The "Narratives" fMRI dataset for evaluating models of naturalistic language comprehension.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
28 09 2021
28 09 2021
Historique:
received:
04
03
2021
accepted:
18
08
2021
entrez:
29
9
2021
pubmed:
30
9
2021
medline:
15
12
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34584100
doi: 10.1038/s41597-021-01033-3
pii: 10.1038/s41597-021-01033-3
pmc: PMC8479122
doi:
Types de publication
Dataset
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
250Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : T32-MH065214
Organisme : NIMH NIH HHS
ID : R01 MH094480
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01-MH112566
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01-MH094480
Organisme : NIBIB NIH HHS
ID : P41 EB019936
Pays : United States
Organisme : NIMH NIH HHS
ID : R01 MH112357
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : DP1-HD091948
Organisme : NICHD NIH HHS
ID : DP1 HD091948
Pays : United States
Organisme : U.S. Department of Health & Human Services | NIH | National Institute of Mental Health (NIMH)
ID : R01-MH112357
Organisme : NIMH NIH HHS
ID : R01 MH112566
Pays : United States
Organisme : United States Department of Defense | Defense Advanced Research Projects Agency (DARPA)
ID : FA8750-18-C-0213
Organisme : NIMH NIH HHS
ID : T32 MH065214
Pays : United States
Informations de copyright
© 2021. The Author(s).
Références
Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S. & Keysers, C. Brain-to-brain coupling: a mechanism for creating and sharing a social world. Trends Cogn. Sci. 16, 114–121 (2012).
pubmed: 22221820
pmcid: 3269540
doi: 10.1016/j.tics.2011.12.007
Berwick, R. C., Friederici, A. D., Chomsky, N. & Bolhuis, J. J. Evolution, brain, and the nature of language. Trends Cogn. Sci. 17, 89–98 (2013).
pubmed: 23313359
doi: 10.1016/j.tics.2012.12.002
Bolhuis, J. J., Beckers, G. J. L., Huybregts, M. A. C., Berwick, R. C. & Everaert, M. B. H. Meaningful syntactic structure in songbird vocalizations? PLoS Biol. 16, e2005157 (2018).
pubmed: 29864124
pmcid: 6002252
doi: 10.1371/journal.pbio.2005157
Townsend, S. W., Engesser, S., Stoll, S., Zuberbühler, K. & Bickel, B. Compositionality in animals and humans. PLoS Biol. 16, e2006425 (2018).
pubmed: 30110319
pmcid: 6093600
doi: 10.1371/journal.pbio.2006425
Hamilton, L. S. & Huth, A. G. The revolution will not be controlled: natural stimuli in speech neuroscience. Lang. Cogn. Neurosci. 35, 573–582 (2020).
pubmed: 32656294
doi: 10.1080/23273798.2018.1499946
Hasson, U., Egidi, G., Marelli, M. & Willems, R. M. Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension. Cognition 180, 135–157 (2018).
pubmed: 30053570
pmcid: 6145924
doi: 10.1016/j.cognition.2018.06.018
Willems, R. M., Nastase, S. A. & Milivojevic, B. Narratives for neuroscience. Trends Neurosci. 43, 271–273 (2020).
pubmed: 32353331
doi: 10.1016/j.tins.2020.03.003
Bookheimer, S. Functional MRI of language: new approaches to understanding the cortical organization of semantic processing. Annu. Rev. Neurosci. 25, 151–188 (2002).
pubmed: 12052907
doi: 10.1146/annurev.neuro.25.112701.142946
Vigneau, M. et al. Meta-analyzing left hemisphere language areas: phonology, semantics, and sentence processing. Neuroimage 30, 1414–1432 (2006).
pubmed: 16413796
doi: 10.1016/j.neuroimage.2005.11.002
Hickok, G. & Poeppel, D. The cortical organization of speech processing. Nat. Rev. Neurosci. 8, 393–402 (2007).
pubmed: 17431404
doi: 10.1038/nrn2113
Price, C. J. The anatomy of language: a review of 100 fMRI studies published in 2009. Ann. N. Y. Acad. Sci. 1191, 62–88 (2010).
pubmed: 20392276
doi: 10.1111/j.1749-6632.2010.05444.x
Price, C. J. A review and synthesis of the first 20 years of PET and fMRI studies of heard speech, spoken language and reading. Neuroimage 62, 816–847 (2012).
pubmed: 22584224
doi: 10.1016/j.neuroimage.2012.04.062
Friederici, A. D. The brain basis of language processing: from structure to function. Physiol. Rev. 91, 1357–1392 (2011).
pubmed: 22013214
doi: 10.1152/physrev.00006.2011
Friederici, A. D. The cortical language circuit: from auditory perception to sentence comprehension. Trends Cogn. Sci. 16, 262–268 (2012).
pubmed: 22516238
doi: 10.1016/j.tics.2012.04.001
Kwong, K. K. et al. Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc. Natl. Acad. Sci. USA 89, 5675–5679 (1992).
pubmed: 1608978
pmcid: 49355
doi: 10.1073/pnas.89.12.5675
Ogawa, S. et al. Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc. Natl. Acad. Sci. USA 89, 5951–5955 (1992).
pubmed: 1631079
pmcid: 402116
doi: 10.1073/pnas.89.13.5951
Logothetis, N. K., Pauls, J., Augath, M., Trinath, T. & Oeltermann, A. Neurophysiological investigation of the basis of the fMRI signal. Nature 412, 150–157 (2001).
pubmed: 11449264
doi: 10.1038/35084005
Logothetis, N. K. What we can do and what we cannot do with fMRI. Nature 453, 869–878 (2008).
pubmed: 18548064
doi: 10.1038/nature06976
Démonet, J. F. et al. The anatomy of phonological and semantic processing in normal subjects. Brain 115, 1753–1768 (1992).
pubmed: 1486459
doi: 10.1093/brain/115.6.1753
Zatorre, R. J., Evans, A. C., Meyer, E. & Gjedde, A. Lateralization of phonetic and pitch discrimination in speech processing. Science 256, 846–849 (1992).
pubmed: 1589767
doi: 10.1126/science.256.5058.846
Belin, P., Zatorre, R. J., Lafaille, P., Ahad, P. & Pike, B. Voice-selective areas in human auditory cortex. Nature 403, 309–312 (2000).
pubmed: 10659849
doi: 10.1038/35002078
Vouloumanos, A., Kiehl, K. A., Werker, J. F. & Liddle, P. F. Detection of sounds in the auditory stream: event-related fMRI evidence for differential activation to speech and nonspeech. J. Cogn. Neurosci. 13, 994–1005 (2001).
pubmed: 11595101
doi: 10.1162/089892901753165890
Dapretto, M. & Bookheimer, S. Y. Form and content: dissociating syntax and semantics in sentence comprehension. Neuron 24, 427–432 (1999).
pubmed: 10571235
doi: 10.1016/S0896-6273(00)80855-7
Ben-Shachar, M., Hendler, T., Kahn, I., Ben-Bashat, D. & Grodzinsky, Y. The neural reality of syntactic transformations: evidence from functional magnetic resonance imaging. Psychol. Sci. 14, 433–440 (2003).
