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Le Petit Prince multilingual naturalistic fMRI corpus
Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension in the brain. However, prior naturalistic stimuli have typically been restricted to a single language, which limited generalizability beyond small typological domain...
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/PMC9424229/ https://www.ncbi.nlm.nih.gov/pubmed/36038567 http://dx.doi.org/10.1038/s41597-022-01625-7 |
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author | Li, Jixing Bhattasali, Shohini Zhang, Shulin Franzluebbers, Berta Luh, Wen-Ming Spreng, R. Nathan Brennan, Jonathan R. Yang, Yiming Pallier, Christophe Hale, John |
author_facet | Li, Jixing Bhattasali, Shohini Zhang, Shulin Franzluebbers, Berta Luh, Wen-Ming Spreng, R. Nathan Brennan, Jonathan R. Yang, Yiming Pallier, Christophe Hale, John |
author_sort | Li, Jixing |
collection | PubMed |
description | Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension in the brain. However, prior naturalistic stimuli have typically been restricted to a single language, which limited generalizability beyond small typological domains. Here we present the Le Petit Prince fMRI Corpus (LPPC–fMRI), a multilingual resource for research in the cognitive neuroscience of speech and language during naturalistic listening (OpenNeuro: ds003643). 49 English speakers, 35 Chinese speakers and 28 French speakers listened to the same audiobook The Little Prince in their native language while multi-echo functional magnetic resonance imaging was acquired. We also provide time-aligned speech annotation and word-by-word predictors obtained using natural language processing tools. The resulting timeseries data are shown to be of high quality with good temporal signal-to-noise ratio and high inter-subject correlation. Data-driven functional analyses provide further evidence of data quality. This annotated, multilingual fMRI dataset facilitates future re-analysis that addresses cross-linguistic commonalities and differences in the neural substrate of language processing on multiple perceptual and linguistic levels. |
format | Online Article Text |
id | pubmed-9424229 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-94242292022-08-31 Le Petit Prince multilingual naturalistic fMRI corpus Li, Jixing Bhattasali, Shohini Zhang, Shulin Franzluebbers, Berta Luh, Wen-Ming Spreng, R. Nathan Brennan, Jonathan R. Yang, Yiming Pallier, Christophe Hale, John Sci Data Data Descriptor Neuroimaging using more ecologically valid stimuli such as audiobooks has advanced our understanding of natural language comprehension in the brain. However, prior naturalistic stimuli have typically been restricted to a single language, which limited generalizability beyond small typological domains. Here we present the Le Petit Prince fMRI Corpus (LPPC–fMRI), a multilingual resource for research in the cognitive neuroscience of speech and language during naturalistic listening (OpenNeuro: ds003643). 49 English speakers, 35 Chinese speakers and 28 French speakers listened to the same audiobook The Little Prince in their native language while multi-echo functional magnetic resonance imaging was acquired. We also provide time-aligned speech annotation and word-by-word predictors obtained using natural language processing tools. The resulting timeseries data are shown to be of high quality with good temporal signal-to-noise ratio and high inter-subject correlation. Data-driven functional analyses provide further evidence of data quality. This annotated, multilingual fMRI dataset facilitates future re-analysis that addresses cross-linguistic commonalities and differences in the neural substrate of language processing on multiple perceptual and linguistic levels. Nature Publishing Group UK 2022-08-29 /pmc/articles/PMC9424229/ /pubmed/36038567 http://dx.doi.org/10.1038/s41597-022-01625-7 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/) . |
spellingShingle | Data Descriptor Li, Jixing Bhattasali, Shohini Zhang, Shulin Franzluebbers, Berta Luh, Wen-Ming Spreng, R. Nathan Brennan, Jonathan R. Yang, Yiming Pallier, Christophe Hale, John Le Petit Prince multilingual naturalistic fMRI corpus |
title | Le Petit Prince multilingual naturalistic fMRI corpus |
title_full | Le Petit Prince multilingual naturalistic fMRI corpus |
title_fullStr | Le Petit Prince multilingual naturalistic fMRI corpus |
title_full_unstemmed | Le Petit Prince multilingual naturalistic fMRI corpus |
title_short | Le Petit Prince multilingual naturalistic fMRI corpus |
title_sort | le petit prince multilingual naturalistic fmri corpus |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9424229/ https://www.ncbi.nlm.nih.gov/pubmed/36038567 http://dx.doi.org/10.1038/s41597-022-01625-7 |
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