Simulation

The Virtual Brain

Create personalised brain models and simulate multi-scale networks

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.

Our offer

  • Open-source platform for constructing and simulating personalised brain network models
  • Ready-to-go image-processing pipelines to generate structural and functional data for personalised brain models
  • Simulation-ready brain image data
  • TVB-NEST co-simulation software to simulate the brain at multiple scales
  • Extra-fast optimised TVB code for high-performance computing
  • All software is available containerised and ready for system-independent execution

Tools

Access TVB in the Cloud or download and run it elsewhere

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 make the best of the TVB resources.

TVB EBRAINS Cloud Simulator

Download TVB Standalone from the TVB website

Download TVB Container

TVB Collaboratory

Simulate the brain at multiple scales with TVB-Multiscale Co-Simulation

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 either Python wrapper integration or TVB-NEST-Elephant (link to Elephant tools and resources page) co-simulation. TVB-Multiscale Co-Simulation is also available for download as a Docker container.

TVB-Multiscale Simulation Web App

Download TVB-multiscale Container

Access TVB-NEST integration wrapper via the Collaboratory

Watch TVB-NEST integration wrapper video

Run the TVB-NEST-Elephant co-simulation workflow

Watch TVB-NEST-Elephant workflow video

Increase simulation speed using FAST-TVB

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.

Fast-TVB Collaboratory

Watch video how to use Fast-TVB

Get the container Image of Fast TVB

TVB workflow for inferring a spatial map of the hyperexcitable zone in patient brains with epilepsy

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 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.

Access and use TVB-inverse

Process your MRI data sets with the TVB Image Processing Pipeline

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 give outputs that can be used for further analysis or to construct personalised brain models. In addition, outputs comply to the BIDS standards, are annotated with metadata following the MINDS schema and 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.

Image Processing Pipeline Collaboratory

Download the demo jupyter notebook

Watch overview video that explains each step

Find TVB ready-processed imaging data in the EBRAINS Knowledge Graph

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.

Find the dataset in the Knowledge Graph

Interactive Atlas Viewer in Multiple Languages

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, plus Arabic, German, Hebrew and Polish.

Brain Atlas Viewer

Background

How to use TVB-on-EBRAINS

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, which in turn makes it possible to produce generalised results - a precondition for being able to use these results to develop potential medical treatments, therapies or diagnostic procedures.

How to use:

  1. Run pre-installed software in the Cloud with web access and HPC back end.
  2. Pull execution-ready Docker containers from Docker Hub.
  • Execute the containers on EBRAINS HPC, using secure container environments like Sarus or Shifter.
  • Operate the containers on supercomputers directly from a web browser via Jupyter notebooks.
  • Log in directly to a shell of the supercomputer via SSH and run the software.
  1. Download software or execution ready containers and run them on a system of your choice, including your local machine.

With the INCF TVB Training Space you can watch didactic tutorials on how to use TVB and the TVB integrated workflows on EBRAINS. Use the video tutorial platform EduPack to get acquainted quickly with TVB.

Watch short video about TVB on EBRAINS Watch full video about TVB on EBRAINS Watch mini-videos explaining TVB on EBRAINS The Virtual Brain Cloud project website INCF TVB Training Space The Virtual Brain project website

User Story

A demonstration of TVB-on-EBRAINS

TVB-on-EBRAINS includes a variety of simulation tools, pipelines, and data sets. Read about and explore TVB-EBRAINS integrated workflows:

  1. Brain simulation and neuroimaging workflows require personal medical data that is subject to data protection regulation. To protect personal data, the services rely on end-to-end encryption and access control.

  2. EBRAINS provides several core services:

  • 'Drive' is a service for hosting and sharing files
  • 'Wiki' and 'Office' allow users to create workspaces and documents for collaborative research
  • 'Lab' hosts sandboxed JupyterLab instances for running live code
  • 'OpenShift' orchestrates different services and provides resources for interactive computing
  • 'HPC' offers supercomputing back ends for resource-intensive computations

  1. Core services interact with the different deployments of TVB services via a RESTful API and Unicore for communication with supercomputers.

  2. TVB services are deployed in the form of a Web GUI, container images, Python notebooks, Python libraries and high-performance back end codes.

  3. The TVB Processing Pipeline produces structural and functional connectomes from MRI data and its outputs can be ingested by Knowledge Graph and annotated with openMINDS metadata, which allows the connectomes to be re-used in other services.

  4. The connectors show interactions between different components (colour group connectors for different deployments)

User story
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