pubmed: 12930473
doi: 10.1111/1467-9280.01459
Noppeney, U. & Price, C. J. An FMRI study of syntactic adaptation. J. Cogn. Neurosci. 16, 702–713 (2004).
pubmed: 15165357
doi: 10.1162/089892904323057399
Patterson, K., Nestor, P. J. & Rogers, T. T. Where do you know what you know? The representation of semantic knowledge in the human brain. Nat. Rev. Neurosci. 8, 976–987 (2007).
pubmed: 18026167
doi: 10.1038/nrn2277
Binder, J. R., Desai, R. H., Graves, W. W. & Conant, L. L. Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies. Cereb. Cortex 19, 2767–2796 (2009).
pubmed: 19329570
pmcid: 2774390
doi: 10.1093/cercor/bhp055
Fedorenko, E., Hsieh, P.-J., Nieto-Castañón, A., Whitfield-Gabrieli, S. & Kanwisher, N. New method for fMRI investigations of language: defining ROIs functionally in individual subjects. J. Neurophysiol. 104, 1177–1194 (2010).
pubmed: 20410363
pmcid: 2934923
doi: 10.1152/jn.00032.2010
Mahowald, K. & Fedorenko, E. Reliable individual-level neural markers of high-level language processing: a necessary precursor for relating neural variability to behavioral and genetic variability. Neuroimage 139, 74–93 (2016).
pubmed: 27261158
doi: 10.1016/j.neuroimage.2016.05.073
Braga, R. M., DiNicola, L. M., Becker, H. C. & Buckner, R. L. Situating the left-lateralized language network in the broader organization of multiple specialized large-scale distributed networks. J. Neurophysiol. 124, 1415–1448 (2020).
pubmed: 32965153
pmcid: 8356783
doi: 10.1152/jn.00753.2019
Jäncke, L., Wüstenberg, T., Scheich, H. & Heinze, H.-J. Phonetic perception and the temporal cortex. Neuroimage 15, 733–746 (2002).
pubmed: 11906217
doi: 10.1006/nimg.2001.1027
Obleser, J., Zimmermann, J., Van Meter, J. & Rauschecker, J. P. Multiple stages of auditory speech perception reflected in event-related FMRI. Cereb. Cortex 17, 2251–2257 (2007).
pubmed: 17150986
doi: 10.1093/cercor/bhl133
Petersen, S. E., Fox, P. T., Posner, M. I., Mintun, M. & Raichle, M. E. Positron emission tomographic studies of the cortical anatomy of single-word processing. Nature 331, 585–589 (1988).
pubmed: 3277066
doi: 10.1038/331585a0
Wise, R. et al. Distribution of cortical neural networks involved in word comprehension and word retrieval. Brain 114, 1803–1817 (1991).
pubmed: 1884179
doi: 10.1093/brain/114.4.1803
Poldrack, R. A. et al. Functional specialization for semantic and phonological processing in the left inferior prefrontal cortex. Neuroimage 10, 15–35 (1999).
pubmed: 10385578
doi: 10.1006/nimg.1999.0441
Just, M. A., Carpenter, P. A., Keller, T. A., Eddy, W. F. & Thulborn, K. R. Brain activation modulated by sentence comprehension. Science 274, 114–116 (1996).
pubmed: 8810246
doi: 10.1126/science.274.5284.114
Kuperberg, G. R. et al. Common and distinct neural substrates for pragmatic, semantic, and syntactic processing of spoken sentences: an fMRI study. J. Cogn. Neurosci. 12, 321–341 (2000).
pubmed: 10771415
doi: 10.1162/089892900562138
Ni, W. et al. An event-related neuroimaging study distinguishing form and content in sentence processing. J. Cogn. Neurosci. 12, 120–133 (2000).
pubmed: 10769310
doi: 10.1162/08989290051137648
Scott, S. K., Blank, C. C., Rosen, S. & Wise, R. J. Identification of a pathway for intelligible speech in the left temporal lobe. Brain 123, 2400–2406 (2000).
pubmed: 11099443
doi: 10.1093/brain/123.12.2400
Vandenberghe, R., Nobre, A. C. & Price, C. J. The response of left temporal cortex to sentences. J. Cogn. Neurosci. 14, 550–560 (2002).
pubmed: 12126497
doi: 10.1162/08989290260045800
Humphries, C., Binder, J. R., Medler, D. A. & Liebenthal, E. Syntactic and semantic modulation of neural activity during auditory sentence comprehension. J. Cogn. Neurosci. 18, 665–679 (2006).
pubmed: 16768368
pmcid: 1635792
doi: 10.1162/jocn.2006.18.4.665
Yarkoni, T., Speer, N. K. & Zacks, J. M. Neural substrates of narrative comprehension and memory. NeuroImage 41, 1408–1425 (2008).
Brennan, J. et al. Syntactic structure building in the anterior temporal lobe during natural story listening. Brain Lang. 120, 163–173 (2012).
pubmed: 20472279
doi: 10.1016/j.bandl.2010.04.002
Brennan, J. R., Stabler, E. P., Van Wagenen, S. E., Luh, W.-M. & Hale, J. T. Abstract linguistic structure correlates with temporal activity during naturalistic comprehension. Brain Lang. 157–158, 81–94 (2016).
pubmed: 27208858
pmcid: 4893969
doi: 10.1016/j.bandl.2016.04.008
Nastase, S. A., Goldstein, A. & Hasson, U. Keep it real: rethinking the primacy of experimental control in cognitive neuroscience. Neuroimage 222, 117254 (2020).
pubmed: 32800992
doi: 10.1016/j.neuroimage.2020.117254
Wehbe, L. et al. Simultaneously uncovering the patterns of brain regions involved in different story reading subprocesses. PLoS One 9, e112575 (2014).
pubmed: 25426840
pmcid: 4245107
doi: 10.1371/journal.pone.0112575
Huth, A. G., de Heer, W. A., Griffiths, T. L., Theunissen, F. E. & Gallant, J. L. Natural speech reveals the semantic maps that tile human cerebral cortex. Nature 532, 453–458 (2016).
pubmed: 27121839
pmcid: 4852309
doi: 10.1038/nature17637
Goldberg, Y. Neural network methods for natural language processing. Synth. Lectures Hum. Lang. Technol. 10, 1–309 (2017).
doi: 10.1007/978-3-031-02165-7
Baroni, M. Linguistic generalization and compositionality in modern artificial neural networks. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190307 (2020).
pubmed: 31840578
doi: 10.1098/rstb.2019.0307
Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) 4171–4186 (Association for Computational Linguistics, 2019).
Radford, A. et al. Language models are unsupervised multitask learners. OpenAI Blog (2019).
Turney, P. D. & Pantel, P. From frequency to meaning: vector space models of semantics. J. Artif. Intell. Res. 37, 141–188 (2010).
doi: 10.1613/jair.2934
Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. in Advances in Neural Information Processing Systems 26 (eds. Burges, C. J. C., Bottou, L., Welling, M., Ghahramani, Z. & Weinberger, K. Q.) 3111–3119 (Curran Associates, Inc., 2013).
