The data-driven approach mainly focusses on the analysis of neurological diseases with methods of machine learning in order to identify characteristic properties of the brain structure and brain function as clinical phenotypes. The model-driven approach builds on our findings from the data-driven approach. It formalizes a mechanistic relationship between clinical data and brain function via equations; in doing so, this relationship can be meaningfully interpreted, and it extends and goes beyond mere observations. Simulated brain models exert high congruence with actual brain data (compare Figure 2).
Figure 2: Brain networks can be realistically simulated with dynamical models. The simulated brain network (left) shows high conformity to the actual clinical data (right). © Xenia Kobeleva
This kind of modelling might enable medical doctors to adapt their treatments based on patient-specific mathematical models.
Methods we use in the research group: