Computational Neurology

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 a data-driven and a modelling-based approach (see Figure 1). This integration helps generating a better (qualitative) and robust (quantitative) understanding of pathophysiological processes of neurological diseases, as well as their diagnosis and treatment.

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 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:

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

Explore dynamical consequences of neurostimulation using neural mass modeling

Personalization of neurostimulation can improve its efficacy whilst taking into account the increasing complexity of the corresponding technology and the amount of technical choices (regarding stimulation location and stimulation settings). In this project we aim to create a whole-brain neural mass model that can accurately predict the effect of different neurostimulation settings in healthy and neurologically impaired individuals.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210