Manning, C. D., Clark, K., Hewitt, J., Khandelwal, U. & Levy, O. Emergent linguistic structure in artificial neural networks trained by self-supervision. Proc. Natl. Acad. Sci. USA 117, 30046–30054 (2020).
pubmed: 32493748
pmcid: 7720155
doi: 10.1073/pnas.1907367117
Breiman, L. Statistical modeling: the two cultures. Stat. Sci. 16, 199–231 (2001).
doi: 10.1214/ss/1009213726
Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12, 1100–1122 (2017).
pubmed: 28841086
pmcid: 6603289
doi: 10.1177/1745691617693393
Varoquaux, G. & Poldrack, R. A. Predictive models avoid excessive reductionism in cognitive neuroimaging. Curr. Opin. Neurobiol. 55, 1–6 (2019).
pubmed: 30513462
doi: 10.1016/j.conb.2018.11.002
Hasson, U., Nastase, S. A. & Goldstein, A. Direct fit to nature: an evolutionary perspective on biological and artificial neural networks. Neuron 105, 416–434 (2020).
pubmed: 32027833
pmcid: 7096172
doi: 10.1016/j.neuron.2019.12.002
LeCun, Y., Cortes, C. & Burges, C. J. MNIST handwritten digit database. (2010).
Krizhevsky, A. Learning multiple layers of features from tiny images. (University of Toronto, 2009).
Milham, M. P. et al. Assessment of the impact of shared brain imaging data on the scientific literature. Nat. Commun. 9, 2818 (2018).
pubmed: 30026557
pmcid: 6053414
doi: 10.1038/s41467-018-04976-1
Biswal, B. B. et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA 107, 4734–4739 (2010).
pubmed: 20176931
pmcid: 2842060
doi: 10.1073/pnas.0911855107
Van Essen, D. C. et al. The WU-Minn Human Connectome Project: an overview. Neuroimage 80, 62–79 (2013).
pubmed: 23684880
doi: 10.1016/j.neuroimage.2013.05.041
Shafto, M. A. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing. BMC Neurol. 14, 204 (2014).
pubmed: 25412575
pmcid: 4219118
doi: 10.1186/s12883-014-0204-1
Taylor, J. R. et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data repository: structural and functional MRI, MEG, and cognitive data from a cross-sectional adult lifespan sample. Neuroimage 144, 262–269 (2017).
pubmed: 26375206
doi: 10.1016/j.neuroimage.2015.09.018
Alexander, L. M. et al. An open resource for transdiagnostic research in pediatric mental health and learning disorders. Sci Data 4, 170181 (2017).
pubmed: 29257126
pmcid: 5735921
doi: 10.1038/sdata.2017.181
Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nat. Neurosci. 17, 1510–1517 (2014).
pubmed: 25349916
doi: 10.1038/nn.3818
Poldrack, R. A. et al. Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat. Rev. Neurosci. 18, 115–126 (2017).
pubmed: 28053326
pmcid: 6910649
doi: 10.1038/nrn.2016.167
Poldrack, R. A., Gorgolewski, K. J. & Varoquaux, G. Computational and informatic advances for reproducible data analysis in neuroimaging. Annu. Rev. Biomed. Data Sci. 2, 119–138 (2019).
doi: 10.1146/annurev-biodatasci-072018-021237
Ferguson, A. R., Nielson, J. L., Cragin, M. H., Bandrowski, A. E. & Martone, M. E. Big data from small data: data-sharing in the ‘long tail’ of neuroscience. Nat. Neurosci. 17, 1442–1447 (2014).
pubmed: 25349910
pmcid: 4728080
doi: 10.1038/nn.3838
Hanke, M. et al. A high-resolution 7-Tesla fMRI dataset from complex natural stimulation with an audio movie. Sci Data 1, 140003 (2014).
pubmed: 25977761
pmcid: 4322572
doi: 10.1038/sdata.2014.3
Hanke, M. et al. A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Sci Data 3, 160092 (2016).
pubmed: 27779621
pmcid: 5079121
doi: 10.1038/sdata.2016.92
Aly, M., Chen, J., Turk-Browne, N. B. & Hasson, U. Learning naturalistic temporal structure in the posterior medial network. J. Cogn. Neurosci. 30, 1345–1365 (2018).
pubmed: 30004848
pmcid: 6211568
doi: 10.1162/jocn_a_01308
DuPre, E., Hanke, M. & Poline, J.-B. Nature abhors a paywall: how open science can realize the potential of naturalistic stimuli. Neuroimage 216, 116330 (2020).
pubmed: 31704292
doi: 10.1016/j.neuroimage.2019.116330
Aliko, S., Huang, J., Gheorghiu, F. & Meliss, S. & Skipper, J. I. A naturalistic neuroimaging database for understanding the brain using ecological stimuli. Sci Data 7, 347 (2020).
pubmed: 33051448
pmcid: 7555491
doi: 10.1038/s41597-020-00680-2
Richardson, H., Lisandrelli, G., Riobueno-Naylor, A. & Saxe, R. Development of the social brain from age three to twelve years. Nat. Commun. 9, 1027 (2018).
pubmed: 29531321
pmcid: 5847587
doi: 10.1038/s41467-018-03399-2
Finn, E. S., Corlett, P. R., Chen, G., Bandettini, P. A. & Constable, R. T. Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative. Nat. Commun. 9, 2043 (2018).
pubmed: 29795116
pmcid: 5966466
doi: 10.1038/s41467-018-04387-2
Chen, J. et al. Accessing real-life episodic information from minutes versus hours earlier modulates hippocampal and high-order cortical dynamics. Cereb. Cortex 26, 3428–3441 (2016).
pubmed: 26240179
pmcid: 4961013
doi: 10.1093/cercor/bhv155
Chen, J. et al. Shared memories reveal shared structure in neural activity across individuals. Nat. Neurosci. 20, 115–125 (2017).
pubmed: 27918531
doi: 10.1038/nn.4450
O’Connor, D. et al. The Healthy Brain Network Serial Scanning Initiative: a resource for evaluating inter-individual differences and their reliabilities across scan conditions and sessions. GigaScience 6, 1–14 (2017).
pubmed: 28369458
pmcid: 5466711
doi: 10.1093/gigascience/giw011
Haxby, J. V. et al. A common, high-dimensional model of the representational space in human ventral temporal cortex. Neuron 72, 404–416 (2011).
pubmed: 22017997
pmcid: 3201764
doi: 10.1016/j.neuron.2011.08.026
Nastase, S. A. et al. Attention Selectively Reshapes the Geometry of Distributed Semantic Representation. Cereb. Cortex 27, 4277–4291 (2017).
pubmed: 28591837
pmcid: 6248820
doi: 10.1093/cercor/bhx138
Nastase, S. A., Halchenko, Y. O., Connolly, A. C., Gobbini, M. I. & Haxby, J. V. Neural responses to naturalistic clips of behaving animals in two different task contexts. Front. Neurosci. 12, 316 (2018).
pubmed: 29867327
pmcid: 5962655
doi: 10.3389/fnins.2018.00316
Castello, M. V. di O., di Oleggio Castello, M. V., Chauhan, V., Jiahui, G. & Ida Gobbini, M. An fMRI dataset in response to ‘The Grand Budapest Hotel’, a socially-rich, naturalistic movie. Scientific Data vol. 7 (2020).
