Welcome to the website of the SEIZURE project, led by Carole Lartizien and [Julien Jung]and funded by the French National Research Agency - ANR (ANR-24-CE45-4399) from Dec. 2024 to Nov. 2028.
Context - Epilepsy is resistant to drug treatment for 30% of patients. For those patients, surgical resection of the brain epileptogenic zone (EZ) is the best option. No biomarker allows to perfectly detect the EZ and surgical outcome is not a trivial task.
Recent progress in statistical image processing of magnetic resonance (MRI) and positron emission (PET) imaging has allowed the detection and phenotyping of subtle lesions, however, some remain hard or impossible to detect. Magnetoencephalography (MEG) offers the possibility to record brief electrophysiological events which propagate within dynamic neural networks including the epileptic focus. Promising advances in these signals analysis open the prospect to characterize brain dysfunctions induced by epilepsy. The anatomical approach with MRI and PET and the functional approach with MEG have largely evolved in parallel. The clinical integration of the diagnostic information conveyed by these different modalities is still empirically performed by epileptologists.
Objectives - The aim of the SEIZURE project is to design a statistical analysis framework for the identification of the EZ and predict surgical outcome based on the multi-modal, high dimensional, multi-scale and heterogeneous neurological patient data including multiparametric MR (mp-MRI) and PET imagings, electrophysiological fingerprints of epilepsy with MEG and clinical data. The proposed analysis framework will leverage the most advanced methods statistical machine learning for image and signal graph processing as well as fusion of heterogeneous data.
Our main research hypothesis is that artificial intelligence (AI) techniques offer the best-suited framework for addressing this challenging issue and allow a quantum leap in diagnostic performance of epilepsy patients based on the objective and robust detection of the EZ. We also hypothesise that an integrated transdisciplinary approach is crucial to the success of such a project.
SEIZURE is a highly transdisciplinary project combining expertise in epilepsy neuroimaging, clinical epileptology as well as core and applied machine learning for signal and image analysis. It will be based on a multimodal database (>200 patients) already collected by the clinician partner.
SEIZURE will produce innovative methodological tools in ML for neurological data analysis and bring these tools into clinical epileptology for objective identification of the EZ and robust prediction of surgery outcome.
If you are interested in collaborating with us or if you simply want to have more information, please do not hesitate to contact us!