Estimating and Analyzing Neural Flow Using Signal Processing on Graphs

Neural communication is fundamentally linked to the brain's overall state and health status. 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 local field potential (LFP) measurements of brain activity of two non-human primates to estimate the communication flow during a stimulation experiment. Comparisons with a baseline model demonstrate that adding the neural flow can improve LFP 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.