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  • Published:2020-12-11 01:00:00.0

    • Stroke

    LEARN: Modelling Strokes within TVB

    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

    Abstract

    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.

    byMichael Burgstahler

  • Published:2020-12-10 01:00:00.0

    • Stroke

    LEARN: TVB Clinical Applications - Stroke Recovery & Dementia

    This lecture presents two recent clinical case studies using TVB: stroke recovery and dementia (due to Alzheimer’s Disease (AD)).

    Using a multi-scale neurophysiological model based on empirical multi-modal neuroimaging data, we show how local and global biophysical parameters characterize changes in  individualized patient-specific brain dynamics, predict recovery of motor function for stroke patients, and correlate with individual differences in cognition for AD patients.

    Topics covered in this lesson by Randy McIntosh

    • General introduction to stroke
    • TVB simulation workflow for stroke patients
    • Structural reconstruction: “Virtual Brain Transplant”
    • Parameter space exploration and fitting
    • General introduction to dementia
    • TVB simulation workflow for patients with dementia
    • Pre-processing issues related to atrophy
    • Two-stage modeling: sub- and full-brain network model
    • Cognition predictor: model parameters vs. metrics of neuroimaging data

    byMichael Burgstahler

  • Published:2020-12-09 01:00:00.0

    • Stroke

    LEARN: Hands-On: Modeling stroke brain

    Manipulate the default connectome provided with TVB to see how structural lesions effect brain dynamics

    In this hands-on session you will insert lesions into the connectome within the TVB graphical user interface. Afterwards the modified connectome will be used for simulations and the resulting activity will be analysed using functional connectivity.

    Topics covered in this lesson by Paul Triebkorn

    • Manipulation of the connectome
    • Parameter space exploration
    • Functional connectivity analysis

    byMichael Burgstahler

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