In January 2021, the Human Brain Project has launched the HBP Innovation Awards to recognize project researchers in their role of “innovators”. The awards will give internal and external visibility to their efforts towards the exploitation of their research results.

The first Innovation Award – for the first semester 2021 – was granted to Viktor Jirsa and the Virtual Epileptic Patient (VEP) team.

Says Viktor Jirsa:

The advantage of VEP is that it provides a balanced judgement of the contribution of the various factors influencing seizures, including regional epileptogenicity, the patient’s brain connectivity, but also electrode placements.

Furthermore, it can simulate brain activity, test clinical interventions, and reveal brain activity, which is not accessible otherwise. For instance, sometimes a clinician would like to have an extra electrode in the patient’s brain, which could not be implanted originally, and VEP can simulate the electrode and generate the missing data.

A first version of the VEP technology is currently tested by medical users within the clinical trial EPINOV. The trial study is conducted in 13 hospital centers in France and has started in June 2019.

The study will last four years and aims at guiding therapeutic strategies to improve surgical prognosis. It will include about 400 prospective patients (adults and children over 12) who have been diagnosed with drug-resistant epilepsy and identified as potential candidates for resective epilepsy surgery.

The EPINOV Trial is the largest randomized multi-site trial ever conducted in epilepsy surgery and has been funded by the French scientific excellence program “Investissements d’Avenir” (Investment in the Future) entitled «Recherche Hospitalo-Universitaire en santé» (RHU) operated by the National Research Agency (ANR).

Read the full interview with Viktor Jirsa in this PDF magazine

Learn how to simulate seizure events and epilepsy in The Virtual Brain.

We will look at the paper: On the Nature of Seizure Dynamics which describes a new local model called the Epileptor, and apply this same model in The Virtual Brain. This is part 1 of 2 in a series explaining how to use the Epileptor.

In this part, we focus on setting up the parameters.

Topics covered in this lesson by Paul Triebkorn

• Overview of modelling seizure in The Virtual Brain
• Overview of The Epileptor
• Overview of the 5 differential equations of The Epileptor
• Configuring parameters for modelling seizure in The Virtual Brain

Related publication

On the modelling of seizure dynamics, published in Brain, 2014 by Jirsa et al.

doi: 10.1093/brain/awu133

In this lecture we will focus on a paper called “The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread”. Within their work, the authors used the epileptor model to simulate a patient's individual seizure.

To understand the concept we will have a closer look at the equations of the epileptor model and particular the epileptogenicity index which controls the excitability of each brain region.

Subsequently, we will begin to setup the epileptogenic zone in our own brain network model with TVB.

Topics covered in this lesson by Paul Triebkorn

• Local epileptor model and epileptogenic zone
• Setting up a brain network for epilepsy

Related publication

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread, published in NeuroImage, January 2017 by V.K. Jirsa, T. Proix, D. Perdikis, M.M. Woodman, H. Wanga, J. Gonzalez-Martinez, C. Bernard, C.Bénar, M. Guye, P. Chauvela, F. Bartolomei

doi: 10.1016/j.neuroimage.2016.04.049

Abstract

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results.

We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients.

Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions.

Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

After introducing the local epileptor model, we will now use it in a large scale brain simulation. We again focus on the paper “The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread”.

Two simulations with different epileptogenicity across the network are visualized to show the difference in seizure spread across the cortex.

Topics covered in this lesson by Paul Triebkorn

• Running epileptic seizure simulations in TVB

Related publication

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread, published in NeuroImage, January 2017 by V.K. Jirsa, T. Proix, D. Perdikis, M.M. Woodman, H. Wanga, J. Gonzalez-Martinez, C. Bernard, C.Bénar, M. Guye, P. Chauvela, F. Bartolomei

doi: 10.1016/j.neuroimage.2016.04.049

Abstract

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results.

We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients.

Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions.

Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

This lecture gives an overview on the article “Individual brain structure and modelling predict seizure propagation” where 15 subjects with epilepsy were modelled to predict individual epileptogenic zones.

