Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset

doi:10.3389/fnins.2017.00639

	Alzheimer Classification Using a Minimum Spanning Tree of High-Order Functional Network on fMRI Dataset
	Guo, Hao2,3 ; Liu, Lei 3; Chen, Junjie 3; Xu, Yong 1; Jie, Xiang 3
刊名	FRONTIERS IN NEUROSCIENCE
	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
DOI	10.3389/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.
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关键词	RESTING-STATE FMRI ; DYNAMIC BRAIN CONNECTIVITY ; GRAPH-THEORY ; DISEASE ; EPILEPSY ; AMYGDALA ; MEMORY ; FLUCTUATIONS ; ARCHITECTURE ; PERFORMANCE
WOS研究方向	Neurosciences & Neurology
语种	英语
出版者	FRONTIERS MEDIA SA
WOS记录号	WOS:000416808900001
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
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/28222]
专题	中国科学院自动化研究所
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.