  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.
