Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis
Abstract Background Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We...
Main Authors: | , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2018-04-01
|
Series: | Translational Neurodegeneration |
Subjects: | |
Online Access: | http://link.springer.com/article/10.1186/s40035-018-0115-y |
id |
doaj-35640d570fb24eff93c80e7fe549faf4 |
---|---|
record_format |
Article |
spelling |
doaj-35640d570fb24eff93c80e7fe549faf42020-11-24T22:16:19ZengBMCTranslational Neurodegeneration2047-91582018-04-017111210.1186/s40035-018-0115-yDivergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysisJia-Xing Cheng0Hong-Ying Zhang1Zheng-Kun Peng2Yao Xu3Hui Tang4Jing-Tao Wu5Jun Xu6Department of Neurology, Northern Jiangsu People’s Hospital, Yangzhou UniversityDepartment of Radiology, Northern Jiangsu People’s Hospital, Yangzhou UniversityDepartment of Radiology, Northern Jiangsu People’s Hospital, Yangzhou UniversityDepartment of Neurology, Northern Jiangsu People’s Hospital, Yangzhou UniversityMedical Experimental Center, Northern Jiangsu People’s Hospital, Yangzhou UniversityDepartment of Radiology, Northern Jiangsu People’s Hospital, Yangzhou UniversityDepartment of Neurology, Beijing TianTan Hospital, Capital Medical UniversityAbstract Background Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD). Methods Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches. Results We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks. Conclusions Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.http://link.springer.com/article/10.1186/s40035-018-0115-ySmall worldAlzheimer’s diseaseDiffusion kurtosis imagingBrain networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jia-Xing Cheng Hong-Ying Zhang Zheng-Kun Peng Yao Xu Hui Tang Jing-Tao Wu Jun Xu |
spellingShingle |
Jia-Xing Cheng Hong-Ying Zhang Zheng-Kun Peng Yao Xu Hui Tang Jing-Tao Wu Jun Xu Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis Translational Neurodegeneration Small world Alzheimer’s disease Diffusion kurtosis imaging Brain networks |
author_facet |
Jia-Xing Cheng Hong-Ying Zhang Zheng-Kun Peng Yao Xu Hui Tang Jing-Tao Wu Jun Xu |
author_sort |
Jia-Xing Cheng |
title |
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis |
title_short |
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis |
title_full |
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis |
title_fullStr |
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis |
title_full_unstemmed |
Divergent topological networks in Alzheimer’s disease: a diffusion kurtosis imaging analysis |
title_sort |
divergent topological networks in alzheimer’s disease: a diffusion kurtosis imaging analysis |
publisher |
BMC |
series |
Translational Neurodegeneration |
issn |
2047-9158 |
publishDate |
2018-04-01 |
description |
Abstract Background Brain consists of plenty of complicated cytoarchitecture. Gaussian-model based diffusion tensor imaging (DTI) is far from satisfactory interpretation of the structural complexity. Diffusion kurtosis imaging (DKI) is a tool to determine brain non-Gaussian diffusion properties. We investigated the network properties of DKI parameters in the whole brain using graph theory and further detected the alterations of the DKI networks in Alzheimer’s disease (AD). Methods Magnetic resonance DKI scanning was performed on 21 AD patients and 19 controls. Brain networks were constructed by the correlation matrices of 90 regions and analyzed through graph theoretical approaches. Results We found small world characteristics of DKI networks not only in the normal subjects but also in the AD patients; Grey matter networks of AD patients tended to be a less optimized network. Moreover, the divergent small world network features were shown in the AD white matter networks, which demonstrated increased shortest paths and decreased global efficiency with fiber tractography but decreased shortest paths and increased global efficiency with other DKI metrics. In addition, AD patients showed reduced nodal centrality predominantly in the default mode network areas. Finally, the DKI networks were more closely associated with cognitive impairment than the DTI networks. Conclusions Our results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases. |
topic |
Small world Alzheimer’s disease Diffusion kurtosis imaging Brain networks |
url |
http://link.springer.com/article/10.1186/s40035-018-0115-y |
work_keys_str_mv |
AT jiaxingcheng divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT hongyingzhang divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT zhengkunpeng divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT yaoxu divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT huitang divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT jingtaowu divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis AT junxu divergenttopologicalnetworksinalzheimersdiseaseadiffusionkurtosisimaginganalysis |
_version_ |
1725790719045533696 |