Characterizing spatiotemporal population receptive fields in human visual cortex with fMRI

AbstractThe use of fMRI and computational modeling has advanced understanding of spatial characteristics of population receptive fields (pRFs) in human visual cortex. However, we know relatively little about the spatiotemporal characteristics of pRFs because neurons’ temporal properties are one to two orders of magnitude faster than fMRI BOLD responses. Here, we developed an image-computable framework to estimate spatiotemporal pRFs from fMRI data. First, we developed a simulation software that predicts fMRI responses to a time varying visual input given a spatiotemporal pRF model and solves the model parameters. The simulator revealed that ground-truth spatiotemporal parameters can be accurately recovered at the millisecond resolution from synthesized fMRI responses. Then, using fMRI and a novel stimulus paradigm, we mapped spatiotemporal pRFs in individual voxels across human visual cortex in 10 participants. We find that a compressive spatiotemporal (CST) pRF model better explains fMRI responses than a conventional spatial pRF model across visual areas spanning the dorsal, lateral, and ventral streams. Further, we find three organizational principles of spatiotemporal pRFs: (i) from early to later areas within a visual stream, spatial and temporal integration windows of pRFs progressively increase in size and show greater compressive nonlinearities, (ii) later visual areas show diverging spatial and temporal integration windows across streams, and (iii) within early visual areas (V1-V3), both spatial and temporal integration windows systematically increase with eccentricity. Together, this computational framework and empirical results open exciting new possibilities for modeling and measuring fine-grained spatiotemporal dynamics of neural responses in the human brain using fMRI.Significance StatementWe developed a computational framework for estimating spatiotemporal receptive fields of neural populations using fMRI. This framework pushes the boundary of fMRI measurements, enabling quantitative evaluation of neural spatial and temporal processing windows at the resolution of visual degrees and milliseconds, which was thought to be unattainable with fMRI. We not only replicate well-established visual field and pRF size maps, but also estimates of temporal summation windows from electrophysiology. Notably, we find that spatial and temporal windows as well as compressive nonlinearities progressively increase from early to later visual areas in multiple visual processing streams. Together, this framework opens exciting new possibilities for modeling and measuring fine-grained spatiotemporal dynamics of neural responses in the human brain using fMRI.