Our interdisciplinary team develops and implements algorithms for analyzing structural data (especially diffusion weighted). We implement new techniques to derive and optimize tractograms of the human brain in order to derive specific markers within the brain white matter to investigate neurodegenerative and especially psychiatric disorders. For example, we showed that our novel optimized fiber density marker shows higher sensitivity for schizophrenia and major depressive disorders compared to standard measures such as fractional anisotropy.
Furthermore, we adapt and improve MRS methods for specific questions in the field of psychiatry and psychology. An important aspect of this work is the quantification of the MRS data. Quantification not only requires artifact-free acquisition with sufficient signal to noise ratio, but also methods to correct other factors that influence signal detection such as coil loading and sensitivity. For example, our combination of the metabolite cycling technique for localization, and a quantification approach based on the principle of reciprocity allows us to reliably determine metabolite concentrations from small volumes in the human brain.
With our work we aim at translating these cutting edge technologies to the clinics to the benefit of patients.
Keywords: Sequence development, functional brain imaging, structural brain imaging, spectroscopic imaging, clinical neuroscience, psychiatric disorders