Abstract #1677

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

Li Zheng¹, Long Qian¹, Dandan Zheng², and Jiahong Gao^3,4

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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