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Scaling Properties of Human Brain Functional Networks

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Artificial Neural Networks and Machine Learning – ICANN 2016 (ICANN 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9886))

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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.

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Acknowledgments

The work has been supported by the European Research Council under the EUs 7th Framework Programme (FP7/2007-2013)/ERC grant agreement no. 341196 to P. Verschure. Data were provided [in part] by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University.

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Authors and Affiliations

  1. Laboratory of Synthetic Perceptive, Emotive and Cognitive Systems (SPECS), N-RAS, DTIC, Universitat Pompeu Fabra (UPF), Barcelona, Spain

    Riccardo Zucca, Xerxes D. Arsiwalla & Paul F. M. J. Verschure

  2. California Institute of Technology, Pasadena, USA

    Hoang Le

  3. Department of Psychiatry, Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Mikail Rubinov

  4. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Mikail Rubinov

  5. Catalan Institute of Advanced Studies (ICREA), Barcelona, Spain

    Paul F. M. J. Verschure

Authors
  1. Riccardo Zucca
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  2. Xerxes D. Arsiwalla
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  3. Hoang Le
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  4. Mikail Rubinov
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  5. Paul F. M. J. Verschure
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Correspondence to Riccardo Zucca .

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Editors and Affiliations

  1. University of Lausanne, Lausanne, Switzerland

    Alessandro E.P. Villa

  2. University of Lausanne, Lausanne, Switzerland

    Paolo Masulli

  3. Universitat Politécnica de Catalunya, Terrrassa, Spain

    Antonio Javier Pons Rivero

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© 2016 Springer International Publishing Switzerland

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Zucca, R., Arsiwalla, X.D., Le, H., Rubinov, M., Verschure, P.F.M.J. (2016). Scaling Properties of Human Brain Functional Networks. In: Villa, A., Masulli, P., Pons Rivero, A. (eds) Artificial Neural Networks and Machine Learning – ICANN 2016. ICANN 2016. Lecture Notes in Computer Science(), vol 9886. Springer, Cham. https://doi.org/10.1007/978-3-319-44778-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-44778-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44777-3

  • Online ISBN: 978-3-319-44778-0

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