Multi-modal imaging of functional systems in the human brainstem

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The brainstem is the most important part of the brain, when it comes to sustaining our life. Despite its tremendous importance, it has been largely neglected by human neuroscience. The most important reason for this neglect lies in the poor performance of standard neuroscientific measurement methods, like functional magnetic resonance imaging (fMRI), in this part of the brain, which is mainly due to the elevated level of physiological noise. fMRI investigations have so far been restricted to basic activation studies of single brainstem nuclei, while investigations on functional connectivity have been mostly unsuccessful due to problems in the application of independent component analysis (ICA). This key method suffers from severe problems in the brainstem as standard approaches are unable to suppress physiological noise to a point, where signals of neuronal origin become the major source of variance in the data. The central goal of this project is to improve a new brainstem-fMRI approach, recently developed by the applicant, to a point where it can be used to reliably measure neuronal activity of single nuclei as well as inter-nuclear and nucleo-cortical connectivity. The new approach uses a radically different approach of physiological noise suppression and can be applied to standard fMRI datasets. To reach this goal, we will first optimize and motivate some ad hoc choices for parameters in previous successful analyses, like the number of dimensions for the ICA decomposition or the exact shape of the brainstem anatomical mask. Afterwards, the applicant together with the academic partner from the Martinos Center for Biomedical Imaging of Harvard University Boston will acquire a sample of 20-30 high-resolution anatomical scans on the local 7-Tesla high-field MRI scanner and develop a data fusion approach to combine these structural data with an existing 3-Tesla functional dataset of more than 100 subjects acquired by the applicant. The aim is to greatly improve the identification of anatomical structures underlying activation clusters in the brainstem. Finally, we will apply the improved method to three existing datasets of common chronic pain syndroms recently acquired by the academic partner. These include data on fibromyalgia, low back pain and carpal tunnel syndrome. The aim is to identify nuclei of relevance for pain modulatory processes and investigate, whether these nuclei show pathologically altered connectivity in chronic pain patients. Questions of interest are, if altered brainstem intrinsic connectivity may be a common mechanism to different pain syndromes, and whether neocortical or brainstem centers play the most important role in chronic pain.