Understanding migraine using dynamic network biomarkers
Year: 2014
Type: article
Abstract: 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.
Source: Cephalalgia
Institutions: Humboldt-Universität zu Berlin, University of Aberdeen, Potsdam Institute for Climate Impact Research, Leiden University Medical Center, Institute of Industrial Science, The University of Tokyo, The University of Tokyo, Wageningen University & Research, University Medical Center Hamburg-Eppendorf
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W2067458807