The Virtual Brain: a simulator of primate brain network dynamics

By Paula Sanz-Leon, Stuart Knock, Michael Marmaduke Woodman, Lia Domide, Jochen Mersmann, Anthony Randal McIntosh, Viktor Jirsa. Front. Neuroinform., 2013

503 citations recorded by the source corpus.

We present The Virtual Brain (TVB), a neuroinformatics platform for full brain network simulations using biologically realistic connectivity. This simulation environment enables the model-based inference of neurophysiological mechanisms across different brain scales that underlie the generation of macroscopic neuroimaging signals including functional MRI (fMRI), EEG and MEG. Researchers from different backgrounds can benefit from an integrative software platform including a supporting framework for data management (generation, organization, storage, integration and sharing) and a simulation core written in Python. TVB allows the reproduction and evaluation of personalized configurations of the brain by using individual subject data. This personalization facilitates an exploration of the consequences of pathological changes in the system, permitting to investigate potential ways to counteract such unfavorable processes. The architecture of TVB supports interaction with MATLAB packages, for example, the well known Brain Connectivity Toolbox. TVB can be used in a client-server configuration, such that it can be remotely accessed through the Internet thanks to its web-based HTML5, JS, and WebGL graphical user interface. TVB is also accessible as a standalone cross-platform Python library and application, and users can interact with the scientific core through the scripting interface IDLE, enabling easy modeling, development and debugging of the scientific kernel. This second interface makes TVB extensible by combining it with other libraries and modules developed by the Python scientific community. In this article, we describe the theoretical background and foundations that led to the development of TVB, the architecture and features of its major software components as well as potential neuroscience applications.

Subfields: Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/c0d276f4cff85c2120a90c87b32eb5ad5b0f30bb

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In Cognitive Neuroscience

Citations in the corpus

Mathematical framework for large-scale brain network modeling in The Virtual Brain (2015, unknown): “Also, see Spiegler and Jirsa (2013) for guidelines about choosing the spatial distribution function for a given geometry of the cortex model in numerical implementations of BNMs (Sanz-Leon et al., 2013).”
The Virtual Epileptic Patient: Individualized whole‐brain models of epilepsy spread (2017, background): “All steps can be performed within the neuroinformatics platform The Virtual Brain (see http://www.thevirtualbrain.org; (Sanz Leon et al., 2013, 2015 Spiegler and Jirsa, 2013)) and its associated pipelines (Schirner et al., 2015; Proix et al., 2016).”
Functional connectivity dynamics: Modeling the switching behavior of the resting state (2015, background): “They are easily implemented within dedicated simulation environments such as The Virtual Brain (Sanz Leon et al., 2013) and are capable ofreproducing time-averaged restingstate FC. Ithas been pointed out, however, that purely statistical models, which predict FC on the basis of local and global…”
Questions and controversies in the study of time-varying functional connectivity in resting fMRI (2020, unknown): “A variety of simulation tools are available to help researchers evaluate how TVFC methods perform under a range of different data-generating conditions (Erhardt, Allen, Wei, Eichele, & Calhoun, 2012; Sanz Leon et al., 2013; W. H. Thompson, Richter, Plaven-Sigray, & Fransson, 2018; Welvaert & Rosseel”