A natural language fMRI dataset for voxelwise encoding models
Abstract Speech comprehension is a complex process that draws on humans’ abilities to extract lexical information, parse syntax, and form semantic understanding. These sub-processes have traditionally been studied using separate neuroimaging experiments that attempt to isolate specific effects of in...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-08-01
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| Series: | Scientific Data |
| Online Access: | https://doi.org/10.1038/s41597-023-02437-z |
| _version_ | 1797453988300324864 |
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| author | Amanda LeBel Lauren Wagner Shailee Jain Aneesh Adhikari-Desai Bhavin Gupta Allyson Morgenthal Jerry Tang Lixiang Xu Alexander G. Huth |
| author_facet | Amanda LeBel Lauren Wagner Shailee Jain Aneesh Adhikari-Desai Bhavin Gupta Allyson Morgenthal Jerry Tang Lixiang Xu Alexander G. Huth |
| author_sort | Amanda LeBel |
| collection | DOAJ |
| description | Abstract Speech comprehension is a complex process that draws on humans’ abilities to extract lexical information, parse syntax, and form semantic understanding. These sub-processes have traditionally been studied using separate neuroimaging experiments that attempt to isolate specific effects of interest. More recently it has become possible to study all stages of language comprehension in a single neuroimaging experiment using narrative natural language stimuli. The resulting data are richly varied at every level, enabling analyses that can probe everything from spectral representations to high-level representations of semantic meaning. We provide a dataset containing BOLD fMRI responses recorded while 8 participants each listened to 27 complete, natural, narrative stories (~6 hours). This dataset includes pre-processed and raw MRIs, as well as hand-constructed 3D cortical surfaces for each participant. To address the challenges of analyzing naturalistic data, this dataset is accompanied by a python library containing basic code for creating voxelwise encoding models. Altogether, this dataset provides a large and novel resource for understanding speech and language processing in the human brain. |
| first_indexed | 2024-03-09T15:30:45Z |
| format | Article |
| id | doaj.art-f9b02f5d43724a31ae65413c9c761b45 |
| institution | Directory Open Access Journal |
| issn | 2052-4463 |
| language | English |
| last_indexed | 2024-03-09T15:30:45Z |
| publishDate | 2023-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Data |
| spelling | doaj.art-f9b02f5d43724a31ae65413c9c761b452023-11-26T12:18:47ZengNature PortfolioScientific Data2052-44632023-08-0110111210.1038/s41597-023-02437-zA natural language fMRI dataset for voxelwise encoding modelsAmanda LeBel0Lauren Wagner1Shailee Jain2Aneesh Adhikari-Desai3Bhavin Gupta4Allyson Morgenthal5Jerry Tang6Lixiang Xu7Alexander G. Huth8Helen Wills Neuroscience Institute, University of California, BerkeleyDepartment of Psychiatry and Biobehavioral Sciences, University of CaliforniaDepartment of Computer Science, The University of Texas at AustinDepartment of Computer Science, The University of Texas at AustinDepartment of Computer Science, The University of Texas at AustinDepartment of Neuroscience, The University of Texas at AustinDepartment of Computer Science, The University of Texas at AustinDepartment of Physics, The University of Texas at AustinDepartment of Computer Science, The University of Texas at AustinAbstract Speech comprehension is a complex process that draws on humans’ abilities to extract lexical information, parse syntax, and form semantic understanding. These sub-processes have traditionally been studied using separate neuroimaging experiments that attempt to isolate specific effects of interest. More recently it has become possible to study all stages of language comprehension in a single neuroimaging experiment using narrative natural language stimuli. The resulting data are richly varied at every level, enabling analyses that can probe everything from spectral representations to high-level representations of semantic meaning. We provide a dataset containing BOLD fMRI responses recorded while 8 participants each listened to 27 complete, natural, narrative stories (~6 hours). This dataset includes pre-processed and raw MRIs, as well as hand-constructed 3D cortical surfaces for each participant. To address the challenges of analyzing naturalistic data, this dataset is accompanied by a python library containing basic code for creating voxelwise encoding models. Altogether, this dataset provides a large and novel resource for understanding speech and language processing in the human brain.https://doi.org/10.1038/s41597-023-02437-z |
| spellingShingle | Amanda LeBel Lauren Wagner Shailee Jain Aneesh Adhikari-Desai Bhavin Gupta Allyson Morgenthal Jerry Tang Lixiang Xu Alexander G. Huth A natural language fMRI dataset for voxelwise encoding models Scientific Data |
| title | A natural language fMRI dataset for voxelwise encoding models |
| title_full | A natural language fMRI dataset for voxelwise encoding models |
| title_fullStr | A natural language fMRI dataset for voxelwise encoding models |
| title_full_unstemmed | A natural language fMRI dataset for voxelwise encoding models |
| title_short | A natural language fMRI dataset for voxelwise encoding models |
| title_sort | natural language fmri dataset for voxelwise encoding models |
| url | https://doi.org/10.1038/s41597-023-02437-z |
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