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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doaj-db62bac54d66497183979919416167d52020-11-25T00:59:42ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962014-05-01810.3389/fninf.2014.0004675614Reproducibility of graph metrics of human brain structural networksJeffrey Thomas Duda0Phil eCook1James eGee2University of PennsylvaniaUniversity of PennsylvaniaUniversity of PennsylvaniaRecent 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. <br/>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. These 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<br/>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.http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00046/fullBrainconnectivitynetworkStructuretractographygraph |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jeffrey Thomas Duda Phil eCook James eGee |
spellingShingle |
Jeffrey Thomas Duda Phil eCook James eGee Reproducibility of graph metrics of human brain structural networks Frontiers in Neuroinformatics Brain connectivity network Structure tractography graph |
author_facet |
Jeffrey Thomas Duda Phil eCook James eGee |
author_sort |
Jeffrey Thomas Duda |
title |
Reproducibility of graph metrics of human brain structural networks |
title_short |
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_sort |
reproducibility of graph metrics of human brain structural networks |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroinformatics |
issn |
1662-5196 |
publishDate |
2014-05-01 |
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. <br/>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. These 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<br/>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. |
topic |
Brain connectivity network Structure tractography graph |
url |
http://journal.frontiersin.org/Journal/10.3389/fninf.2014.00046/full |
work_keys_str_mv |
AT jeffreythomasduda reproducibilityofgraphmetricsofhumanbrainstructuralnetworks AT philecook reproducibilityofgraphmetricsofhumanbrainstructuralnetworks AT jamesegee reproducibilityofgraphmetricsofhumanbrainstructuralnetworks |
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1725216628605452288 |