Multi-Manifold Diffeomorphic Metric Mapping for Cortical Registration with its Applications in Brain Structural and Functional Studies

Abstract

The human cortex is a convoluted sheet that forms sulco-gyral patterns to allow a large surface area inside the skull. Thus, in terms of distance measured along the cortex, functionally distinct regions are geometrically distant but close to each other in volume space. Because of this complexity, one of the main challenges in brain structural and functional MRI studies is to optimize the alignment of the cortical structures across individuals. In this thesis, we first develop a new diffeomorphic mapping algorithm, multi-manifold large deformation diffeomorphic metric mapping (MM-LDDMM), for morphing the cortical hemispheric surfaces using the geometry of sulcal curves (1-dimensional manifold) and cortical surface (2-dimensional manifold) in their own coordinates. This registration algorithm could better align both local regions and global shape patterns compared to previous registrations in the LDDMM framework and the spherical registrations implemented in CARET and FreeSurfer softwares.

Once the registration method is developed, we subsequently apply it in a structural study for generating a cortical surface template. As average template generation is based on registration, only good registration would give representative template over a population. We generate an average template for a sample of subjects including young healthy adults to healthy elders as well as dementia patients with the MM-LDDMM registration. It maintains the detailed sulco-gyral pattern but not limited to major deep sulci. This template is representative for the population in terms of its metric distance to each subject in the population and would be useful in the shape study in a variety of neurodegenerative diseases and healthy aging.

Other than in structural studies, good brain registration is also required in functional studies to locate which brain region is related to a particular function. While the functional connectivity of the brain in the early childhood is not clear but very important for our understanding of the normal brain development, we conduct resting state functional MRI analyses based on MM-LDDMM registration. We analyze the resting state functional connectivity of 6-year-old children?s brain with a large sample and identify the primary, higher order networks and default mode network (DMN). This study suggests that intrinsic functional networks of the brain are formed with well-developed visual and somatomotor networks but developing auditory, attention, executive networks, and DMN at six years of age. Moreover, we investigate the resting state functional connectivities development between 6 and 10 years old children and examine their relations with cognitive performance to better understand the functional development during early childhood. Using the seed correlation method and graph theoretical analyses, we report that, during early development, both regional activation and functional interactions between regions, especially for those in frontal networks, are changing prominently, which can be partly due to structural changes and has important relationship with cognitive performance for executive functions. This study provides new information about normal neurodevelopmental trajectories during early childhood, which could enable us to better understand any abnormal developments for those neurodevelopmental disorders.