Understanding migraine using dynamic network biomarkers

Markus A. Dahlem*, Juergen Kurths, Michel D. Ferrari, Kazuyuki Aihara, Marten Scheffer, Arne May

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)


Background Mathematical modeling approaches are becoming ever more established in clinical neuroscience. They provide insight that is key to understanding 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 dynamic network biomarker of migraine.

Original languageEnglish
Pages (from-to)627-630
Number of pages4
Issue number7
Early online date16 Sept 2014
Publication statusPublished - Jun 2015

Bibliographical note

Date of Acceptance: 03/08/2014

This work was supported by the Seventh Framework EUproject EuroHeadPain (#602633) to AM and MF and the FIRST program from JSPS, initiated by CSTP to KA


  • migraine
  • tipping point
  • premonitory symptoms
  • triggers
  • early-warning signals
  • critical transitions
  • complex diseases
  • attacks
  • aura


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