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Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings

To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resol...

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Autores principales: Ye, Zhiwen, Shelton, Andrew M., Shaker, Jordan R., Boussard, Julien, Colonell, Jennifer, Manavi, Sahar, Chen, Susu, Windolf, Charlie, Hurwitz, Cole, Namima, Tomoyuki, Pedraja, Federico, Weiss, Shahaf, Raducanu, Bogdan, Ness, Torbjørn V., Einevoll, Gaute T., Laurent, Gilles, Sawtell, Nathaniel B., Bair, Wyeth, Pasupathy, Anitha, Lopez, Carolina Mora, Dutta, Barun, Paninski, Liam, Siegle, Joshua H., Koch, Christof, Olsen, Shawn R., Harris, Timothy D., Steinmetz, Nicholas A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Cold Spring Harbor Laboratory 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473688/
https://www.ncbi.nlm.nih.gov/pubmed/37662298
http://dx.doi.org/10.1101/2023.08.23.554527
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author Ye, Zhiwen
Shelton, Andrew M.
Shaker, Jordan R.
Boussard, Julien
Colonell, Jennifer
Manavi, Sahar
Chen, Susu
Windolf, Charlie
Hurwitz, Cole
Namima, Tomoyuki
Pedraja, Federico
Weiss, Shahaf
Raducanu, Bogdan
Ness, Torbjørn V.
Einevoll, Gaute T.
Laurent, Gilles
Sawtell, Nathaniel B.
Bair, Wyeth
Pasupathy, Anitha
Lopez, Carolina Mora
Dutta, Barun
Paninski, Liam
Siegle, Joshua H.
Koch, Christof
Olsen, Shawn R.
Harris, Timothy D.
Steinmetz, Nicholas A.
author_facet Ye, Zhiwen
Shelton, Andrew M.
Shaker, Jordan R.
Boussard, Julien
Colonell, Jennifer
Manavi, Sahar
Chen, Susu
Windolf, Charlie
Hurwitz, Cole
Namima, Tomoyuki
Pedraja, Federico
Weiss, Shahaf
Raducanu, Bogdan
Ness, Torbjørn V.
Einevoll, Gaute T.
Laurent, Gilles
Sawtell, Nathaniel B.
Bair, Wyeth
Pasupathy, Anitha
Lopez, Carolina Mora
Dutta, Barun
Paninski, Liam
Siegle, Joshua H.
Koch, Christof
Olsen, Shawn R.
Harris, Timothy D.
Steinmetz, Nicholas A.
author_sort Ye, Zhiwen
collection PubMed
description To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To overcome these limitations, we developed a silicon probe with significantly smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device measures neuronal activity at ultra-high densities (>1300 sites per mm, 10 times higher than previous probes), with 6 μm center-to-center spacing and low noise. This device effectively comprises an implantable voltage-sensing camera that captures a planar image of a neuron’s electrical field. We introduce a new spike sorting algorithm optimized for these probes and use it to find that the yield of visually-responsive neurons in recordings from mouse visual cortex improves ~3-fold. Recordings across multiple brain regions and four species revealed a subset of unexpectedly small extracellular action potentials not previously reported. Further experiments determined that, in visual cortex, these do not correspond to major subclasses of interneurons and instead likely reflect recordings from axons. Finally, using ground-truth identification of cortical inhibitory cell types with optotagging, we found that cell type was discriminable with approximately 75% success among three types, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, sampling bias, and cell type classification.
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spelling pubmed-104736882023-09-02 Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings Ye, Zhiwen Shelton, Andrew M. Shaker, Jordan R. Boussard, Julien Colonell, Jennifer Manavi, Sahar Chen, Susu Windolf, Charlie Hurwitz, Cole Namima, Tomoyuki Pedraja, Federico Weiss, Shahaf Raducanu, Bogdan Ness, Torbjørn V. Einevoll, Gaute T. Laurent, Gilles Sawtell, Nathaniel B. Bair, Wyeth Pasupathy, Anitha Lopez, Carolina Mora Dutta, Barun Paninski, Liam Siegle, Joshua H. Koch, Christof Olsen, Shawn R. Harris, Timothy D. Steinmetz, Nicholas A. bioRxiv Article To study the neural basis of behavior, we require methods to sensitively and accurately measure neural activity at single neuron and single spike resolution. Extracellular electrophysiology is a principal method for achieving this, but it has biases in the neurons it detects and it imperfectly resolves their action potentials. To overcome these limitations, we developed a silicon probe with significantly smaller and denser recording sites than previous designs, called Neuropixels Ultra (NP Ultra). This device measures neuronal activity at ultra-high densities (>1300 sites per mm, 10 times higher than previous probes), with 6 μm center-to-center spacing and low noise. This device effectively comprises an implantable voltage-sensing camera that captures a planar image of a neuron’s electrical field. We introduce a new spike sorting algorithm optimized for these probes and use it to find that the yield of visually-responsive neurons in recordings from mouse visual cortex improves ~3-fold. Recordings across multiple brain regions and four species revealed a subset of unexpectedly small extracellular action potentials not previously reported. Further experiments determined that, in visual cortex, these do not correspond to major subclasses of interneurons and instead likely reflect recordings from axons. Finally, using ground-truth identification of cortical inhibitory cell types with optotagging, we found that cell type was discriminable with approximately 75% success among three types, a significant improvement over lower-resolution recordings. NP Ultra improves spike sorting performance, sampling bias, and cell type classification. Cold Spring Harbor Laboratory 2023-08-25 /pmc/articles/PMC10473688/ /pubmed/37662298 http://dx.doi.org/10.1101/2023.08.23.554527 Text en https://creativecommons.org/licenses/by-nc/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (https://creativecommons.org/licenses/by-nc/4.0/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format for noncommercial purposes only, and only so long as attribution is given to the creator.
spellingShingle Article
Ye, Zhiwen
Shelton, Andrew M.
Shaker, Jordan R.
Boussard, Julien
Colonell, Jennifer
Manavi, Sahar
Chen, Susu
Windolf, Charlie
Hurwitz, Cole
Namima, Tomoyuki
Pedraja, Federico
Weiss, Shahaf
Raducanu, Bogdan
Ness, Torbjørn V.
Einevoll, Gaute T.
Laurent, Gilles
Sawtell, Nathaniel B.
Bair, Wyeth
Pasupathy, Anitha
Lopez, Carolina Mora
Dutta, Barun
Paninski, Liam
Siegle, Joshua H.
Koch, Christof
Olsen, Shawn R.
Harris, Timothy D.
Steinmetz, Nicholas A.
Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title_full Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title_fullStr Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title_full_unstemmed Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title_short Ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
title_sort ultra-high density electrodes improve detection, yield, and cell type specificity of brain recordings
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10473688/
https://www.ncbi.nlm.nih.gov/pubmed/37662298
http://dx.doi.org/10.1101/2023.08.23.554527
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