As acquiring bigger data becomes easier in experimental brain science, com-putational and statistical brain science must achieve similar advances to fullycapitalize on these data. Tackling these problems will benefit from a moreexplicit and concerted effort to work together. Specifically, brain sciencecan be further democratized by harnessing the power of community-driven tools, which both are built by and benefit from many different people with different backgroundsand expertise. This perspective can be applied across modalities and scales and enables collabora-tions across previously siloed communities.

We develop methods for accurate image registration between images of different modalities, possibly in the presence of damaged or missing tissue. We use this approach to build the first 3D reconstruction of tau tangle pathology in the medial temporal lobe for an individual who died of Alzheimer's disease.

We developed a generative algorithm for registration of micron resolution serial section microscopy images to the Allen reference atlas.

We design diffeomorphic techniques to estimate unobserved data between sparsely sampled slices in cardiac MRI.

We derived the Cramer Rau bound for estimating volume changes from deformable image registration, which is a lower bound on the variance of an estimator. We demonstrated its implications for image registration performance in asymmetric methods versus symmetric methods, the former generally performing better.