Prediction of the seizure suppression effect by electrical stimulation via a computational modeling approach

Sora Ahn, Sumin Jo, Sang Beom Jun, Hyang Woon Lee, Seungjun Lee

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7 Scopus citations


In this paper, we identified factors that can affect seizure suppression via electrical stimulation by an integrative study based on experimental and computational approach. Preferentially, we analyzed the characteristics of seizure-like events (SLEs) using our previous in vitro experimental data. The results were analyzed in two groups classified according to the size of the effective region, in which the SLE was able to be completely suppressed by local stimulation. However, no significant differences were found between these two groups in terms of signal features or propagation characteristics (i.e., propagation delays, frequency spectrum, and phase synchrony). Thus, we further investigated important factors using a computational model that was capable of evaluating specific influences on effective region size. In the proposed model, signal transmission between neurons was based on two different mechanisms: synaptic transmission and the electrical field effect. We were able to induce SLEs having similar characteristics with differentially weighted adjustments for the two transmission methods in various noise environments. Although the SLEs had similar characteristics, their suppression effects differed. First of all, the suppression effect occurred only locally where directly received the stimulation effect in the high noise environment, but it occurred in the entire network in the low noise environment. Interestingly, in the same noise environment, the suppression effect was different depending on SLE propagation mechanism; only a local suppression effect was observed when the influence of the electrical field transmission was very weak, whereas a global effect was observed with a stronger electrical field effect. These results indicate that neuronal activities synchronized by a strong electrical field effect respond more sensitively to partial changes in the entire network. In addition, the proposed model was able to predict that stimulation of a seizure focus region is more effective for suppression. In conclusion, we confirmed the possibility of a computational model as a simulation tool to analyze the efficacy of deep brain stimulation (DBS) and investigated the key factors that determine the size of an effective region in seizure suppression via electrical stimulation.

Original languageEnglish
Article number39
JournalFrontiers in Computational Neuroscience
StatePublished - 29 May 2017

Bibliographical note

Funding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Planning (MSIP) (2014R1A2A2A1A11052763 to SL, 2014R1A2A2A09052449 to SBJ and 2014R1A2A1A11052103 to HWL), the MSIP as GFP (CISS-2012M3A6A054204 to SBJ) and grants of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health &Welfare (HI14C1989 to HWL).

Publisher Copyright:
© 2017 Ahn, Jo, Jun, Lee and Lee.


  • Computational model
  • Electrical field effect
  • Electrical stimulation
  • In vitro experiment
  • Seizure propagation mechanism
  • Seizure suppression


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