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Network analysis in detection of early-stage mild cognitive impairment
Ni, Huangjing2,3,7; Qin, Jiaolong2,3,8; Zhou, Luping4; Zhao, Zhigen5; Wang, Jun6; Hou, Fengzhen1; Alzheimers Dis Neuroimaging Initia
2017-07-15
发表期刊PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
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
DOI10.1016/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
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15269
专题脑网络组研究中心
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
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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.
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