title: Departure from Network Equilibrium (DNE): an efficient and scalable measure of instantaneous network dynamics, with an application to magnetoencephalography.
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.
authors.affiliation: Brain Sciences Center (11B), Department of Veterans Affairs, Minneapolis Health Care System, One Veterans Drive, Minneapolis, MN, 55417, USA.
authors.forename: Margaret Y
authors.initials: MY
authors.lastname: Mahan
authors.forename: Arthur C
authors.initials: AC
authors.lastname: Leuthold
authors.forename: Apostolos P
authors.initials: AP
authors.lastname: Georgopoulos
mesh_terms: D017677:Age Distribution; D001921:Brain / Q000502:physiology*; D006801:Humans; D015225:Magnetoencephalography / Q000379:methods; D008954:Models, Biological; D009415:Nerve Net / Q000502:physiology*; D013647:Task Performance and Analysis