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<p>Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. <i>CNeuroMod-THINGS</i> meets this need by capturing neural representations for a wide set of semantic concepts using well-characterized images in a new densely-sampled, large-scale fMRI dataset. Importantly, <i>CNeuroMod-THINGS</i> exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For <i>CNeuroMod-THINGS</i>, four CNeuroMod participants each completed 33–36 sessions of a continuous recognition paradigm using 4320 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model human vision in controlled and naturalistic settings.</p>

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CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月29日

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  • Marie St-Laurent,
  • Basile Pinsard,
  • Oliver Contier,
  • Elizabeth DuPre,
  • Katja Seeliger,
  • Valentina Borghesani,
  • Julie A. Boyle,
  • Lune Bellec,
  • Martin N. Hebart

摘要

Data-hungry neuro-AI modelling requires ever larger neuroimaging datasets. CNeuroMod-THINGS meets this need by capturing neural representations for a wide set of semantic concepts using well-characterized images in a new densely-sampled, large-scale fMRI dataset. Importantly, CNeuroMod-THINGS exploits synergies between two existing projects: the THINGS initiative (THINGS) and the Courtois Project on Neural Modelling (CNeuroMod). THINGS has developed a common set of thoroughly annotated images broadly sampling natural and man-made objects which is used to acquire a growing collection of multimodal neural responses. Meanwhile, CNeuroMod is acquiring hundreds of hours of fMRI data from a core set of participants during controlled and naturalistic tasks, including visual tasks like movie watching and videogame playing. For CNeuroMod-THINGS, four CNeuroMod participants each completed 33–36 sessions of a continuous recognition paradigm using 4320 images from the THINGS stimulus set spanning 720 categories. We report behavioural and neuroimaging metrics that showcase the quality of the data. By bridging together large existing resources, CNeuroMod-THINGS expands our capacity to model human vision in controlled and naturalistic settings.