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Network Analysis of Time Series: Novel Approaches to Network Neuroscience
In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial,...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874015/ https://www.ncbi.nlm.nih.gov/pubmed/35221887 http://dx.doi.org/10.3389/fnins.2021.787068 |
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author | Varley, Thomas F. Sporns, Olaf |
author_facet | Varley, Thomas F. Sporns, Olaf |
author_sort | Varley, Thomas F. |
collection | PubMed |
description | In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches. |
format | Online Article Text |
id | pubmed-8874015 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-88740152022-02-26 Network Analysis of Time Series: Novel Approaches to Network Neuroscience Varley, Thomas F. Sporns, Olaf Front Neurosci Neuroscience In the last two decades, there has been an explosion of interest in modeling the brain as a network, where nodes correspond variously to brain regions or neurons, and edges correspond to structural or statistical dependencies between them. This kind of network construction, which preserves spatial, or structural, information while collapsing across time, has become broadly known as “network neuroscience.” In this work, we provide an alternative application of network science to neural data: network-based analysis of non-linear time series and review applications of these methods to neural data. Instead of preserving spatial information and collapsing across time, network analysis of time series does the reverse: it collapses spatial information, instead preserving temporally extended dynamics, typically corresponding to evolution through some kind of phase/state-space. This allows researchers to infer a, possibly low-dimensional, “intrinsic manifold” from empirical brain data. We will discuss three methods of constructing networks from nonlinear time series, and how to interpret them in the context of neural data: recurrence networks, visibility networks, and ordinal partition networks. By capturing typically continuous, non-linear dynamics in the form of discrete networks, we show how techniques from network science, non-linear dynamics, and information theory can extract meaningful information distinct from what is normally accessible in standard network neuroscience approaches. Frontiers Media S.A. 2022-02-11 /pmc/articles/PMC8874015/ /pubmed/35221887 http://dx.doi.org/10.3389/fnins.2021.787068 Text en Copyright © 2022 Varley and Sporns. https://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 Varley, Thomas F. Sporns, Olaf Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title | Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title_full | Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title_fullStr | Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title_full_unstemmed | Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title_short | Network Analysis of Time Series: Novel Approaches to Network Neuroscience |
title_sort | network analysis of time series: novel approaches to network neuroscience |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8874015/ https://www.ncbi.nlm.nih.gov/pubmed/35221887 http://dx.doi.org/10.3389/fnins.2021.787068 |
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