Title: Reproducibility and robustness of graph measures of the associative-semantic network
Authors: Wang, Yu ×
Nelissen, Natalie
Adamczuk, Kate
De Weer, An-Sofie
Vandenbulcke, Mathieu
Sunaert, Stefan
Vandenberghe, Rik
Dupont, Patrick #
Issue Date: 2014
Publisher: Public Library of Sciene
Series Title: PLoS One vol:9 issue:12 pages:e115215
Article number: 10.1371/journal.pone.0115215
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.
ISSN: 1932-6203
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Laboratory for Cognitive Neurology
Research Group Psychiatry
Translational MRI (+)
× corresponding author
# (joint) last author

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