RT Journal Article
SR Electronic
T1 A massive 7T fMRI dataset to bridge cognitive and computational neuroscience
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.02.22.432340
DO 10.1101/2021.02.22.432340
A1 Allen, Emily J.
A1 St-Yves, Ghislain
A1 Wu, Yihan
A1 Breedlove, Jesse L.
A1 Dowdle, Logan T.
A1 Caron, Brad
A1 Pestilli, Franco
A1 Charest, Ian
A1 Hutchinson, J. Benjamin
A1 Naselaris, Thomas
A1 Kay, Kendrick
YR 2021
UL http://biorxiv.org/content/early/2021/02/22/2021.02.22.432340.abstract
AB Extensive sampling of neural activity during rich cognitive phenomena is critical for robust understanding of brain function. We present the Natural Scenes Dataset (NSD), in which high-resolution fMRI responses to tens of thousands of richly annotated natural scenes are measured while participants perform a continuous recognition task. To optimize data quality, we develop and apply novel estimation and denoising techniques. Simple visual inspections of the NSD data reveal clear representational transformations along the ventral visual pathway. Further exemplifying the inferential power of the dataset, we use NSD to build and train deep neural network models that predict brain activity more accurately than state-of-the-art models from computer vision. NSD also includes substantial resting-state and diffusion data, enabling network neuroscience perspectives to constrain and enhance models of perception and memory. Given its unprecedented scale, quality, and breadth, NSD opens new avenues of inquiry in cognitive and computational neuroscience.Competing Interest StatementThe authors have declared no competing interest.