Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance

Yuan, Augustine(Xiaoran); Colonell, Jennifer; Lebedeva, Anna; Okun, Michael; Charles, Adam; Harris, Timothy D.

Abstract

Accurate tracking of the same neurons across multiple days is crucial for studying changes in neuronal activity during learning and adaptation. Advances in high-density extracellular electrophysiology recording probes, such as Neuropixels, provide a promising avenue to accomplish this goal. Identifying the same neurons in multiple recordings is, however, complicated by non-rigid movement of the tissue relative to the recording sites (drift) and loss of signal from some neurons. Here, we propose a neuron tracking method that can identify the same cells independent of firing statistics, that are used by most existing methods. Our method is based on between-day non-rigid alignment of spike-sorted clusters. We verified the same cell identity in mice using measured visual receptive fields. This method succeeds on datasets separated from 1 to 47 days, with an 84% average recovery rate.

Citation

Yuan, A., Colonell, J., Lebedeva, A., Okun, M., Charles, A., & Harris, T. D. (2024). Multi-day neuron tracking in high-density electrophysiology recordings using earth mover's distance. eLife, 12, Article 92495. https://doi.org/10.7554/eLife.92495.3

• Journal Article Type: Article
• Acceptance Date: Sep 18, 2023
• Online Publication Date: Jul 10, 2024
• Publication Date: Jul 10, 2024
• Deposit Date: Dec 25, 2023
• Publicly Available Date: Jan 4, 2024
• Journal: eLife
• Electronic ISSN: 2050-084X
• Publisher: eLife Sciences Publications
• Peer Reviewed: Peer Reviewed
• Volume: 12
• Article Number: 92495
• DOI: https://doi.org/10.7554/eLife.92495.3
• Public URL: https://nottingham-repository.worktribe.com/output/28997085
• Publisher URL: https://elifesciences.org/articles/92495
• Additional Information: © 2023, Yuan et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.