中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 智能制造技术与系统研究中心
  5. 智能机器人团队
Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

文献类型：期刊论文

作者Guo, Hao2,3; Liu, Lei3; Chen, Junjie3; Xu, Yong1; Jie, Xiang3
刊名FRONTIERS IN NEUROSCIENCE
出版日期2017-12-01
卷号11页码:18
关键词Alzheimer's disease fMRI minimum spanning tree high-order functional connectivity network feature selection classification
ISSN号1662-453X
DOI10.3389/fnins.2017.00639
通讯作者Jie, Xiang(xiangjie_tyut@sina.com)
英文摘要Functional magnetic resonance imaging (fMRI) is one of the most useful methods to generate functional connectivity networks of the brain. However, conventional network generation methods ignore dynamic changes of functional connectivity between brain regions. Previous studies proposed constructing high-order functional connectivity networks that consider the time-varying characteristics of functional connectivity, and a clustering method was performed to decrease computational cost. However, random selection of the initial clustering centers and the number of clusters negatively affected classification accuracy, and the network lost neurological interpretability. Here we propose a novel method that introduces the minimum spanning tree method to high-order functional connectivity networks. As an unbiased method, the minimum spanning tree simplifies high-order network structure while preserving its core framework. The dynamic characteristics of time series are not lost with this approach, and the neurological interpretation of the network is guaranteed. Simultaneously, we propose a multi-parameter optimization framework that involves extracting discriminative features from the minimum spanning tree high-order functional connectivity networks. Compared with the conventional methods, our resting-state fMRI classification method based on minimum spanning tree high-order functional connectivity networks greatly improved the diagnostic accuracy for Alzheimer's disease.
WOS关键词RESTING-STATE FMRI ; DYNAMIC BRAIN CONNECTIVITY ; GRAPH-THEORY ; DISEASE ; EPILEPSY ; AMYGDALA ; MEMORY ; FLUCTUATIONS ; ARCHITECTURE ; PERFORMANCE
资助项目National Natural Science Foundation of China[61472270] ; National Natural Science Foundation of China[61402318] ; National Natural Science Foundation of China[61672374] ; Natural Science Foundation of Shanxi Province[201601D021073] ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi[2016139]
WOS研究方向Neurosciences & Neurology
语种英语
WOS记录号WOS:000416808900001
出版者FRONTIERS MEDIA SA
资助机构National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi
源URL[http://ir.ia.ac.cn/handle/173211/28222]  
专题自动化研究所_智能制造技术与系统研究中心_智能机器人团队
通讯作者Jie, Xiang
作者单位1.Shanxi Med Univ, Hosp 1, Dept Psychiat, Taiyuan, Shanxi, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China
3.Taiyuan Univ Technol, Coll Comp Sci & Technol, Dept Software Engn, Taiyuan, Shanxi, Peoples R China
推荐引用方式
GB/T 7714		 Guo, Hao,Liu, Lei,Chen, Junjie,et al. Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset[J]. FRONTIERS IN NEUROSCIENCE,2017,11:18.
APA Guo, Hao,Liu, Lei,Chen, Junjie,Xu, Yong,&Jie, Xiang.(2017).Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset.FRONTIERS IN NEUROSCIENCE,11,18.
MLA Guo, Hao,et al."Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset".FRONTIERS IN NEUROSCIENCE 11(2017):18.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。