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  Le Petit Prince Hong Kong (LPPHK): Naturalistic fMRI and EEG data from older Cantonese speakers

Momenian, M., Ma, Z., Wu, S., Wang, C., Brennan, J., Hale, J., et al. (2024). Le Petit Prince Hong Kong (LPPHK): Naturalistic fMRI and EEG data from older Cantonese speakers. Scientific Data, 11(1): 992. doi:10.1038/s41597-024-03745-8.

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 Creators:
Momenian, Mohammad1, 2, Author
Ma, Zhengwu3, Author
Wu, Shuyi3, Author
Wang, Chengcheng3, Author
Brennan, Jonathan4, Author
Hale, John5, Author
Meyer, Lars6, 7, Author                 
Li, Jixing3, Author
Affiliations:
1Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, China, ou_persistent22              
2Research Institute for Smart Ageing (RISA), The Hong Kong Polytechnic University, China, ou_persistent22              
3Department of Linguistics and Translation, City University of Hong Kong, China, ou_persistent22              
4Department of Linguistics, University of Michigan, Ann Arbor, MI, USA, ou_persistent22              
5Department of Linguistics, University of Georgia, Athens, GA, USA, ou_persistent22              
6Max Planck Research Group Language Cycles, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_3025666              
7Department of Phoniatrics and Pedaudiology, Münster University, Germany, ou_persistent22              

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Free keywords: Cognitive ageing; Language
 Abstract: Currently, the field of neurobiology of language is based on data from only a few Indo-European languages. The majority of this data comes from younger adults neglecting other age groups. Here we present a multimodal database which consists of task-based and resting state fMRI, structural MRI, and EEG data while participants over 65 years old listened to sections of the story The Little Prince in Cantonese. We also provide data on participants' language history, lifetime experiences, linguistic and cognitive skills. Audio and text annotations, including time-aligned speech segmentation and prosodic information, as well as word-by-word predictors such as frequency and part-of-speech tagging derived from natural language processing (NLP) tools are included in this database. Both MRI and EEG data diagnostics revealed that the data has good quality. This multimodal database could advance our understanding of spatiotemporal dynamics of language comprehension in the older population and help us study the effects of healthy aging on the relationship between brain and behaviour.

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Language(s): eng - English
 Dates: 2023-12-252024-08-052024-09-112024-09-11
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41597-024-03745-8
PMID: 39261552
PMC: PMC11390913
 Degree: -

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Project name : -
Grant ID : A0035856
Funding program : -
Funding organization : Hong Kong Polytechnic University
Project name : -
Grant ID : 7200747
Funding program : -
Funding organization : City University of Hong Kong

Source 1

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Title: Scientific Data
  Abbreviation : Sci. Data
Source Genre: Journal
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Publ. Info: London, United Kingdom : Nature Publishing Group
Pages: - Volume / Issue: 11 (1) Sequence Number: 992 Start / End Page: - Identifier: ISSN: 2052-4463
CoNE: https://pure.mpg.de/cone/journals/resource/2052-4463