Cargando…
Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification
Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological at...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829545/ https://www.ncbi.nlm.nih.gov/pubmed/33505965 http://dx.doi.org/10.3389/fcell.2020.610569 |
_version_ | 1783641193445326848 |
---|---|
author | Jiao, Zhuqing Ji, Yixin Zhang, Jiahao Shi, Haifeng Wang, Chuang |
author_facet | Jiao, Zhuqing Ji, Yixin Zhang, Jiahao Shi, Haifeng Wang, Chuang |
author_sort | Jiao, Zhuqing |
collection | PubMed |
description | Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer’s disease (AD). |
format | Online Article Text |
id | pubmed-7829545 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78295452021-01-26 Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification Jiao, Zhuqing Ji, Yixin Zhang, Jiahao Shi, Haifeng Wang, Chuang Front Cell Dev Biol Cell and Developmental Biology Brain functional networks constructed via regularization has been widely used in early mild cognitive impairment (eMCI) classification. However, few methods can properly reflect the similarities and differences of functional connections among different people. Most methods ignore some topological attributes, such as connection strength, which may delete strong functional connections in brain functional networks. To overcome these limitations, we propose a novel method to construct dynamic functional networks (DFN) based on weighted regularization (WR) and tensor low-rank approximation (TLA), and apply it to identify eMCI subjects from normal subjects. First, we introduce the WR term into the DFN construction and obtain WR-based DFNs (WRDFN). Then, we combine the WRDFNs of all subjects into a third-order tensor for TLA processing, and obtain the DFN based on WR and TLA (WRTDFN) of each subject in the tensor. We calculate the weighted-graph local clustering coefficient of each region in each WRTDFN as the effective feature, and use the t-test for feature selection. Finally, we train a linear support vector machine (SVM) classifier to classify the WRTDFNs of all subjects. Experimental results demonstrate that the proposed method can obtain DFNs with the scale-free property, and that the classification accuracy (ACC), the sensitivity (SEN), the specificity (SPE), and the area under curve (AUC) reach 87.0662% ± 0.3202%, 83.4363% ± 0.5076%, 90.6961% ± 0.3250% and 0.9431 ± 0.0023, respectively. We also achieve the best classification results compared with other comparable methods. This work can effectively improve the classification performance of DFNs constructed by existing methods for eMCI and has certain reference value for the early diagnosis of Alzheimer’s disease (AD). Frontiers Media S.A. 2021-01-11 /pmc/articles/PMC7829545/ /pubmed/33505965 http://dx.doi.org/10.3389/fcell.2020.610569 Text en Copyright © 2021 Jiao, Ji, Zhang, Shi and Wang. 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 | Cell and Developmental Biology Jiao, Zhuqing Ji, Yixin Zhang, Jiahao Shi, Haifeng Wang, Chuang Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title | Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title_full | Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title_fullStr | Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title_full_unstemmed | Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title_short | Constructing Dynamic Functional Networks via Weighted Regularization and Tensor Low-Rank Approximation for Early Mild Cognitive Impairment Classification |
title_sort | constructing dynamic functional networks via weighted regularization and tensor low-rank approximation for early mild cognitive impairment classification |
topic | Cell and Developmental Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7829545/ https://www.ncbi.nlm.nih.gov/pubmed/33505965 http://dx.doi.org/10.3389/fcell.2020.610569 |
work_keys_str_mv | AT jiaozhuqing constructingdynamicfunctionalnetworksviaweightedregularizationandtensorlowrankapproximationforearlymildcognitiveimpairmentclassification AT jiyixin constructingdynamicfunctionalnetworksviaweightedregularizationandtensorlowrankapproximationforearlymildcognitiveimpairmentclassification AT zhangjiahao constructingdynamicfunctionalnetworksviaweightedregularizationandtensorlowrankapproximationforearlymildcognitiveimpairmentclassification AT shihaifeng constructingdynamicfunctionalnetworksviaweightedregularizationandtensorlowrankapproximationforearlymildcognitiveimpairmentclassification AT wangchuang constructingdynamicfunctionalnetworksviaweightedregularizationandtensorlowrankapproximationforearlymildcognitiveimpairmentclassification |