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:
We invite applications for a master's thesis for contributing to developing models of personalized transcranial magnetic stimulation (TMS) in neurodegenerative diseases (e.g., Alzheimer’s Disease). The project touches topics of probabilistic modelling, data analysis and optimization.
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.
We invite applications for students' projects and bachelor/master theses for contributing to developing models of pathological influences of neurodegeneratives diseases (i.e., Alzheimer’s Disease) on brain activity. The project touches topics of whole-brain dynamics, biomarkers, and effective connectivity (i.e., directional influences of brain regions onto each other).
The Institut für Neuroinformatik (INI) is a research unit of the Faculty of Computer Science at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and 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 psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.
Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany
Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210
Riccardo Leone 