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Le Petit Prince multilingual naturalistic fMRI corpus.

ABSTRACT: 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.

SUBMITTER: Li J

PROVIDER: S-EPMC9424229 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Le Petit Prince multilingual naturalistic fMRI corpus.

Li Jixing J Bhattasali Shohini S Zhang Shulin S Franzluebbers Berta B Luh Wen-Ming WM Spreng R Nathan RN Brennan Jonathan R JR Yang Yiming Y Pallier Christophe C Hale John J

Scientific data 20220829 1

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: ds00 ...[more]

PMID: 36038567

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Project description:Studies involving multivariate pattern analysis (MVPA) of BOLD fMRI data generally attribute the success of the information-theoretic approach to BOLD signal contrast on the fine spatial scale of millimeters facilitating the classification or decoding of perceptual stimuli. However, to date MVPA studies that have actually explored fMRI resolutions at less than 2 mm voxel size are rare and limited to small sets of unnatural stimuli (like visual gratings) as well as specific sub-regions of the brain, notably the primary somatosensory cortices. To investigate what spatial scale best supports high information extraction under more general conditions this study combined naturalistic movie stimuli with high-resolution fMRI at 7 T and linear discriminant analysis (LDA) of global and local BOLD signal patterns. Contrary to predictions, LDA and similar classifiers reached a maximum in classification accuracy (CA) at a smoothed resolution close to 3 mm, well above the 1.2 mm voxel size of the fMRI acquisition. Maximal CAs around 90% were contingent upon global fMRI signal patterns comprising 4 k-16 k of the most reactive voxels distributed sparsely throughout the occipital and ventro-temporal cortices. A Searchlight analysis of local fMRI patterns largely confirmed the global results, but also revealed a small subset of brain regions in early visual cortex showing limited increases in CA with higher resolution. Principal component analysis of the global and local fMRI signal patterns suggested that reproducible neuronal contributions were spatially auto-correlated and smooth, while other components of higher spatial frequency were likely related to physiological noise and responsible for the reduced CA at higher resolution. Systematic differences between experiments and subjects suggested that higher CA was significantly correlated with more consistent behavior revealed by eye tracking. Thus, the optimal resolution of fMRI data for MVPA was mainly limited by physiological noise of high spatial frequency as well as behavioral (in-)consistency.

Dataset Information

Le Petit Prince multilingual naturalistic fMRI corpus.

Publications

Le Petit Prince multilingual naturalistic fMRI corpus.

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