Connectivity analysis focuses on the interaction between brain regions. Such relationships inform us about patterns of neural communication and may heighten our understanding of neurological disorders. Here, we propose a generative framework that uses anatomical and functional connectivity information to find impairments within a clinical population. Anatomical connectivity is measured via Diffusion Weighted Imaging (DWI), and functional connectivity is assessed using resting-state functional Magnetic Resonance Imaging (fMRI). We first develop a probabilistic model to merge information from DWI tractography and resting-state fMRI correlations to infer latent templates of connectivity within the brain. We also present an intuitive extension to population studies and demonstrate that our model learns stable differences between a control and a schizophrenia population. Despite the promise of our joint model, connectivity results are difficult to interpret and validate given our region centric knowledge of the brain. To alleviate these concerns, we present a novel approach to identify regions, which we call disease foci, associated with the disorder based on connectivity information. This allows us to aggregate pairwise connectivity changes into a region-based representation of the disease. Once again, we use a probabilistic formulation: latent variables specify a template organization of the brain, which we indirectly observe through resting-state fMRI correlations and DWI tractography. The inference algorithm simultaneously identifies both the afflicted regions and the network of aberrant connectivity. Finally, we extend the region-based model to include multiple collections of foci, which we call disease clusters. Preliminary results suggest that as the number of clusters increases, the refined model explains progressively more of the functional differences between the populations.