Learning Time-Varying Multi-Region Brain Communications via Scalable Markovian Gaussian Processes

TL;DR

This paper introduces a scalable framework using Markovian Gaussian Processes to analyze dynamic, time-varying neural communications across multiple brain regions, effectively handling large datasets and revealing evolving interaction patterns.

Contribution

The novel Adaptive Delay Model combines Gaussian Processes with State Space Models and parallel inference to efficiently capture dynamic brain communications in large neural datasets.

Findings

Successfully identifies directionality and temporal dynamics of neural communication.

Scales efficiently to large multi-region neural recordings.

Validates approach on synthetic and real neural data.

Abstract

Understanding and constructing brain communications that capture dynamic communications across multiple regions is fundamental to modern system neuroscience, yet current methods struggle to find time-varying region-level communications or scale to large neural datasets with long recording durations. We present a novel framework using Markovian Gaussian Processes to learn brain communications with time-varying temporal delays from multi-region neural recordings, named Adaptive Delay Model (ADM). Our method combines Gaussian Processes with State Space Models and employs parallel scan inference algorithms, enabling efficient scaling to large datasets while identifying concurrent communication patterns that evolve over time. This time-varying approach captures how brain region interactions shift dynamically during cognitive processes. Validated on synthetic and multi-region neural recordings datasets, our approach discovers both the directionality and temporal dynamics of neural communication. This work advances our understanding of distributed neural computation and provides a scalable tool for analyzing dynamic brain networks.