Understanding migraine using dynamic network biomarkers

Markus A Dahlem; Jürgen Kurths; Michel D Ferrari; Kazuyuki Aihara; Marten Scheffer; Arne May

doi:10.1177/0333102414550108

Understanding migraine using dynamic network biomarkers

Standard

Understanding migraine using dynamic network biomarkers. / Dahlem, Markus A; Kurths, Jürgen; Ferrari, Michel D; Aihara, Kazuyuki; Scheffer, Marten; May, Arne.

In: CEPHALALGIA, 16.09.2014.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Dahlem, MA, Kurths, J, Ferrari, MD, Aihara, K, Scheffer, M & May, A 2014, 'Understanding migraine using dynamic network biomarkers', CEPHALALGIA. https://doi.org/10.1177/0333102414550108

APA

Dahlem, M. A., Kurths, J., Ferrari, M. D., Aihara, K., Scheffer, M., & May, A. (2014). Understanding migraine using dynamic network biomarkers. CEPHALALGIA. https://doi.org/10.1177/0333102414550108

Vancouver

Dahlem MA, Kurths J, Ferrari MD, Aihara K, Scheffer M, May A. Understanding migraine using dynamic network biomarkers. CEPHALALGIA. 2014 Sep 16. https://doi.org/10.1177/0333102414550108

Bibtex

@article{dc86623cd5d24f258dc743f58ceb62b1,

title = "Understanding migraine using dynamic network biomarkers",

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.",

author = "Dahlem, {Markus A} and J{\"u}rgen Kurths and Ferrari, {Michel D} and Kazuyuki Aihara and Marten Scheffer and Arne May",

note = "{\textcopyright} International Headache Society 2014 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.",

year = "2014",

month = sep,

day = "16",

doi = "10.1177/0333102414550108",

language = "English",

journal = "CEPHALALGIA",

issn = "0333-1024",

publisher = "SAGE Publications",

}

RIS

TY - JOUR

T1 - Understanding migraine using dynamic network biomarkers

AU - Dahlem, Markus A

AU - Kurths, Jürgen

AU - Ferrari, Michel D

AU - Aihara, Kazuyuki

AU - Scheffer, Marten

AU - May, Arne

PY - 2014/9/16

Y1 - 2014/9/16

N2 - 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.

AB - 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.

U2 - 10.1177/0333102414550108

DO - 10.1177/0333102414550108

M3 - SCORING: Journal article

C2 - 25228683

JO - CEPHALALGIA

JF - CEPHALALGIA

SN - 0333-1024

ER -