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DOI: 10.1055/s-0043-125303
Intrakranielle EEG Netzwerkanalysen bei fokalen Epilepsien
Analyses of Intracranial EEG Networks in Focal EpilepsiesPublication History
Publication Date:
13 April 2018 (online)
Zusammenfassung
Die Theorie der Netzwerke hat sich zu einem bedeutsamen Werkzeug bei der Analyse komplexer Systeme entwickelt und ihre Methoden werden erfolgreich angewendet, um die Dynamik epileptischer Anfälle zu verstehen. In diesem Artikel wird eine nicht-technische Einführung in die Konzepte der Netzwerktheorie gegeben. Ausgehend von intrakraniellen EEG Signalen wird demonstriert wie daraus funktionale Netzwerke hergeleitet werden können und wie sich diese Netzwerke visualisieren und analysieren lassen. Wichtige Begriffe wie „Knoten“, „Verbindung“ und „Knoten-Zentralität“ werden erklärt und ein experimentell prüfbares Modell der Netzwerkdynamik fokaler Anfälle wird vorgestellt. Dieses Modell impliziert, dass die hierarchische und modulare Netzwerkstruktur unseres Gehirns dazu prädestiniert, dass lokale neurogliale Aktivität unkontrollierbar wird. Die „Neuro-Netzwerkwissenschaft“ dürfte in naher Zukunft zu besserer Diagnostik und Therapie für Epilepsiepatienten führen.
Abstract
Network theory has become a powerful tool to assess complex systems in our highly interconnected world. The methods involved have also successfully been applied to uncover the network nature of epilepsies. Here a non-technical introduction into the concepts of network science is given. Using intracranial EEG signals it is demonstrated step by step how functional networks may be derived and how these networks can be visualized and analysed. The fundamental notions of “nodes”, “links” and “node centrality” are explained and an experimentally testable hypothetical model of network seizure dynamics is presented. This model suggests that the hierarchical and modular network structure of brains may predispose local neuro-glial activity to become uncontrolled, which might explain the surprisingly high prevalence of epilepsies. Network neuroscience can be expected to help develop better diagnostics and therapeutics for epilepsy in the near future.
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