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arXiv · 1702.00768

Scaling Properties of Human Brain Functional Networks

Riccardo Zucca·Xerxes D. Arsiwalla·Hoang Le·Mikail Rubinov·Paul Verschure
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Abstract

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.

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Riccardo Zucca, Xerxes D. Arsiwalla, Hoang Le, Mikail Rubinov, Paul Verschure. 2017-02-02. Scaling Properties of Human Brain Functional Networks. https://doi.org/10.1007/978-3-319-44778-0_13

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