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Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification
High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FC...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305547/ https://www.ncbi.nlm.nih.gov/pubmed/30618582 http://dx.doi.org/10.3389/fnins.2018.00959 |
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author | Zhou, Yueying Zhang, Limei Teng, Shenghua Qiao, Lishan Shen, Dinggang |
author_facet | Zhou, Yueying Zhang, Limei Teng, Shenghua Qiao, Lishan Shen, Dinggang |
author_sort | Zhou, Yueying |
collection | PubMed |
description | High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance. |
format | Online Article Text |
id | pubmed-6305547 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63055472019-01-07 Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification Zhou, Yueying Zhang, Limei Teng, Shenghua Qiao, Lishan Shen, Dinggang Front Neurosci Neuroscience High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance. Frontiers Media S.A. 2018-12-18 /pmc/articles/PMC6305547/ /pubmed/30618582 http://dx.doi.org/10.3389/fnins.2018.00959 Text en Copyright © 2018 Zhou, Zhang, Teng, Qiao and Shen. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Zhou, Yueying Zhang, Limei Teng, Shenghua Qiao, Lishan Shen, Dinggang Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title_full | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title_fullStr | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title_full_unstemmed | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title_short | Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification |
title_sort | improving sparsity and modularity of high-order functional connectivity networks for mci and asd identification |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6305547/ https://www.ncbi.nlm.nih.gov/pubmed/30618582 http://dx.doi.org/10.3389/fnins.2018.00959 |
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