Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1783720634071646208 |
---|---|
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%. |
format | Online Article Text |
id | pubmed-8269664 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT xieqingsong constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT zhangxiangfei constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT rekikislem constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT chenxiaobo constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT maoning constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT shendinggang constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder AT zhaofeng constructinghighorderfunctionalconnectivitynetworkbasedoncentralmomentfeaturesfordiagnosisofautismspectrumdisorder |