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

Marie St-Laurent1,2, Basile Pinsard3, Oliver Contier4

  • 1Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany. laurentm@cbs.mpg.de.

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|January 29, 2026
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Summary

The CNeuroMod-THINGS dataset offers large-scale fMRI data for neuro-AI modeling. It captures neural representations of semantic concepts using well-characterized images, advancing human vision research.

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Cognitive Science

Background:

  • Neuro-AI modeling demands extensive neuroimaging datasets.
  • Existing initiatives like THINGS and CNeuroMod provide valuable resources.

Purpose of the Study:

  • To create a large-scale, densely-sampled fMRI dataset (CNeuroMod-THINGS) for neuro-AI modeling.
  • To capture neural representations of diverse semantic concepts using well-characterized images.
  • To leverage synergies between the THINGS and CNeuroMod projects.

Main Methods:

  • Four participants underwent 33-36 fMRI sessions each.
  • A continuous recognition paradigm was employed using 4320 images from the THINGS stimulus set (720 categories).
  • Behavioral and neuroimaging metrics were collected to ensure data quality.

Main Results:

  • The CNeuroMod-THINGS dataset provides high-quality fMRI data.
  • The dataset enables the study of neural representations for a broad range of semantic concepts.
  • It supports research in both controlled and naturalistic settings.

Conclusions:

  • CNeuroMod-THINGS expands the capacity for human vision modeling.
  • The dataset facilitates advancements in neuro-AI by providing rich neural data.
  • It demonstrates the value of integrating existing large-scale research initiatives.