This paper presents a paradigm for population-based studies using geodesic connectivity maps computed from DTI (Diffusion Tensor Imaging) data. Geodesic connectivity maps provide a measure of connectivity characterization between different regions of the brain using a tensor-based measure that combines anisotropy and orientation information. A connectivity map is constructed relative to a given region in a template space. Population based statistical analysis is then undertaken on these connectivity maps, pertaining to regions of interest. Group-wise changes in these connectivity maps therefore indicate potential disruptions of pathways connecting the ROI with the rest of the brain. This general paradigm for connectivity-based analysis is applied herein to a specific problem, namely the study of white matter maturation in a developing mouse brain, by performing voxel-wise linear regression on connectivity maps computed from several white matter regions. The study shows a significant effect of age whereas no effect of sex is observed.