A Caltech Library Service

Scaling Properties of Human Brain Functional Networks

Zucca, Riccardo and Arsiwalla, Xerxes D. and Le, Hoang and Rubinov, Mikail and Verschure, Paul F. M. J. (2016) Scaling Properties of Human Brain Functional Networks. In: Artificial Neural Networks and Machine Learning. Lecture Notes in Computer Science. No.9886. Springer , Cham, pp. 107-114. ISBN 978-3-319-44777-3.

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item:


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 (10 K 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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription
Zucca, Riccardo0000-0003-4808-6010
Arsiwalla, Xerxes D.0000-0003-1485-1853
Additional Information:© 2016 Springer International Publishing Switzerland.
Subject Keywords:Power-law distributions – Functional connectivity – Generalized pareto – Model fitting – Maximum likelihood – Connectome – Brain networks
Series Name:Lecture Notes in Computer Science
Issue or Number:9886
Record Number:CaltechAUTHORS:20161222-075758856
Persistent URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73126
Deposited By: Tony Diaz
Deposited On:22 Dec 2016 17:16
Last Modified:09 Mar 2020 13:19

Repository Staff Only: item control page