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Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Characterizing Spatiotemporal Population Receptive Fields in Human Visual Cortex with fMRI.

Insub Kim¹, Eline R Kupers², Garikoitz Lerma-Usabiaga^3,4

¹Department of Psychology, Stanford University, Stanford, CA, 94305 insubkim@stanford.edu.

The Journal of Neuroscience : the Official Journal of the Society for Neuroscience

|November 14, 2023

Summary

Researchers developed a new framework to map spatiotemporal population receptive fields (pRFs) using fMRI, revealing how visual information processing changes across the human visual cortex.

Keywords:

fMRI human visual cortex pRF spatiotemporal

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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

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Last Updated: Jul 11, 2025

Topographical Estimation of Visual Population Receptive Fields by fMRI

Topographical Estimation of Visual Population Receptive Fields by fMRI

Published on: February 3, 2015

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain

Published on: May 10, 2012

Area of Science:

Neuroscience
Computational Neuroscience
Visual Neuroscience

Background:

Functional magnetic resonance imaging (fMRI) and computational modeling have advanced understanding of spatial population receptive fields (pRFs) in the human visual cortex.
However, the spatiotemporal characteristics of pRFs remain poorly understood due to the slow temporal resolution of fMRI BOLD responses compared to neuronal activity.

Purpose of the Study:

To develop an image-computable framework for estimating spatiotemporal pRFs from fMRI data.
To investigate the spatiotemporal properties of pRFs across the human visual cortex and identify organizational principles.

Main Methods:

Developed simulation software to predict fMRI responses and recover spatiotemporal pRF parameters at millisecond resolution.
Mapped spatiotemporal pRFs in individual voxels across visual areas using fMRI and a novel stimulus paradigm in 10 participants.
Utilized a compressive spatiotemporal (CST) pRF model to better explain fMRI responses compared to conventional spatial pRF models.

Main Results:

The CST pRF model significantly outperformed the conventional spatial pRF model across dorsal, lateral, and ventral visual streams.
Identified three organizational principles: pRFs increase in size and nonlinearity from early to later visual areas, diverge across streams in later areas, and increase with eccentricity in early visual areas (V1-V3).

Conclusions:

The developed computational framework enables accurate estimation of millisecond-resolution spatiotemporal pRFs from fMRI data.
The findings reveal key organizational principles of spatiotemporal pRFs across the human visual cortex, advancing our understanding of visual information processing.
This work opens new avenues for modeling and measuring fine-grained neural response dynamics using fMRI.