机构：[1]Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, People’s Republic of China[2]Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing 100053, People’s Republic of China神经内科首都医科大学宣武医院[3]Beijing Key Laboratory of Neuromodulation, Beijing 100053, People’s Republic of China[4]College of Bio-information, ChongQing University of Posts and Telecommunications, Chongqing 400065, People’s Republic of China[5]School of Microelectronics and Solid-State Electronics Physiology, University of Electronic Science and Technology of China, Chengdu 610054, People’s Republic of China
The diagnosis of mild cognitive impairment (MCI) is very helpful for early therapeutic interventions of Alzheimer's disease (AD). MCI has been proven to be correlated with disorders in multiple brain areas. In this paper, we used information from resting brain networks at different EEG frequency bands to reliably recognize MCI. Because EEG network analysis is influenced by the reference that is used, we also evaluate the effect of the reference choices on the resting scalp EEG network-based MCI differentiation. The conducted study reveals two aspects: (1) the network-based MCI differentiation is superior to the previously reported classification that uses coherence in the EEG; and (2) the used EEG reference influences the differentiation performance, and the zero approximation technique (reference electrode standardization technique, REST) can construct a more accurate scalp EEG network, which results in a higher differentiation accuracy for MCI. This study indicates that the resting scalp EEG-based network analysis could be valuable for MCI recognition in the future.
基金：
973 program (2011CB707803)
the National Nature Science Foundation of China (nos 61175117, 31070881 and 31100745)
the program for New Century Excellent Talents in University (NCET-12-0089)
the 863 project (2012AA011601)
第一作者机构：[1]Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, People’s Republic of China
通讯作者：
推荐引用方式(GB/T 7714)：
Peng Xu,Xiu Chun Xiong,Qing Xue,et al.Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference[J].PHYSIOLOGICAL MEASUREMENT.2014,35(7):1279-1298.doi:10.1088/0967-3334/35/7/1279.
APA：
Peng Xu,Xiu Chun Xiong,Qing Xue,Yin Tian,Yueheng Peng...&De Zhong Yao.(2014).Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference.PHYSIOLOGICAL MEASUREMENT,35,(7)
MLA：
Peng Xu,et al."Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference".PHYSIOLOGICAL MEASUREMENT 35..7(2014):1279-1298
