As MRI scanner technologies continue to advance, the acquisition of high resolution multivariate data increasingly facilitate more quantitate measures of brain structure and function. However, these more complex data require increasingly advanced computational methods for analysis and visualization. The “Three Pillars” of neuroimaging with MRI that are most common in both basic science research and in clinical settings are high resolution
anatomical (HRA) imaging, diffusion tensor imaging (DTI) and functional MRI (FMRI). Despite the ubiquity and importance of these methods, there remain significant challenges in extracting quantitative information from these data. In this talk I will discuss some recent work on new computational methods for the analysis of morphology from HRA, estimating local physiology and structural connectivity from DTI, and detecting functional brain networks from resting state FMRI that aim to provide more robust and quantitative measures of brain structure and function.