Nastase, S. A. et al. Narratives. OpenNeuro https://doi.org/10.18112/openneuro.ds002345.v1.1.4 (2019).
Gorgolewski, K. J. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3, 160044 (2016).
pubmed: 27326542
pmcid: 4978148
doi: 10.1038/sdata.2016.44
Poldrack, R. A. & Gorgolewski, K. J. OpenfMRI: Open sharing of task fMRI data. Neuroimage 144, 259–261 (2017).
pubmed: 26048618
doi: 10.1016/j.neuroimage.2015.05.073
Hanke, M. et al. datalad/datalad: 0.13.3 (August 28, 2020). Zenodo https://doi.org/10.5281/zenodo.4006562 (2020).
Hanke, M. et al. In defense of decentralized research data management. Neuroforum 27, 17–25 (2021).
Spiers, H. J. & Maguire, E. A. Decoding human brain activity during real-world experiences. Trends Cogn. Sci. 11, 356–365 (2007).
pubmed: 17618161
doi: 10.1016/j.tics.2007.06.002
Hasson, U. & Honey, C. J. Future trends in neuroimaging: neural processes as expressed within real-life contexts. Neuroimage 62, 1272–1278 (2012).
pubmed: 22348879
doi: 10.1016/j.neuroimage.2012.02.004
Matusz, P. J., Dikker, S., Huth, A. G. & Perrodin, C. Are we ready for real-world neuroscience? J. Cogn. Neurosci. 31, 327–338 (2019).
pubmed: 29916793
doi: 10.1162/jocn_e_01276
Sonkusare, S., Breakspear, M. & Guo, C. Naturalistic stimuli in neuroscience: critically acclaimed. Trends Cogn. Sci. 23, 699–714 (2019).
pubmed: 31257145
doi: 10.1016/j.tics.2019.05.004
Redcay, E. & Moraczewski, D. Social cognition in context: a naturalistic imaging approach. Neuroimage 216, 116392 (2020).
pubmed: 31770637
doi: 10.1016/j.neuroimage.2019.116392
Vanderwal, T., Eilbott, J. & Castellanos, F. X. Movies in the magnet: naturalistic paradigms in developmental functional neuroimaging. Dev. Cogn. Neurosci. 36, 100600 (2018).
pubmed: 30551970
pmcid: 6969259
doi: 10.1016/j.dcn.2018.10.004
Kriegeskorte, N., Mur, M. & Bandettini, P. A. Representational similarity analysis—connecting the branches of systems neuroscience. Front. Syst. Neurosci. 2, 4 (2008).
pubmed: 19104670
pmcid: 2605405
Naselaris, T., Kay, K. N., Nishimoto, S. & Gallant, J. L. Encoding and decoding in fMRI. Neuroimage 56, 400–410 (2011).
pubmed: 20691790
doi: 10.1016/j.neuroimage.2010.07.073
Santoro, R. et al. Encoding of natural sounds at multiple spectral and temporal resolutions in the human auditory cortex. PLoS Comput. Biol. 10, e1003412 (2014).
pubmed: 24391486
pmcid: 3879146
doi: 10.1371/journal.pcbi.1003412
de Heer, W. A., Huth, A. G., Griffiths, T. L., Gallant, J. L. & Theunissen, F. E. The hierarchical cortical organization of human speech processing. J. Neurosci. 37, 6539–6557 (2017).
pubmed: 28588065
pmcid: 5511884
doi: 10.1523/JNEUROSCI.3267-16.2017
Kell, A. J. E., Yamins, D. L. K., Shook, E. N., Norman-Haignere, S. V. & McDermott, J. H. A task-optimized neural network replicates human auditory behavior, predicts brain responses, and reveals a cortical processing hierarchy. Neuron 98, 630–644.e16 (2018).
pubmed: 29681533
doi: 10.1016/j.neuron.2018.03.044
Mitchell, T. M. et al. Predicting human brain activity associated with the meanings of nouns. Science 320, 1191–1195 (2008).
pubmed: 18511683
doi: 10.1126/science.1152876
Pereira, F. et al. Toward a universal decoder of linguistic meaning from brain activation. Nat. Commun. 9, 963 (2018).
pubmed: 29511192
pmcid: 5840373
doi: 10.1038/s41467-018-03068-4
Schrimpf, M. et al. The neural architecture of language: integrative reverse-engineering converges on a model for predictive processing. Preprint at https://doi.org/10.1101/2020.06.26.174482 (2020).
Hasson, U., Nir, Y., Levy, I., Fuhrmann, G. & Malach, R. Intersubject synchronization of cortical activity during natural vision. Science 303, 1634–1640 (2004).
pubmed: 15016991
doi: 10.1126/science.1089506
Nastase, S. A., Gazzola, V., Hasson, U. & Keysers, C. Measuring shared responses across subjects using intersubject correlation. Soc. Cogn. Affect. Neurosci. 14, 667–685 (2019).
pubmed: 31099394
pmcid: 6688448
Vanderwal, T. et al. Individual differences in functional connectivity during naturalistic viewing conditions. Neuroimage 157, 521–530 (2017).
pubmed: 28625875
doi: 10.1016/j.neuroimage.2017.06.027
Feilong, M., Nastase, S. A., Guntupalli, J. S. & Haxby, J. V. Reliable individual differences in fine-grained cortical functional architecture. Neuroimage 183, 375–386 (2018).
pubmed: 30118870
doi: 10.1016/j.neuroimage.2018.08.029
Finn, E. S. et al. Idiosynchrony: from shared responses to individual differences during naturalistic neuroimaging. Neuroimage 215, 116828 (2020).
pubmed: 32276065
doi: 10.1016/j.neuroimage.2020.116828
Chen, P.-H. et al. A reduced-dimension fMRI shared response model. in Advances in Neural Information Processing Systems 28 (eds. Cortes, C., Lawrence, N. D., Lee, D. D., Sugiyama, M. & Garnett, R.) 460–468 (Curran Associates, Inc., 2015).
Guntupalli, J. S. et al. A model of representational spaces in human cortex. Cereb. Cortex 26, 2919–2934 (2016).
pubmed: 26980615
pmcid: 4869822
doi: 10.1093/cercor/bhw068
Guntupalli, J. S., Feilong, M. & Haxby, J. V. A computational model of shared fine-scale structure in the human connectome. PLoS Comput. Biol. 14, e1006120 (2018).
pubmed: 29664910
pmcid: 5922579
doi: 10.1371/journal.pcbi.1006120
Van Uden, C. E. et al. Modeling semantic encoding in a common neural representational space. Front. Neurosci. 12, 437 (2018).
pubmed: 30042652
pmcid: 6048235
doi: 10.3389/fnins.2018.00437
Haxby, J. V., Guntupalli, J. S., Nastase, S. A. & Feilong, M. Hyperalignment: modeling shared information encoded in idiosyncratic cortical topographies. eLife 9 (2020).
