Scaling Properties of Human Brain Functional Networks
This addresses a methodological debate in neuroscience about network scaling properties, with implications for understanding hub structures in brain networks, but it is incremental as it builds on prior statistical methods and data.
The study tackled the problem of determining whether human brain functional networks follow a power-law distribution by analyzing high-resolution data from the Human Connectome Project, and found that the degree distributions generally do not support a power-law but instead tend towards the thin-tail limit of the generalized Pareto model.
We investigate scaling properties of human brain functional networks in the resting-state. Analyzing network degree distributions, we statistically test whether their tails scale as power-law or not. Initial studies, based on least-squares fitting, were shown to be inadequate for precise estimation of power-law distributions. Subsequently, methods based on maximum-likelihood estimators have been proposed and applied to address this question. Nevertheless, no clear consensus has emerged, mainly because results have shown substantial variability depending on the data-set used or its resolution. In this study, we work with high-resolution data (10K nodes) from the Human Connectome Project and take into account network weights. We test for the power-law, exponential, log-normal and generalized Pareto distributions. Our results show that the statistics generally do not support a power-law, but instead these degree distributions tend towards the thin-tail limit of the generalized Pareto model. This may have implications for the number of hubs in human brain functional networks.