# BOLD5000: A public fMRI dataset of 5000 images

arXiv: https://arxiv.org/abs/1809.01281 · HTML: https://mlsanity.com/paper/1809.01281/

## Key facts

- Cited by 63 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2018.

## Built on by (most cited first, 30 of 63)

- [Seeing Beyond the Brain: Conditional Diffusion Model with Sparse Masked Modeling for Vision Decoding](https://mlsanity.com/paper/2211.06956/) (2022)
- [Scaling laws for language encoding models in fMRI](https://mlsanity.com/paper/2305.11863/) (2023)
- [Contrast, Attend and Diffuse to Decode High-Resolution Images from Brain Activities](https://mlsanity.com/paper/2305.17214/) (2023)
- [NeuroPictor: Refining fMRI-to-Image Reconstruction via Multi-individual Pretraining and Multi-level Modulation](https://mlsanity.com/paper/2403.18211/) (2024)
- [Controllable Mind Visual Diffusion Model](https://mlsanity.com/paper/2305.10135/) (2023)
- [Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models](https://mlsanity.com/paper/2306.03089/) (2023)
- [Neuro-3D: Towards 3D Visual Decoding from EEG Signals](https://mlsanity.com/paper/2411.12248/) (2024)
- [Psychometry: An Omnifit Model for Image Reconstruction from Human Brain Activity](https://mlsanity.com/paper/2403.20022/) (2024)
- [Scaling laws for decoding images from brain activity](https://mlsanity.com/paper/2501.15322/) (2025)
- [Deep Neural Networks and Brain Alignment: Brain Encoding and Decoding (Survey)](https://mlsanity.com/paper/2307.10246/) (2023)
- [Through their eyes: multi-subject Brain Decoding with simple alignment techniques](https://mlsanity.com/paper/2309.00627/) (2023)
- [Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy](https://mlsanity.com/paper/2401.00430/) (2023)
- [Brain Decodes Deep Nets](https://mlsanity.com/paper/2312.01280/) (2023)
- [Visio-Linguistic Brain Encoding](https://mlsanity.com/paper/2204.08261/) (2022)
- [MinD-3D: Reconstruct High-quality 3D objects in Human Brain](https://mlsanity.com/paper/2312.07485/) (2023)
- [Revealing Vision-Language Integration in the Brain with Multimodal Networks](https://mlsanity.com/paper/2406.14481/) (2024)
- [BrainGuard: Privacy-Preserving Multisubject Image Reconstructions from Brain Activities](https://mlsanity.com/paper/2501.14309/) (2025)
- [A Survey of Emerging Applications of Diffusion Probabilistic Models in MRI](https://mlsanity.com/paper/2311.11383/) (2023)
- [Joint fMRI Decoding and Encoding with Latent Embedding Alignment](https://mlsanity.com/paper/2303.14730/) (2023)
- [Memory Encoding Model](https://mlsanity.com/paper/2308.01175/) (2023)
- [A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli](https://mlsanity.com/paper/2503.15978/) (2025)
- [ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding](https://mlsanity.com/paper/2510.27128/) (2025)
- [Spatial encoding of BOLD fMRI time series for categorizing static images across visual datasets: A pilot study on human vision](https://mlsanity.com/paper/2309.03590/) (2023)
- [MinD-3D++: Advancing fMRI-Based 3D Reconstruction with High-Quality Textured Mesh Generation and a Comprehensive Dataset](https://mlsanity.com/paper/2409.11315/) (2024)
- [Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models](https://mlsanity.com/paper/2411.07121/) (2024)
- [A Survey of fMRI to Image Reconstruction](https://mlsanity.com/paper/2502.16861/) (2025)
- [Image complexity based fMRI-BOLD visual network categorization across visual datasets using topological descriptors and deep-hybrid learning](https://mlsanity.com/paper/2311.08417/) (2023)
- [Reanimating Images using Neural Representations of Dynamic Stimuli](https://mlsanity.com/paper/2406.02659/) (2024)
- [Scaling Vision Transformers for Functional MRI with Flat Maps](https://mlsanity.com/paper/2510.13768/) (2025)
- [Domain Adaptation and Generalization on Functional Medical Images: A Systematic Survey](https://mlsanity.com/paper/2212.03176/) (2022)

## Abstract (arXiv)

Vision science, particularly machine vision, has been revolutionized by introducing large-scale image datasets and statistical learning approaches. Yet, human neuroimaging studies of visual perception still rely on small numbers of images (around 100) due to time-constrained experimental procedures. To apply statistical learning approaches that integrate neuroscience, the number of images used in neuroimaging must be significantly increased. We present BOLD5000, a human functional MRI (fMRI) study that includes almost 5,000 distinct images depicting real-world scenes. Beyond dramatically increasing image dataset size relative to prior fMRI studies, BOLD5000 also accounts for image diversity, overlapping with standard computer vision datasets by incorporating images from the Scene UNderstanding (SUN), Common Objects in Context (COCO), and ImageNet datasets. The scale and diversity of these image datasets, combined with a slow event-related fMRI design, enable fine-grained exploration into the neural representation of a wide range of visual features, categories, and semantics. Concurrently, BOLD5000 brings us closer to realizing Marr's dream of a singular vision science - the intertwined study of biological and computer vision.