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High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays

View ORCID ProfileJason E Chung, Hannah R Joo, Jiang Lan Fan, Daniel F Liu, Alex H Barnett, Supin Chen, Charlotte Geaghan-Breiner, Mattias P Karlsson, Magnus Karlsson, Kye Y Lee, Hexin Liang, Jeremy F Magland, Angela C Tooker, Leslie F Greengard, Vanessa M Tolosa, Loren M Frank
doi: https://doi.org/10.1101/242693
Jason E Chung
University of California, San Francisco;
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  • For correspondence: jason.chung@ucsf.edu
Hannah R Joo
University of California, San Francisco;
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Jiang Lan Fan
University of California, San Francisco;
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Daniel F Liu
University of California, San Francisco;
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Alex H Barnett
Center for Computational Biology, Flatiron Institute;
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Supin Chen
Lawrence Livermore National Laboratory;
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Charlotte Geaghan-Breiner
University of California, San Francisco;
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Mattias P Karlsson
SpikeGadgets LLC
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Magnus Karlsson
SpikeGadgets LLC
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Kye Y Lee
Lawrence Livermore National Laboratory;
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Hexin Liang
University of California, San Francisco;
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Jeremy F Magland
Center for Computational Biology, Flatiron Institute;
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Angela C Tooker
Lawrence Livermore National Laboratory;
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Leslie F Greengard
Center for Computational Biology, Flatiron Institute;
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Vanessa M Tolosa
Lawrence Livermore National Laboratory;
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Loren M Frank
University of California, San Francisco;
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Abstract

The brain is a massive neuronal network, organized into anatomically distributed sub-circuits, with functionally relevant activity occurring at timescales ranging from milliseconds to months. Current methods to monitor neural activity, however, lack the necessary conjunction of anatomical spatial coverage, temporal resolution, and long-term stability to measure this distributed activity. Here we introduce a large-scale, multi-site recording platform that integrates polymer electrodes with a modular stacking headstage design supporting up to 1024 recording channels in freely behaving rats. This system can support months-long recordings from hundreds of well-isolated units across multiple brain regions. Moreover, these recordings are stable enough to track 25% of single units for over a week. This platform enables large-scale electrophysiological interrogation of the fast dynamics and long-timescale evolution of anatomically distributed circuits, and thereby provides a new tool for understanding brain activity.

Footnotes

  • This version of the manuscript has removed Figure 5 from the previous version, along with the corresponding text and supplementary material that illustrated recordings from the songbird. Following deposit of the previous version we discovered that there was a programming and plotting error for the raster data in panel 5b, resulting in recurring patterns across channels that are an artifact of incorrect indexing between neural and song data. In investigating the source of this error, we also found that the recording length of the birdsong datasets (between one and five minutes) influenced the quality of spike sorting and single unit isolation that contribute to the figure. We therefore have removed the figure until we resolve these issues and can provide a corrected version.

Copyright 
The copyright holder for this preprint is the author/funder. All rights reserved. No reuse allowed without permission.
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  • Posted April 28, 2018.

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High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays
Jason E Chung, Hannah R Joo, Jiang Lan Fan, Daniel F Liu, Alex H Barnett, Supin Chen, Charlotte Geaghan-Breiner, Mattias P Karlsson, Magnus Karlsson, Kye Y Lee, Hexin Liang, Jeremy F Magland, Angela C Tooker, Leslie F Greengard, Vanessa M Tolosa, Loren M Frank
bioRxiv 242693; doi: https://doi.org/10.1101/242693
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High-density, long-lasting, and multi-region electrophysiological recordings using polymer electrode arrays
Jason E Chung, Hannah R Joo, Jiang Lan Fan, Daniel F Liu, Alex H Barnett, Supin Chen, Charlotte Geaghan-Breiner, Mattias P Karlsson, Magnus Karlsson, Kye Y Lee, Hexin Liang, Jeremy F Magland, Angela C Tooker, Leslie F Greengard, Vanessa M Tolosa, Loren M Frank
bioRxiv 242693; doi: https://doi.org/10.1101/242693

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