Understanding migraine using dynamical network biomarkers
Abstract
Background: Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understand complex interactions of network phenomena, in general, and interactions within the migraine generator network, in particular. Purpose: In this study, two recent modeling studies on migraine are set in the context of premonitory symptoms that are easy to confuse for trigger factors. This causality confusion is explained, if migraine attacks are initiated by a transition caused by a tipping point. Conclusion: We need to characterize the involved neuronal and autonomic subnetworks and their connections during all parts of the migraine cycle if we are ever to understand migraine. We predict that mathematical models have the potential to dismantle large and correlated fluctuations in such subnetworks as a dynamical network biomarker of migraine.
Keywords
Cite
@article{arxiv.1404.6126,
title = {Understanding migraine using dynamical network biomarkers},
author = {Markus A. Dahlem and Jürgen Kurths and Michel D. Ferrari and Kazuyuki Aihara and Marten Scheffer and Arne May},
journal= {arXiv preprint arXiv:1404.6126},
year = {2014}
}
Comments
4 pages, 1 figure