The Virtual Brain

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Learning by doing.

  • LEARN: Modelling Strokes within TVB

    • Stroke

    Learn how to simulate strokes with the simulation platform, The Virtual Brain

    We will go through two papers: Functional Mechanisms of Recovery after Stroke: Modeling with The Virtual Brain and The Virtual Brain: Modeling Biological Correlates of Recovery After Chronic Stroke, and apply the same processes with our own structural connectivity data set in The Virtual Brain.

    Topics covered in this lesson by Paul Triebkorn

    • Overview of modelling strokes in The Virtual Brain
    • Parameter settings for modelling strokes in The Virtual Brain
    • Visualizing results of simulations in The Virtual Brain
    • Parameter space exploration

    Related publication

    Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain, published in eNeuro, March 2016, by Maria Inez Falcon, Jeffrey D. Riley, Viktor Jirsa, Anthony R. McIntosh, E. Elinor Chen and Ana Solodkin

    doi: 10.1523/ENEURO.0158-15.2016


    We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual’s human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke.

    In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6–12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype. Compared with controls, individuals with stroke demonstrated a consistent reduction in conduction velocity, increased local dynamics, and reduced local inhibitory coupling. A negative relationship between local excitation and motor recovery, and a positive correlation between local dynamics and motor recovery were seen.

    TVB reveals a disrupted post-stroke system favoring excitation-over-inhibition and local-over-global dynamics, consistent with existing mammal literature on stroke mechanisms. Our results point to the potential of TVB to determine individualized biomarkers of stroke recovery.