Milivojevic, B., Varadinov, M., Vicente Grabovetsky, A., Collin, S. H. P. & Doeller, C. F. Coding of event nodes and narrative context in the hippocampus. J. Neurosci. 36, 12412–12424 (2016).
pubmed: 27927958
pmcid: 6601969
doi: 10.1523/JNEUROSCI.2889-15.2016
Baldassano, C. et al. Discovering event structure in continuous narrative perception and memory. Neuron 95, 709–721.e5 (2017).
pubmed: 28772125
pmcid: 5558154
doi: 10.1016/j.neuron.2017.06.041
Baldassano, C., Hasson, U. & Norman, K. A. Representation of real-world event schemas during narrative perception. J. Neurosci. 38, 9689–9699 (2018).
pubmed: 30249790
pmcid: 6222059
doi: 10.1523/JNEUROSCI.0251-18.2018
Chang, L. J. et al. Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience. Sci. Adv. 7, eabf7129 (2021).
pubmed: 33893106
pmcid: 8064646
doi: 10.1126/sciadv.abf7129
Heusser, A. C., Fitzpatrick, P. C. & Manning, J. R. Geometric models reveal behavioural and neural signatures of transforming experiences into memories. Nat. Hum. Behav. 5, 905–919 (2021).
pubmed: 33574605
doi: 10.1038/s41562-021-01051-6
Simony, E. et al. Dynamic reconfiguration of the default mode network during narrative comprehension. Nat. Commun. 7, 12141 (2016).
pubmed: 27424918
pmcid: 4960303
doi: 10.1038/ncomms12141
Kim, D., Kay, K., Shulman, G. L. & Corbetta, M. A new modular brain organization of the BOLD signal during natural vision. Cereb. Cortex 28, 3065–3081 (2018).
pubmed: 28981593
doi: 10.1093/cercor/bhx175
Betzel, R. F., Byrge, L., Esfahlani, F. Z. & Kennedy, D. P. Temporal fluctuations in the brain’s modular architecture during movie-watching. Neuroimage 213, 116687 (2020).
pubmed: 32126299
doi: 10.1016/j.neuroimage.2020.116687
Meer, J. N., van der, Breakspear, M., Chang, L. J., Sonkusare, S. & Cocchi, L. Movie viewing elicits rich and reliable brain state dynamics. Nat. Commun. 11, 5004 (2020).
pubmed: 33020473
pmcid: 7536385
doi: 10.1038/s41467-020-18717-w
Brainard, D. H. The Psychophysics Toolbox. Spat. Vis. 10, 433–436 (1997).
pubmed: 9176952
doi: 10.1163/156856897X00357
Kleiner, M., Brainard, D. & Pelli, D. What’s new in Psychtoolbox-3? Perception 36 ECVP Abstract Supplement (2007).
Peirce, J. W. PsychoPy—psychophysics software in Python. J. Neurosci. Methods 162, 8–13 (2007).
pubmed: 17254636
pmcid: 2018741
doi: 10.1016/j.jneumeth.2006.11.017
Peirce, J. W. Generating stimuli for neuroscience using PsychoPy. Front. Neuroinform. 2, 10 (2009).
pubmed: 19198666
pmcid: 2636899
Peirce, J. et al. PsychoPy2: experiments in behavior made easy. Behav. Res. Methods 51, 195–203 (2019).
pubmed: 30734206
pmcid: 6420413
doi: 10.3758/s13428-018-01193-y
DuPre, E., Hanke, M. & Poline, J.-B. Nature abhors a paywall: how open science can realize the potential of naturalistic stimuli. Neuroimage 216, 116330 (2019).
pubmed: 31704292
doi: 10.1016/j.neuroimage.2019.116330
McNamara, Q., De La Vega, A. & Yarkoni, T. Developing a comprehensive framework for multimodal feature extraction. in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 1567–1574 (ACM, 2017).
Ochshorn, R. M. & Hawkins, M. Gentle: a robust yet lenient forced aligner built on Kaldi. (2016).
Povey, D. et al. The Kaldi speech recognition toolkit. in IEEE 2011 workshop on automatic speech recognition and understanding (IEEE Signal Processing Society, 2011).
Cieri, C., Miller, D. & Walker, K. The Fisher Corpus: a resource for the next generations of speech-to-text. Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC) 4, 69–71 (2004).
Nichols, T. E. et al. Best practices in data analysis and sharing in neuroimaging using MRI. Nat. Neurosci. 20, 299–303 (2017).
pubmed: 28230846
pmcid: 5685169
doi: 10.1038/nn.4500
Gulban, O. F. et al. poldracklab/pydeface: v2.0.0. Zenodo https://doi.org/10.5281/zenodo.3524401 (2019).
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat. Methods 16, 111–116 (2019).
pubmed: 30532080
doi: 10.1038/s41592-018-0235-4
Esteban, O. et al. fMRIPrep: a robust preprocessing pipeline for functional MRI. Zenodo https://doi.org/10.5281/zenodo.3724468 (2020).
Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in python. Front. Neuroinform. 5, 13 (2011).
pubmed: 21897815
pmcid: 3159964
doi: 10.3389/fninf.2011.00013
Esteban, O. et al. nipy/nipype: 1.4.2. Zenodo https://doi.org/10.5281/zenodo.3668316 (2020).
Abraham, A. et al. Machine learning for neuroimaging with scikit-learn. Front. Neuroinform. 8, 14 (2014).
pubmed: 24600388
pmcid: 3930868
doi: 10.3389/fninf.2014.00014
Kurtzer, G. M., Sochat, V. & Bauer, M. W. Singularity: scientific containers for mobility of compute. PLoS One 12, e0177459 (2017).
pubmed: 28494014
pmcid: 5426675
doi: 10.1371/journal.pone.0177459
Cox, R. W. AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Comput. Biomed. Res. 29, 162–173 (1996).
pubmed: 8812068
doi: 10.1006/cbmr.1996.0014
Cox, R. W. AFNI: what a long strange trip it’s been. Neuroimage 62, 743–747 (2012).
pubmed: 21889996
doi: 10.1016/j.neuroimage.2011.08.056
Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans. Med. Imaging 29, 1310–1320 (2010).
pubmed: 20378467
pmcid: 3071855
doi: 10.1109/TMI.2010.2046908
Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12, 26–41 (2008).
pubmed: 17659998
doi: 10.1016/j.media.2007.06.004
Zhang, Y., Brady, M. & Smith, S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging 20, 45–57 (2001).
pubmed: 11293691
doi: 10.1109/42.906424
Dale, A. M., Fischl, B. & Sereno, M. I. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179–194 (1999).
pubmed: 9931268
doi: 10.1006/nimg.1998.0395
Fischl, B. FreeSurfer. Neuroimage 62, 774–781 (2012).
pubmed: 22248573
doi: 10.1016/j.neuroimage.2012.01.021
Klein, A. et al. Mindboggling morphometry of human brains. PLoS Comput. Biol. 13, e1005350 (2017).
pubmed: 28231282
pmcid: 5322885
doi: 10.1371/journal.pcbi.1005350
Esteban, O., Ciric, R., Markiewicz, C. J., Poldrack, R. A. & Gorgolewski, K. J. TemplateFlow Client: accessing the library of standardized neuroimaging standard spaces. Zenodo https://doi.org/10.5281/zenodo.3981009 (2020).
