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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
Source PublicationPHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
2017-07-15
Volume478Pages:113-119
SubtypeArticle
AbstractThe 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.
KeywordNetwork Analysis Mild Cognitive Impairment Resting-state Functional Magnetic Resonance Imaging Entropy Of The Degree Distribution
WOS HeadingsScience & Technology ; Physical Sciences
DOI10.1016/j.physa.2017.02.044
WOS KeywordEARLY ALZHEIMERS-DISEASE ; MEDIAL TEMPORAL-LOBE ; FALSE DISCOVERY RATE ; FRONTAL LOBES ; MEMORY IMPAIRMENT ; EMPIRICAL BAYES ; EEG SIGNALS ; BRAIN ; DIAGNOSIS ; AMYGDALA
Indexed BySCI
Language英语
Funding OrganizationNational Natural Science Foundation of China(61401518) ; Natural Science Foundation of Jiangsu Province(BK20141432) ; Fundamental Research Funds for the Central Universities(2015PT005)
WOS Research AreaPhysics
WOS SubjectPhysics, Multidisciplinary
WOS IDWOS:000400721600012
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/15269
Collection脑网络组研究中心
Affiliation1.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
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences;  Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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