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Estimation of brain network ictogenicity predicts outcome from epilepsy surgery

Type of publication Peer-reviewed
Publikationsform Original article (peer-reviewed)
Author Goodfellow, M.; Rummel, C.; Abela, E.; Richardson, M. P.; Schindler, K.; Terry, J. R.
Project A Bayesian Inference Approach to Intracranial EEG Seizure Dynamics
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Original article (peer-reviewed)

Journal Scientific Reports
Volume (Issue) 6
Page(s) 29215 - 29215
Title of proceedings Scientific Reports
DOI 10.1038/srep29215

Open Access

URL https://www.nature.com/articles/srep29215
Type of Open Access Publisher (Gold Open Access)

Abstract

Surgery is a valuable option for pharmacologically intractable epilepsy. However, significant post-operative improvements are not always attained. This is due in part to our incomplete understanding of the seizure generating (ictogenic) capabilities of brain networks. Here we introduce an in silico, model-based framework to study the effects of surgery within ictogenic brain networks. We find that factors conventionally determining the region of tissue to resect, such as the location of focal brain lesions or the presence of epileptiform rhythms, do not necessarily predict the best resection strategy. We validate our framework by analysing electrocorticogram (ECoG) recordings from patients who have undergone epilepsy surgery. We find that when post-operative outcome is good, model predictions for optimal strategies align better with the actual surgery undertaken than when post-operative outcome is poor. Crucially, this allows the prediction of optimal surgical strategies and the provision of quantitative prognoses for patients undergoing epilepsy surgery.
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