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  Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease

Zhang, S., Zhao, H., Wang, W., Wang, Z., Luo, X., Hramov, A., Kurths, J. (2023): Edge-centric effective connection network based on muti-modal MRI for the diagnosis of Alzheimer’s disease. - Neurocomputing, 552, 126512.
https://doi.org/10.1016/j.neucom.2023.126512

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 Creators:
Zhang, Shunqi1, Author
Zhao, Haiyan1, Author
Wang, Weiping1, Author
Wang, Zhen1, Author
Luo, Xiong1, Author
Hramov, Alexander1, Author
Kurths, Jürgen2, Author              
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Alzheimer’s disease (AD) is an irreversible neurodegenerative disease. But if AD is detected early, it can greatly reduce the severity of the disease. Functional connection networks (FCNs) can be used for the early diagnosis of AD, but they are undirected graphs and lack the description of causal information. Moreover, most of FCNs take brain regions as nodes, and few studies have been carried out focusing on the connections of the brain network. Although effective connection networks (ECNs) are digraphs, they do not reflect the causal relationships between brain connections. Therefore, we innovatively propose an edge-centric ECN (EECN) to explore the causality of the co-fluctuating connection in brain networks. Firstly, the traditional conditional Granger causality (GC) method is improved for constructing ECNs based on the suppression relationship between structural connection network (SCN) and FCN. Then based on the improved GC method, edge time series and EECNs are constructed. Finally, we perform dichotomous tasks on four stages of AD to verify the accuracy of our proposed method. The results show that this method achieves good results in six classification tasks. Finally, we present some brain connections that may be essential for early AD classification tasks. This study may have a positive impact on the application of brain networks.

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Language(s): eng - English
 Dates: 2023-07-132023-10-01
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neucom.2023.126512
MDB-ID: No data to archive
PIKDOMAIN: RD4 - Complexity Science
Organisational keyword: RD4 - Complexity Science
Research topic keyword: Complex Networks
Research topic keyword: Health
Research topic keyword: Nonlinear Dynamics
Model / method: Nonlinear Data Analysis
 Degree: -

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Title: Neurocomputing
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 552 Sequence Number: 126512 Start / End Page: - Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/neurocomputing
Publisher: Elsevier