Technological and computational advances offer increasing capacity to quantify what neurologists have long appreciated as the complexity of the brain. The information-rich techniques of electro- and magnetoencephalography, as well as structural and functional MRI, are increasingly being examined through the lenses of network theory and oscillations to capture complex brain dynamics. Although still in the early stages of clinical application, these “network” approaches have the potential to shed new light on the diagnosis, prognosis, and treatment of neurological diseases. We review the basic principles of network theory and oscillation dynamics, including the recently discovered “small-world network” concept, and provide and introduction to how these techniques are being applied to routinely available clinical data (such as MRI, EEG, and MEG). Specific clinical applications span normal brain functions (cognition and sleep) and disorders of the brain such as epilepsy, dementia, movement disorders, pain, autism, and schizophrenia. These interrelated approaches respect the fundamentals of anatomically-driven diagnosis while providing a theoretical and practical armamentarium to investigate aspects of neurological disease that may challenge the scope of traditional clinical tools.