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Using Pareto optimality to explore the topology and dynamics of the human connectome
Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performanc...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150305/ https://www.ncbi.nlm.nih.gov/pubmed/25180308 http://dx.doi.org/10.1098/rstb.2013.0530 |
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author | Avena-Koenigsberger, Andrea Goñi, Joaquín Betzel, Richard F. van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Thiran, Jean-Philippe Sporns, Olaf |
author_facet | Avena-Koenigsberger, Andrea Goñi, Joaquín Betzel, Richard F. van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Thiran, Jean-Philippe Sporns, Olaf |
author_sort | Avena-Koenigsberger, Andrea |
collection | PubMed |
description | Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains. |
format | Online Article Text |
id | pubmed-4150305 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
spelling | pubmed-41503052014-10-05 Using Pareto optimality to explore the topology and dynamics of the human connectome Avena-Koenigsberger, Andrea Goñi, Joaquín Betzel, Richard F. van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Thiran, Jean-Philippe Sporns, Olaf Philos Trans R Soc Lond B Biol Sci Articles Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains. The Royal Society 2014-10-05 /pmc/articles/PMC4150305/ /pubmed/25180308 http://dx.doi.org/10.1098/rstb.2013.0530 Text en http://creativecommons.org/licenses/by/4.0/ © 2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
spellingShingle | Articles Avena-Koenigsberger, Andrea Goñi, Joaquín Betzel, Richard F. van den Heuvel, Martijn P. Griffa, Alessandra Hagmann, Patric Thiran, Jean-Philippe Sporns, Olaf Using Pareto optimality to explore the topology and dynamics of the human connectome |
title | Using Pareto optimality to explore the topology and dynamics of the human connectome |
title_full | Using Pareto optimality to explore the topology and dynamics of the human connectome |
title_fullStr | Using Pareto optimality to explore the topology and dynamics of the human connectome |
title_full_unstemmed | Using Pareto optimality to explore the topology and dynamics of the human connectome |
title_short | Using Pareto optimality to explore the topology and dynamics of the human connectome |
title_sort | using pareto optimality to explore the topology and dynamics of the human connectome |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4150305/ https://www.ncbi.nlm.nih.gov/pubmed/25180308 http://dx.doi.org/10.1098/rstb.2013.0530 |
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