1. Introduction to The Virtual Brain

What is TVB, and why does it matter?

Authors
Affiliations

Marius Pille

Berlin Institute of Health at Charité University Medicine

Leon Martin

Berlin Institute of Health at Charité University Medicine

Leon Stefanovski

Charité University Medicine Berlin

This session gives an overview of The Virtual Brain (TVB) — its history, mathematical foundations, and applications in basic and translational neuroscience. It sets the stage for all hands-on notebooks that follow.

1 Topics

1.1 Basic ideas and history

TVB is an open-source, multi-scale brain simulation framework. Learn how it emerged from empirical connectivity science and why whole-brain network models offer a principled window into brain function and dysfunction.

1.2 Mathematical framework

Brain network models are systems of coupled ODEs or SDEs on a structural connectome graph. This section introduces the key equations: local dynamics, coupling functions, long-range delays, and noise.

1.3 Applications in basic research

Connectome-constrained decision dynamics (Schirner, Deco, and Ritter 2023), cortical wave phenomena (Koller, Schirner, and Ritter 2024), and ODE-based separation of task and rest fMRI signals (Kashyap et al. 2025).

1.4 Clinical applications

Mechanistic simulation of Alzheimer’s disease, epilepsy surgery planning, and brain stimulation protocols — linking biophysical parameters to clinical biomarkers.

2 Key concepts

Concept Description
Neural mass model Mean-field ODE describing the activity of a cortical region
Structural connectivity White-matter tractography-derived coupling weights and delays
Coupling function How the activity of connected regions influences each other
Parcellation / atlas Discrete spatial partition mapping brain regions to connectome nodes
BOLD signal Haemodynamic observable linked to simulated neural activity via a balloon model

3 Useful resources

See the workshop agenda.

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References

Kashyap, Amrit, Eloy Geenjaar, Patrik Bey, Kiret Dhindsa, Katharina Glomb, Sergey Plis, Shella Keilholz, and Petra Ritter. 2025. “Using an Ordinary Differential Equation Model to Separate Rest and Task Signals in fMRI.” Nature Communications 16 (1). https://doi.org/10.1038/s41467-025-62491-6.
Koller, Dominik P., Michael Schirner, and Petra Ritter. 2024. “Human Connectome Topology Directs Cortical Traveling Waves and Shapes Frequency Gradients.” Nature Communications 15 (1). https://doi.org/10.1038/s41467-024-47860-x.
Schirner, Michael, Gustavo Deco, and Petra Ritter. 2023. “Learning How Network Structure Shapes Decision-Making for Bio-Inspired Computing.” Nature Communications 14 (1). https://doi.org/10.1038/s41467-023-38626-y.