Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease
文献类型：期刊论文
| 作者 | Li, Rui1,2 ; Yu, Jing1,3; Zhang, Shouzi4; Bao, Feng5; Wang, Pengyun1,2; Huang, Xin1,2; Li, Juan1,2
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| 刊名 | PLOS ONE
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| 出版日期 | 2013 |
| 卷号 | 8期号:12 |
| ISSN号 | 1932-6203 |
| 通讯作者 | Li, J (reprint author), Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Ctr Aging Psychol, Beijing 100101, Peoples R China. |
| 产权排序 | 1 |
| 英文摘要 | Alzheimer's disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI. |
| 学科主题 | Medical psychology |
| WOS标题词 | Science & Technology |
| 类目[WOS] | Multidisciplinary Sciences |
| 研究领域[WOS] | Science & Technology - Other Topics |
| 关键词[WOS] | MILD COGNITIVE IMPAIRMENT ; RESTING-STATE NETWORKS ; INTRINSIC FUNCTIONAL CONNECTIVITY ; INDEPENDENT COMPONENT ANALYSIS ; HUMAN BRAIN ; AUTOBIOGRAPHICAL MEMORY ; CINGULATE CORTEX ; MRI ; FMRI ; SELF |
| 收录类别 | SCI |
| 项目简介 | This work was supported by the National Natural Science Foundation of China (31271108, 31200847, 30911120494 and 31070916), the Knowledge Innovation Project of the Chinese Academy of Sciences (KSCX2-EW-J-8), the CAS/SAFEA International Partnership Program for Creative Research Team (Y2CX131003), and Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y1CX251005). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. |
| 语种 | 英语 |
| WOS记录号 | WOS:000328566700060 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/10820]  |
| 专题 | 心理研究所_中国科学院心理健康重点实验室 |
| 作者单位 | 1.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Ctr Aging Psychol, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Inst Psychol, Magnet Resonance Imaging Res Ctr, Beijing 100101, Peoples R China 3.Southwest Univ, Sch Psychol, Chongqing, Peoples R China 4.Beijing Geriatr Hosp, Beijing, Peoples R China 5.Capital Med Univ, Beijing Anding Hosp, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Rui,Yu, Jing,Zhang, Shouzi,et al. Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease[J]. PLOS ONE,2013,8(12). |
| APA | Li, Rui.,Yu, Jing.,Zhang, Shouzi.,Bao, Feng.,Wang, Pengyun.,...&Li, Juan.(2013).Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease.PLOS ONE,8(12). |
| MLA | Li, Rui,et al."Bayesian Network Analysis Reveals Alterations to Default Mode Network Connectivity in Individuals at Risk for Alzheimer's Disease".PLOS ONE 8.12(2013). |
入库方式： OAI收割
来源：心理研究所
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