Phenomenological network models: Lessons for epilepsy surgery

Jurgen Hebbink* (Corresponding Author), Hil Meijer, Geertjan Huiskamp, Stephanus A. van Gils, Frans Leijten

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

    32 Citations (Scopus)
    135 Downloads (Pure)


    The current opinion in epilepsy surgery is that successful surgery is about removing pathological cortex in the anatomic sense. This contrasts with recent developments in epilepsy research, where epilepsy is seen as a network disease. Computational models offer a framework to investigate the influence of networks, as well as local tissue properties, and to explore alternative resection strategies. Here we study, using such a model, the influence of connections on seizures and how this might change our traditional views of epilepsy surgery. We use a simple network model consisting of four interconnected neuronal populations. One of these populations can be made hyperexcitable, modeling a pathological region of cortex. Using model simulations, the effect of surgery on the seizure rate is studied. We find that removal of the hyperexcitable population is, in most cases, not the best approach to reduce the seizure rate. Removal of normal populations located at a crucial spot in the network, the "driver," is typically more effective in reducing seizure rate. This work strengthens the idea that network structure and connections may be more important than localizing the pathological node. This can explain why lesionectomy may not always be sufficient.

    Original languageEnglish
    Pages (from-to)e147-e151
    Number of pages5
    Issue number10
    Early online date26 Jul 2017
    Publication statusPublished - 4 Oct 2017


    • Computational model
    • Epilepsy surgery
    • Focal epilepsy
    • Network disease


    Dive into the research topics of 'Phenomenological network models: Lessons for epilepsy surgery'. Together they form a unique fingerprint.

    Cite this