Extracting spatial-temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition

TL;DR

This paper adapts dynamic mode decomposition (DMD), a fluid physics algorithm, to analyze large-scale neural recordings, capturing coherent spatial-temporal patterns and enabling new insights into brain activity.

Contribution

It introduces a scalable DMD-based method for analyzing neural data, combining spatial and temporal analysis in a unified framework, validated on human neural recordings.

Findings

Successfully applied DMD to human neural recordings during motor tasks.

Developed a novel DMD-based approach to extract sleep spindle networks.

Demonstrated scalability of DMD to large neural datasets.

Abstract

There is a broad need in the neuroscience community to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes simultaneously recording dynamic brain activity over minutes to hours. Such dynamic datasets are characterized by coherent patterns across both space and time, yet existing computational methods are typically restricted to analysis either in space or in time separately. Here we report the adaptation of dynamic mode decomposition (DMD), an algorithm originally developed for the study of fluid physics, to large-scale neuronal recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes; the resulting analysis combines key features of performing principal components analysis (PCA) in space and power spectral analysis in time. The algorithm scales easily to very large numbers of simultaneously acquired measurements. We validated the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor activation task. Next, we leveraged DMD in combination with machine learning to develop a novel method to extract sleep spindle networks from the same subjects. We suggest that DMD is generally applicable as a powerful method in the analysis and understanding of large-scale recordings of neural activity.