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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. http://resolver.caltech.edu/CaltechAUTHORS:20161222-075758856

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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 (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
http://dx.doi.org/10.1007/978-3-319-44778-0_13DOIArticle
http://link.springer.com/chapter/10.1007%2F978-3-319-44778-0_13PublisherArticle
Additional Information:© 2016 Springer International Publishing Switzerland.
Subject Keywords:Power-law distributions – Functional connectivity – Generalized pareto – Model fitting – Maximum likelihood – Connectome – Brain networks
Record Number:CaltechAUTHORS:20161222-075758856
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20161222-075758856
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:73126
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:22 Dec 2016 17:16
Last Modified:22 Dec 2016 17:16

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