Using computational models to relate structural and functional brain connectivity

Modern imaging methods allow a non-invasive assessment of both structural and functional brain connectivity. This has lead tothe identification of disease-related alterations affecting functional connectivity. The mechanism of how such alterations infunctional connectivity arise in a structured network of interacting neural populations is as yet poorly understood. Here we usea modeling approach to explore the way in which this can arise and to highlight the important role that local populationdynamics can have in shaping emergent spatial functional connectivity patterns. The local dynamics for a neural population istaken to be of the Wilson–Cowan type, whilst the structural connectivity patterns used, describing long-range anatomicalconnections, cover both realistic scenarios (from the CoComac database) and idealized ones that allow for more detailedtheoretical study. We have calculated graph–theoretic measures of functional network topology from numerical simulations ofmodel networks. The effect of the form of local dynamics on the observed network state is quantified by examining thecorrelation between structural and functional connectivity. We document a profound and systematic dependence of thesimulated functional connectivity patterns on the parameters controlling the dynamics. Importantly, we show that a weaklycoupled oscillator theory explaining these correlations and their variation across parameter space can be developed. Thistheoretical development provides a novel way to characterize the mechanisms for the breakdown of functional connectivity indiseases through changes in local dynamics