Estimating and Analyzing Neural Information flow using Signal Processing on Graphs

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 sensorimo-tor 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.

1. INTRODUCTION

A deeper understanding of the internal communication processes in the brain may be crucial for developing more effective treatments for many neurological diseases [1], [2]. Furthermore, with the advancement of electrode arrays that expand across multiple brain areas and provide simultaneous recordings from hundreds of channels, developing novel tools to analyze and model the dynamics of brain neural networks is much needed. Recently, graphs have emerged as promising tools to analyze neural processes. Thereby most research has focused on studying properties of brain graphs that were derived from structural or functional measurements of the brain [3], [4]. More recently, graph signal processing (GSP) has been proposed as a way to study neural signals that are observed at the nodes of an underlying brain network [5], [6], [7]. The central idea of GSP is that, rather than focusing on the graph itself, we are interested in processing signals indexed by the nodes of the graph, for example, by finding a graph spectral representation of the signal, or performing filtering in the graph domain [8]. By focusing on graph signals, this framework therefore offers a principle way to study dynamic network and communication processes in the brain.