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Translational Neuroscience

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Towards dynamical network biomarkers in neuromodulation of episodic migraine

1Department of Physics, AG NLD Cardiovascular Physics, Humboldt-Universität zu Berlin, Robert-Koch-Platz 4, 10115, Berlin, Germany

2Center for Experimental Medicine, Department of Systems Neuroscience, Universitätsklinikum Hamburg-Eppendorf, 20246, Hamburg, Germany

3FIRST, Aihara Innovative Mathematical Modelling Project, Japan Science and Technology Agency, Tokyo, Japan

4Collaborative Research Center for Innovative Mathematical Modelling, Institute of Industrial Science, University of Tokyo, Tokyo, 153-8505, Japan

5Potsdam Institute for Climate Impact Research, 14473, Potsdam, Germany

6Institute for Complex Systems and Mathematical Biology, University of Aberdeen, Aberdeen, AB24 3UE, UK

© 2013 Versita Warsaw. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. (CC BY-NC-ND 3.0)

Citation Information: Translational Neuroscience. Volume 4, Issue 3, Pages 282–294, ISSN (Online) 2081-6936, ISSN (Print) 2081-3856, DOI: 10.2478/s13380-013-0127-0, September 2013

Publication History

Published Online:


Computational methods have complemented experimental and clinical neurosciences and led to improvements in our understanding of the nervous systems in health and disease. In parallel, neuromodulation in form of electric and magnetic stimulation is gaining increasing acceptance in chronic and intractable diseases. In this paper, we firstly explore the relevant state of the art in fusion of both developments towards translational computational neuroscience. Then, we propose a strategy to employ the new theoretical concept of dynamical network biomarkers (DNB) in episodic manifestations of chronic disorders. In particular, as a first example, we introduce the use of computational models in migraine and illustrate on the basis of this example the potential of DNB as early-warning signals for neuromodulation in episodic migraine.

Keywords: Migraine; Neuromodulation; Nonlinear dynamics; Biomarkers; Spreading depression; Hodgkin-Huxley models; Central pattern generator; Pain

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