional magnetic resonance imaging technology. We consider the estimation of task FC during a motor task. Data are publicly available from the Human Connectome Project. One of the approaches for estimation of FC is the implementation of graph-theoretic methods. Since FC refers to the estimation of undirected temporal associations between any two regions in the brain, often including spatially incongruous areas, a graph with vertices corresponding to brain regions of interest and edges corresponding to existing connections between regions is used as a model for FC. We will review various approaches to FC estimation using graph-theoretic methods and propose a novel estimation procedure incorporating structural connectivity estimated by diffusion tensor imaging. We will discuss the comparisons of the proposed approach with other methods for the estimation of FC and their computational efficiency.