Introduction to the Issue on Advanced Signal Processing for Brain Networks

The 16 papers in this special section focused on advanced signal processing techniques for brain networks. Network models of the brain have become an important tool of modern neurosciences to study fundamental organizational principles of brain structure & function. Their connectivity is captured by the so-called connectome, the complete set of structural and functional links of the network. Advancing current methodology remains an important need in the field; e.g., increasing large-scale models; incorporating multimodal information in multiplex graph models; dealing with dynamical aspects of network models; and matching data-driven and theoretical models. These challenges form multiple opportunities to develop and adapt emerging signal processing theories and methods at the interface of graph theory, machine learning, applied statistics, simulation, and so on, to play a key role in analysis and modeling and to bring our understanding of brain networks to the next level for key applications in cognitive and clinical neurosciences, including brain-computer interfaces.