Structural brain network characteristics in patients with episodic and chronic migraine

  • 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 inBackground 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.show moreshow less

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Metadaten
Author:Lars Michels, Nabin Koirala, Sergiu Groppa, Roger Luechinger, Andreas R. Gantenbein, Peter S. Sandor, Spyros Kollias, Franz Riederer, Muthuraman MuthuramanORCiDGND
URN:urn:nbn:de:bvb:384-opus4-1097203
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/109720
ISSN:1129-2369OPAC
ISSN:1129-2377OPAC
Parent Title (English):The Journal of Headache and Pain
Publisher:Springer Science and Business Media LLC
Place of publication:Berlin
Type:Article
Language:English
Year of first Publication:2021
Publishing Institution:Universität Augsburg
Release Date:2023/12/05
Tag:Anesthesiology and Pain Medicine; Neurology (clinical); General Medicine
Volume:22
Issue:1
First Page:8
DOI:https://doi.org/10.1186/s10194-021-01216-8
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Professur für Informatik in der Medizintechnik
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)