With the TVB GUI we will model seizure spread and the effect of lesioning the connectome. The impact of cutting edges in the network on seizure spreading will be visualized.

Topics covered in this lesson by Paul Triebkorn

• Individual epilepsy modelling
• Impact of connectome manipulation on seizure spread

Related publication

Individual brain structure and modelling predict seizure propagation, published in Brain, March 2017 by Timothée Proix, Fabrice Bartolomei, Maxime Guye, Viktor K. Jirsa

doi: 10.1093/brain/awx004

Abstract

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.

Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders.

When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing.

We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome.

In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation.

We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity.

Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.

The simulation of the virtual epileptic patient is presented as an example of advanced brain simulation as a translational approach to deliver improved results in clinics. The fundamentals of epilepsy are explained.

On this basis, the concept of epilepsy simulation is developed. By using an iPython notebook, the detailed process of this approach is explained step by step.

In the end, you are able to perform simple epilepsy simulations your own.

Topics covered in this lesson by Julie Courtiol

• Fundamentals of epilepsy
• How to build a virtual epileptic patient?
• Hand-on-guide through the iPython notebook
• Creating your own epilepsy simulations with TVB

Along the example of a patient with bi-temporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions.

The patient's brain network model is then evaluated via simulation, data fitting and mathematical analysis.

This lecture demonstrates how to develop novel personalized strategies towards therapy and intervention using TVB.

Topics covered in this lesson by Paul Triebkorn

• General introduction to epilepsy
• Workflow of the VEP
• Building step-by-step a VEP using TVB
• VEP evaluation: fitting SEEG data using parameter sweep and inference
• Predicting the Propagation Zone from patient-specific connectome

Explore how to setup an epileptic seizure simulation with the TVB graphical user interface

This lesson will show you how to program the epileptor model in the brain network to simulate a epileptic seizure originating in the hippocampus. It will also show how to upload and view mouse connectivity data, as well as give a short introduction to the python script interface of TVB.

Topics covered in this lesson by Paul Triebkorn

• Setting up the epileptor model
• Simulation of an epileptic seizure
• Mouse connectivity
• TVB python script interface

This lecture on modeling epilepsy using TVB by Julie Courtiol is part of the TVB Node 10 series, a 4 day workshop dedicated to learning about The Virtual Brain, brain imaging, brain simulation, personalised brain models, TVB use cases, etc. TVB is a full brain simulation platform.

Topics covered in this lesson by Julie Courtiol

• Introduction to epilepsy
• How to model epilepsy with TVB
• TVB epilepy use cases

An overview of the Bayesian Virtual Epileptic Patient (BVEP), a research use case using TVB supported on the EBRAINS infrastructure.

Topics covered in this lesson by Meysam Hashemi

• Bayesian model inversion
• BVEP workflow
• Jupyter notebook example
• Python & Stan interfaces
• PyMC3 example
• Comparing Stan & PyMC3 accuracy

On the quiet Saturday morning of January 6th, 2018, an eager scientist tapped the trackpad – unknowingly making history and quite a few people dance on tables, toasting with leftover champagne from New Year's eve: Because the 10,000th copy of The Virtual Brain was downloaded!

The story of this impressive achievement in modern neuroscience started 10 years ago, in a pub in Chicago where Viktor and Randy had more than one beer in the afterglow of an OHBM meeting – and a crazy idea: Running a scientifically useful, even individualized human brain simulator on arbitrary laptops and yet be scalable to HPC clusters.

Today, The Virtual Brain (TVB) has become an internationally acclaimed, open source neuroscience software platform, available for free on Windows, Mac and Linux. Every day, a sizable global community of active researchers are using TVB to analyze, understand and help treat diseases like Epilepsy, Stroke and Alzheimer's Disease.

