Virtual brain twins for stimulation in epilepsy

Estimating the epileptogenic zone network (EZN) is an important part of the diagnosis of drug-resistant focal epilepsy and has a pivotal role in treatment and intervention. Virtual brain twins provide a modeling method for personalized diagnosis and treatment. They integrate patient-specific brain topography with structural connectivity from anatomical neuroimaging such as magnetic resonance imaging, and dynamic activity from functional recordings such as electroencephalography (EEG) and stereo-EEG (SEEG). Seizures show rich spatial and temporal features in functional recordings, which can be exploited to estimate the EZN. Stimulation-induced seizures can provide important and complementary information. Here we consider invasive SEEG stimulation and non-invasive temporal interference stimulation as a complementary approach. This paper offers a high-resolution virtual brain twin framework for EZN diagnosis based on stimulation-induced seizures. It provides an important methodological and conceptual basis to make the transition from invasive to non-invasive diagnosis and treatment of drug-resistant focal epilepsy.

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Publication Details

Journal
Nature Computational Science
Published
2025-08-05
DOI
https://doi.org/10.1038/s43588-025-00841-6
Citations
8
Primary Topic
EEG and Brain-Computer Interfaces
Type
article
Field-Weighted Citation Impact
11.45

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article

Virtual brain twins for stimulation in epilepsy

Borana Dollomaja, Jean-Didier Lemaréchal, Paul Triebkorn, Viktor Jirsa et al.
8 citations
Nature Computational Science
EEG and Brain-Computer Interfaces
11.45
article

Virtual brain twins for stimulation in epilepsy

Borana Dollomaja, Jean-Didier Lemaréchal, Paul Triebkorn, Viktor Jirsa, Gian Marco Duma, Adam Williamson, Julia Makhalova, Huifang Wang, Fabrice Bartolomei
article en
8 citations

Abstract

Estimating the epileptogenic zone network (EZN) is an important part of the diagnosis of drug-resistant focal epilepsy and has a pivotal role in treatment and intervention. Virtual brain twins provide a modeling method for personalized diagnosis and treatment. They integrate patient-specific brain topography with structural connectivity from anatomical neuroimaging such as magnetic resonance imaging, and dynamic activity from functional recordings such as electroencephalography (EEG) and stereo-EEG (SEEG). Seizures show rich spatial and temporal features in functional recordings, which can be exploited to estimate the EZN. Stimulation-induced seizures can provide important and complementary information. Here we consider invasive SEEG stimulation and non-invasive temporal interference stimulation as a complementary approach. This paper offers a high-resolution virtual brain twin framework for EZN diagnosis based on stimulation-induced seizures. It provides an important methodological and conceptual basis to make the transition from invasive to non-invasive diagnosis and treatment of drug-resistant focal epilepsy.

Nature Computational ScienceVol. 5(9)
Centre National de la Recherche Scientifique (FR), Inserm (FR), Aix-Marseille Université (FR), Institut de Neurosciences des Systèmes (FR), Centre de Résonance Magnétique Biologique et Médicale (FR), IRCCS Eugenio Medea (IT), Hôpital de la Timone (FR)
Fondation Aix-Marseille Universite, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, HORIZON EUROPE Framework Programme
Good health and well-being
Openalex Percentile: Top 1%
EEG and Brain-Computer Interfaces
11.45
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