Repeatability analysis of global and local metrics of brain structural networks

Andreotti, Jennifer; Jann, Kay; Melie-Garcìa, Lester; Giezendanner, Stéphanie; Dierks, Thomas; Federspiel, Andrea (2014). Repeatability analysis of global and local metrics of brain structural networks. Brain connectivity, 4(3), pp. 203-220. Mary Ann Liebert 10.1089/brain.2013.0202

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Text brain%2E2013%2E0202.pdf - Published Version Available under License Publisher holds Copyright. This is a copy of an article published in the Brain connectivity [©2014 copyright Mary Ann Liebert, Inc.]; Brain connectivity is available online at: http://online.liebertpub.com. Download (1MB) | Preview

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

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