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Updated: May 24, 2026

Topographical Estimation of Visual Population Receptive Fields by fMRI
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Modeling center-surround configurations in population receptive fields using fMRI.

Wietske Zuiderbaan1, Ben M Harvey, Serge O Dumoulin

  • 1Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, The Netherlands. w.zuiderbaan@uu.nl

Journal of Vision
|March 13, 2012
PubMed
Summary

A new difference-of-Gaussians (DoG) model improves functional Magnetic Resonance Imaging (fMRI) analysis of visual receptive fields by incorporating surround suppression and negative fMRI responses. This enhanced model better explains neural activity in early visual areas.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Visual System Research

Background:

  • Antagonistic center-surround receptive fields are fundamental to visual processing.
  • Negative functional Magnetic Resonance Imaging (fMRI) responses, indicating activity below baseline, are observed in the visual cortex.
  • Current population receptive field (pRF) models do not account for surround suppression or negative fMRI signals.

Purpose of the Study:

  • To extend the population receptive field (pRF) model to include surround suppression and negative fMRI responses.
  • To develop a novel pRF model using a difference-of-Gaussians (DoG) function.
  • To compare the explanatory power of the new DoG-based pRF model against the conventional Gaussian model.

Main Methods:

  • Utilized neural model-based fMRI data analysis.

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Last Updated: May 24, 2026

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  • Implemented a difference-of-Gaussians (DoG) function to model the pRF, allowing for surround suppression and negative responses.
  • Compared the goodness-of-fit between the DoG pRF model and the conventional Gaussian pRF model.

    • Main Results:

    • The DoG pRF model significantly increased the variance explained compared to the conventional model, particularly in early visual areas (V1/2/3).
    • The DoG model effectively captured fMRI signals below baseline (negative responses) and demonstrated surround suppression.
    • Surround size estimates increased with eccentricity and across visual areas, while the suppression index decreased from V1 to V2.

    Conclusions:

    • The DoG-based pRF model provides a more accurate representation of visual receptive fields, including surround suppression and negative responses.
    • This enhanced modeling approach offers a non-invasive method to investigate center-surround receptive field properties in the human visual system.
    • The findings highlight the importance of incorporating antagonistic surround mechanisms in understanding visual cortical organization.