Neuroimaging and Interindividual Differences

The project group uses MRI-based techniques to understand the neurophysiological basis of work-related factors and to develop ways to modulate them.

Prof. Dr. Erhan Genç
Ardeystr. 67
44139 Dortmund
Dr. Erhan Genc

Research Group Leader

The project group addresses the question of the extent to which interindividual differences in work-relevant factors (e.g. cognitive performance or personality structure) are associated with interindividual differences in the structural, functional and metabolic constitution of the brain. To obtain data, the project group combines various MRI-based techniques with psychometric, genetic and electrophysiological methods. Relevant correlations are not only examined in cross-section, but also in the form of longitudinal studies (e.g. Dortmund Vitality Study). In addition, the project group uses various brain stimulation techniques to gain insights into the basic mechanisms of said factors and to modulate them.

Focus 1: Interindividual differences in work-related factors can be attributed to variations in brain structure, function and metabolism as well as genetic factors. To understand these relationships, data are collected using modern imaging and (poly)genetic methods and analysed using statistical methods from the field of machine learning.

Focus 2: In many respects, it is desirable to modulate work-relevant factors non-invasively. To this end, the findings from statistical investigations can be used to develop individualised brain stimulation protocols. Such protocols allow effective modulation of factors such as cognitive performance and can be used experimentally to gain an even deeper understanding of the underlying physiological processes.

Focus 3: Anxiety disorders can contribute to lost employment and reduced productivity. It is therefore of great interest to identify factors that promote unlearning of inappropriate fear responses (extinction learning). To this end, the functional connectivity of the brain is being investigated for learning-related dynamics using 7T MRI imaging.


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