In the first part of this talk I will go over some recent results related to consistent vertex classification for a class of latent position graph models. This work relies on a spectral embedding of the adjacency matrix and the use of k-nearest-neighbors classifiers. In the second part of the talk I will discuss applying these techniques to graphs derived from diffusion tensor MRI. Each vertex in these graphs corresponds to a voxel in the original images. Using gyral based regions of interest (ROI) as the class labels, we demonstrate classification error rates indicating that ROI signal is present in the graph. This is some of the first work to consider networks built at the voxel level.