Prof. Dr. Xenia Kobeleva

Research interest

In the Computational Neurology group, we answer clinical relevant questions in the field of neuropsychiatry using computational methods. We analyze clinical and neuroimaging data using both data-driven and theory-based modeling. This integration helps generating a better (qualitative) and robust (quantitative) understanding of pathophysiological processes of neurological diseases, as well as their diagnosis and treatment.

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.

Methods

• Dynamical modelling (dynamic mean field, Hopf, etc.)
• Analyses of connectivity (structural connectivity, functional connectivity, dynamical functional connectivity, effective connectivity)
• Pre-processing of structural and fMRI data (including preprocessing pipelines)
• Analyses of resting state fMRI and task fMRI
• Neurophysiology (TMS, EMG, EEG)
• Medical informatics
• Open science

Master theses

We welcome students who are interested in experimental and theoretical analyses of pathological oscillations in neurodegeneration and their modulation with neurostimulation, as well as explorations of dynamical models and model fitting.

Suggestions for Master projects in the department Computational Neurology

Website

click here to find out more about the department of Computational Neurology

click here for the INI-Website of the department of Computational Neurology