Characterizing Spatiotemporal Population Receptive Fields in Human Visual Cortex with fMRI

• Published in: The Journal of neuroscience 2024-01, Vol.44 (2), p.e0803232023
• Main Authors: Kim, Insub, Kupers, Eline R, Lerma-Usabiaga, Garikoitz, Grill-Spector, Kalanit
• Format: Article
• Language: English
• Subjects: Computational neuroscience; Cortex (temporal); Functional magnetic resonance imaging; Mathematical models; Parameters; Streams; Visual cortex; Visual fields
• ISSN: 0270-6474; 1529-2401; 1529-2401
• DOI: 10.1523/JNEUROSCI.0803-23.2023

The 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 (both females and males). 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: (1) from early to later areas within a visual stream, spatial and temporal windows of pRFs progressively increase in size and show greater compressive nonlinearities, (2) later visual areas show diverging spatial and temporal windows across streams, and (3) within early visual areas (V1-V3), both spatial and temporal 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 using fMRI.