Elsevier

NeuroImage

Volume 111, 1 May 2015, Pages 385-430
NeuroImage

Mathematical framework for large-scale brain network modeling in The Virtual Brain

https://doi.org/10.1016/j.neuroimage.2015.01.002Get rights and content
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Highlights

  • We present mathematical description of the components of a brain network model.

  • A direct link is provided between the equations and their implementation in TVB.

  • Arbitrary neural mass models can be embedded within the general brain network model.

  • The framework also supports neural field models, permitting model cross-validation.

  • Biophysical observation models enable a direct comparison with experimental data.

Abstract

In this article, we describe the mathematical framework of the computational model at the core of the tool The Virtual Brain (TVB), designed to simulate collective whole brain dynamics by virtualizing brain structure and function, allowing simultaneous outputs of a number of experimental modalities such as electro- and magnetoencephalography (EEG, MEG) and functional Magnetic Resonance Imaging (fMRI). The implementation allows for a systematic exploration and manipulation of every underlying component of a large-scale brain network model (BNM), such as the neural mass model governing the local dynamics or the structural connectivity constraining the space time structure of the network couplings. Here, a consistent notation for the generalized BNM is given, so that in this form the equations represent a direct link between the mathematical description of BNMs and the components of the numerical implementation in TVB. Finally, we made a summary of the forward models implemented for mapping simulated neural activity (EEG, MEG, sterotactic electroencephalogram (sEEG), fMRI), identifying their advantages and limitations.

Keywords

Brain network models
Neural mass
Neural field
Whole brain dynamics
Forward solutions
Cortical surface
Brain simulator
EEG
MEG
fMRI
BOLD

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