Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

• Published in: Cell reports methods Vol. 1; no. 7; p. 100101
• Main Authors: Cao, Liang, Varga, Viktor, Chen, Zhe S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 22-11-2021; Elsevier
• Subjects: clusterless decoding; hippocampus; local field potential; lower rank factorization; memory replay; position decoding; prospective decoding; spatiotemporal patterns; traveling waves; unsupervised learning; local field potential; position decoding; clusterless decoding; lower rank factorization; hippocampus; memory replay; prospective decoding; spatiotemporal patterns; traveling waves; unsupervised learning
• ISSN: 2667-2375; 2667-2375
• DOI: 10.1016/j.crmeth.2021.100101

Spatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.
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•Rodent hippocampal field potentials provide spatial readouts in run and ripples•Unsupervised learning reveals latent structures from spatiotemporal patterns•Optical flow method identifies spatiotemporal patterns from multisite recordings•Prospective decoding predicts animals' decisions in goal-directed navigation
The amplitude and phase information derived from high-density electrophysiological recordings represents rich and complementary information carriers to transfer information in the brain. We develop supervised and unsupervised learning methods to extract such information from large-scale rodent hippocampal recordings during various behavioral states and apply them in position decoding, replay analysis, and decision prediction.
Cao et al. develop supervised and unsupervised methods to extract amplitude and phase information from high-density rodent hippocampal electrophysiological recordings and demonstrate their use in position decoding, replay analysis, and decision prediction.