The study of brain networks has developed extensively over the last couple of decades. By contrast, techniques for the statistical analysis of these networks are less developed. Here, 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. We identify, among other variables, that the amount of sleep the days before the scan is a relevant variable that must be controlled. Finally, we discuss the potential bias in neuroimaging findings that is generated by some behavioural and brain structure variables.
De Risto Ilmoniemi
De William Lytton
De Claudia Vargas
De Daniel Fraiman
De Oswaldo Baffa Filho
De Leonardo Cohen
De Yves Frégnac
De Maria Elisa Pimentel Piemonte , Rafael Bassi Stern
De Fabrizia Mealli
De Sylvain Le Corff
