Research Interests
In my work, I use a combination of multi-modal data assessment and computational modeling to deepen our understanding of the human brain in health and disease. Using computational models of learning and decision-making to probe disease-induced changes in brain and behavior allows me to move from descriptive to mechanistic levels of understanding. Working with a multi-modal assessment approach I try to bridge between information about brain structure, brain function, autonomic nervous system function, all the way to symptom assessments to gain novel comprehensive insights into the dopaminergic and noradrenergic system in health and disease. The ultra-high field (7 tesla) MR scanner allows me to map the structural integrity of midbrain nuclei at highest resolution. This allows me to investigate the relationship between the individual spatial pattern of neurodegeneration in Parkinson’s disease and the patient’s clinical symptoms.An important analysis tool for my work is our novel mapping approach which generalizes receptive field mapping from sensory neuroscience to cognitive domains, revealing topographic principles underlying cognitive processes in the brain. Using this technique, I have recently initiated a project to test novel theories about how the human brain encodes a diverse range of reward prediction errors and how Parkinson’s disease might affect this encoding in a specific way.
