Title: Statistical comparison of (brain) networks,
Abstract: The study of random networks in a neuroscientific context has developed
extensively over the last couple of decades. By contrast, techniques for the
statistical analysis of these networks are less developed. In this paper, we
focus on the statistical comparison of brain networks in a nonparametric
framework and discuss the associated detection and identification problems. We
tested network differences between groups with an analysis of variance (ANOVA)
test we developed specifically for networks. We also propose and analyse the
behaviour of a new statistical procedure designed to identify different
subnetworks. As an example, we show the application of this tool in
resting-state fMRI data obtained from the Human Connectome Project. Finally, we
discuss the potential bias in neuroimaging findings that is generated by some
behavioural and brain structure variables. Our method can also be applied to
other kind of networks such as protein interaction networks, gene networks or
social networks.