Written with help of Paul Triebkorn, MD & Andreas Spiegler, PhD

Indeed, neuroscience is changing very quickly in the spirit of open science. This involves not only sharing big datasets but also creating new, open-source tools that allow for testing research hypotheses derived in the clinics. One such tool, for instance, is The Virtual Brain (TVB): a software developed to launch mesoscale simulations of the human brain.

The human brain connects across multiple levels of organization: from cortical microcircuits built from dense networks of interconnected neurons to large resting-state networks competing with each other. These levels are relevant while addressing different cognitive paradigms.

For instance, according to Quiroga et al. (2005), a single neuron within the visual cortex reacted to a particular face (now referred to as the Jennifer Aniston cell). However, cognitive dimensions that are subject to most clinical studies, usually engage mesoscale brain networks: groups of interconnected brain regions which can be found with the use of interventional cognitive studies combined with classic neuroimaging techniques such as functional Magnetic Resonance Imaging or electroencephalography.

Why Simulate Brain Activity With The Virtual Brain?

The Virtual Brain models neuronal activity on this mesoscale level of organization. Why to even simulate brain activity? This simulator, notably, predicts neuroimaging recordings derived from a model specified by the user and can serve to study subject-specific local sources of cognitive deficits.

What Are The Model’s Three Basic Features?

Three basic sets of features define the model:

1. matrix of connectivity. This is, in fact, either anatomical connectivity or a combination of anatomical and functional connectivity. Anatomical connectivity, notably, reflects the width of neuronal fibers projecting between different brain regions within the chosen network. Researchers can derive this matrix from a Diffusion Tensor Imaging study. Functional connectivity, on the other hand, reflects the strength of statistical associations between activity in different brain regions. There is a variety of techniques to quantify the strength of functional connectivity in the brain in fMRI or EEG / MEG, therefore, the derivation of this matrix depends on the research questions specified by the user and the properties of the datasets in the particular study.
2. a matrix of associated delays, notably, in communication between brain regions. These delays have a crucial influence on the dynamics in the network, since they function as rhythm generators.
3. a dynamical model that, notably, aims to capture the nature of dynamic interaction between brain regions. Currently, TVB implements a range of neural mass models. Dynamical models, notably, differ in terms of potential applications. For instance, Jansen & Rit model is a three-node dynamical system used to simulate alpha rhythms in the cortex, while Wong-Wang model is a system of two coupled non-linear equations, dedicated to modeling decision making in two-choice paradigms (winner-takes-all dynamics). Therefore, it is important to mind the tested predictions and choose the model which can best address the investigated research problem. The TVB software allows for further fine-tuning of the dynamical model and tracking the generated dynamics in a simple two-node case before applying it to complex datasets (Fig. 1).