| Network-Based Statistic Show Aberrant Functional Connectivity in Alzheimer's Disease | |
Zhan, Yafeng1,2,3; Yao, Hongxiang4; Wang, Pan5 ; Zhou, Bo5; Zhang, Zengqiang5; Guo, Yan'e5; An, Ningyu4; Ma, Jianhua1; Zhang, Xi5; Liu, Yong2,3; Liu Yong
| |
| Source Publication | IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
 |
| 2016-10-01 | |
| Volume | 10Issue:7Pages:1182-1188 |
| Subtype | Article |
| Abstract | 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. |
| Keyword | Alzheimer's Disease Components Of Interest Mild Cognitive Impairment Network-based Statistic |
| WOS Headings | Science & Technology ; Technology |
| DOI | 10.1109/JSTSP.2016.2600298 |
| WOS Keyword | MILD COGNITIVE IMPAIRMENT ; DEFAULT-MODE NETWORK ; RESTING-STATE FMRI ; VENTRAL ATTENTION SYSTEMS ; HUMAN BRAIN ; DISCONNECTION SYNDROME ; MRI ; MEMORY ; DISEASE,ALZHEIMERS ; DORSAL |
| Indexed By | SCI ; SSCI |
| Language | 英语 |
| Funding Organization | 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) |
| WOS Research Area | Engineering |
| WOS Subject | Engineering, Electrical & Electronic |
| WOS ID | WOS:000385576200006 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/13199 |
| Collection | 脑网络组研究 |
| Corresponding Author | Liu Yong |
| Affiliation | 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 |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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. |
| Files in This Item: | Download All | |||||
| File Name/Size | DocType | Version | Access | License | ||
| Yafeng_JSTSP.pdf（465KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment