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Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods
An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357637/ https://www.ncbi.nlm.nih.gov/pubmed/28373838 http://dx.doi.org/10.3389/fncom.2017.00014 |
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author | Monti, Ricardo P. Lorenz, Romy Hellyer, Peter Leech, Robert Anagnostopoulos, Christoforos Montana, Giovanni |
author_facet | Monti, Ricardo P. Lorenz, Romy Hellyer, Peter Leech, Robert Anagnostopoulos, Christoforos Montana, Giovanni |
author_sort | Monti, Ricardo P. |
collection | PubMed |
description | An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project. |
format | Online Article Text |
id | pubmed-5357637 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-53576372017-04-03 Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods Monti, Ricardo P. Lorenz, Romy Hellyer, Peter Leech, Robert Anagnostopoulos, Christoforos Montana, Giovanni Front Comput Neurosci Neuroscience An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project. Frontiers Media S.A. 2017-03-20 /pmc/articles/PMC5357637/ /pubmed/28373838 http://dx.doi.org/10.3389/fncom.2017.00014 Text en Copyright © 2017 Monti, Lorenz, Hellyer, Leech, Anagnostopoulos and Montana. 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) or licensor 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 Monti, Ricardo P. Lorenz, Romy Hellyer, Peter Leech, Robert Anagnostopoulos, Christoforos Montana, Giovanni Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title_full | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title_fullStr | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title_full_unstemmed | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title_short | Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |
title_sort | decoding time-varying functional connectivity networks via linear graph embedding methods |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5357637/ https://www.ncbi.nlm.nih.gov/pubmed/28373838 http://dx.doi.org/10.3389/fncom.2017.00014 |
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