Fonov, V. S., Evans, A. C., McKinstry, R. C., Almli, C. R. & Collins, D. L. Unbiased nonlinear average age-appropriate brain templates from birth to adulthood. Neuroimage 47, S102 (2009).
doi: 10.1016/S1053-8119(09)70884-5
Evans, A. C., Janke, A. L., Collins, D. L. & Baillet, S. Brain templates and atlases. Neuroimage 62, 911–922 (2012).
pubmed: 22248580
doi: 10.1016/j.neuroimage.2012.01.024
Fischl, B., Sereno, M. I., Tootell, R. B. & Dale, A. M. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum. Brain Mapp. 8, 272–284 (1999).
pubmed: 10619420
pmcid: 6873338
doi: 10.1002/(SICI)1097-0193(1999)8:4<272::AID-HBM10>3.0.CO;2-4
Huntenburg, J. M. Evaluating nonlinear coregistration of BOLD EPI and T1w images. (Freie Universität Berlin, 2014).
Wang, S. et al. Evaluation of Field Map and Nonlinear Registration Methods for Correction of Susceptibility Artifacts in Diffusion MRI. Front. Neuroinform. 11, 17 (2017).
pubmed: 28270762
pmcid: 5318394
doi: 10.3389/fninf.2017.00017
Treiber, J. M. et al. Characterization and Correction of Geometric Distortions in 814 Diffusion Weighted Images. PLoS One 11, e0152472 (2016).
pubmed: 27027775
pmcid: 4814112
doi: 10.1371/journal.pone.0152472
Greve, D. N. & Fischl, B. Accurate and robust brain image alignment using boundary-based registration. Neuroimage 48, 63–72 (2009).
pubmed: 19573611
doi: 10.1016/j.neuroimage.2009.06.060
Jenkinson, M., Bannister, P., Brady, M. & Smith, S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 17, 825–841 (2002).
pubmed: 12377157
doi: 10.1006/nimg.2002.1132
Smith, S. M. et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23, S208–19 (2004).
pubmed: 15501092
doi: 10.1016/j.neuroimage.2004.07.051
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W. & Smith, S. M. FSL. Neuroimage 62, 782–790 (2012).
pubmed: 21979382
doi: 10.1016/j.neuroimage.2011.09.015
Cox, R. W. & Hyde, J. S. Software tools for analysis and visualization of fMRI data. NMR Biomed. 10, 171–178 (1997).
pubmed: 9430344
doi: 10.1002/(SICI)1099-1492(199706/08)10:4/5<171::AID-NBM453>3.0.CO;2-L
Lanczos, C. Evaluation of Noisy Data. J. Soc. Ind. Appl. Math. B Numer. Anal. 1, 76–85 (1964).
Power, J. D. et al. Methods to detect, characterize, and remove motion artifact in resting state fMRI. Neuroimage 84, 320–341 (2014).
pubmed: 23994314
doi: 10.1016/j.neuroimage.2013.08.048
Behzadi, Y., Restom, K., Liau, J. & Liu, T. T. A component based noise correction method (CompCor) for BOLD and perfusion based fMRI. Neuroimage 37, 90–101 (2007).
pubmed: 17560126
doi: 10.1016/j.neuroimage.2007.04.042
Satterthwaite, T. D. et al. An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data. Neuroimage 64, 240–256 (2013).
pubmed: 22926292
doi: 10.1016/j.neuroimage.2012.08.052
Pajula, J. & Tohka, J. Effects of spatial smoothing on inter-subject correlation based analysis of FMRI. Magn. Reson. Imaging 32, 1114–1124 (2014).
pubmed: 24970023
doi: 10.1016/j.mri.2014.06.001
Nastase, S. A., Liu, Y.-F., Hillman, H., Norman, K. A. & Hasson, U. Leveraging shared connectivity to aggregate heterogeneous datasets into a common response space. Neuroimage 217, 116865 (2020).
pubmed: 32325212
doi: 10.1016/j.neuroimage.2020.116865
Chung, M. K. et al. Cortical thickness analysis in autism with heat kernel smoothing. Neuroimage 25, 1256–1265 (2005).
pubmed: 15850743
doi: 10.1016/j.neuroimage.2004.12.052
Hagler, D. J. Jr, Saygin, A. P. & Sereno, M. I. Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data. Neuroimage 33, 1093–1103 (2006).
pubmed: 17011792
doi: 10.1016/j.neuroimage.2006.07.036
Triantafyllou, C., Hoge, R. D. & Wald, L. L. Effect of spatial smoothing on physiological noise in high-resolution fMRI. Neuroimage 32, 551–557 (2006).
pubmed: 16815038
doi: 10.1016/j.neuroimage.2006.04.182
Friedman, L., Glover, G. H., Krenz, D. & Magnotta, V., FIRST BIRN. Reducing inter-scanner variability of activation in a multicenter fMRI study: role of smoothness equalization. Neuroimage 32, 1656–1668 (2006).
pubmed: 16875843
doi: 10.1016/j.neuroimage.2006.03.062
Simony, E. & Chang, C. Analysis of stimulus-induced brain dynamics during naturalistic paradigms. Neuroimage 216, 116461 (2019).
pubmed: 31843711
doi: 10.1016/j.neuroimage.2019.116461
Ciric, R. et al. Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. Neuroimage 154, 174–187 (2017).
pubmed: 28302591
doi: 10.1016/j.neuroimage.2017.03.020
Parkes, L., Fulcher, B., Yücel, M. & Fornito, A. An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. Neuroimage 171, 415–436 (2018).
pubmed: 29278773
doi: 10.1016/j.neuroimage.2017.12.073
Muschelli, J. et al. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 96, 22–35 (2014).
pubmed: 24657780
doi: 10.1016/j.neuroimage.2014.03.028
Lindquist, M. A., Geuter, S., Wager, T. D. & Caffo, B. S. Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Hum. Brain Mapp. 40, 2358–2376 (2019).
pubmed: 30666750
pmcid: 6865661
doi: 10.1002/hbm.24528
Halchenko, Y. O. & Hanke, M. Open is not enough. Let’s take the next step: an integrated, community-driven computing platform for neuroscience. Front. Neuroinform. 6, 22 (2012).
pubmed: 23055966
pmcid: 3458431
doi: 10.3389/fninf.2012.00022
Hanke, M. & Halchenko, Y. O. Neuroscience runs on GNU/Linux. Front. Neuroinform. 5, 8 (2011).
pubmed: 21779243
pmcid: 3133852
doi: 10.3389/fninf.2011.00008
Walt, S., van der, Colbert, S. C. & Varoquaux, G. The NumPy Array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22–30 (2011).
doi: 10.1109/MCSE.2011.37
Harris, C. R. et al. Array programming with NumPy. Nature 585, 357–362 (2020).
pubmed: 32939066
pmcid: 7759461
doi: 10.1038/s41586-020-2649-2
Jones, E., Oliphant, T. & Peterson, P. SciPy: open source scientific tools for Python (2001).
Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17, 261–272 (2020).
pubmed: 32015543
pmcid: 7056644
doi: 10.1038/s41592-019-0686-2
McKinney, W. Data structures for statistical computing in Python. in Proceedings of the 9th Python in Science Conference 51–56 (2010).
Brett, M. et al. nipy/nibabel: 3.1.1. Zenodo https://doi.org/10.5281/zenodo.3924343 (2020).
Perez, F. & Granger, B. E. IPython: a system for interactive scientific computing. Computing in Science Engineering 9, 21–29 (2007).
doi: 10.1109/MCSE.2007.53
Kluyver, T. et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. in Positioning and Power in Academic Publishing: Players, Agents and Agendas (eds. Loizides, F. & Schmidt, B.) 87–90 (IOS Press, 2016).
Jette, M. A., Yoo, A. B. & Grondona, M. SLURM: Simple Linux Utility for Resource Management. in Job Scheduling Strategies for Parallel Processing (eds. Feitelson, D., Rudolph, L. & Schwiegelshohn, U.) 44–60 (Springer, Berlin, Heidelberg, 2003).
Saad, Z. S., Reynolds, R. C., Argall, B., Japee, S. & Cox, R. W. SUMA: an interface for surface-based intra- and inter-subject analysis with AFNI. 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro 2, 1510–1513 (2004).
Saad, Z. S. & Reynolds, R. C. SUMA. Neuroimage 62, 768–773 (2012).
pubmed: 21945692
doi: 10.1016/j.neuroimage.2011.09.016
Hunter, J. D. Matplotlib: A 2D Graphics Environment. Comput. Sci. Eng. 9, 90–95 (2007).
doi: 10.1109/MCSE.2007.55
Lerner, Y., Honey, C. J., Silbert, L. J. & Hasson, U. Topographic mapping of a hierarchy of temporal receptive windows using a narrated story. J. Neurosci. 31, 2906–2915 (2011).
pubmed: 21414912
pmcid: 3089381
doi: 10.1523/JNEUROSCI.3684-10.2011
Ben-Yakov, A., Honey, C. J., Lerner, Y. & Hasson, U. Loss of reliable temporal structure in event-related averaging of naturalistic stimuli. Neuroimage 63, 501–506 (2012).
pubmed: 22813575
doi: 10.1016/j.neuroimage.2012.07.008
Regev, M., Honey, C. J., Simony, E. & Hasson, U. Selective and invariant neural responses to spoken and written narratives. J. Neurosci. 33, 15978–15988 (2013).
pubmed: 24089502
pmcid: 3787506
doi: 10.1523/JNEUROSCI.1580-13.2013
Stephens, G. J., Honey, C. J. & Hasson, U. A place for time: the spatiotemporal structure of neural dynamics during natural audition. J. Neurophysiol. 110, 2019–2026 (2013).
pubmed: 23926041
pmcid: 3841928
doi: 10.1152/jn.00268.2013
Lerner, Y., Honey, C. J., Katkov, M. & Hasson, U. Temporal scaling of neural responses to compressed and dilated natural speech. J. Neurophysiol. 111, 2433–2444 (2014).
pubmed: 24647432
pmcid: 4044438
doi: 10.1152/jn.00497.2013
Liu, Y. et al. Measuring speaker-listener neural coupling with functional near infrared spectroscopy. Sci. Rep. 7, 43293 (2017).
pubmed: 28240295
pmcid: 5327440
doi: 10.1038/srep43293
Vodrahalli, K. et al. Mapping between fMRI responses to movies and their natural language annotations. Neuroimage 180, 223–231 (2018).
pubmed: 28648889
doi: 10.1016/j.neuroimage.2017.06.042
Yeshurun, Y., Nguyen, M. & Hasson, U. Amplification of local changes along the timescale processing hierarchy. Proc. Natl. Acad. Sci. USA 114, 9475–9480 (2017).
pubmed: 28811367
pmcid: 5584410
doi: 10.1073/pnas.1701652114
Zuo, X. et al. Temporal integration of narrative information in a hippocampal amnesic patient. Neuroimage 213, 116658 (2020).
pubmed: 32084563
doi: 10.1016/j.neuroimage.2020.116658
Gross, J. et al. Speech rhythms and multiplexed oscillatory sensory coding in the human brain. PLoS Biol. 11, e1001752 (2013).
pubmed: 24391472
pmcid: 3876971
doi: 10.1371/journal.pbio.1001752
Blank, I. A. & Fedorenko, E. Domain-general brain regions do not track linguistic input as closely as language-selective regions. J. Neurosci. 37, 9999–10011 (2017).
pubmed: 28871034
pmcid: 5637122
doi: 10.1523/JNEUROSCI.3642-16.2017
Iotzov, I. et al. Divergent neural responses to narrative speech in disorders of consciousness. Ann Clin Transl Neurol 4, 784–792 (2017).
pubmed: 29159190
pmcid: 5682119
doi: 10.1002/acn3.470
Loiotile, R. E., Cusack, R. & Bedny, M. Naturalistic audio-movies and narrative synchronize ‘visual’ cortices across congenitally blind but not sighted individuals. J. Neurosci. 39, 8940–8948 (2019).
pubmed: 31548238
pmcid: 6832681
doi: 10.1523/JNEUROSCI.0298-19.2019
Lositsky, O. et al. Neural pattern change during encoding of a narrative predicts retrospective duration estimates. Elife 5 (2016).
Yeshurun, Y. et al. Same story, different story: the neural representation of interpretive frameworks. Psychol. Sci. 28, 307–319 (2017).
pubmed: 28099068
pmcid: 5348256
doi: 10.1177/0956797616682029
Regev, M. et al. Propagation of Information Along the Cortical Hierarchy as a Function of Attention While Reading and Listening to Stories. Cereb. Cortex 29, 4017–4034 (2019).
pubmed: 30395174
doi: 10.1093/cercor/bhy282
Chien, H.-Y. S. & Honey, C. J. Constructing and forgetting temporal context in the human cerebral cortex. Neuron 106, 675–686.e11 (2020).
pubmed: 32164874
pmcid: 7244383
doi: 10.1016/j.neuron.2020.02.013
Zadbood, A., Chen, J., Leong, Y. C., Norman, K. A. & Hasson, U. How we transmit memories to other brains: constructing shared neural representations via communication. Cereb. Cortex 27, 4988–5000 (2017).
pubmed: 28922834
pmcid: 6057550
doi: 10.1093/cercor/bhx202
Heider, F. & Simmel, M. An experimental study of apparent behavior. Am. J. Psychol. 57, 243–259 (1944).
doi: 10.2307/1416950
Nguyen, M., Vanderwal, T. & Hasson, U. Shared understanding of narratives is correlated with shared neural responses. Neuroimage 184, 161–170 (2019).
pubmed: 30217543
doi: 10.1016/j.neuroimage.2018.09.010
Chang, C. H. C., Lazaridi, C., Yeshurun, Y., Norman, K. A. & Hasson, U. Relating the past with the present: Information integration and segregation during ongoing narrative processing. J. Cogn. Neurosci. 33, 1106–1128 (2021).
pubmed: 34428791
doi: 10.1162/jocn_a_01707
Visconti di Oleggio Castello, M. et al. ReproNim/reproin 0.6.0. Zenodo https://doi.org/10.5281/zenodo.3625000 (2020).
