Statistical comparison of (brain) networks

TL;DR

This paper develops nonparametric statistical methods for comparing brain networks, including an ANOVA test and a subnetwork identification procedure, demonstrated on fMRI data from the Human Connectome Project.

Contribution

It introduces new statistical tools specifically designed for the comparison and analysis of brain networks, addressing a gap in existing neuroscientific methods.

Findings

The ANOVA test effectively detects differences between groups.

The subnetwork identification procedure successfully isolates distinct network components.

Application to fMRI data illustrates practical utility and potential biases.

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.