gms | German Medical Science

71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC)
9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie

Deutsche Gesellschaft für Neurochirurgie (DGNC) e. V.

21.06. - 24.06.2020

Graph theoretical network changes in presurgical epilepsy patients are independent of the underlying pathology

Graphentheoretische Netzwerkveränderungen in prächirurgischen Epilepsiepatienten sind unabhängig von der zugrundeliegenden Pathologie

Meeting Abstract

  • presenting/speaker Trakolis Leonidas - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Sabine Rona - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Monika Fudali - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Thomas Wuttke - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • Holger Lerche - Eberhard Karls Universität Tübingen, Abteilung Neurologie mit Schwerpunkt für Epileptologie, Tübingen, Deutschland
  • Jürgen Honegger - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland
  • presenting/speaker Georgios Naros - Eberhard Karls Universität Tübingen, Klinik für Neurochirurgie, Tübingen, Deutschland

Deutsche Gesellschaft für Neurochirurgie. 71. Jahrestagung der Deutschen Gesellschaft für Neurochirurgie (DGNC), 9. Joint Meeting mit der Japanischen Gesellschaft für Neurochirurgie. sine loco [digital], 21.-24.06.2020. Düsseldorf: German Medical Science GMS Publishing House; 2020. DocV061

doi: 10.3205/20dgnc065, urn:nbn:de:0183-20dgnc0650

Published: June 26, 2020

© 2020 Leonidas et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Objective: Several aspects of abnormal reorganization have been elucidated using a graph theory approach in describing the cortical network in presurgical epilepsy patients. While most studies concentrate on temporal lobe epilepsy, less is known about epilepsies caused by other pathologies, such as tumor, dysplasia, cavernoma, etc. The present study uses the principles of graph theory to support the hypothesis that the epileptogenic hemisphere shows similar graph theoretical network changes independent of the underlying pathology.

Methods: This retrospective study enrolled 25 patients (31.7 ± 25.6 years) with epilepsy deriving from various pathologies. These were divided in two groups: patients with tumour-induced epilepsy (n=15) and patients with epilepsy due to other type of pathology (n=10). Individual 3D MR images were coregistered to a brain atlas covering 88 region-of-interest (ROI). Finally, DTI connectivity between these ROIs and graph theory parameter (i.e. density and betweenness centrality) were calculated.

Results: The epileptogenic hemisphere shows higher density (0.33±0.06 and 0.29±0.06, p<0.05) but lower betweenness centrality (0.057±0.008 and 0.060±0.006, p<0.05) in comparison to the non-epileptogenic hemisphere. There was no group effect of the underlying pathology.

Conclusion: Our preliminary results show chronic epileptic attacks causing graph theoretical network changes within the epileptogenic hemisphere. These changes are independent of the underlying pathology.