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1024-channel electrophysiological recordings in macaque V1 and V4 during resting state
Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined restin...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917124/ https://www.ncbi.nlm.nih.gov/pubmed/35277528 http://dx.doi.org/10.1038/s41597-022-01180-1 |
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author | Chen, Xing Morales-Gregorio, Aitor Sprenger, Julia Kleinjohann, Alexander Sridhar, Shashwat van Albada, Sacha J. Grün, Sonja Roelfsema, Pieter R. |
author_facet | Chen, Xing Morales-Gregorio, Aitor Sprenger, Julia Kleinjohann, Alexander Sridhar, Shashwat van Albada, Sacha J. Grün, Sonja Roelfsema, Pieter R. |
author_sort | Chen, Xing |
collection | PubMed |
description | Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity. In this paper, we provide the dataset, describe the raw and processed data formats and data acquisition methods, and indicate how the data can be used to yield new insights into the ‘background’ activity that influences the processing of visual information in our brain. |
format | Online Article Text |
id | pubmed-8917124 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-89171242022-03-28 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state Chen, Xing Morales-Gregorio, Aitor Sprenger, Julia Kleinjohann, Alexander Sridhar, Shashwat van Albada, Sacha J. Grün, Sonja Roelfsema, Pieter R. Sci Data Data Descriptor Co-variations in resting state activity are thought to arise from a variety of correlated inputs to neurons, such as bottom-up activity from lower areas, feedback from higher areas, recurrent processing in local circuits, and fluctuations in neuromodulatory systems. Most studies have examined resting state activity throughout the brain using MRI scans, or observed local co-variations in activity by recording from a small number of electrodes. We carried out electrophysiological recordings from over a thousand chronically implanted electrodes in the visual cortex of non-human primates, yielding a resting state dataset with unprecedentedly high channel counts and spatiotemporal resolution. Such signals could be used to observe brain waves across larger regions of cortex, offering a temporally detailed picture of brain activity. In this paper, we provide the dataset, describe the raw and processed data formats and data acquisition methods, and indicate how the data can be used to yield new insights into the ‘background’ activity that influences the processing of visual information in our brain. Nature Publishing Group UK 2022-03-11 /pmc/articles/PMC8917124/ /pubmed/35277528 http://dx.doi.org/10.1038/s41597-022-01180-1 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Chen, Xing Morales-Gregorio, Aitor Sprenger, Julia Kleinjohann, Alexander Sridhar, Shashwat van Albada, Sacha J. Grün, Sonja Roelfsema, Pieter R. 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title | 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title_full | 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title_fullStr | 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title_full_unstemmed | 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title_short | 1024-channel electrophysiological recordings in macaque V1 and V4 during resting state |
title_sort | 1024-channel electrophysiological recordings in macaque v1 and v4 during resting state |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917124/ https://www.ncbi.nlm.nih.gov/pubmed/35277528 http://dx.doi.org/10.1038/s41597-022-01180-1 |
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