Abstract
Developing accurate, generalizable models of visual representation requires extensive stimulus sampling due to the vast dimensionality of visual inputs. The aim of this study was to design, collect, and pre-process a massive, high-quality fMRI dataset that can be used to advance our understanding of visual representation in the human brain. Using ultra-high-field fMRI (7T, whole-brain, T2*-weighted gradient-echo EPI, 1.8-mm resolution, 1.6-s TR), we measured BOLD responses while each of 8 participants viewed 9,000–10,000 distinct, color natural scenes (22,500–30,000 trials) in 30–40 weekly scan sessions over the course of a year. As participants fixated a central point, they performed a long-term continuous recognition task in which they judged whether they had seen each image at any time during the experiment, either in the current scan session or any previous scan session. Data collection is now complete and includes functional data during the continuous recognition task, as well as resting-state data, retinotopy, category localizers, anatomical data (T1, T2, diffusion, venogram, angiogram), physiological data (pulse, respiration), eye-tracking data, and additional behavioral assessments outside the scanner. Pre-processing and data quality assessments indicate that the data are of exceptional quality, with participants having nearly perfect response rates, high task performance, and low head motion, and with brain images having high contrast-to-noise ratio and spatial stability across scan sessions. Both the raw and pre-processed data will be made publicly available to the scientific community. Because of its unprecedented scale and richness, NSD (http://naturalscenesdataset.org) can be used to explore diverse neuroscientific questions with high power at the level of individual subjects and to develop models of the complex series of transformations occurring throughout the visual hierarchy.