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There is a broad need in the neuroscience community to understand andvisualize large-scale recordings of neural activity, big data acquired by tensor hundreds of electrodes simultaneously recording dynamic brain activity overminutes to hours. Such dynamic datasets are characterized by coherent patternsacross both space and time, yet existing computational methods are typicallyrestricted to analysis either in space or in time separately. Here we reportthe adaptation of dynamic mode decomposition (DMD), an algorithm originallydeveloped for the study of fluid physics, to large-scale neuronal recordings.DMD is a modal decomposition algorithm that describes high-dimensional dynamicdata using coupled spatial-temporal modes; the resulting analysis combines keyfeatures of performing principal components analysis (PCA) in space and powerspectral analysis in time. The algorithm scales easily to very large numbers ofsimultaneously acquired measurements. We validated the DMD approach onsub-dural electrode array recordings from human subjects performing a knownmotor activation task. Next, we leveraged DMD in combination with machinelearning to develop a novel method to extract sleep spindle networks from thesame subjects. We suggest that DMD is generally applicable as a powerful methodin the analysis and understanding of large-scale recordings of neural activity.

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