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Abstract #1431

Brain Network Atlas Estimation using Centered Graph Shrinkage with Application to Developing and Aging Brains

Islem Rekik1, Gang Li1, Minjeong Kim1, Weili Lin1, and Dinggang Shen1,2

1Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States, 2Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea

Learning how to average brain networks (i.e., build a brain network atlas) constitutes a key step in creating a reliable ‘mean’ representation of a set of normal brains, which can be used to spot deviations from the normal network atlas (i.e., abnormal cases). However, this topic remains largely unexplored in neuroimaging field. In this work, we propose a network atlas estimation framework through a non-linear diffusion along the local neighbors of each node (network) in a graph. Our evaluation on both developing and aging datasets showed a better ‘centeredness’ of our atlas in comparison with the state-of-the-art network fusion method.

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