DREDge: robust motion correction for high-density extracellular recordings across species.

IF 36.1 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Charlie Windolf, Han Yu, Angelique C Paulk, Domokos Meszéna, William Muñoz, Julien Boussard, Richard Hardstone, Irene Caprara, Mohsen Jamali, Yoav Kfir, Duo Xu, Jason E Chung, Kristin K Sellers, Zhiwen Ye, Jordan Shaker, Anna Lebedeva, R T Raghavan, Eric Trautmann, Max Melin, João Couto, Samuel Garcia, Brian Coughlin, Margot Elmaleh, David Christianson, Jeremy D W Greenlee, Csaba Horváth, Richárd Fiáth, István Ulbert, Michael A Long, J Anthony Movshon, Michael N Shadlen, Mark M Churchland, Anne K Churchland, Nicholas A Steinmetz, Edward F Chang, Jeffrey S Schweitzer, Ziv M Williams, Sydney S Cash, Liam Paninski, Erdem Varol
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引用次数: 0

Abstract

High-density microelectrode arrays have opened new possibilities for systems neuroscience, but brain motion relative to the array poses challenges for downstream analyses. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from action potential data, DREDge enables automated, high-temporal-resolution motion tracking in local field potential data. In human intraoperative recordings, DREDge's local field potential-based tracking reliably recovered evoked potentials and single-unit spike sorting. In recordings of deep probe insertions in nonhuman primates, DREDge tracked motion across centimeters of tissue and several brain regions while mapping single-unit electrophysiological features. DREDge reliably improved motion correction in acute mouse recordings, especially in those made with a recent ultrahigh-density probe. Applying DREDge to recordings from chronic implantations in mice yielded stable motion tracking despite changes in neural activity between experimental sessions. These advances enable automated, scalable registration of electrophysiological data across species, probes and drift types, providing a foundation for downstream analyses of these rich datasets.

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来源期刊
Nature Methods
Nature Methods 生物-生化研究方法
CiteScore
58.70
自引率
1.70%
发文量
326
审稿时长
1 months
期刊介绍: Nature Methods is a monthly journal that focuses on publishing innovative methods and substantial enhancements to fundamental life sciences research techniques. Geared towards a diverse, interdisciplinary readership of researchers in academia and industry engaged in laboratory work, the journal offers new tools for research and emphasizes the immediate practical significance of the featured work. It publishes primary research papers and reviews recent technical and methodological advancements, with a particular interest in primary methods papers relevant to the biological and biomedical sciences. This includes methods rooted in chemistry with practical applications for studying biological problems.
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