Network analysis in detection of early-stage mild cognitive impairment

doi:10.1016/j.physa.2017.02.044

	Network analysis in detection of early-stage mild cognitive impairment
	Ni, Huangjing2,3,7 ; Qin, Jiaolong2,3,8 ; Zhou, Luping 4; Zhao, Zhigen 5; Wang, Jun 6; Hou, Fengzhen 1; Alzheimers Dis Neuroimaging Initia
发表期刊	PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
	2017-07-15
卷号	478 页码:113-119
文章类型	Article
摘要	The detection and intervention for early-stage mild cognitive impairment (EMCI) is of vital importance However, the pathology of EMCI remains largely unknown, making it be challenge to the clinical diagnosis. In this paper, the resting-state functional magnetic resonance imaging (rs-fMRI) data derived from EMCI patients and normal controls are analyzed using the complex network theory. We construct the functional connectivity (FC) networks and employ the local false discovery rate approach to successfully detect the abnormal functional connectivities appeared in the EMCI patients. Our results demonstrate the abnormal functional connectivities have appeared in the EMCI patients, and the affected brain regions are mainly distributed in the frontal and temporal lobes In addition, to quantitatively characterize the statistical properties of FCs in the complex network, we herein employ the entropy of the degree distribution (E-DD) index and some other well established measures, i.e., clustering coefficient (Cc) and the efficiency of graph (E-G). Eventually, we found that the E-DD index, better than the widely used Cc and EG measures, may serve as an assistant and potential marker for the detection of EMCI. (C) 2017 Elsevier B.V. All rights reserved.
关键词	Network Analysis Mild Cognitive Impairment Resting-state Functional Magnetic Resonance Imaging Entropy Of The Degree Distribution
WOS标题词	Science & Technology ; Physical Sciences
DOI	10.1016/j.physa.2017.02.044
关键词[WOS]	EARLY ALZHEIMERS-DISEASE ; MEDIAL TEMPORAL-LOBE ; FALSE DISCOVERY RATE ; FRONTAL LOBES ; MEMORY IMPAIRMENT ; EMPIRICAL BAYES ; EEG SIGNALS ; BRAIN ; DIAGNOSIS ; AMYGDALA
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61401518) ; Natural Science Foundation of Jiangsu Province(BK20141432) ; Fundamental Research Funds for the Central Universities(2015PT005)
WOS研究方向	Physics
WOS类目	Physics, Multidisciplinary
WOS记录号	WOS:000400721600012
引用统计	被引频次：10[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/15269
专题	脑图谱与类脑智能实验室_脑网络组研究
作者单位	1.China Pharmaceut Univ, Key Lab Biomed Funct Mat, Nanjing 210009, Jiangsu, Peoples R China 2.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 4.Univ Wollongong, Sch Comp & Informat Technol, Wollongong, NSW 2522, Australia 5.Temple Univ, Dept Stat, Fox Sch Business, Philadelphia, PA 19122 USA 6.Nanjing Univ Posts & Telecommun, Sch Geog & Biol Informat, Nanjing 210003, Jiangsu, Peoples R China 7.Nanjing Univ, Sch Elect Sci & Engn, Nanjing 210093, Jiangsu, Peoples R China 8.Southeast Univ, Res Ctr Learning Sci, Nanjing 210096, Jiangsu, Peoples R China
第一作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Ni, Huangjing,Qin, Jiaolong,Zhou, Luping,et al. Network analysis in detection of early-stage mild cognitive impairment[J]. PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,2017,478:113-119.
APA	Ni, Huangjing.,Qin, Jiaolong.,Zhou, Luping.,Zhao, Zhigen.,Wang, Jun.,...&Alzheimers Dis Neuroimaging Initia.(2017).Network analysis in detection of early-stage mild cognitive impairment.PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS,478,113-119.
MLA	Ni, Huangjing,et al."Network analysis in detection of early-stage mild cognitive impairment".PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 478(2017):113-119.