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

arXiv: https://arxiv.org/abs/2507.09024 · HTML: https://mlsanity.com/paper/2507.09024/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 55 references that are in the map.
- First posted on arXiv 11 Jul 2025.

## Builds on (most cited first, 4 of 4)

- [BOLD5000: A public fMRI dataset of 5000 images](https://mlsanity.com/paper/1809.01281/) (2018)
- [Pupil: An Open Source Platform for Pervasive Eye Tracking and Mobile Gaze-based Interaction](https://mlsanity.com/paper/1405.0006/) (2014)
- [The Algonauts Project 2025 Challenge: How the Human Brain Makes Sense of Multimodal Movies](https://mlsanity.com/paper/2501.00504/) (2024)
- [Revealing interpretable object representations from human behavior](https://mlsanity.com/paper/1901.02915/) (2019)

## Built on by (most cited first, 2 of 2)

- [Platonic Projection Structures: Operator-Induced Observability in Representation Learning](https://mlsanity.com/paper/2607.05175/) (2026)
- [Fast Whole-Brain, Geometry-Aware Functional Alignment for Cross-Subject Decoding](https://mlsanity.com/paper/2607.10931/) (2026)

## Abstract (arXiv)

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 large-scale 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 approximately 4000 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 broad slices of the human visual experience.