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

By Paula Sanz-Leon, Stuart Knock, Andreas Spiegler, Viktor Jirsa. NeuroImage, 2015

336 citations recorded by the source corpus.

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.

View Paper: https://www.semanticscholar.org/paper/23be65f2b2fee15432e3086c0d98a6610709848f

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Citations in the corpus

Development of large-scale functional networks from birth to adulthood: a guide to neuroimaging literature (2017, background): “Given that macroscale FC is modulated by both interregional connectivity strength as well as local population dynamics (Sanz-Leon et al., 2015), alterations in SC-FC coupling could potentially point to important shifts in the underlying drivers of various network phenomena.”
The Kuramoto model in complex networks (2015, background): “Other recent works have investigated further aspects of the relation between structure and dynamics of phase oscillators in anatomical and functional cortical networks [214, 378, 401–406], which includes studies on the effects of remote synchronization [407–409], a toolbox for large-scale simulation”
Quilting the Brain: Whole-Brain iEEG Reconstruction via Incomplete Observation Linear Mixed Models (2026, unknown): “These technologies o(cid:27)er whole-brain coverage, enabling researchers to infer large-scale functional network architectures and global dynamic propagation (Sanz-Leon et al., 2015; Breakspear, 2017; Deco et al., 2008).”