Evidence map›Paper›PMID 29719719›Full record

ArticleTranslational neurodegeneration2018

Divergent topological networks in Alzheimer's disease: a diffusion kurtosis imaging analysis.

Jia-Xing Cheng, Hong-Ying Zhang, Zheng-Kun Peng, Yao Xu, Hui Tang, Jing-Tao Wu, Jun Xu

Abstract read
In one paragraph

Article in Translational neurodegeneration, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

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  4. Microstructural brain abnormalities and associated neurocognitive dysfunction in obstructive sleep apnea: a pilot study with diffusion kurtosis imaging.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jia-Xing ChengDepartment of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Hong-Ying ZhangDepartment of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Zheng-Kun PengDepartment of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Yao XuDepartment of Neurology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Hui TangMedical Experimental Center, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Jing-Tao WuDepartment of Radiology, Northern Jiangsu People's Hospital, Yangzhou University, Yangzhou, 225001 China.
Jun Xu4Department of Neurology, Beijing TianTan Hospital, Capital Medical University, Beijing, 100050 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBrain 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).

methodsMagnetic 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.

resultsWe 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.

conclusionsOur results suggest that DKI might be superior to DTI and could serve as a novel approach to understand the pathogenic mechanisms in neurodegenerative diseases.

Indexed as

Alzheimer’s diseaseBrain networksDiffusion kurtosis imagingSmall world

Identifiers

PMID29719719
PMCPMC5921324

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.