TVB's user base is growing by more than 6,000 per year and the scientific groundwork has been cited in close to 100 peer-reviewed publications. Large research facilities at Charité in Berlin, AMU in Marseille and Baycrest in Toronto have constructed and simulated hundreds of individual, Connectome-based brain network models and published their findings. Well over 10 million CPU core hours went into TVB simulations, running on average MacBooks, faculty Linux servers and supercomputers like JURECA in Jülich. Starting this year, the French Epinov project will use TVB to guide surgical strategies in 10 clinics by modeling the individual brains of 400 epilepsy patients.

What enabled the success of TVB is its singular focus on delivering practical results for novel clinical applications – not in 10 years but today, on every PC and Mac. It's the only software that can generate brain imaging signals for any person, and even in animal models, reasonably fast and with scalable fidelity.

While large-scale research initiatives have been trying to simulate neurons and small brain regions at the cellular level on massively parallel hardware, they are years away from clinical applications. TVB, however, accelerates this process and reduces complexity on the micro level to attain the macro organization: A TVB model of a patient's brain generates sufficiently accurate EEG, MEG, BOLD and SEEG signals despite the complexity of a micrometer of neuronal tissue, which is reduced by a factor of a million through methods known from statistics. The key is to keep the geometry of the brain's shape and its folds precise on a millimeter level.

This smart reduction of complexity has earned TVB worldwide recognition as demonstrated by invitations to participate at neuroinformatics events such as INCF conferences, and workshops dedicated to High Performance Computing (HPC) (such as organized by NSG). TVB is used as a reference tool for use of HPC resources in the neuroscience community and directly links to other large-scale neuroinformatics efforts such as the Allen Institute’s Mouse atlas or the Human Brain Project (HBP).

TVB members disseminate their know-how, e.g. through international TVB Node workshops, by mentoring students in the Google Summer of Code program and supporting code contributors via GitHub. Also, the public at large can experience TVB technology in a playful way at the MyVirtualDream events and the upcoming Brainmodes smartphone app.

Over the past 10 years and 10,000 downloads, TVB has evolved from a few haphazard equations scribbled on a bar coaster to a revolutionary platform for computational brain modeling. During this time, the TVB team has received around $20 million in generous funding, largely carried by the James S. McDonnell Foundation.

Up to 25 people made the TVB team in some phases, working hard to realize the vision of founding scientists Viktor, Randy and Petra. Major kudos goes to Jochen and Lia who hammered out the software architecture in multiple "code jams", Michael who gave TVB a brand identity and UI, and the steady hand from Tanya steering the group through many showcases at the Society for Neuroscience and the Node workshop series.

It's been an epic journey and we're all proud to start this new year on a high note! I guess we're excused to enjoy a cold beer now – for science, you know!

Download the TVB@10000 illustration and spread the word!

Leading TVB scientist Dr. Petra Ritter was invited to speak about The Virtual Brain and its clinical applications at the „Future Medicine“ Science Match 2016 in Berlin, Germany. This conference was part of the Berlin Science Week 2016.

In the Bolle Festsäle "Future Medicine" Science Match brought together the rising stars of science from top institutions in Berlin, Germany, and the world with proven game-changers.

They presented in one day cutting-edge translational medicine and showed how their work will turn advances in life science research into benefits for patients and people. In five sessions the topics Personalized Medicine, Digital Health, Start-Up Ecosystems, Regenerative Medicine and Cell Engineering were discussed.

Developed by German newspaper The Tagesspiegel, Science Match is an innovative format for connecting science, business, society, media and new talent. In keynotes and 3-minute presentations German and international researchers will feature their innovations in life sciences. Attracting up to 800 participants, the target audiences of this event are peers, young professionals, healthcare industry, venture capital, funding institutions and policy makers.

Finally published and available online:

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread

By Jirsa VK, Proix T, Perdikis D, Woodman MM, Wang H, Gonzalez-Martinez J, Bernard C, Bénar C, Guye M, Chauvel P, Bartolomei F

Great work by the TVB scientists and their team in Marseille!

Head over to our publications list to see more papers about neuroscience with The Virtual Brain!