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EPI-SURGE

Computational Modeling of Large-Scale Epileptic Networks for Surgical Targeting


The goal of the Episurge project is to provide clinicians with novel, complementary tools that will increase surgery success and minimize invasiveness for the treatment of focal drug resistant epilepsy (DRE). The most efficient treatment of DRE is resective surgery, which is underused and has barely improved its outcome of seizure-free patients for the past 50 years. Our common solution towards a better management of DRE is a bioinformatic approach using personalized brain models, derived from each patient’s own anatomy. A personalized virtual brain is constructed from the patient’s non-invasive neuroimaging data. Computer simulations of the virtual brain for each DRE patient generate predictions for personalized surgery procedures. The predictions of the model will be validated in mouse models of epilepsy and compared to empirical clinical data, in particular surgical outcome. The virtual brain technology will also test in silico novel highly innovative therapeutic solutions; such as spatially distributed multi-focal laser microsurgery, which is currently used in Cleveland. If our joint project is successful, treatment of DRE will transform into a minimally invasive procedure with significantly reduced pre-surgical evaluation time, morbidity, mortality rates, and surgery failures (especially in the most difficult cases). This will dramatically increase treatment utilization in DRE and will transform the lives of millions of patients.


CONTACT INFORMATION

Cleveland Site: Dr. Jorge Gonzalez-Martinez

Marseille site: Dr. Viktor Jirsa


 
 

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Improving EPilepsy surgery management and progNOsis using Virtual brain technology

Thousands of patients with drug resistant focal epilepsy (DRE) undergo resective brain surgery with the aim of achieving seizure freedom. Despite technical advances, the success rate of epilepsy surgery in terms of seizure freedom has not greatly improved, remaining at around 50%. Epilepsy surgical failure can be due to the non-resection or insufficient modulation of the important nodes and pathways that characterize the epileptogenic network. For any candidate for epilepsy surgery, the critical factor in deciding upon the treatment strategy is the correct estimation of surgery outcome. No reliable procedure currently exists to combine the various prognostic factors for a given patient. This leads to great uncertainty on an individual scale in predicting the effects of surgery. The aim of this project is to guide surgical strategies to improve epilepsy surgical prognosis by a novel approach of large–scale brain modeling based on individual epileptic patient data. Retrospective pilot studies have confirmed the feasibility of this approach and shown promising results in terms of predictability of surgical results. Our project consists of a multicenter trial involving 10 epilepsy surgery centers, using our state of the art medical neuroinformatic technology. The Virtual Brain is a modeling technology comprising a dynamic network model of the human adult brain. The functions of the brain model are constrained by personalized human anatomical information derived from non-invasive brain imaging. Personalized modeled data generated by TVB will improve accuracy of localisation of the epileptogenic zone, guide optimal surgical strategy and improve surgery outcome. The prospective, randomized trial will include subjects with DRE who undergo presurgical evaluation and surgical decision-making will be taken in multidisciplinary patient management conferences. In the virtualized group, detailed patient anatomy, structural connectivity and sEEG will serve as input for the virtual patient (VEP). An individualized VEP report indicating the most epileptogenic regions will be provided to each investigator. Surgical decision will be made by multidisciplinary patient management conferences considering the VEP results. We will compare: 1 | surgical outcome at 1 year in terms of seizure frequency, 2 | influence of VEP results on surgical strategies and surgical morbidity. VEP will also be performed retrospectively in operated patients from the control group and the results of the virtualization will be compared to the actual surgery outcome. We will work towards translating these technologies to the market, which will revolutionize the management of epilepsy surgical candidates and improve prognosis of epilepsy surgery.

 
 
 
 

PROJECT PARTNERS