Structural brain network characteristics in patients with episodic and chronic migraine

The Journal of Headache and Pain

Peter S. Sandor¹  Andreas R. Gantenbein¹  Franz Riederer²  Lars Michels³  Spyros Kollias³  Nabin Koirala⁴  Roger Luechinger⁵  Muthuraman Muthuraman⁶  Sergiu Groppa⁶ 

[1] Department of Neurology and Neurorehabilitation, RehaClinic, CH-5330, Bad Zurzach, Switzerland;Department of Neurology, University Hospital Zurich, CH-8091, Zurich, Switzerland;Department of Neurology, Clinic Hietzing and Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Wolkerssbergenstrasse 1, AT-1130, Vienna, Austria;University of Zurich, Faculty of Medicine, Rämistrasse 100, CH-8091, Zurich, Switzerland;Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland;Haskins Laboratories, New Haven, Connecticut, USA;Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany;Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland;Section of Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing unit, Department of Neurology, Focus Program Translational Neuroscience (FTN), University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany;

关键词: Migraine;    Episodic;    Chronic;    Graph theory;    Connectivity;   

DOI  :  10.1186/s10194-021-01216-8

来源: Springer

【 摘 要 】

BackgroundMigraine is a primary headache disorder that can be classified into an episodic (EM) and a chronic form (CM). Network analysis within the graph-theoretical framework based on connectivity patterns provides an approach to observe large-scale structural integrity. We test the hypothesis that migraineurs are characterized by a segregated network.Methods19 healthy controls (HC), 17 EM patients and 12 CM patients were included. Cortical thickness and subcortical volumes were computed, and topology was analyzed using a graph theory analytical framework and network-based statistics. We further used support vector machines regression (SVR) to identify whether these network measures were able to predict clinical parameters.ResultsNetwork based statistics revealed significantly lower interregional connectivity strength between anatomical compartments including the fronto-temporal, parietal and visual areas in EM and CM when compared to HC. Higher assortativity was seen in both patients’ group, with higher modularity for CM and higher transitivity for EM compared to HC. For subcortical networks, higher assortativity and transitivity were observed for both patients’ group with higher modularity for CM. SVR revealed that network measures could robustly predict clinical parameters for migraineurs.ConclusionWe found global network disruption for EM and CM indicated by highly segregated network in migraine patients compared to HC. Higher modularity but lower clustering coefficient in CM is suggestive of more segregation in this group compared to EM. The presence of a segregated network could be a sign of maladaptive reorganization of headache related brain circuits, leading to migraine attacks or secondary alterations to pain.

【 授权许可】

CC BY