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Abstract #2845

Joint Brain Connectivity Estimation from Diffusion and Functional MRI Using a Network Flow Model

Shu-Hsien Chu 1 , Keshab K. Parhi 1 , and Christophe Lenglet 1

1 University of Minnesota, Minneapolis, Minnesota, United States

In the paper, a novel brain network is proposed with nodes as brain regions, links as possible white matter fiber bundles, flow as electrochemical signal, link capacities characterized by fiber strength based on diffusion MRI, and node demands as neural reaction estimated from functional MRI. The signaling pathways are discovered through solving the proposed brain network model. Comparing with the connectivity derived from either diffusion MRI, functional MRI, or a joint model using the expectation-maximization algorithm presented in a prior work, the proposed model finds the maximum true connections with fewest number of false connections.

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