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 incorporate possible mechanisms of neural communication, 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 the 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 co

SUBMITTER: Schwock F 

PROVIDER: S-EPMC10925120 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature