  Many recent efforts in computational modeling of macro-scale brain dynamics
have begun to take a data-driven approach by incorporating structural and/or
functional information derived from subject data. Here, we discuss recent work
using personalized brain network models to study structure-function
relationships in human brains. We describe the steps necessary to build such
models and show how this computational approach can provide previously
unobtainable information through the ability to perform virtual experiments.
Finally, we present examples of how personalized brain network models can be
used to gain insight into the effects of local stimulation and improve surgical
outcomes in epilepsy.
