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Reproducibility of graph metrics of human brain structural networks

Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient po...

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Autores principales: Duda, Jeffrey T., Cook, Philip A., Gee, James C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019854/
https://www.ncbi.nlm.nih.gov/pubmed/24847245
http://dx.doi.org/10.3389/fninf.2014.00046
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author Duda, Jeffrey T.
Cook, Philip A.
Gee, James C.
author_facet Duda, Jeffrey T.
Cook, Philip A.
Gee, James C.
author_sort Duda, Jeffrey T.
collection PubMed
description Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. The reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are used to examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm.
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spelling pubmed-40198542014-05-20 Reproducibility of graph metrics of human brain structural networks Duda, Jeffrey T. Cook, Philip A. Gee, James C. Front Neuroinform Neuroscience Recent interest in human brain connectivity has led to the application of graph theoretical analysis to human brain structural networks, in particular white matter connectivity inferred from diffusion imaging and fiber tractography. While these methods have been used to study a variety of patient populations, there has been less examination of the reproducibility of these methods. A number of tractography algorithms exist and many of these are known to be sensitive to user-selected parameters. The methods used to derive a connectivity matrix from fiber tractography output may also influence the resulting graph metrics. Here we examine how these algorithm and parameter choices influence the reproducibility of proposed graph metrics on a publicly available test-retest dataset consisting of 21 healthy adults. The dice coefficient is used to examine topological similarity of constant density subgraphs both within and between subjects. Seven graph metrics are examined here: mean clustering coefficient, characteristic path length, largest connected component size, assortativity, global efficiency, local efficiency, and rich club coefficient. The reproducibility of these network summary measures is examined using the intraclass correlation coefficient (ICC). Graph curves are created by treating the graph metrics as functions of a parameter such as graph density. Functional data analysis techniques are used to examine differences in graph measures that result from the choice of fiber tracking algorithm. The graph metrics consistently showed good levels of reproducibility as measured with ICC, with the exception of some instability at low graph density levels. The global and local efficiency measures were the most robust to the choice of fiber tracking algorithm. Frontiers Media S.A. 2014-05-07 /pmc/articles/PMC4019854/ /pubmed/24847245 http://dx.doi.org/10.3389/fninf.2014.00046 Text en Copyright © 2014 Duda, Cook and Gee. http://creativecommons.org/licenses/by/3.0/ 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
Duda, Jeffrey T.
Cook, Philip A.
Gee, James C.
Reproducibility of graph metrics of human brain structural networks
title Reproducibility of graph metrics of human brain structural networks
title_full Reproducibility of graph metrics of human brain structural networks
title_fullStr Reproducibility of graph metrics of human brain structural networks
title_full_unstemmed Reproducibility of graph metrics of human brain structural networks
title_short Reproducibility of graph metrics of human brain structural networks
title_sort reproducibility of graph metrics of human brain structural networks
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4019854/
https://www.ncbi.nlm.nih.gov/pubmed/24847245
http://dx.doi.org/10.3389/fninf.2014.00046
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