Lars Michels1, Nabin Koirala2,3, Franz Riederer4,5, Muthuraman Muthuraman3, Sergiu Groppa3, Roger Luechinger6, Andreas R Gantenbein7,8, Peter S Sandor7,8, Spyros Kollias9. 1. Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland. lars.michels@usz.ch. 2. Haskins Laboratories, New Haven, Connecticut, USA. 3. 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. 4. Department of Neurology, Clinic Hietzing and Karl Landsteiner Institute for Clinical Epilepsy Research and Cognitive Neurology, Wolkerssbergenstrasse 1, AT-1130, Vienna, Austria. 5. University of Zurich, Faculty of Medicine, Rämistrasse 100, CH-8091, Zurich, Switzerland. 6. Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland. 7. Department of Neurology and Neurorehabilitation, RehaClinic, Bad Zurzach, CH-5330, Switzerland. 8. Department of Neurology, University Hospital Zurich, CH-8091, Zurich, Switzerland. 9. Department of Neuroradiology, University Hospital Zurich, Sternwartstr. 6, CH-8091, Zurich, Switzerland.
Abstract
BACKGROUND: Migraine 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. METHODS: 19 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. RESULTS: Network 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. CONCLUSION: We 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.
BACKGROUND:Migraine 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. METHODS: 19 healthy controls (HC), 17 EMpatients and 12 CMpatients 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. RESULTS: Network 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. CONCLUSION: We found global network disruption for EM and CM indicated by highly segregated network in migrainepatients 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.
Authors: Nicole Schmitz; Enrico B Arkink; Marieke Mulder; Katya Rubia; Faiza Admiraal-Behloul; Guus G Schoonman; Mark C Kruit; Michel D Ferrari; Mark A van Buchem Journal: Neurosci Lett Date: 2008-05-16 Impact factor: 3.046
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