EPINET · Detection of brain patterns for the characterisation of epileptic networks

Detection of brain patterns for the characterisation of epileptic networks

Every year 2.4 million people in the world are diagnosed with epilepsy and approximately 25% of them respond poorly to drug treatment. Selected patients are offered the opportunity to improve seizure control through a surgical procedure, with overall improvement of quality of life which is is strictly dependent on the level of seizure-freedom after surgery. A good delineation of the seizure-onset zone (SOZ), which is the area responsible for the generation of the seizures, relies on the discovery of specific biomarkers of seizure propensity of specific brain regions; among them, high-frequency oscillations (HFOs) measured in intracranial electroencephalogram (iEEG) have gained much attention in the last years due to their strict correlation to the SOZ. The EPINET project aimed at developing a better understanding of the role of HFOs in the generation of seizure and at designing tools and algorithms for a better identification of the SOZ in patients affected by drug-resistant epilepsy. Three research objectives (RO) were identified: to develop quantitative methods for the automated detection of intracranial and extracranial HFOs; to develop a reliable and robust set of methodologies for the totally non-invasive recognition of HFOs; to evaluate the role of brain oscillations in the high frequency range to delineate the epileptogenic zone. At the end of the project the three ROs were fully achieved with the development of a complete set of routines and algorithms, named EPINETLAB, for the detection of HFOs and the identification of the SOZ. The tool, freely available upon request, is intended to support clinicians in the presurgical work-up, being user-friendly and fully documented in each single part. Moreover, a database of iEEG data from 60 patients, collected over three different European centres and of MEG data from 13 paediatric patients, collected at the Birmingham Children’s Hospital (BCH), allowed a robust validation of the implemented algorithms both with invasive and non-invasive recording technique, which was another aim of the project. EPINET allowed the fellow to become an independent computational neuroscientist, thanks to the highly multidisciplinary nature of the activities and the expertise of the Aston University/BCH research teams and to the secondment at Micromed, an French company whose R&D unit is in Italy with a 30-year track record of development and commercialization of solutions for neurophysiology. The fellow developed knowledge of the ethical and practical standard to which all clinical research is conducted, thanks to the collaboration with the BCH and the Aston Brain Centre, which provided her with the skills needed to understand how a clinical protocol is conducted and to run one on her own. Moreover the fellow acquired training in ethics, safety, data protection and intellectual property, very important features for the process of becoming an independent scientist. And she improved her networking background and her exposure to the epilepsy scientific community, thanks to the collaboration with different European centres and to the attendance to many national and international epilepsy-related congresses.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз