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Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder

The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially l...

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Autores principales: Xie, Qingsong, Zhang, Xiangfei, Rekik, Islem, Chen, Xiaobo, Mao, Ning, Shen, Dinggang, Zhao, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269664/
https://www.ncbi.nlm.nih.gov/pubmed/34268010
http://dx.doi.org/10.7717/peerj.11692
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author Xie, Qingsong
Zhang, Xiangfei
Rekik, Islem
Chen, Xiaobo
Mao, Ning
Shen, Dinggang
Zhao, Feng
author_facet Xie, Qingsong
Zhang, Xiangfei
Rekik, Islem
Chen, Xiaobo
Mao, Ning
Shen, Dinggang
Zhao, Feng
author_sort Xie, Qingsong
collection PubMed
description The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.
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spelling pubmed-82696642021-07-14 Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder Xie, Qingsong Zhang, Xiangfei Rekik, Islem Chen, Xiaobo Mao, Ning Shen, Dinggang Zhao, Feng PeerJ Bioinformatics The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%. PeerJ Inc. 2021-07-06 /pmc/articles/PMC8269664/ /pubmed/34268010 http://dx.doi.org/10.7717/peerj.11692 Text en © 2021 Xie et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.
spellingShingle Bioinformatics
Xie, Qingsong
Zhang, Xiangfei
Rekik, Islem
Chen, Xiaobo
Mao, Ning
Shen, Dinggang
Zhao, Feng
Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title_full Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title_fullStr Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title_full_unstemmed Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title_short Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
title_sort constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder
topic Bioinformatics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8269664/
https://www.ncbi.nlm.nih.gov/pubmed/34268010
http://dx.doi.org/10.7717/peerj.11692
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