Literature DB >> 25114082

Characterizing topological patterns in amnestic mild cognitive impairment by quantitative water diffusivity.

Bing Zhang1, Xin Zhang1, Fang Zhang2, Ming Li1, Christopher G Schwarz3, Jiange Zhang2, Zhenyu Yin4, Lai Qian4, Hui Zhao4, Kun Wang1, Chuanshuai Tian1, Haiping Yu1, Weibo Chen5, Fangfei Lu1, Wenbo Wu1, Qing X Yang6, Yun Xu4, Bin Zhu1.   

Abstract

Mean diffusivity (MD) derived from diffusion tensor imaging has shown its ability to assess the microscopic structural integrity damage of gray matter in amnestic mild cognitive impairment (aMCI), a prodromal stage of Alzheimer's disease (AD). However, little is known about the small world topology networks constructed by cortical MD in cognitive disease. In this work, we measured the cortical MD in the entire brain in patients with aMCI (n = 30) and AD (n = 30) compared with cognitive-normal (CNs) controls (n = 30), and then constructed the cortical diffusivity network by using graph-theoretical analysis. Compared with CNs, patients with aMCI and AD showed abnormal small-world property of cortical diffusivity networks (higher degree of clustering and longer path length), reflecting a less optimal topological organization. Moreover, the mean degree of connections of network in aMCI patients was characterized by lower than CNs but higher than AD. In addition, 11 hub regions were identified by negative correlations between MD and the score of Montreal Cognitive Assessment after multiple regression analysis, including bilateral hippocampi and related limbic system. Among those hub regions, the connectivity of the right olfactory cortex and middle orbital gyrus to the rest of brain regions were disrupted earlier than the other 9 regions in aMCI when compared to CN. In conclusion, the change of cortical diffusivity in topological network organization, mean degree of connections, and disrupted hub regions in aMCI may serve to identify patients in the prodromal stage of AD and reflect microstructural deterioration of neurodegeneration.

Entities:  

Keywords:  Alzheimer's disease; amnestic mild cognitive impairment; mean diffusivity; small world networks

Mesh:

Year:  2015        PMID: 25114082     DOI: 10.3233/JAD-140882

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  6 in total

1.  Abnormal organization of white matter networks in patients with subjective cognitive decline and mild cognitive impairment.

Authors:  Xiao-Ni Wang; Yang Zeng; Guan-Qun Chen; Yi-He Zhang; Xuan-Yu Li; Xu-Yang Hao; Yang Yu; Meng Zhang; Can Sheng; Yu-Xia Li; Yu Sun; Hong-Yan Li; Yang Song; Kun-Cheng Li; Tian-Yi Yan; Xiao-Ying Tang; Ying Han
Journal:  Oncotarget       Date:  2016-08-02

2.  The Abnormality of Topological Asymmetry between Hemispheric Brain White Matter Networks in Alzheimer's Disease and Mild Cognitive Impairment.

Authors:  Cheng Yang; Suyu Zhong; Xiaolong Zhou; Long Wei; Lijia Wang; Shengdong Nie
Journal:  Front Aging Neurosci       Date:  2017-08-07       Impact factor: 5.750

3.  MEG Beamformer-Based Reconstructions of Functional Networks in Mild Cognitive Impairment.

Authors:  Maria E López; Marjolein M A Engels; Elisabeth C W van Straaten; Ricardo Bajo; María L Delgado; Philip Scheltens; Arjan Hillebrand; Cornelis J Stam; Fernando Maestú
Journal:  Front Aging Neurosci       Date:  2017-04-25       Impact factor: 5.750

4.  Weighted Random Support Vector Machine Clusters Analysis of Resting-State fMRI in Mild Cognitive Impairment.

Authors:  Xia-An Bi; Qian Xu; Xianhao Luo; Qi Sun; Zhigang Wang
Journal:  Front Psychiatry       Date:  2018-07-25       Impact factor: 4.157

5.  Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly-available nodes approach.

Authors:  Xiaopan Zhang; Junhong Liu; Yuan Chen; Yanan Jin; Jingliang Cheng
Journal:  Brain Behav       Date:  2021-01-03       Impact factor: 2.708

6.  Effective differentiation of mild cognitive impairment by functional brain graph analysis and computerized testing.

Authors:  Rok Požar; Bruno Giordani; Voyko Kavcic
Journal:  PLoS One       Date:  2020-03-16       Impact factor: 3.752

  6 in total

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