EPINOV — Improving EPilepsy surgery management and progNOsis using Virtual brain technology

Overview

EPINOV was a French national RHU (Recherche Hospitalo-Universitaire) clinical trial and research project, funded by ANR under PIA3 and running from January 2018 to November 2023. With a €5.3M budget, it was the first randomised controlled trial (NCT03643016) to test whether personalised whole-brain simulation could improve surgical outcomes in drug-resistant focal epilepsy. The trial enrolled 356 patients across 12 French epilepsy surgery centres. It was led by a team at the Institut de Neurosciences des Systèmes (INS, INSERM/AMU Marseille), with AP-HM, Hospices Civils de Lyon, and Dassault Systèmes as partners.

The Virtual Epileptic Patient (VEP)

EPINOV’s core intervention was the Virtual Epileptic Patient: a personalised whole-brain network model built for each patient from their own SEEG (stereoelectroencephalography) data and structural MRI/DTI connectivity, used to model the epileptogenic zone and seizure propagation pre-surgically and predict optimal resection targets. The models were built on the Virtual Brain (TVB) framework, so EPINOV was scientifically founded on TVB while remaining operationally independent, with its own RCT protocol, funding, and clinical sites. After the trial, EPINOV published an open dataset of 30 VEP models on EBRAINS, with the underlying SEEG data, connectivity matrices, and simulation parameters.

Connections

• relatedTo: ANR Open Science Policy (EPINOV was ANR/PIA3-funded and subject to ANR open science requirements)
• registeredIn: ClinicalTrials.gov
• registeredIn: EBRAINS (VEP open dataset of 30 personalised models deposited with a persistent identifier via the EBRAINS Knowledge Graph)
• relatedTo: The Virtual Brain (the Virtual Epileptic Patient intervention is built on the TVB simulation framework)
• relatedTo: Human Brain Project (TVB, the framework underpinning the VEP methodology, was developed within the Human Brain Project)
• relatedTo: Virtual Brain Twin (direct successor project)

Resources

• https://clinicaltrials.gov/study/NCT03643016 (RCT registration, NCT03643016)
• https://ins-amu.fr (INS, Institut de Neurosciences des Systèmes, lead institution)
• https://search.kg.ebrains.eu (EBRAINS Knowledge Graph, VEP dataset)
• https://doi.org/10.1016/j.neuroimage.2017.04.020 (Jirsa et al. 2017, NeuroImage, VEP methodology)