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Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment
Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC esti...
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/PMC5808180/ https://www.ncbi.nlm.nih.gov/pubmed/29467643 http://dx.doi.org/10.3389/fninf.2018.00003 |
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author | Zhou, Yueying Qiao, Lishan Li, Weikai Zhang, Limei Shen, Dinggang |
author_facet | Zhou, Yueying Qiao, Lishan Li, Weikai Zhang, Limei Shen, Dinggang |
author_sort | Zhou, Yueying |
collection | PubMed |
description | Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method. |
format | Online Article Text |
id | pubmed-5808180 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-58081802018-02-21 Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment Zhou, Yueying Qiao, Lishan Li, Weikai Zhang, Limei Shen, Dinggang Front Neuroinform Neuroscience Functional connectivity (FC) network has been becoming an increasingly useful tool for understanding the cerebral working mechanism and mining sensitive biomarkers for neural/mental disease diagnosis. Currently, Pearson's Correlation (PC) is the simplest and most commonly used scheme in FC estimation. Despite its empirical effectiveness, PC only encodes the low-order (i.e., second-order) statistics by calculating the pairwise correlations between network nodes (brain regions), which fails to capture the high-order information involved in FC (e.g., the correlations among different edges in a network). To address this issue, we propose a novel FC estimation method based on Matrix Variate Normal Distribution (MVND), which can capture both low- and high-order correlations simultaneously with a clear mathematical interpretability. Specifically, we first generate a set of BOLD subseries by the sliding window scheme, and for each subseries we construct a temporal FC network by PC. Then, we employ the constructed FC networks as samples to estimate the final low- and high-order FC networks by maximizing the likelihood of MVND. To illustrate the effectiveness of the proposed method, we conduct experiments to identify subjects with Mild Cognitive Impairment (MCI) from Normal Controls (NCs). Experimental results show that the fusion of low- and high-order FCs can generally help to improve the final classification performance, even though the high-order FC may contain less discriminative information than its low-order counterpart. Importantly, the proposed method for simultaneous estimation of low- and high-order FCs can achieve better classification performance than the two baseline methods, i.e., the original PC method and a recent high-order FC estimation method. Frontiers Media S.A. 2018-02-06 /pmc/articles/PMC5808180/ /pubmed/29467643 http://dx.doi.org/10.3389/fninf.2018.00003 Text en Copyright © 2018 Zhou, Qiao, Li, Zhang 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 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 Qiao, Lishan Li, Weikai Zhang, Limei Shen, Dinggang Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title | Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title_full | Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title_fullStr | Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title_full_unstemmed | Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title_short | Simultaneous Estimation of Low- and High-Order Functional Connectivity for Identifying Mild Cognitive Impairment |
title_sort | simultaneous estimation of low- and high-order functional connectivity for identifying mild cognitive impairment |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5808180/ https://www.ncbi.nlm.nih.gov/pubmed/29467643 http://dx.doi.org/10.3389/fninf.2018.00003 |
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