The motivation of this thesis is the in vivo dissection of the human brain's white matter from diffusion magnetic resonance imaging. This procedure isolates the human brain's white matter tracts that play a role in a particular function or disorder of the brain so they can be analysed. Manually performing this task requires a great knowledge of brain anatomy and several hours of work. Hence, the development of a technique to automatically perform the identification of white matter structures is of utmost importance. The brain is organized in networks that are made up of tracts connecting different regions. These networks are important for the development of brain functions such as language or vision. Moreover, lesions and cognitive disorders are sometimes better explained by disconnection mechanisms between cerebral regions than by damage of those regions. Despite several decades of tracing these networks in the brain, our knowledge of cerebral connections has progressed very little since the beginning of the last century. Recently, we have seen a spectacular development of magnetic resonance imaging (MRI) techniques for the study of the living human brain. One technique for exploring white matter tissue characteristics and pathways in vivo is Diffusion MRI (dMRI). Particularly, dMRI-based tractography facilitates tracing the white matter tracts in vivo. Overall, dMRI is a promising technique to explore the anatomical basis of human cognition and its disorders. This thesis has several contributions. We develop the means for the automatic dissection of WM tracts from dMRI, this is based on a mathematical framework for the WM and its tracts based on the Gaussian process formalism. Using this framework, we develop techniques to find group differences in the white matter, particularly between healthy and schizophrenic subjects and to improve the visualization and representation of spinal cord MRI images.
