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Quantification of retinotopic maps with a Gaussian process modeling.

Sebastian Waz1,2, Yalin Wang3,4, Zhong-Lin Lu5,6,7,8

  • 1Center for Neural Science, New York University, New York, NY, USA.

Journal of Vision
|July 28, 2025
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Summary

This study introduces a new Gaussian process model for retinotopic mapping, improving accuracy in quantifying visual cortex organization. The method enhances boundary detection and reduces topological errors in functional magnetic resonance imaging (fMRI) data.

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Area of Science:

  • Neuroscience
  • Human Brain Mapping
  • Visual Cortex Research

Background:

  • Retinotopic mapping using fMRI is crucial for understanding visual cortex organization.
  • Current methods face challenges with limited resolution, low signal-to-noise ratio, and lack automated visual area segregation.
  • Population receptive field (PRF) models aid estimation but struggle with topological accuracy.

Purpose of the Study:

  • To develop an improved method for retinotopic map quantification in the human visual cortex.
  • To address challenges in modeling cortical topology and automatically segregate visual areas.
  • To enhance the accuracy and reliability of visual area boundary delineation.

Main Methods:

  • Implemented an extended polar angle parametrization.
  • Introduced cortical anchor point identification.
  • Utilized a Gaussian process model for map estimation, outperforming linear regression.

Main Results:

  • Reduced topological violations in retinotopic maps from 49.2% to 31.5%.
  • Automatically defined precise boundaries between six discrete visual areas with a mean 95% credible interval width of 0.104 π rad.
  • Estimated foveal confluence location to be systematically more dorsal and medial than the occipital pole.

Conclusions:

  • The Gaussian process modeling approach offers a more accurate and reliable method for quantifying retinotopic maps.
  • This method improves the delineation of visual areas and their boundaries.
  • Findings provide a more precise understanding of visual field representations on the human cortex.