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作者:

Zhao, Yu (Zhao, Yu.) | Yao, Zhijun (Yao, Zhijun.) | Zheng, Weihao (Zheng, Weihao.) | Yang, Jing (Yang, Jing.) | Ding, Zhijie (Ding, Zhijie.) | Li, Mi (Li, Mi.) (学者:栗觅) | Lu, Shengfu (Lu, Shengfu.)

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EI Scopus

摘要:

Mild cognitive impairment (MCI) is a transition stage between normal aging and dementia. Brain network has been proven to occupy an important role in the study of differences in Alzheimer's disease (AD) and MCI. However, there is little knowledge about individual metabolic network abnormities which might be sensitive features in the prediction of MCI progression. In this paper, we constructed the individual metabolic network based on longitudinal Fluorodeoxyglucose positron emission tomography (FDG-PET) of 33 progress MCI (pMCI) patients and 46 stable MCI (sMCI) patients from the Alzheimer's disease Neuroimaging Initiative (ADNI). Firstly, PET images of each time point are normalized with the Yakushev normalization procedure and registered to the Brainnetome Atlas (BNA) template. Then the combination of rough distance and precision distance is utilized for accurate evaluation of between-region dissimilarity and calculated the correlation coefficient of the network. Finally, correlative feature selected by Lasso shows a significant promotion in classification performance compare with the metabolic intensity, achieving an accuracy of 89.9% and area under the receiver operating characteristic curve of 0.892. What's more, the combination of multi time points also suggests a better classification result than single time point. This finding may predict disease course in individuals with mild cognitive impairment. © 2017 IEEE.

关键词:

Bioinformatics Classification (of information) Forecasting Metabolism Neurodegenerative diseases Neuroimaging Positron emission tomography

作者机构:

  • [ 1 ] [Zhao, Yu]Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
  • [ 2 ] [Yao, Zhijun]Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
  • [ 3 ] [Zheng, Weihao]Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
  • [ 4 ] [Yang, Jing]Lanzhou University Second Hospital, Lanzhou, China
  • [ 5 ] [Ding, Zhijie]Tianshui City Psychiatric Hospital, Tianshui, China
  • [ 6 ] [Li, Mi]International WIC Institute, Beijing University of Technology, Beijing, China
  • [ 7 ] [Lu, Shengfu]International WIC Institute, Beijing University of Technology, Beijing, China

通讯作者信息:

  • [yao, zhijun]gansu provincial key laboratory of wearable computing, school of information science and engineering, lanzhou university, lanzhou, china

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年份: 2017

卷: 2017-January

页码: 1894-1899

语种: 英文

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SCOPUS被引频次: 6

ESI高被引论文在榜: 0 展开所有

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