The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

Samuel A. Nastase; Yun Fei Liu; Hanna Hillman; Asieh Zadbood; Liat Hasenfratz; Neggin Keshavarzian; Janice Chen; Christopher J. Honey; Yaara Yeshurun; Mor Regev; Mai Nguyen; Claire H.C. Chang; Christopher Baldassano; Olga Lositsky; Erez Simony; Michael A. Chow; Yuan Chang Leong; Paula P. Brooks; Emily Micciche; Gina Choe; Ariel Goldstein; Tamara Vanderwal; Yaroslav O. Halchenko; Kenneth A. Norman; Uri Hasson

doi:10.1038/s41597-021-01033-3

The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

Samuel A. Nastase, Yun Fei Liu, Hanna Hillman, Asieh Zadbood, Liat Hasenfratz, Neggin Keshavarzian, Janice Chen, Christopher J. Honey, Yaara Yeshurun, Mor Regev, Mai Nguyen, Claire H.C. Chang, Christopher Baldassano, Olga Lositsky, Erez Simony, Michael A. Chow, Yuan Chang Leong, Paula P. Brooks, Emily Micciche, Gina ChoeAriel Goldstein, Tamara Vanderwal, Yaroslav O. Halchenko, Kenneth A. Norman, Uri Hasson

Research output: Contribution to journal › Article › peer-review

18 Scopus citations

Abstract

The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

Original language	English (US)
Article number	250
Journal	Scientific Data
Volume	8
Issue number	1
DOIs	https://doi.org/10.1038/s41597-021-01033-3
State	Published - Dec 2021

All Science Journal Classification (ASJC) codes

Information Systems
Education
Library and Information Sciences
Statistics and Probability
Computer Science Applications
Statistics, Probability and Uncertainty

Access to Document

10.1038/s41597-021-01033-3

Cite this

Nastase, S. A., Liu, Y. F., Hillman, H., Zadbood, A., Hasenfratz, L., Keshavarzian, N., Chen, J., Honey, C. J., Yeshurun, Y., Regev, M., Nguyen, M., Chang, C. H. C., Baldassano, C., Lositsky, O., Simony, E., Chow, M. A., Leong, Y. C., Brooks, P. P., Micciche, E., ... Hasson, U. (2021). The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension. Scientific Data, 8(1), Article 250. https://doi.org/10.1038/s41597-021-01033-3

@article{fe481d7a57ac4a8cb364ff59481da17f,

title = "The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension",

abstract = "The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.",

author = "Nastase, {Samuel A.} and Liu, {Yun Fei} and Hanna Hillman and Asieh Zadbood and Liat Hasenfratz and Neggin Keshavarzian and Janice Chen and Honey, {Christopher J.} and Yaara Yeshurun and Mor Regev and Mai Nguyen and Chang, {Claire H.C.} and Christopher Baldassano and Olga Lositsky and Erez Simony and Chow, {Michael A.} and Leong, {Yuan Chang} and Brooks, {Paula P.} and Emily Micciche and Gina Choe and Ariel Goldstein and Tamara Vanderwal and Halchenko, {Yaroslav O.} and Norman, {Kenneth A.} and Uri Hasson",

note = "Funding Information: We thank Leigh Nystrom, Mark Pinsk, Garrett McGrath, and the administrative staff at the Scully Center for the Neuroscience of Mind and Behavior and the Princeton Neuroscience Institute, as well as Elizabeth McDevitt, Anne Mennen, and members of Pygers support group. We thank Franklin Feingold for assistance in data sharing, as well as Chris Gorgolewski, Tal Yarkoni, Satrajit S. Ghosh, Avital Hahamy, Mohamed Amer, Indranil Sur, Xiao Lin, and Ajay Divarakian for helpful feedback on the data and analysis. This work was supported by the National Institutes of Health (NIH) grants R01-MH094480 (U.H.), DP1-HD091948 (U.H.), R01-MH112566 (U.H.), R01-MH112357 (K.A.N., U.H), T32-MH065214 (K.A.N), by the Defense Advanced Research Projects Agency (DARPA) Brain-to-Brain Seedling contract number FA8750-18-C-0213 (U.H.), and by the Intel Corporation. The views, opinions, and/or conclusions contained in this paper are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied of the NIH, DARPA, or Intel. Publisher Copyright: {\textcopyright} 2021, The Author(s).",

