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Dataset of human intracranial recordings during famous landmark identification

For most people, recalling information about familiar items in a visual scene is an effortless task, but it is one that depends on coordinated interactions of multiple, distributed neural components. We leveraged the high spatiotemporal resolution of direct intracranial recordings to better delineat...

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Autores principales: Woolnough, Oscar, Kadipasaoglu, Cihan M., Conner, Christopher R., Forseth, Kiefer J., Rollo, Patrick S., Rollo, Matthew J., Baboyan, Vatche G., Tandon, Nitin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803828/
https://www.ncbi.nlm.nih.gov/pubmed/35102154
http://dx.doi.org/10.1038/s41597-022-01125-8
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author Woolnough, Oscar
Kadipasaoglu, Cihan M.
Conner, Christopher R.
Forseth, Kiefer J.
Rollo, Patrick S.
Rollo, Matthew J.
Baboyan, Vatche G.
Tandon, Nitin
author_facet Woolnough, Oscar
Kadipasaoglu, Cihan M.
Conner, Christopher R.
Forseth, Kiefer J.
Rollo, Patrick S.
Rollo, Matthew J.
Baboyan, Vatche G.
Tandon, Nitin
author_sort Woolnough, Oscar
collection PubMed
description For most people, recalling information about familiar items in a visual scene is an effortless task, but it is one that depends on coordinated interactions of multiple, distributed neural components. We leveraged the high spatiotemporal resolution of direct intracranial recordings to better delineate the network dynamics underpinning visual scene recognition. We present a dataset of recordings from a large cohort of humans while they identified images of famous landmarks (50 individuals, 52 recording sessions, 6,775 electrodes, 6,541 trials). This dataset contains local field potential recordings derived from subdural and penetrating electrodes covering broad areas of cortex across both hemispheres. We provide this pre-processed data with behavioural metrics (correct/incorrect, response times) and electrode localisation in a population-normalised cortical surface space. This rich dataset will allow further investigation into the spatiotemporal progression of multiple neural processes underlying visual processing, scene recognition and cued memory recall.
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spelling pubmed-88038282022-02-07 Dataset of human intracranial recordings during famous landmark identification Woolnough, Oscar Kadipasaoglu, Cihan M. Conner, Christopher R. Forseth, Kiefer J. Rollo, Patrick S. Rollo, Matthew J. Baboyan, Vatche G. Tandon, Nitin Sci Data Data Descriptor For most people, recalling information about familiar items in a visual scene is an effortless task, but it is one that depends on coordinated interactions of multiple, distributed neural components. We leveraged the high spatiotemporal resolution of direct intracranial recordings to better delineate the network dynamics underpinning visual scene recognition. We present a dataset of recordings from a large cohort of humans while they identified images of famous landmarks (50 individuals, 52 recording sessions, 6,775 electrodes, 6,541 trials). This dataset contains local field potential recordings derived from subdural and penetrating electrodes covering broad areas of cortex across both hemispheres. We provide this pre-processed data with behavioural metrics (correct/incorrect, response times) and electrode localisation in a population-normalised cortical surface space. This rich dataset will allow further investigation into the spatiotemporal progression of multiple neural processes underlying visual processing, scene recognition and cued memory recall. Nature Publishing Group UK 2022-01-31 /pmc/articles/PMC8803828/ /pubmed/35102154 http://dx.doi.org/10.1038/s41597-022-01125-8 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
Woolnough, Oscar
Kadipasaoglu, Cihan M.
Conner, Christopher R.
Forseth, Kiefer J.
Rollo, Patrick S.
Rollo, Matthew J.
Baboyan, Vatche G.
Tandon, Nitin
Dataset of human intracranial recordings during famous landmark identification
title Dataset of human intracranial recordings during famous landmark identification
title_full Dataset of human intracranial recordings during famous landmark identification
title_fullStr Dataset of human intracranial recordings during famous landmark identification
title_full_unstemmed Dataset of human intracranial recordings during famous landmark identification
title_short Dataset of human intracranial recordings during famous landmark identification
title_sort dataset of human intracranial recordings during famous landmark identification
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8803828/
https://www.ncbi.nlm.nih.gov/pubmed/35102154
http://dx.doi.org/10.1038/s41597-022-01125-8
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