  Correlating neural communication in brain networks with behavior and
cognition can provide fundamental insights into the functionality of both
healthy and diseased brains. We demonstrate how communication in the brain can
be estimated from recorded neural activity using concepts from graph signal
processing. The communication is modeled as a flow signals on the edges of a
graph and naturally arises from a graph diffusion process. We apply the
diffusion model to micro-electrocorticography (ECoG) recordings from
sensorimotor cortex of two non-human primates to estimate the neural
communication flow during excitatory optogenetics. Comparisons with a baseline
model demonstrate that adding the neural flow can improve ECoG predictions.
Finally, we demonstrate how the neural flow can be decomposed into a gradient
and rotational component and show that the gradient component depends on the
location of stimulation. This technique, for the first time, offers the
opportunity to study neural communication on an unprecedented spatiotemporal
scale.
