Le Petit Prince multilingual naturalistic fMRI corpus.


Journal

Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192

Informations de publication

Date de publication:
29 08 2022
Historique:
received: 07 10 2021
accepted: 10 08 2022
entrez: 29 8 2022
pubmed: 30 8 2022
medline: 1 9 2022
Statut: epublish

Résumé

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.

Identifiants

pubmed: 36038567
doi: 10.1038/s41597-022-01625-7
pii: 10.1038/s41597-022-01625-7
pmc: PMC9424229
doi:

Types de publication

Dataset Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

530

Subventions

Organisme : National Science Foundation (NSF)
ID : 1903783
Organisme : National Science Foundation (NSF)
ID : 1607251

Informations de copyright

© 2022. The Author(s).

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Auteurs

Jixing Li (J)

New York University Abu Dhabi, Neuroscience of Language Lab, Abu Dhabi, UAE. jixingli@cityu.edu.hk.
Department of Linguistics and Translation, City University of Hong Kong, Hong Kong, Hong Kong. jixingli@cityu.edu.hk.

Shohini Bhattasali (S)

University of Maryland, Department of Linguistics & Institute of Advanced Computer Studies, College Park, MD, 20742, USA.

Shulin Zhang (S)

University of Georgia, Department of Linguistics, Athens, GA, 30602, USA.

Berta Franzluebbers (B)

University of Georgia, Department of Linguistics, Athens, GA, 30602, USA.

Wen-Ming Luh (WM)

National Institute on Aging, Baltimore, MD, 21225, USA.

R Nathan Spreng (RN)

Laboratory of Brain and Cognition, Montreal Neurological Institute, Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montreal, QC, H3A 2B4, Canada.

Jonathan R Brennan (JR)

Department of Linguistics, University of Michigan, Ann Arbor, MI48109, USA.

Yiming Yang (Y)

Jiangsu Key Laboratory of Language and Cognitive Neuroscience, Jiangsu Normal University, Xuzhou, 221116, China.

Christophe Pallier (C)

Cognitive Neuroimaging Unit, INSERM, CEA, CNRS, Universit Paris-Saclay, NeuroSpin center, Gif-sur-Yvette, 91191, France.

John Hale (J)

University of Georgia, Department of Linguistics, Athens, GA, 30602, USA. jthale@uga.edu.

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Classifications MeSH