This exploratory analysis tests the suitability of deep residual networks to learn neuroanatomical abnormalities from the structural MRI (sMRI) modality, and utility of dynamic (i.e. time-varying) functional connectivity approaches in delineating discriminative functional MRI (fMRI) features to predict progression of individuals with mild cognitive impairment to Alzheimer’s disease. Results demonstrate better than state-of-the-art prediction performance using the structural MRI modality alone. Multimodal prediction performed significantly better than unimodal sMRI or fMRI predictions, thus corroborating the benefits of predicting in the augmented space. Results also corroborate the diagnostic utility of the sMRI and fMRI features used to make the predictions.