The Virtual Brain

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  • LEARN: Modeling brain dynamics in brain tumor patients using The Virtual Brain

    • Special applications

    Tutorial on how to simulate brain tumor brains with TVB (reproducing publication: Marinazzo et al. 2020 Neuroimage). This tutorial comprises a didactic video, jupyter notebooks, and full data set for the construction of virtual brains from patients and health controls.

    Related publication

    Modeling Brain Dynamics in Brain Tumor Patients Using the Virtual Brain, published in eNeuro, May 2018, by Hannelore Aerts, Michael Schirner, Ben Jeurissen, Dirk Van Roost, Eric Achten, Petra Ritter and Daniele Marinazzo

    doi: 10.1523/ENEURO.0083-18.2018


    Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, noninvasive neuroimaging techniques such as functional MRI and diffusion-weighted imaging fiber tracking are currently employed.

    However, taking into account this information is often still insufficient, as the complex nonlinear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention.

    Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.

    As a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong–Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.

    Results showed

    • (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters;
    • (2) local model parameters that can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain;
    • and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

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