Virtual Brains

In this project we use brain modelling to recreate the behaviour and function of the human brain using computer models. The idea is to create “Digital Twins” for each subject and obtain a low-dimensional representation of their individual brain dynamics.

We are using a whole brain modelling approach. To keep this computationally feasible, we combine all synapses of a brain region to one node and treat them as one unit in the signal processing. The regions are determined by brain atlases, e.g. the Desikan Killiany atlas. At each of these nodes we run a neural mass model (NMM), primarily the Reduced Wong Wang-Deco model, which emulates the processing of signals at this node. The connections between the nodes are determined through the number of streamlines, which is recovered from DTI measurements. With this model we are then able to simulate resting state fMRI signal. To individualize the brain model we run an optimization of the models´ parameters with the aim to achieve a high correlation between the simulated and the empirically measured resting state fMRI of each individual subject.

The goal of this project is to combine state-of-the-art machine learning with these personalized Digital Twins and thereby moving the field from the purely predictive analytics-based approach towards interpretable models which enable mathematically principled intervention design.