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. 

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