Current knowledge of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS) is limited by poor understanding of how they progress through the central nervous system. In these patients, novel neuroimaging techniques may help to elucidate the spatial, time-dependent expansion of the underlying pathology across brain networks. Few magnetic resonance imaging (MRI) longitudinal studies have been published on ALS, and the true potential of MRI as a marker for monitoring disease progression has yet to be defined. Longitudinal analyses of ALS patients showed a decrease of cortical thickness in motor, temporal, and fronto-parietal cortices, as well as diffusion tensor MRI changes in the corticospinal tract, corpus callosum and frontal regions. The recent development and application of graph theoretical tools to MRI connectivity research offer a unique opportunity to explore principles of network-based neurodegeneration and to address unanswered questions. The application of graph theory on brain connectivity data put previous MRI findings in a new perspective, suggesting that node properties are likely to play a critical role in the pathophysiology of neurodegenerative diseases. Moreover, a major strength of graph theory is that it can be used to generate and test competing generative models designed to explain observed variations across a range of topological properties in the disease. Network science experiments will pave the way to the development of novel tools for understanding the biological underpinnings of ALS, and to identifying individualized, early interventions to modify disease progression.