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Abstract #2378

Extraction of Both Dynamic Functional and Structural Connectivity from Resting-state fMRI for MCI Classification

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

1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, CHAPEL HILL, NC, United States

In this abstract, we show that the diagnosis accuracy of mild cognitive impairment (MCI) can be significantly improved by integrating dynamic information contained in the traditional functional connectivity (FC) from grey matter (GM) regions and the functional correlation tensors (FCT) from white matter (WM) regions, both computed from resting-state fMRI (RS-fMRI). The advantages of our method include: 1) dynamic FC is exploited to reveal rich time-varying information in FC, and 2) the anatomical structure information within WM can be well incorporated in RS-fMRI.

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