Computational Neurology

Computationale Neurologie
Klinik und Poliklinik für Neurologie
Dr. Xenia Kobeleva

In order to answer clinically relevant questions, computational neurology combines neuroimaging and clinical data. Computational neurology combines a data-based approach and a modelling-based approach; this integration not only allows for contributions to better understand neurological diseases, but also to improve diagnostics and therapies in neurology. The data-based approach assesses neurodegenerative diseases by using machine-learning techniques in order to associate clinical phenotypes with characteristics of brain structure and function.

The modelling-based approach builds on these insights. Using mathematical concepts, this approach creates mechanistic and interpretable relationships between clinical data and brain activity, which is beyond a pure descriptive value. These kinds of models can be used to test potential treatments in a virtual setting. Therefore, computational neurology contributes to a deeper understanding of neurological diseases and to the development of new diagnostics and therapies. 


Die Computationale Neurologie verbindet einen datengetriebenen Ansatz (links oben), einen modellierungsgetriebenen Ansatz (links unten) und klinische Daten (rechts).. © Xenia Kobeleva
Mithilfe von dynamischen Modellen können Hirnnetzwerke realistisch simuliert werden. Im Vergleich zu empirisch erhobenen Daten (rechts) zeigt die Modellierung (links) eine hohe Übereinstimmung. © Xenia Kobeleva


  • Dynamic modelling (Dynamic Mean Field model, Hopf model, etc.)
  • Connectivity analyses (structural connectivity, static functional connectivity, dynamic functional connectivity, effective connectivity)
  • Structural and functional MRI preprocessing (and creation of preprocessing pipelines)
  • Resting state and task-based fMRI analyses
  • Neurophysiology (TMS, EMG, EEG)
  • Medical informatics
  • Open science