Publications and Citations

Primary Citation

If you find VBI useful in your research, please cite our main publication:

@article{VBI,
  title={Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models},
  author={Ziaeemehr, Abolfazl and Woodman, Marmaduke and Domide, Lia and Petkoski, Spase and Jirsa, Viktor and Hashemi, Meysam},
  DOI={10.7554/elife.106194.1},
  url={http://dx.doi.org/10.7554/eLife.106194.1},
  publisher={eLife Sciences Publications, Ltd},
  year={2025},
  abstract = {Network neuroscience has proven essential for understanding the principles and mechanisms
  underlying complex brain (dys)function and cognition. In this context, whole-brain network modeling–
  also known as virtual brain modeling–combines computational models of brain dynamics (placed at each network node)
  with individual brain imaging data (to coordinate and connect the nodes), advancing our understanding of
  the complex dynamics of the brain and its neurobiological underpinnings. However, there remains a critical
  need for automated model inversion tools to estimate control (bifurcation) parameters at large scales
  associated with neuroimaging modalities, given their varying spatio-temporal resolutions.
  This study aims to address this gap by introducing a flexible and integrative toolkit for efficient Bayesian inference
  on virtual brain models, called Virtual Brain Inference (VBI). This open-source toolkit provides fast simulations,
  taxonomy of feature extraction, efficient data storage and loading, and probabilistic machine learning algorithms,
  enabling biophysically interpretable inference from non-invasive and invasive recordings.
  Through in-silico testing, we demonstrate the accuracy and reliability of inference for commonly used
  whole-brain network models and their associated neuroimaging data. VBI shows potential to improve hypothesis
  evaluation in network neuroscience through uncertainty quantification, and contribute to advances in precision
  medicine by enhancing the predictive power of virtual brain models.}
}

DOI: 10.7554/elife.106194.1

Zenodo DOI: 10.5281/zenodo.14795543

Publications Using VBI

Below is a list of publications that have used VBI in their research. If you have published work using VBI and would like it listed here, please open an issue or submit a pull request.

2023 - The virtual multiple sclerosis patient: on the clinical-radiological paradox

Sorrentino, P and Pathak, A and Ziaeemehr, A and Troisi Lopez, E and Cipriano, L and Romano, A and Sparaco, M and Quarantelli, M and Banerjee, A and Sorrentino, G and others

medRxiv preprint, Cold Spring Harbor Laboratory Press.

This study addresses the clinical-radiological paradox in multiple sclerosis using virtual patient modeling with VBI, exploring the disconnect between clinical symptoms and radiological findings in MS patients.

2024 - Simulation-based inference on virtual brain models of disorders

Hashemi, Meysam and Ziaeemehr, Abolfazl and Woodman, Marmaduke M and Fousek, Jan and Petkoski, Spase and Jirsa, Viktor K

Machine Learning: Science and Technology, Volume 5, Issue 3, Pages 035019.

This publication demonstrates the application of simulation-based inference techniques using VBI for parameter estimation on virtual brain models, particularly focusing on brain disorders and neurological conditions.

2024 - Principles and operation of virtual brain twins

Hashemi, Meysam and Depannemaecker, Damien and Saggio, Marisa and Triebkorn, Paul and Rabuffo, Giovanni and Fousek, Jan and Ziaeemehr, Abolfazl and Sip, Viktor and Athanasiadis, Anastasios and Breyton, Martin and others

bioRxiv preprint, Cold Spring Harbor Laboratory.

This publication explores the fundamental principles and operational aspects of virtual brain twins, demonstrating the application of VBI in creating personalized brain models for precision medicine and understanding individual brain dynamics.

2024 - The virtual multiple sclerosis patient

Sorrentino, P and Pathak, A and Ziaeemehr, A and Lopez, E Troisi and Cipriano, L and Romano, A and Sparaco, M and Quarantelli, M and Banerjee, A and Sorrentino, G and others

iScience, Volume 27, Issue 7. Publisher: Elsevier.

This study presents a virtual patient model for multiple sclerosis, utilizing VBI for personalized modeling and understanding of disease progression and treatment responses in MS patients.

2025 - The virtual parkinsonian patient

Angiolelli, Marianna and Depannemaecker, Damien and Agouram, Hasnae and Régis, Jean and Carron, Romain and Woodman, Marmaduke and Chiodo, Letizia and Triebkorn, Paul and Ziaeemehr, Abolfazl and Hashemi, Meysam and others

npj Systems Biology and Applications, Volume 11, Issue 1, Pages 40. Publisher: Nature Publishing Group UK London.

This publication presents a virtual patient model for Parkinson’s disease, demonstrating the application of VBI in modeling parkinsonian brain dynamics and potential therapeutic interventions.

2025 - Mapping brain lesions to conduction delays: the next step for personalized brain models in Multiple Sclerosis

Mazzara, Camille and Ziaeemehr, Abolfazl and Troisi Lopez, E and Cipriano, Lorenzo and Angiolelli, Marianna and Sparaco, Maddalena and Quarantelli, Mario and Granata, Carmine and Sorrentino, Giuseppe and Hashemi, Meysam and others

Human Brain Mapping, Volume 46, Issue 7, Pages e70219. Publisher: Wiley Online Library.

This study advances personalized brain modeling in Multiple Sclerosis by mapping brain lesions to conduction delays, utilizing VBI to create more accurate and clinically relevant virtual MS patient models.

2025 - Mapping global brain reconfigurations following local targeted manipulations

Rabuffo, Giovanni and Lokossou, Houefa-Armelle and Li, Zengmin and Ziaee-Mehr, Abolfazl and Hashemi, Meysam and Quilichini, Pascale P. and Ghestem, Antoine and Arab, Ouafae and Esclapez, Monique and Verma, Parul and Raj, Ashish and Gozzi, Alessandro and Sorrentino, Pierpaolo and Chuang, Kai-Hsiang and Perles-Barbacaru, Teodora-Adriana and Viola, Angèle and Jirsa, Viktor K. and Bernard, Christophe

Proceedings of the National Academy of Sciences, Volume 122, Issue 16, Pages e2405706122. DOI: 10.1073/pnas.2405706122

This study investigates how local targeted brain manipulations lead to global brain reconfigurations, utilizing VBI for analyzing the complex dynamics and inference on virtual brain models.

Note

This section will be updated as new publications using VBI become available. If you have used VBI in your research, we would love to hear about it!

Funding Acknowledgments

This research has received funding from:

European Union:

• EU’s Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreements:

  • No. 101147319 (EBRAINS 2.0 Project)

  • No. 101137289 (Virtual Brain Twin Project)

  • No. 101057429 (project environMENTAL)

France:

• Government grant managed by the Agence Nationale de la Recherche:

  • Reference ANR-22-PESN-0012 (France 2030 program)

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

How to Submit Your Publication

If you have used VBI in your research and would like your publication listed here:

1. Open an Issue: Create a new issue on our GitHub repository with the label “publication”

2. Submit a Pull Request: Add your publication details to this file following the template format

3. Email Us: Contact the development team with your publication details

Required Information:

• Citation in standard academic format

• DOI link (if available)

We appreciate you acknowledging VBI in your work and helping us track the impact of this toolkit in the research community!