ArticleScientific data2023
A large-scale fMRI dataset for the visual processing of naturalistic scenes.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Medial temporal default mode network selectively encodes autobiographical visual imagery.Science advances · 2026Article
- Triple-N dataset: large-scale fMRI-guided dense recordings of nonhuman primate neural responses to natural scenes.Nature neuroscience · 2026Article
- Contrastive learning to fine-tune feature extraction models for the visual cortex.PLoS computational biology · 2026Article
- A 5.0 T Ultra-High-Field fMRI Dataset for Naturalistic Visual Scene Processing.Scientific data · 2026Article
- A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning.Scientific data · 2026Article
- Rethinking Naturalistic Movie Neuroimaging Through Film Form.Behavioral sciences (Basel, Switzerland) · 2026Review
- A large-scale fMRI dataset for vision-language semantic association.Scientific data · 2026Article
- Quantification of planar cortical magnification with optimal transport and topological smoothing.NeuroImage · 2026Article
- CNeuroMod-THINGS, a densely-sampled fMRI dataset for visual neuroscience.Scientific data · 2026Article
- Simulated Complex Cells Contribute to Object Recognition Through Representational Untangling.Neural computation · 2026Article
- Synthesis and perceptual scaling of high-resolution naturalistic images using Stable Diffusion.Behavior research methods · 2025Article
- A large-scale MEG and EEG dataset for object recognition in naturalistic scenes.Scientific data · 2025Article
- A multi-subject and multi-session EEG dataset for modelling human visual object recognition.Scientific data · 2025Article
- Social brain responses to natural scene images of social interactions.Social cognitive and affective neuroscience · 2025Article
- Principles of intensive human neuroimaging.Trends in neurosciences · 2024Article
- RECONSTRUCTING RETINAL VISUAL IMAGES FROM 3T FMRI DATA ENHANCED BY UNSUPERVISED LEARNING.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
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Authors and funding
6 authors.
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Abstract
One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.
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