The nodes of brain networks can be identified using three different definitions of connectivity between regions. Structural connectivity (SC) describes the anatomical connections between regions, functional connectivity (FC) identifies whether the activity of two regions increases and decreases coherently, while effective connectivity (EC) attempts to describe the brain not in terms of EEG or MRI signals, but the underlying neuronal populations which produce them. Each of these measures can be estimated using multiple different data acquisition and analysis techniques. For example, SC can be determined from diffusion tensor imaging (DTI) MRI scans, which are sensitive to the diffusion of water in white matter tracts, or from measurements of cortical thickness. Similarly, FC can be calculated from electroencephalography (EEG) or functional MRI (fMRI) measurements. Understanding how these different measures of connectivity are related, and how measurements of human brain function and structure can be combined to produce a unified picture, is not straightforward. Few studies have acquired the high quality data with multiple techniques that is required for such an undertaking. A further complication is that of defining model networks which are of sufficient complexity to provide a realistic test of any methodological developments, while being sufficiently well-characterised to allow developments to be validated. We will overcome this issue in a novel way by building on decades of invasive neurophysiological experiments which have characterised the networks responsible for the generation of thalamocortical oscillations (TCO), electrophysiological events that are generated by interactions between cortical and thalamic network nodes. TCO can be hallmarks of normal brain function (alpha rhythm, sleep spindles and K-complexes), or pathophysiology, of which the most obvious are generalised spike-wave discharges, characteristic of generalised epilepsy.