The Virtual Brain (TVB) is an open-source platform for constructing and simulating personalised brain network models. The TVB-on-EBRAINS ecosystem includes a variety of prepackaged modules, integrated simulation tools, pipelines and data sets for easy and immediate use on EBRAINS. Process your large cohort databases and use these results to develop potential medical treatments, therapies or diagnostic procedures.
Run TVB simulations remotely from your local computer via the TVB EBRAINS Cloud simulator with HPC back end (free EBRAINS account required) or download the TVB Docker container or TVB standalone (OS-compiled), via the TVB website.
See the TVB Collaboratory for tips on how EBRAINS can help you maximise the TVB resources.
Launch TVB EBRAINS Cloud Simulator Download TVB Standalone from the TVB website Download TVB Container Get access to the TVB Collaboratory
Couple large-scale brain models from TVB with spiking networks from NEST Simulator (link to tool page for NEST Simulator). For example, you may study the impact of realistic large-scale brain network activity on spiking neurons and networks, or even replace entire brain areas by spiking networks and study emerging mean field dynamics.
Access the multi-scale features of TVB in one click via the Web App. Easily set up and run TVB-NEST workflows, choosing Python wrapper integration or TVB-NEST-Elephant co-simulation. TVB-Multiscale Co-Simulation is also available for download as a Docker container.
Launch TVB-Multiscale Simulation Web App
Download TVB-multiscale Container
Exploit parallel supercomputing architectures with multi-threaded TVB simulations to perform faster-than-real-time simulations of large networks with FAST-TVB. You can use the convenient platform-independent Docker container on your computer or supercomputer.
Use TVB in a sophisticated model inversion approach: uncover the model’s secret parameter values and identify the zone with hyperexcitable parameters; the epileptogenic zone, by applying the statistical methods used in the EPINOV trial.
This approach combines best-in-class simulator technology with the best available inference techniques and is extensible, enabling its application on other pathologies. A set of Jupyter notebooks takes you step-by-step through the entire workflow.
The TVB Image Processing Pipeline takes multi-modal MRI data sets (anatomical, functional or diffusion MRI) as input. Your output includes structural and functional connectomes, region-average fMRI time series, brain surface triangulations, EEG electrode positions, lead field matrices and brain parcellations. The pipeline takes data sets in BIDS standard format and gives outputs that can be used for further analysis or to construct personalised brain models. In addition, outputs comply with the BIDS standards, are annotated with metadata following the MINDS schema and are adapted for sharing via the EBRAINS Knowledge Graph. Run our Jupyter notebook example to see how the TVB processing pipeline can be executed from the web browser.
Use processed TVB ready brain scans to run your simulations. This reference data set contains a series of brain scans from a total of 25 tumour patients and 11 healthy controls, organised in the BIDS format.
A Virtual Standard Brain model and its network parcellations are available in an interactive atlas viewer. Explore brain parcellations used by TVB, the functional relevance of different brain areas and networks and their location and shapes. Select areas based on either anatomy or function. Descriptions are available in English, Arabic, German, Hebrew and Polish.
The Virtual Brain integration in EBRAINS allows users to have an end-to-end experience of personalised brain model creation and multi-scale brain simulation, using high-performance computing in the Cloud. This makes it possible to process large-cohort databases and produce generalised results - a precondition for using these results to develop potential medical treatments, therapies or diagnostic procedures.
a) Run pre-installed software in the Cloud with web access and HPC back end.
b) Pull execution-ready Docker containers from Docker Hub.
c) Download software or execution-ready containers and run them on a system of your choice, including your local machine.
d) Download software or execution-ready containers and run them on a system of your choice, including your local machine.
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