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Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease

文献类型：期刊论文

作者Zhan, Yafeng1,2,3; Yao, Hongxiang4; Wang, Pan5; Zhou, Bo5; Zhang, Zengqiang5; Guo, Yan'e5; An, Ningyu4; Ma, Jianhua1; Zhang, Xi5; Liu, Yong2,3
刊名IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
出版日期2016-10-01
卷号10期号:7页码:1182-1188
关键词Alzheimer's Disease Components Of Interest Mild Cognitive Impairment Network-based Statistic
DOI10.1109/JSTSP.2016.2600298
文献子类Article
英文摘要Alzheimer's disease (AD) and mild cognitive impairment (MCI) have been associated with impaired communication among large-scale brain networks. Given nature that interconnected subnetworks are responsible for daily behavior than a single pair of functional connectivity, it is valid to use a network-based statistic (NBS) method to exploit the clustering structure of connectivity alterations in AD/MCI. We explored abnormal network components using NBS based on resting-state functional magnetic resonance imaging (fMRI) connectivity in a sample of patients with AD (N = 35), MCI (N = 27) and age-matched healthy subjects (N = 27). The results demonstrated that patients had reduced functional connectivity strength in several components, including the default mode network, sensorimotor network, visual-sensory network, and visual-attention network. In patients with AD, the functional connectivity of these components of interest (COIs) exhibited greater attenuation than that in MCI subjects compared with normal cognition. A greater degree of cognitive impairment was correlated with a greater decrease in functional connectivity in the identified COIs. These results indicate that the neurodegenerative disruption of fMRI connectivity is widely distributed in several networks in AD/MCI. These profiles deepen our understanding of the neural basis of AD/MCI dysfunction and indicate the potential of resting-state fMRI measures as biomarkers or predictors of AD.
WOS关键词MILD COGNITIVE IMPAIRMENT ; DEFAULT-MODE NETWORK ; RESTING-STATE FMRI ; VENTRAL ATTENTION SYSTEMS ; HUMAN BRAIN ; DISCONNECTION SYNDROME ; MRI ; MEMORY ; DISEASE,ALZHEIMERS ; DORSAL
WOS研究方向Engineering
语种英语
WOS记录号WOS:000385576200006
资助机构National Natural Science Foundation of China(81571062 ; Youth Innovation Promotion Association CAS(2014119) ; Natural Science Foundation of Beijing(7152096) ; Beijing Nova Program(Z1511000003150112) ; National Laboratory of Pattern Recognition(201407344) ; 81471120 ; 61431012)
源URL[http://ir.ia.ac.cn/handle/173211/13199]  
专题自动化研究所_脑网络组研究中心
通讯作者Liu Yong
作者单位1.Southern Med Univ, Sch Biomed Engn, Guangzhou 510515, Guangdong, Peoples R China
2.Chinese Acad Sci, Brainnetome Ctr, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Dept Radiol, Beijing 100853, Peoples R China
5.Chinese Peoples Liberat Army Gen Hosp, Inst Gerontol & Geriatr, Dept Neurol, Beijing 100853, Peoples R China
推荐引用方式
GB/T 7714		 Zhan, Yafeng,Yao, Hongxiang,Wang, Pan,et al. Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease[J]. IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,2016,10(7):1182-1188.
APA Zhan, Yafeng.,Yao, Hongxiang.,Wang, Pan.,Zhou, Bo.,Zhang, Zengqiang.,...&Liu Yong.(2016).Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease.IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING,10(7),1182-1188.
MLA Zhan, Yafeng,et al."Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease".IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 10.7(2016):1182-1188.

入库方式： OAI收割

来源：自动化研究所

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