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The problem of thresholding in small-world network analysis

Langer, Nicolas and Pedroni, Andreas and Jäncke, Lutz. (2013) The problem of thresholding in small-world network analysis. PLoS ONE, Vol. 8, H. 1 , e53199.

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Official URL: http://edoc.unibas.ch/dok/A6135377

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Abstract

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.
Faculties and Departments:07 Faculty of Psychology > Departement Psychologie > Ehemalige Einheiten Psychologie > Social and Affective Neuroscience (Knoch)
UniBasel Contributors:Pedroni, Andreas
Item Type:Article, refereed
Article Subtype:Research Article
Bibsysno:Link to catalogue
Publisher:Public Library of Science
e-ISSN:1932-6203
Note:Publication type according to Uni Basel Research Database: Journal article
Identification Number:
Last Modified:31 Aug 2018 06:39
Deposited On:19 Jul 2013 07:35

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