Diffusion MRI and Connectomics will advance the study of brain connectivity using diffusion imaging and its powerful extensions. This project will use Deep Learning to develop tract-based statistical analysis tools and adaptive connectivity mapping approaches. It will enable analysis of large diffusion imaging datasets totaling over 10,000 subjects.

TR&D2 became an area of interest as a result of ongoing work to improve maps of brain connectivity and identify the factors that affect it. The resulting transition from computational anatomy (assessed with structural MRI) to DTI and HARDI expanded the scope of LONIR to meet the changing needs of the field and our collaborators.

During the most recent P41 grant period, LONIR investigated automated tract extraction, methods of diffusion modeling that are more sensitive to disease, and adaptive techniques to detect changes in neural networks. The current project focuses on increasing power when analyzing brain connectivity and handling unprecedentedly large datasets.