Assessing statistical significance when partitioning large-scale brain networks

Multivariate analysis of structural and functional brain imaging data can be used to produce network models of interaction or similarity between different brain structures. Graph partitioning methods can then be used to identify distinct subnetworks that may provide insight into the organization of the human brain. Although several efficient partitioning algorithms have been proposed, and their properties studied thoroughly, there has been limited work addressing the statistical significance of the resulting partitions. We present a new method to estimate the statistical significance of a network structure based on modularity. We derive a numerical approximation of the distribution of modularity on random graphs, and use this distribution to calculate a threshold that controls the type I error rate in partitioning graphs. We demonstrate the technique in application to brain subnetworks identified from diffusion-based fiber tracking data and from resting state fMRI data.