A multi-modal approach for examining dopamine andnoradrenaline in health and Parkinson's Disease

In this presentation I want to give an overview over the methods and conceptual approach that I use to deepen our understanding of the human brain in health and disease. In my work, I use multiple measurement modalities in combination with computational modelling, focusing on the dopaminergic and noradrenergic systems and their dysfunction in Parkinson’s disease. I apply computational models of learning and decision-making to probe disease-induced changes in brain and behavior, moving from descriptive to mechanistic levels of understanding.
Using published and preliminary data, I will showcase our multi-modal assessment approach bridging between information about brain structure, brain function, autonomic nervous system function, all the way to disease symptoms to gain novel comprehensive insights into the dopaminergic and noradrenergic system in health and disease. Using ultra-high resolution (7 tesla) structural MR data we have shown that neurodegeneration in the noradrenergic locus coeruleus and the dopaminergic substantia nigra occurs non-uniformly and that different sub-regions are associated with different motor- and non-motor symptoms. Preliminary data suggest that we can measure functional changes in Parkinson’s disease in brain regions as small as the locus coeruleus. We find that these perturbations are normalized by medication and that they are reflected in autonomic responses (pupil size).
Focusing more on the functional MR data, I will present our novel analysis framework which generalizes receptive field mapping from sensory neuroscience to cognitive domains, revealing topographic principles underlying cognitive processes in the brain. I will present our findings that the brain simultaneously computes value estimates based on longer and shorter-term memory integration. I will then explain how we have further developed the technique and how I will use it in a recently initiated 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.
Giving a broad introduction to my conceptual approach and the tools I use, I hope to touch upon many potential areas of overlap with the work and interests of other LFIN members.