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Brain Network Dynamics Adhere to a Power Law

The temporal dynamics of complex networks such as the Internet are characterized by a power scaling between the temporal mean and dispersion of signals at each network node. Here we tested the hypothesis that the temporal dynamics of the brain networks are characterized by a similar power law. This...

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Autores principales: Tomasi, Dardo G., Shokri-Kojori, Ehsan, Volkow, Nora D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306388/
https://www.ncbi.nlm.nih.gov/pubmed/28261049
http://dx.doi.org/10.3389/fnins.2017.00072
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author Tomasi, Dardo G.
Shokri-Kojori, Ehsan
Volkow, Nora D.
author_facet Tomasi, Dardo G.
Shokri-Kojori, Ehsan
Volkow, Nora D.
author_sort Tomasi, Dardo G.
collection PubMed
description The temporal dynamics of complex networks such as the Internet are characterized by a power scaling between the temporal mean and dispersion of signals at each network node. Here we tested the hypothesis that the temporal dynamics of the brain networks are characterized by a similar power law. This realization could be useful to assess the effects of randomness and external modulators on the brain network dynamics. Simulated data using a well-stablished random diffusion model allowed us to predict that the temporal dispersion of the amplitude of low frequency fluctuations (ALFF) and that of the local functional connectivity density (lFCD) scale with their temporal means. We tested this hypothesis in open-access resting-state functional magnetic resonance imaging datasets from 66 healthy subjects. A robust power law emerged from the temporal dynamics of ALFF and lFCD metrics, which was insensitive to the methods used for the computation of the metrics. The scaling exponents (ALFF: 0.8 ± 0.1; lFCD: 1.1 ± 0.1; mean ± SD) decreased with age and varied significantly across brain regions; multimodal cortical areas exhibited lower scaling exponents, consistent with a stronger influence of external inputs, than limbic and subcortical regions, which exhibited higher scaling exponents, consistent with a stronger influence of internal randomness. Findings are consistent with the notion that external inputs govern neuronal communication in the brain and that their relative influence differs between brain regions. Further studies will assess the potential of this metric as biomarker to characterize neuropathology.
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spelling pubmed-53063882017-03-03 Brain Network Dynamics Adhere to a Power Law Tomasi, Dardo G. Shokri-Kojori, Ehsan Volkow, Nora D. Front Neurosci Neuroscience The temporal dynamics of complex networks such as the Internet are characterized by a power scaling between the temporal mean and dispersion of signals at each network node. Here we tested the hypothesis that the temporal dynamics of the brain networks are characterized by a similar power law. This realization could be useful to assess the effects of randomness and external modulators on the brain network dynamics. Simulated data using a well-stablished random diffusion model allowed us to predict that the temporal dispersion of the amplitude of low frequency fluctuations (ALFF) and that of the local functional connectivity density (lFCD) scale with their temporal means. We tested this hypothesis in open-access resting-state functional magnetic resonance imaging datasets from 66 healthy subjects. A robust power law emerged from the temporal dynamics of ALFF and lFCD metrics, which was insensitive to the methods used for the computation of the metrics. The scaling exponents (ALFF: 0.8 ± 0.1; lFCD: 1.1 ± 0.1; mean ± SD) decreased with age and varied significantly across brain regions; multimodal cortical areas exhibited lower scaling exponents, consistent with a stronger influence of external inputs, than limbic and subcortical regions, which exhibited higher scaling exponents, consistent with a stronger influence of internal randomness. Findings are consistent with the notion that external inputs govern neuronal communication in the brain and that their relative influence differs between brain regions. Further studies will assess the potential of this metric as biomarker to characterize neuropathology. Frontiers Media S.A. 2017-02-14 /pmc/articles/PMC5306388/ /pubmed/28261049 http://dx.doi.org/10.3389/fnins.2017.00072 Text en Copyright © 2017 Tomasi, Shokri-Kojori and Volkow. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Tomasi, Dardo G.
Shokri-Kojori, Ehsan
Volkow, Nora D.
Brain Network Dynamics Adhere to a Power Law
title Brain Network Dynamics Adhere to a Power Law
title_full Brain Network Dynamics Adhere to a Power Law
title_fullStr Brain Network Dynamics Adhere to a Power Law
title_full_unstemmed Brain Network Dynamics Adhere to a Power Law
title_short Brain Network Dynamics Adhere to a Power Law
title_sort brain network dynamics adhere to a power law
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5306388/
https://www.ncbi.nlm.nih.gov/pubmed/28261049
http://dx.doi.org/10.3389/fnins.2017.00072
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