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Large-Scale Mapping of Axonal Arbors Using High-Density Microelectrode Arrays.
Bullmann, Torsten; Radivojevic, Milos; Huber, Stefan T; Deligkaris, Kosmas; Hierlemann, Andreas; Frey, Urs.
Afiliação
  • Bullmann T; RIKEN Quantitative Biology Center, RIKEN, Kobe, Japan.
  • Radivojevic M; Graduate School of Informatics, Kyoto University, Kyoto, Japan.
  • Huber ST; Carl Ludwig Institute for Physiology, University of Leipzig, Leipzig, Germany.
  • Deligkaris K; Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
  • Hierlemann A; RIKEN Quantitative Biology Center, RIKEN, Kobe, Japan.
  • Frey U; RIKEN Quantitative Biology Center, RIKEN, Kobe, Japan.
Front Cell Neurosci ; 13: 404, 2019.
Article em En | MEDLINE | ID: mdl-31555099
ABSTRACT
Understanding the role of axons in neuronal information processing is a fundamental task in neuroscience. Over the last years, sophisticated patch-clamp investigations have provided unexpected and exciting data on axonal phenomena and functioning, but there is still a need for methods to investigate full axonal arbors at sufficient throughput. Here, we present a new method for the simultaneous mapping of the axonal arbors of a large number of individual neurons, which relies on their extracellular signals that have been recorded with high-density microelectrode arrays (HD-MEAs). The segmentation of axons was performed based on the local correlation of extracellular signals. Comparison of the results with both, ground truth and receiver operator characteristics, shows that the new segmentation method outperforms previously used methods. Using a standard HD-MEA, we mapped the axonal arbors of 68 neurons in <6 h. The fully automated method can be extended to new generations of HD-MEAs with larger data output and is estimated to provide data of axonal arbors of thousands of neurons within recording sessions of a few hours.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2019 Tipo de documento: Article