Langer, Nicolas and Pedroni, Andreas and Jäncke, Lutz. (2013) The problem of thresholding in smallworld 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 smallworld characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous smallworld connectivity analysis of resting EEGdata we explored implications of a commonly used analysis approach. This common course of analysis is to compare smallworld characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of intersubject 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 smallworld 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 nonindependency 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 
eISSN:  19326203 
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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