« Virtual Brain »-based interpretation of electrophysiological Signals in epilepsy – VIBRATIONS

In this project, we propose to simulate in a biologically realistic way MEG and EEG fields produced bdifferent configurations of brain sources, which will differ in terms of spatial and dynamic characteristics. Our research hypothesis is that computational and biophysical models can bring crucial information to clinically interpret the signals measured by MEG and EEG. In particular, they can help to efficiently address some complementary questions faced by epileptologists when analyzing electrophysiological data.

Our strategy will be three-fold:
i) We will construct virtual brain models with both dynamic aspects (reproducing both hyperexcitability and hypersynchronisation alterations observed in the epileptic brain) and a realistic geometry based on actual tractography measures performed in patients
PROGRAMME DE RECHERCHE
TRANSLATIONNELLE EN SANTE
PRTS
EDITION 2013
Projet
VIBRATIONS
DOCUMENT SCIENTIFIQUE
ii) We will explore the parameter space though large-scale simulations of source configurations, using parallel computing implemented on a computer cluster.
iii) We will confront the results of these simulations to simultaneous recordings of EEG, MEG and intracerebral EEG (stereotactic EEG, SEEG). The models will be tuned on SEEG signals, and tested versus the surface signals in order to validate the ability of the models to represent real MEG and EEG signals.

In progress

Our project constitutes a translational effort from theoretical neuroscience and mathematics towards clinical investigation. A first output of the project will be a database of simulations, which will permit in a given situation to assess the number of configurations that could have given rise to the observed signals in EEG, MEG and SEEG. A second – and major - output of the project will be to give the clinician access to a software platform which will allow for testing possible configurations of hyperexcitable regions in a user-friendly way. Moreover, representative examples will be made available to the community through a website, which will permit its use in future studies aimed at confronting the results of different signal processing methods on the same ‘ground truth’ data.

In progress

Epilepsy is a major neurological disorder, affecting of the order of 0.5 to 1% of the population. It is a very invalidating disease, with high impact on quality of life. In a large proportion of cases, medication cannot prevent seizures; surgical removal of the regions responsible for seizures is then the only way to cure patients. However, results crucially depend on the correct delineation of the epileptogenic zone.

Several methods are used during presurgical evaluation. Intracerebral EEG, an invasive method, permits to record directly within the brain. In contrast, electrophysiology (Magnetoencephalography MEG, and Electroencephalography, EEG) constitute a non-invasive way to estimate the location and extent of regions from which the epileptic discharges originate.

However, the non-invasive methods suffer from difficulties hampering routine clinical use, including:
- The generation of large amounts of data, especially following recent advances in terms of number of channels that can be acquired simultaneously. This calls for advanced methods for multidimensional signal interpretation. Several methods exist, based on mathematical assumptions, whose validity and impact on the results are not yet clearly defined.
- The difficulty in recording deep brain regions with complex architectony (hippocampus, amygdala, thalamus). The actual detectability of these regions from MEG and EEG, and the best signal processing strategies are a subject of intense debate.
- Non-invasive measures mostly record interictal activity (irritative region). It is not clear how the subset of regions to be removed can be best delineated from these recordings (definition of a ‘primary irritative zone’).

In this context, computational modeling, under the form of a “virtual brain” is a powerful tool to investigate the impact of different configurations of the sources on the measures, in a well-controlled environment.
In this project, we propose to simulate in a biologically realistic way MEG and EEG fields produced by different configurations of brain sources, which will differ in terms of spatial and dynamic characteristics. Our research hypothesis is that computational and biophysical models can bring crucial information to clinically interpret the signals measured by MEG and EEG. In particular, they can help to efficiently address some complementary questions faced by epileptologists when analyzing electrophysiological data.

Our strategy will be three-fold:
i) We will construct virtual brain models with both dynamic aspects (reproducing both hyperexcitability and hypersynchronisation alterations observed in the epileptic brain) and a realistic geometry based on actual tractography measures performed in patients
ii) We will explore the parameter space though large-scale simulations of source configurations, using parallel computing implemented on a computer cluster.
iii) We will confront the results of these simulations to simultaneous recordings of EEG, MEG and intracerebral EEG (stereotaxic EEG, SEEG). The models will be tuned on SEEG signals, and tested versus the surface signals in order to validate the ability of the models to represent real MEG and EEG signals.

Our project constitutes a translational effort from theoretical neuroscience and mathematics towards clinical investigation. A first output of the project will be a database of simulations, which will permit in a given situation to assess the number of configurations that could have given rise to the observed signals in EEG, MEG and SEEG. A second – and major - output of the project will be to give the clinician access to a software platform which will allow for testing possible configurations of hyperexcitable regions in a user-friendly way. Moreover, representative examples will be made available to the community through a website, which will permit its use in future studies aimed at confronting the results of different signal processing methods on the same ‘ground truth’ data.