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Resolving Structural Variability in Network Models and the Brain

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured...

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Detalles Bibliográficos
Autores principales: Klimm, Florian, Bassett, Danielle S., Carlson, Jean M., Mucha, Peter J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3967917/
https://www.ncbi.nlm.nih.gov/pubmed/24675546
http://dx.doi.org/10.1371/journal.pcbi.1003491
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author Klimm, Florian
Bassett, Danielle S.
Carlson, Jean M.
Mucha, Peter J.
author_facet Klimm, Florian
Bassett, Danielle S.
Carlson, Jean M.
Mucha, Peter J.
author_sort Klimm, Florian
collection PubMed
description Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data.
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spelling pubmed-39679172014-04-01 Resolving Structural Variability in Network Models and the Brain Klimm, Florian Bassett, Danielle S. Carlson, Jean M. Mucha, Peter J. PLoS Comput Biol Research Article Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. Public Library of Science 2014-03-27 /pmc/articles/PMC3967917/ /pubmed/24675546 http://dx.doi.org/10.1371/journal.pcbi.1003491 Text en © 2014 Klimm et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Klimm, Florian
Bassett, Danielle S.
Carlson, Jean M.
Mucha, Peter J.
Resolving Structural Variability in Network Models and the Brain
title Resolving Structural Variability in Network Models and the Brain
title_full Resolving Structural Variability in Network Models and the Brain
title_fullStr Resolving Structural Variability in Network Models and the Brain
title_full_unstemmed Resolving Structural Variability in Network Models and the Brain
title_short Resolving Structural Variability in Network Models and the Brain
title_sort resolving structural variability in network models and the brain
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3967917/
https://www.ncbi.nlm.nih.gov/pubmed/24675546
http://dx.doi.org/10.1371/journal.pcbi.1003491
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