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

Workshop Dates: 12-13 May 2026

About

Participants learn core concepts and methods for personalized whole-brain network modeling with The Virtual Brain (TVB). The workflow combines individualized model definition, simulation, and analysis with ontology-driven model specification and gradient-based optimization (Martin et al. 2025; Pille et al. 2025).

The Virtual Brain Ontology (TVB-O) provides a knowledge framework for FAIR model specification and automated code generation, while connecting model metadata to biomedical ontologies (Martin et al. 2025). TVB-Optim adds gradient-based fitting of large parameter spaces in whole-brain models, with scaling for computationally intensive optimization experiments (Pille et al. 2025).

Relevant examples of mechanistic and translational whole-brain modeling include connectome-constrained decision dynamics (Schirner, Deco, and Ritter 2023), connectome-driven cortical wave phenomena (Koller, Schirner, and Ritter 2024), and ODE-based separation of task and rest fMRI signals (Kashyap et al. 2025).

Useful links:

  • The Virtual Brain: https://thevirtualbrain.org
  • TVB-O: https://virtual-twin.github.io/tvbo
  • TVB-Optim: https://virtual-twin.github.io/tvboptim

Format

Two days of hands-on sessions: theory and code for specifying, running, analyzing, and optimizing dynamical brain network models, followed by a mini hackathon.

Agenda

Day 1

Block 1 — 09:00–12:00

  1. Intro to Brain Network Modelling — notebook
  2. Defining a FAIR brain network model
  3. Setup & first examples — see setup
  4. Applications in basic and clinical research

Block 2 — 13:00–15:00

  1. Bifurcation theory — notebook
    1. Parameter exploration & inference I — notebook

Block 3 — 15:30–17:30

    1. Parameter exploration & inference II
  2. Stimulating the brain

Day 2 — Mini Hackathon

Block 1 — 09:00–11:30

  • Pitch your research question
  • Form small groups
  • Plotting the brain with bsplot

Block 2 — 12:00–13:30

  • Group work on your own data
  • Checkpoints and wrap-up

We help participants work on their own research questions. Bring your own data if possible.

Target Audience

Researchers in computational neuroscience: data scientists, clinicians, engineers, and curious participants. Prior TVB experience helps but is not required.

Requirements

  • Basic Python programming.
  • Notebooks run online via EBRAINS (account required).
  • Limited support for local setups.
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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.
Martin, Leon, Konstantin Buelau, Marius Pille, Rico Andre Schmitt, Christoph Huettl, Jil M Meier, Halgurd Taher, Dionysios Perdikis, Michael Schirner, and Petra Ritter. 2025. “The Virtual Brain Ontology: A Digital Knowledge Framework for Reproducible Brain Network Modeling.” bioRxiv, November. https://doi.org/10.1101/2025.11.19.689211.
Pille, Marius, Leon Martin, Emilius Richter, Dionysios Perdikis, Michael Schirner, and Petra Ritter. 2025. “Fast and Easy Whole-Brain Network Model Parameter Estimation with Automatic Differentiation.” bioRxiv, November. https://doi.org/10.1101/2025.11.18.689003.
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.