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Abstract #1694

Hybrid high-order resting-state functional connectivity networks for mild cognitive impairment diagnosis

Yu Zhang1, Han Zhang1, Xiaobo Chen1, and Dinggang Shen1

1Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States

This study proposes a novel approach named “hybrid high-order FC networks” to explore the higher-level interactions among brain regions for improving the diagnosis performance of early mild cognitive impairment. We first construct the low-order network and the topographical similarity-based high-order network. With the two-level FC networks, we propose to construct a new “associated high-order network”, which is formed by estimating the higher-level interactions between the high-order sub-networks and low-order sub-networks. We further devise a multi-kernel learning strategy to integrate the dynamic networks of the three different levels. A high diagnosis accuracy of 91.5 % demonstrates effectiveness of our proposed approach.

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