Departure from Network Equilibrium (DNE): An efficient and scalable measure of instantaneous network dynamics, with an application to magnetoencephalography

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Abstract

The assessment of the dynamic status of a network is currently unavailable. It is important to know how far a network is away from its equilibrium (as an indicator of instability) at a moment, and over periods of time. Here, we introduce the Departure from Network Equilibrium (DNE), a new measure of instantaneous network dynamics. DNE is simple, fast to compute, and scalable with network size. We present the results of its application on white noise networks (as a basis) and on networks derived from magnetoencephalographic recordings from the human brain.

Original languageEnglish (US)
Pages (from-to)225-236
Number of pages12
JournalExperimental Brain Research
Volume232
Issue number1
DOIs
StatePublished - Jan 2014

Bibliographical note

Funding Information:
Acknowledgments This work was supported by the American Legion Brain Sciences Chair. This material is based upon work partially supported by the National Science Foundation under Grant No. 00006595. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation.

Keywords

  • Brain
  • Equilibrium
  • Magnetoencephalography
  • Networks
  • Scalability

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