September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images
Author Affiliations & Notes
  • Oliver Contier
    Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    Max Planck School of Cognition, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
  • Martin N. Hebart
    Vision and Computational Cognition Group, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Adam H. Dickter
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Lina Teichmann
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Alexis Kidder
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Anna Corriveau
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Charles Zheng
    Machine Learning Core, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Maryam Vaziri-Pashkam
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Charles Baker
    Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
  • Footnotes
    Acknowledgements  The Intramural Research Program of the National Institutes of Health (grant nos ZIA-MH-002909 and ZIC-MH002968), under National Institute of Mental Health Clinical Study Protocol 93-M-1070 (NCT00001360), and by a research group grant awarded to M.N.H. by the Max Planck Society.
Journal of Vision September 2021, Vol.21, 2633. doi:https://doi.org/10.1167/jov.21.9.2633
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      Oliver Contier, Martin N. Hebart, Adam H. Dickter, Lina Teichmann, Alexis Kidder, Anna Corriveau, Charles Zheng, Maryam Vaziri-Pashkam, Charles Baker; THINGS-fMRI/MEG: A large-scale multimodal neuroimaging dataset of responses to natural object images. Journal of Vision 2021;21(9):2633. doi: https://doi.org/10.1167/jov.21.9.2633.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

A detailed understanding of visual object representations in brain and behavior is fundamentally limited by the number of stimuli that can be presented in any one experiment. Ideally, the space of objects should be sampled in a representative manner, with (1) maximal breadth of the stimulus material and (2) minimal bias in the object categories. Such a dataset would allow the detailed study of object representations and provide a basis for testing and comparing computational models of vision and semantics. Towards this end, we recently developed the large-scale object image database THINGS of more than 26,000 images of 1,854 object concepts sampled representatively from the American English language (Hebart et al., 2019). Here we introduce THINGS-fMRI and THINGS-MEG, two large-scale brain imaging datasets using functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG). Over the course of 12 scanning sessions, 7 participants (fMRI: n = 3, MEG: n = 4) were presented with images from the THINGS database (fMRI: 8,740 images of 720 concepts, MEG: 22,448 images of 1,854 concepts) while they carried out an oddball detection task. To reduce noise, participants’ heads were stabilized and repositioned between sessions using custom head casts. To facilitate the use by other researchers, the data were converted to the Brain Imaging Data Structure format (BIDS; Gorgolewski et al., 2016) and preprocessed with fMRIPrep (Esteban et al., 2018). Estimates of the noise ceiling and general quality control demonstrate overall high data quality, with only small overall displacement between sessions. By carrying out a broad and representative multimodal sampling of object representations in humans, we hope this dataset to be of use for visual neuroscience and computational vision research alike.

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