Elevated ictal brain network ictogenicity enables prediction of optimal seizure control
Lopes, M; Richardson, MP; Abela, E; et al.Rummel, C; Schindler, K; Goodfellow, M; Terry, J
Date: 1 March 2018
Article
Journal
Frontiers in Neurology
Publisher
Frontiers Media
Publisher DOI
Abstract
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 ...
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 electroencephalography (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.
Mathematics and Statistics
Faculty of Environment, Science and Economy
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