Name
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 powerlaw, 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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-44778-0_13 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2016, PT I |
Keywords | DocType | Volume |
Power-law distributions, Functional connectivity, Generalized pareto, Model fitting, Maximum likelihood, Connectome, Brain networks | Journal | 9886 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Riccardo Zucca | 1 | 70 | 11.03 |
| Xerxes D. Arsiwalla | 2 | 84 | 17.84 |
| Hoang Le | 3 | 0 | 0.34 |
| Mikail Rubinov | 4 | 1153 | 49.20 |
| Paul F. M. J. Verschure | 5 | 677 | 116.64 |