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Reproducibility and robustness of graph measures of the associative-semantic network.

Wang Y, Nelissen N, Adamczuk K, De Weer AS, Vandenbulcke M, Sunaert S, Vandenberghe R, Dupont P - PLoS ONE (2014)

Bottom Line: The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network.Local graph measures are very variable in terms of reproducibility and should be interpreted with care.For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.

ABSTRACT
Graph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks derived from a task fMRI experiment during explicit associative-semantic processing of words and pictures. The nodes of the network were defined using an independent study and the connectivity was based on the partial correlation of the time series between any pair of nodes. The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network. Furthermore, group based binary networks should be derived from groups of sufficient size and the lower the density the more subjects are required to obtain robust values. Local graph measures are very variable in terms of reproducibility and should be interpreted with care. For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information.

No MeSH data available.


Test-retest variability (%) between independent groups for binary (over a range of densities) and weighted (w) networks.: global efficiency; : the characteristic path length; : the mean betweenness centrality; : the mean local efficiency; : the mean clustering coefficient.
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pone-0115215-g004: Test-retest variability (%) between independent groups for binary (over a range of densities) and weighted (w) networks.: global efficiency; : the characteristic path length; : the mean betweenness centrality; : the mean local efficiency; : the mean clustering coefficient.

Mentions: Test-retest variability for the different global graph measures are shown for the split-half case (Fig. 3) and the between-independent groups case (Fig. 4) for binary and weighted networks.


Reproducibility and robustness of graph measures of the associative-semantic network.

Wang Y, Nelissen N, Adamczuk K, De Weer AS, Vandenbulcke M, Sunaert S, Vandenberghe R, Dupont P - PLoS ONE (2014)

Test-retest variability (%) between independent groups for binary (over a range of densities) and weighted (w) networks.: global efficiency; : the characteristic path length; : the mean betweenness centrality; : the mean local efficiency; : the mean clustering coefficient.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4264875&req=5

pone-0115215-g004: Test-retest variability (%) between independent groups for binary (over a range of densities) and weighted (w) networks.: global efficiency; : the characteristic path length; : the mean betweenness centrality; : the mean local efficiency; : the mean clustering coefficient.
Mentions: Test-retest variability for the different global graph measures are shown for the split-half case (Fig. 3) and the between-independent groups case (Fig. 4) for binary and weighted networks.

Bottom Line: The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network.Local graph measures are very variable in terms of reproducibility and should be interpreted with care.For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information.

View Article: PubMed Central - PubMed

Affiliation: Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.

ABSTRACT
Graph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks derived from a task fMRI experiment during explicit associative-semantic processing of words and pictures. The nodes of the network were defined using an independent study and the connectivity was based on the partial correlation of the time series between any pair of nodes. The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network. Furthermore, group based binary networks should be derived from groups of sufficient size and the lower the density the more subjects are required to obtain robust values. Local graph measures are very variable in terms of reproducibility and should be interpreted with care. For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information.

No MeSH data available.