Estimating and Analyzing Neural Information Flow Using Signal Processing on Graphs

TL;DR

This paper introduces a novel graph signal processing approach to estimate and analyze neural communication flow in the brain, using diffusion models on ECoG data to reveal insights into neural interactions during stimulation.

Contribution

It presents a new method to model neural communication as flow signals on graphs, enabling detailed analysis of neural interactions with improved prediction and interpretability.

Findings

Neural flow estimation improves ECoG prediction accuracy.

Neural flow can be decomposed into gradient and rotational components.

Gradient component varies with stimulation location.

Abstract

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.