  The ultimate goal of cognitive neuroscience is to understand the mechanistic
neural processes underlying the functional organization of the brain. Key to
this study is understanding structure of both the structural and functional
connectivity between anatomical regions. In this paper we follow previous work
in developing a simple dynamical model of the brain by simulating its various
regions as Kuramoto oscillators whose coupling structure is described by a
complex network. However in our simulations rather than generating synthetic
networks, we simulate our synthetic model but coupled by a real network of the
anatomical brain regions which has been reconstructed from diffusion tensor
imaging (DTI) data. By using an information theoretic approach that defines
direct information flow in terms of causation entropy (CSE), we show that we
can more accurately recover the true structural network than either of the
popular correlation or LASSO regression techniques. We demonstrate the
effectiveness of our method when applied to data simulated on the realistic DTI
network, as well as on randomly generated small-world and Erd\"os-R\'enyi (ER)
networks.
