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A naturalistic neuroimaging database for understanding the brain using ecological stimuli
Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the f...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555491/ https://www.ncbi.nlm.nih.gov/pubmed/33051448 http://dx.doi.org/10.1038/s41597-020-00680-2 |
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author | Aliko, Sarah Huang, Jiawen Gheorghiu, Florin Meliss, Stefanie Skipper, Jeremy I. |
author_facet | Aliko, Sarah Huang, Jiawen Gheorghiu, Florin Meliss, Stefanie Skipper, Jeremy I. |
author_sort | Aliko, Sarah |
collection | PubMed |
description | Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world. |
format | Online Article Text |
id | pubmed-7555491 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-75554912020-10-19 A naturalistic neuroimaging database for understanding the brain using ecological stimuli Aliko, Sarah Huang, Jiawen Gheorghiu, Florin Meliss, Stefanie Skipper, Jeremy I. Sci Data Data Descriptor Neuroimaging has advanced our understanding of human psychology using reductionist stimuli that often do not resemble information the brain naturally encounters. It has improved our understanding of the network organization of the brain mostly through analyses of ‘resting-state’ data for which the functions of networks cannot be verifiably labelled. We make a ‘Naturalistic Neuroimaging Database’ (NNDb v1.0) publically available to allow for a more complete understanding of the brain under more ecological conditions during which networks can be labelled. Eighty-six participants underwent behavioural testing and watched one of 10 full-length movies while functional magnetic resonance imaging was acquired. Resulting timeseries data are shown to be of high quality, with good signal-to-noise ratio, few outliers and low movement. Data-driven functional analyses provide further evidence of data quality. They also demonstrate accurate timeseries/movie alignment and how movie annotations might be used to label networks. The NNDb can be used to answer questions previously unaddressed with standard neuroimaging approaches, progressing our knowledge of how the brain works in the real world. Nature Publishing Group UK 2020-10-13 /pmc/articles/PMC7555491/ /pubmed/33051448 http://dx.doi.org/10.1038/s41597-020-00680-2 Text en © The Author(s) 2020 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/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Aliko, Sarah Huang, Jiawen Gheorghiu, Florin Meliss, Stefanie Skipper, Jeremy I. A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title | A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title_full | A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title_fullStr | A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title_full_unstemmed | A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title_short | A naturalistic neuroimaging database for understanding the brain using ecological stimuli |
title_sort | naturalistic neuroimaging database for understanding the brain using ecological stimuli |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7555491/ https://www.ncbi.nlm.nih.gov/pubmed/33051448 http://dx.doi.org/10.1038/s41597-020-00680-2 |
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