Inferring Neural Communication Dynamics from Field Potentials Using Graph Diffusion Autoregression

Abstract

Estimating dynamic network communication is attracting increased attention, spurred by rapid advancements in multi-site neural recording technologies and efforts to better understand cognitive processes. Yet, traditional methods, which infer communication from statistical dependencies among distributed neural recordings, face core limitations: they do not model neural interactions in a biologically plausible way, neglect spatial information from the recording setup, and yield predominantly static estimates that cannot capture rapid changes in the brain. To address these issues, we introduce a graph diffusion autoregressive model. Designed for distributed field potential recordings, our model combines vector autoregression with a network communication process to produce a high-resolution communication signal. We successfully validated the model on simulated neural activity and recordings from subdural and intracortical micro-electrode arrays placed in macaque sensorimotor cortex demonstrating its ability to describe rapid communication dynamics induced by optogenetic stimulation, changes in resting state communication, and the trial-by-trial variability during a reach task.

A large-scale optogenetic neurophysiology platform for improving accessibility in NHP behavioral experiments

“…Our MMAD molding process did not require any special facilities or tools aside from the custom mold pieces, and the miniature ECoG array is commercially available (Ripple Neuro Inc.). We have previously demonstrated the MMAD’s capability of electrical stimulation (Devon J Griggs et al ., 2021; Khateeb et al ., 2022; Zhou et al ., 2022, 2023; Schwock et al ., 2024), which opens further experimental opportunities, especially given that optogenetic and electrical stimulation have been shown to work synergistically (Ohayon et al ., 2013; Hart et al ., 2020). Furthermore, we predict our work will spur the development of MMADs with higher channel counts of smaller and transparent electrodes to increase electrophysiological resolution and optical access (Belloir et al ., 2022; Vargo et al ., 2023).…”