title: Estimation of brain network ictogenicity predicts outcome from epilepsy surgery.
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.
authors.affiliation: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.|Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.|EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.
authors.forename: M
authors.initials: M
authors.lastname: Goodfellow
authors.affiliation: Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland.
authors.forename: C
authors.initials: C
authors.lastname: Rummel
authors.affiliation: Support Center for Advanced Neuroimaging (SCAN), University Institute for Diagnostic and Interventional Neuroradiology, University of Bern, Switzerland.
authors.forename: E
authors.initials: E
authors.lastname: Abela
authors.affiliation: EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.|Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
authors.forename: M P
authors.initials: MP
authors.lastname: Richardson
authors.affiliation: Department of Neurology, University of Bern, Switzerland.
authors.forename: K
authors.initials: K
authors.lastname: Schindler
authors.affiliation: College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.|Centre for Biomedical Modelling and Analysis, University of Exeter, Exeter, UK.|EPSRC Centre for Predictive Modelling in Healthcare, University of Exeter, Exeter, UK.
authors.forename: J R
authors.initials: JR
authors.lastname: Terry
mesh_terms: D000328:Adult; D001921:Brain / Q000503:physiopathology*; D000069279:Drug Resistant Epilepsy / Q000503:physiopathology* / Q000601:surgery*; D000069280:Electrocorticography / Q000379:methods; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011184:Postoperative Period; D012640:Seizures / Q000503:physiopathology / Q000601:surgery; D055815:Young Adult