About the research group
The Computational Neurology Group is a research group at Ruhr University Bochum (RUB), consisting of Prof. Dr. Xenia Kobeleva and her team.
Background
We conduct interdisciplinary research at the intersection between neurology and computational modelling. This is reflected in our unique organizational affiliation, which extends to both RUB’s Faculty of Medicine (primary affiliation) and its Faculty of Computer Science (secondary affiliation).
Based on this dual organizational affiliation, many of our activities are based in RUB’s interdisciplinary Institute for Neural Computation (INI). We are also part of the Bernstein Node Bochum, RUB’s Science Hub Neuroscience, and RUB’s International Graduate School of Neuroscience.
Our team is young, motivated, international, and genuinely interdisciplinary, bringing together expertise from medicine, neuroscience, mathematics/quantitative sciences, and psychology. Besides a direct connection to clinical neurology, we are intensively collaborating with leading experts for mathematics and computer science.
Research questions
In the Computational Neurology Group, we investigate clinically relevant questions in the field of neuropsychiatry (especially questions about neurodegenerative diseases) using computational approaches. In doing so, we bridge clinical neuroscience (neurostimulation, symptoms, networks and treatment of neurodegenerative diseases) and computational neuroscience (whole-brain network neural modeling, parameter inference, control theory), contributing to a growing body of translational research that connects computational methods to real-world clinical data and patient-oriented questions.
Research strategy: Combining data- and model-driven approaches
Our research focuses on combining a data-driven approach and a model-driven approach, aiming for tuning our mathematical models so that they accurately replicate clinical brain activity data (see Figure 1).
Figure 1: Computational neurology connects a data-driven approach (top left), a model-based approach (bottom left), and clinical data (right). © Xenia Kobeleva
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 activity 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
These mathematical models can enable medical doctors to adapt their treatments based on patient-specific model parameters.
Research methods
In the Computational Neurology Group, we combine several methods from medical research, mathematics, and computer science. Specifically, we specialize in:
- Dynamical modelling (dynamic mean field, Hopf, etc.)
- Analyses of connectivity (structural connectivity, functional connectivity, dynamical functional connectivity, effective connectivity)
- Neurostimulation (including TMS-EEG)
- Preprocessing of structural and functional MRT data (including preprocessing pipelines)
- Analyses and design of fMRI and EEG studies
- Neurophysiology (TMS, EMG, EEG)
Social benefit of our research
We are firm believers that research transparency and outreach to patients is the necessary foundation that legitimizes our academic work.
Following the principles of Open Science, we thoroughly document our research inputs, methodologies, and outputs, make them publicly available and subsequentially publish our results in Open Access. By doing so, we enable third-party replication of our results in different contexts, validating or falsifying our findings.
We also engage in several outreach activities (communicating our research results to patients affected) and maintain strong relationships to patient interest groups (see website section “non-expert info”). As such, we investigate not only research questions that are clinically relevant, but also relevant to the individuals affected by neurodegenerative diseases (whether these individuals are actual patients or family/care-givers).