year = "2021",

month = dec,

doi = "10.1038/s41597-021-01033-3",

language = "English (US)",

volume = "8",

journal = "Scientific Data",

issn = "2052-4463",

publisher = "Nature Publishing Group",

number = "1",

}

Nastase, SA, Liu, YF, Hillman, H, Zadbood, A, Hasenfratz, L, Keshavarzian, N, Chen, J, Honey, CJ, Yeshurun, Y, Regev, M, Nguyen, M, Chang, CHC, Baldassano, C, Lositsky, O, Simony, E, Chow, MA, Leong, YC, Brooks, PP, Micciche, E, Choe, G, Goldstein, A, Vanderwal, T, Halchenko, YO, Norman, KA & Hasson, U 2021, 'The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension', Scientific Data, vol. 8, no. 1, 250. https://doi.org/10.1038/s41597-021-01033-3

TY - JOUR

T1 - The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

AU - Nastase, Samuel A.

AU - Liu, Yun Fei

AU - Hillman, Hanna

AU - Zadbood, Asieh

AU - Hasenfratz, Liat

AU - Keshavarzian, Neggin

AU - Chen, Janice

AU - Honey, Christopher J.

AU - Yeshurun, Yaara

AU - Regev, Mor

AU - Nguyen, Mai

AU - Chang, Claire H.C.

AU - Baldassano, Christopher

AU - Lositsky, Olga

AU - Simony, Erez

AU - Chow, Michael A.

AU - Leong, Yuan Chang

AU - Brooks, Paula P.

AU - Micciche, Emily

AU - Choe, Gina

AU - Goldstein, Ariel

AU - Vanderwal, Tamara

AU - Halchenko, Yaroslav O.

AU - Norman, Kenneth A.

AU - Hasson, Uri

N1 - Funding Information: We thank Leigh Nystrom, Mark Pinsk, Garrett McGrath, and the administrative staff at the Scully Center for the Neuroscience of Mind and Behavior and the Princeton Neuroscience Institute, as well as Elizabeth McDevitt, Anne Mennen, and members of Pygers support group. We thank Franklin Feingold for assistance in data sharing, as well as Chris Gorgolewski, Tal Yarkoni, Satrajit S. Ghosh, Avital Hahamy, Mohamed Amer, Indranil Sur, Xiao Lin, and Ajay Divarakian for helpful feedback on the data and analysis. This work was supported by the National Institutes of Health (NIH) grants R01-MH094480 (U.H.), DP1-HD091948 (U.H.), R01-MH112566 (U.H.), R01-MH112357 (K.A.N., U.H), T32-MH065214 (K.A.N), by the Defense Advanced Research Projects Agency (DARPA) Brain-to-Brain Seedling contract number FA8750-18-C-0213 (U.H.), and by the Intel Corporation. The views, opinions, and/or conclusions contained in this paper are those of the authors and should not be interpreted as representing the official views or policies, either expressed or implied of the NIH, DARPA, or Intel. Publisher Copyright: © 2021, The Author(s).

PY - 2021/12

Y1 - 2021/12

N2 - The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

AB - The “Narratives” collection aggregates a variety of functional MRI datasets collected while human subjects listened to naturalistic spoken stories. The current release includes 345 subjects, 891 functional scans, and 27 diverse stories of varying duration totaling ~4.6 hours of unique stimuli (~43,000 words). This data collection is well-suited for naturalistic neuroimaging analysis, and is intended to serve as a benchmark for models of language and narrative comprehension. We provide standardized MRI data accompanied by rich metadata, preprocessed versions of the data ready for immediate use, and the spoken story stimuli with time-stamped phoneme- and word-level transcripts. All code and data are publicly available with full provenance in keeping with current best practices in transparent and reproducible neuroimaging.

UR - http://www.scopus.com/inward/record.url?scp=85116052357&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85116052357&partnerID=8YFLogxK

U2 - 10.1038/s41597-021-01033-3

DO - 10.1038/s41597-021-01033-3

M3 - Article

C2 - 34584100

AN - SCOPUS:85116052357

SN - 2052-4463

VL - 8

JO - Scientific Data

JF - Scientific Data

IS - 1

M1 - 250

ER -

The “Narratives” fMRI dataset for evaluating models of naturalistic language comprehension

Abstract

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this