Original Research ARTICLE
Elevated Ictal Brain Network Ictogenicity Enables Prediction of Optimal Seizure Control
- 1Living Systems Institute, University of Exeter, United Kingdom
- 2Wellcome Trust Centre for Biomedical Modelling and Analysis, University of Exeter, United Kingdom
- 3EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, United Kingdom
- 4Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London, United Kingdom
- 5Support Center for Advanced Neuroimaging (SCAN), University of Bern, Switzerland
- 6Department of Neurology, University Hospital Bern, Switzerland
Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed Brain Network Ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial EEG (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e. a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post- seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.
Keywords: epilepsy surgery, ictogenic network, intracranial EEG, network dynamics, neural mass model
Received: 31 Oct 2017;
Accepted: 12 Feb 2018.
Edited by:Udaya Seneviratne, Monash Medical Centre, Australia
Reviewed by:Leonardo Bonilha, Medical University of South Carolina, United States
Maxime Guye, Aix-Marseille Université, France
Copyright: © 2018 Lopes, Richardson, Abela, Rummel, Schindler, Goodfellow and Terry. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: PhD. Marinho A. Lopes, University of Exeter, Living Systems Institute, Exeter, United Kingdom, firstname.lastname@example.org