Dynamic Decomposition of Spatiotemporal Neural Signals

TL;DR

This paper introduces a novel data analysis framework that decomposes complex neural signals into meaningful components using a linearized model based on stochastic differential equations and Gaussian process regression, enhancing interpretation of neural dynamics.

Contribution

The paper presents a new method for decomposing neural signals into rhythmic and non-rhythmic components using a linearized dynamic model, applicable to complex spatiotemporal data.

Findings

Effective in identifying oscillatory modulations in neural signals

Robust against structured temporal and spatiotemporal noise

Demonstrated on magnetoencephalographic data

Abstract

Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.