Halchenko, Y. et al. nipy/heudiconv v0.8.0. Zenodo https://doi.org/10.5281/zenodo.3760062 (2020).
Lin, X. et al. Data-efficient mutual information neural estimator. Preprint at https://arxiv.org/abs/1905.03319 (2019).
Mennes, M., Biswal, B. B., Castellanos, F. X. & Milham, M. P. Making data sharing work: the FCP/INDI experience. Neuroimage 82, 683–691 (2013).
pubmed: 23123682
doi: 10.1016/j.neuroimage.2012.10.064
Kennedy, D. N., Haselgrove, C., Riehl, J., Preuss, N. & Buccigrossi, R. The NITRC image repository. Neuroimage 124, 1069–1073 (2016).
pubmed: 26044860
doi: 10.1016/j.neuroimage.2015.05.074
Nastase, S. A. et al. Narratives Dataset. FCP/INDI https://doi.org/10.15387/fcp_indi.retro.Narratives (2021).
Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016).
pubmed: 26978244
pmcid: 4792175
doi: 10.1038/sdata.2016.18
Gorgolewski, K. J. et al. BIDS apps: improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods. PLoS Comput. Biol. 13, e1005209 (2017).
pubmed: 28278228
pmcid: 5363996
doi: 10.1371/journal.pcbi.1005209
Cox, R. W. et al. A (sort of) new image data format standard: NIfTI-1. in 10th Annual Meeting of the Organization for Human Brain Mapping, Budapest, Hungary (2004).
Li, X., Morgan, P. S., Ashburner, J., Smith, J. & Rorden, C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J. Neurosci. Methods 264, 47–56 (2016).
pubmed: 26945974
doi: 10.1016/j.jneumeth.2016.03.001
Wagner, A. S. et al. The DataLad Handbook. Zenodo https://doi.org/10.5281/zenodo.3905791 (2020).
Esteban, O. et al. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. PLoS One 12, e0184661 (2017).
pubmed: 28945803
pmcid: 5612458
doi: 10.1371/journal.pone.0184661
Esteban, O. et al. MRIQC: advancing the automatic prediction of image quality in MRI from unseen sites. Zenodo https://doi.org/10.5281/zenodo.3352432 (2019).
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L. & Petersen, S. E. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage 59, 2142–2154 (2012).
pubmed: 22019881
doi: 10.1016/j.neuroimage.2011.10.018
Forman, S. D. et al. Improved assessment of significant activation in functional magnetic resonance imaging (fMRI): use of a cluster‐size threshold. Magn. Reson. Med. 33, 636–647 (1995).
pubmed: 7596267
doi: 10.1002/mrm.1910330508
Krüger, G. & Glover, G. H. Physiological noise in oxygenation-sensitive magnetic resonance imaging. Magn. Reson. Med. 46, 631–637 (2001).
pubmed: 11590638
doi: 10.1002/mrm.1240
Ojemann, J. G. et al. Anatomic localization and quantitative analysis of gradient refocused echo-planar fMRI susceptibility artifacts. Neuroimage 6, 156–167 (1997).
pubmed: 9344820
doi: 10.1006/nimg.1997.0289
Hasson, U., Malach, R. & Heeger, D. J. Reliability of cortical activity during natural stimulation. Trends Cogn. Sci. 14, 40–48 (2010).
pubmed: 20004608
doi: 10.1016/j.tics.2009.10.011
Nili, H. et al. A toolbox for representational similarity analysis. PLoS Comput. Biol. 10, e1003553 (2014).
pubmed: 24743308
pmcid: 3990488
doi: 10.1371/journal.pcbi.1003553
Silver, N. C. & Dunlap, W. P. Averaging correlation coefficients: should Fisher’s z transformation be used? Journal of Applied Psychology 72, 146–148 (1987).
doi: 10.1037/0021-9010.72.1.146
Cohen, J. D. et al. Computational approaches to fMRI analysis. Nat. Neurosci. 20, 304–313 (2017).
pubmed: 28230848
pmcid: 5457304
doi: 10.1038/nn.4499
Kumar, M. et al. BrainIAK tutorials: user-friendly learning materials for advanced fMRI analysis. PLoS Comp. Biol. 16, e1007549 (2020).
doi: 10.1371/journal.pcbi.1007549
Kumar, M. et al. BrainIAK: the brain imaging analysis kit. Preprint at https://doi.org/10.31219/osf.io/db2ev (2020).
Glasser, M. F. et al. A multi-modal parcellation of human cerebral cortex. Nature 536, 171–178 (2016).
pubmed: 27437579
pmcid: 4990127
doi: 10.1038/nature18933
Mills, K. HCP-MMP1.0 projected on fsaverage. figshare https://doi.org/10.6084/m9.figshare.3498446.v2 (2016).
Aguirre, G. K., Zarahn, E. & D’esposito, M. The variability of human, BOLD hemodynamic responses. Neuroimage 8, 360–369 (1998).
pubmed: 9811554
doi: 10.1006/nimg.1998.0369
Handwerker, D. A., Ollinger, J. M. & D’Esposito, M. Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses. Neuroimage 21, 1639–1651 (2004).
pubmed: 15050587
doi: 10.1016/j.neuroimage.2003.11.029
Binder, J. R. et al. Human temporal lobe activation by speech and nonspeech sounds. Cereb. Cortex 10, 512–528 (2000).
pubmed: 10847601
doi: 10.1093/cercor/10.5.512
Zatorre, R. J., Belin, P. & Penhune, V. B. Structure and function of auditory cortex: music and speech. Trends Cogn. Sci. 6, 37–46 (2002).
pubmed: 11849614
doi: 10.1016/S1364-6613(00)01816-7
Chen, G. et al. Untangling the relatedness among correlations, part I: nonparametric approaches to inter-subject correlation analysis at the group level. Neuroimage 142, 248–259 (2016).
pubmed: 27195792
doi: 10.1016/j.neuroimage.2016.05.023
Chen, G., Taylor, P. A., Shin, Y.-W., Reynolds, R. C. & Cox, R. W. Untangling the relatedness among correlations, Part II: inter-subject correlation group analysis through linear mixed-effects modeling. Neuroimage 147, 825–840 (2017).
pubmed: 27751943
doi: 10.1016/j.neuroimage.2016.08.029
Chen, G. et al. Untangling the relatedness among correlations, part III: inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning. Neuroimage 216, 116474 (2020).
pubmed: 31884057
doi: 10.1016/j.neuroimage.2019.116474
Markiewicz, C. J. et al. poldracklab/fitlins. Zenodo https://doi.org/10.5281/zenodo.5120201 (2021).
de la Vega, A., Blair, R. & Yarkoni, T. neuroscout/neuroscout. Zenodo https://doi.org/10.5281/zenodo.4456028 (2021).
Yarkoni, T. et al. PyBIDS: Python tools for BIDS datasets. J. Open Source Softw. 4 (2019).