Independent thesis Advanced level (professional degree), 20 credits / 30 HE credits
The dopamine system innervates diﬀerent regions of the brain via different pathways, and regulates motor function, cognitive behavior and emotions as well as motivation and reward. The binding to dopamine receptors in different brain regions is therefore important for different functions.
With aid of Positron Emission Tomography (PET) it is possible to investigate receptor binding in different regions for different neurotransmitters in vivo. There are different kinds of dopamine receptors, these subtypes are grouped into two categories, D1-like and D2- like receptors that have different intracellular signalling pathways. The dopamine receptors are distributed over the entire brain, but the receptor densities vary over different regions. The relationship between receptor density for different regions and across subjects are poorly understood. Radioligands used in this study was 11C-SCH23390 for D1 binding and 11C-Raclopride for D2 binding. Regions included in this investigation was striatum, amygdala, hippocampus, anterior cingulated cortex, orbitofrontal cortex, lateral temporal cortex and lateral parietal cortex. Manual region of interests were manually delineated for striatal areas, and the Anatomical Automatic Labelling in SPM5 were used for obtaining the BP values for extrastriatal areas. The participants in this study were 30 healthy volunteering male subjects with average age of 24.9 (std 2.7). No linear Pearson correlations were found between D1 and D2 for neither of the included regions. With aid of principal component analysis and the clustering techniques K-means and Fuzzy c-means, the subjects were divided into different clusters depending on their individual dopamine D1 and D2 receptor binding proﬁles. One subgroup had high D1−levels, and low D2−levels, the other group low D1−levels and high D2−levels. These formations were not distinguishable by visual inspection of the group as a single entity, but were identiﬁed by the cluster algorithms.
2012. , 39 p.