Parametric distributions for assessing significance in modular partitions of brain networks

Abstract

Brain networks are often explored with graph theoretical approaches, and community structures identified using modularity-based partitions. Despite the popularity of these methods, the significance of the obtained subnetworks is largely unaddressed in the literature. We present two parametric methods to assess the statistical significance of network partitions, and therefore control against spurious subnetworks that may arise in random graphs, rather than self-organized brain networks. We evaluate these methods with simulated data and resting state fMRI data.

To cut or not to cut? Assessing the modular structure of brain networks

“…It has been shown that scaled and shifted Gamma distributions can well approximate the Tracy-Widom distribution, either by directly minimizing the difference between the two distributions (Vlok and Olivier (2012)), or by matching their first, second, and third moments (Wei and Tirkkonen (2011); Chiani (2012)). In (Chang et al (2013)) we showed that such an approach led to inferior numerical results than the Tracy-Widom distribution.…”