THINGS-fMRI is a high-density functional magnetic resonance imaging (fMRI) dataset for object cognition research, released by the National Institute of Mental Health of the National Institutes of Health (NIH), the Max Planck Institute for Human Cognition and Brain Sciences in Germany, and the University of Giessen Medical School, among other institutions. It aims to systematically characterize the human brain's visual and semantic representation of objects in the real world.

This dataset belongs to THINGS-data, and the relevant research papers are as follows:THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behavior. THINGS-data contains 1,854 object concepts and 26,107 manually selected and labeled images of natural scene objects.

In the fMRI experiment, subjects viewed images of objects from the THINGS image database during the scan, while whole-brain BOLD signals were recorded to analyze the spatial representation distribution of objects in the brain. Three subjects completed 12 scan sessions, viewing a total of 8,740 unique images covering 720 object categories. The images were presented rapidly and sequentially, with subjects maintaining central fixation. An anomalous image detection task ensured attentional engagement, and some images were repeated across different sessions to support the analysis of representation stability and reproducibility.

In addition to task-oriented functional data, the dataset also provides rich structural and auxiliary scanning information, including high-resolution T1/T2 structural images, vascular imaging (TOF, T2*), field maps, functional localization experiments, retinal topological localization data, and resting-state functional connectivity data, providing support for multi-level brain functional modeling.

In addition, the dataset also includes various preprocessing and derivative results, such as fMRIPrep brain mask, cortical tile maps (pycortex), and single-trial response estimates (ICA-betas).