Project Summary Diffusion MRI analysis is the primary tool for studying brain white matter microstructure. Adolescent brain development involves significant microstructural changes due to brain reorganization and neural pruning. The trajectory of these changes can be altered by the onset of mental disorders, and longitudinal analysis of diffusion imaging is needed to capture these alterations. Our proposal aims to make available an open source software platform for exploratory analysis of longitudinal diffusion MRI datasets, and to apply it to study the early onset of bipolar disorder in the Adolescent Brain Cognitive Development (ABCD) data. Diffusion tensor imaging is not sufficient to distinguish the different types of changes that occur during adolescent brain development, so our software tool will support more advanced diffusion models such as Neurite Orientation Dispersion and Density Imaging. This takes advantage of the high angular resolution of ABCD diffusion images. The exploratory tools we develop will employ the widely used Tract-Based Spatial Statistics (TBSS) technique, which is a dimensionality reduction that is well suited for exploratory analysis. Going beyond standard TBSS, we will adapt the technique to work with improved registration techniques, to work with more advanced diffusion models, and to provide developmental trajectories based on longitudinal imaging data. The TBSS methodology requires high quality image alignment. In order to achieve this, we will develop a deep learning model for pairwise registration of diffusion datasets, speeding up an important step in diffusion MRI analysis. By generating fiber orientation distributions from the ABCD diffusion imaging, our registration model will align fiber orientations in addition to anatomical structures. These tools will be made available in an open source software platform. While there are many tools for exploring diffusion MRI data, this one will be unique in its support for longitudinal datasets with high angular resolution imaging.