The detailed in-vivo characterization of subcortical brain structures is essential not only to understand the basic organizational principles of the healthy brain but also for the study of the involvement of the basal ganglia in brain disorders. The particular tissue properties of basal ganglia - most importantly their high iron content, strongly affect the contrast of magnetic resonance imaging (MRI) images, hampering the accurate automated assessment of these regions. This technical challenge explains the substantial controversy in the literature about the magnitude, directionality and neurobiological interpretation of basal ganglia structural changes estimated from MRI and computational anatomy techniques. My scientific project addresses the pertinent need for accurate automated delineation of basal ganglia using two complementary strategies:
? Empirical testing of the utility of novel imaging protocols to provide superior contrast in the basal ganglia and to quantify brain tissue properties;
? Improvement of the algorithms for the reliable automated detection of basal ganglia and thalamus
Previous research demonstrated that MRI protocols based on magnetization transfer (MT) saturation maps provide optimal grey-white matter contrast in subcortical structures compared with the widely used Tl-weighted (Tlw) images (Helms et al., 2009). Under the assumption of a direct impact of brain tissue properties on MR contrast my first study addressed the question of the mechanisms underlying the regional specificities effect of the basal ganglia. I used established whole-brain voxel-based methods to test for grey matter volume differences between MT and Tlw imaging protocols with an emphasis on subcortical structures. I applied a regression model to explain the observed grey matter differences from the regionally specific impact of brain tissue properties on the MR contrast.
The results of my first project prompted further methodological developments to create adequate priors for the basal ganglia and thalamus allowing optimal automated delineation of these structures in a probabilistic tissue classification framework. I established a standardized workflow for manual labelling of the basal ganglia, thalamus and cerebellar dentate to create new tissue probability maps from quantitative MR maps featuring optimal grey-white matter contrast in subcortical areas. The validation step of the new tissue priors included a comparison of the classification performance with the existing probability maps.
In my third project I continued investigating the factors impacting automated brain tissue classification that result in interpretational shortcomings when using Tlw MRI data in the framework of computational anatomy. While the intensity in Tlw images is predominantly