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

Margaret Y. Mahan; Arthur C. Leuthold; Apostolos P. Georgopoulos

doi:10.1007/s00221-013-3733-8

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

Margaret Y. Mahan, Arthur C. Leuthold, Apostolos P. Georgopoulos

Neuroscience

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

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 language	English (US)
Pages (from-to)	225-236
Number of pages	12
Journal	Experimental Brain Research
Volume	232
Issue number	1
DOIs	https://doi.org/10.1007/s00221-013-3733-8
State	Published - 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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10.1007/s00221-013-3733-8

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

Abstract

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