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Abstract #1677

CEEMD-based Multi-Spectrum Brain Networks for Identification of MCI

Li Zheng1, Long Qian1, Dandan Zheng2, and Jiahong Gao3,4

1Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, China, People's Republic of, 2GE Healthcare, MR Research China, Beijing, Beijing, China, People's Republic of, 3Beijing City Key Lab for Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing, China, People's Republic of, 4Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China, People's Republic of

The early detection of MCI is of paramount importance for possible delay of the transition from MCI to AD. Recently, several resting-state fMRI based neural imaging studies have been applied for MCI diagnosis by the aid of pattern classification recently. In current study, CEEMD-based high-dimensional pattern classification framework was proposed to identify MCI individuals from subjects who experience normal aging with an accuracy of 93.3 percent, compared to conventional method for brain oscillation separation. In addition, the most discriminant regions selected by our method also reflected the association with MCI, to some degree.

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