Imparting functional meaning to neuroanatomical location has been among the greatest challenges to neuroscientists. The characterization of the brain architecture responsible for human cognition received a boost in momentum with the emergence of in vivo functional and structural neuroimaging technology over the past 25 years. Yet, individual variability in cortical gyrification and the patterns of blood flow-related activity measured using fMRI and positron emission tomography complicated direct comparisons across subjects without spatially accounting for overall brain size and shape. This realization resulted in considerable effort now involving the collective works of neuroscientists, computer scientists, and mathematicians to develop common brain atlas spaces against which the regions of activity may be accurately referenced. We examine recent developments in brain imaging and computational anatomy that have greatly expanded our ability to analyze brain structure and function. The enormous diversity of brain maps and imaging methods has spurred the development of population-based digital brain atlases. Atlases store information on how the brain varies across age and gender, across time, in health and disease, and in large human populations. We describe how brain atlases, and the computational tools that align new data sets with them, facilitate comparison of brain data across experiments, laboratories, and from different imaging devices. The major philosophies are presented that underlie the construction of probabilistic atlases, which store information on anatomic and functional variability in a population. Algorithms that create composite brain maps and atlases based on multiple subjects are examined. We show that group patterns of cortical organization, asymmetry, and disease-specific trends that may not be apparent in individual brain maps can be resolved. Finally, we describe the development of four-dimensional maps that store information on the dynamics of brain change in development and disease.