Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset | |
Guo, Hao1,2; Liu, Lei1; Chen, Junjie1; Xu, Yong3; Jie, Xiang1 | |
发表期刊 | FRONTIERS IN NEUROSCIENCE |
ISSN | 1662-453X |
2017-12-01 | |
卷号 | 11页码:18 |
通讯作者 | Jie, Xiang(xiangjie_tyut@sina.com) |
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. |
关键词 | Alzheimer's disease fMRI minimum spanning tree high-order functional connectivity network feature selection classification |
DOI | 10.3389/fnins.2017.00639 |
关键词[WOS] | RESTING-STATE FMRI ; DYNAMIC BRAIN CONNECTIVITY ; GRAPH-THEORY ; DISEASE ; EPILEPSY ; AMYGDALA ; MEMORY ; FLUCTUATIONS ; ARCHITECTURE ; PERFORMANCE |
收录类别 | SCI |
语种 | 英语 |
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] ; 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] |
项目资助者 | National Natural Science Foundation of China ; Natural Science Foundation of Shanxi Province ; Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi |
WOS研究方向 | Neurosciences & Neurology |
WOS类目 | Neurosciences |
WOS记录号 | WOS:000416808900001 |
出版者 | FRONTIERS MEDIA SA |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/28223 |
专题 | 智能制造技术与系统研究中心_智能机器人 |
通讯作者 | Jie, Xiang |
作者单位 | 1.Taiyuan Univ Technol, Coll Comp Sci & Technol, Dept Software Engn, Taiyuan, Shanxi, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China 3.Shanxi Med Univ, Hosp 1, Dept Psychiat, Taiyuan, Shanxi, Peoples R China |
第一作者单位 | 模式识别国家重点实验室 |
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. |
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