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

Journal of Neuroscience Methods
Bingni W BruntonJ Nathan Kutz

Abstract

There is a broad need in neuroscience to understand and visualize large-scale recordings of neural activity, big data acquired by tens or hundreds of electrodes recording dynamic brain activity over minutes to hours. Such 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 studying fluid physics, to large-scale neural recordings. DMD is a modal decomposition algorithm that describes high-dimensional dynamic data using coupled spatial-temporal modes. The algorithm is robust to variations in noise and subsampling rate; it scales easily to very large numbers of simultaneously acquired measurements. We first validate the DMD approach on sub-dural electrode array recordings from human subjects performing a known motor task. Next, we combine DMD with unsupervised clustering, developing a novel method to extract spindle networks during sleep. We uncovered several distinct sleep spindle networks identifiable by their stereotypical cortical distribution patterns, frequency, and duration. DMD is closel...Continue Reading

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Citations

Mar 24, 2016·IEEE Transactions on Bio-medical Engineering·Chet T MoritzRajesh P N Rao
Jan 12, 2017·PLoS Computational Biology·James M KunertJ Nathan Kutz
Jun 11, 2017·Journal of Neuroscience Methods·Ankit ParekhIndu Ayappa
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Nov 6, 2018·PLoS Computational Biology·Keisuke FujiiYoshinobu Kawahara
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Aug 26, 2021·Journal of the Royal Society, Interface·Nathaniel J LindenBingni W Brunton

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