September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Retinotopic mapping as a methodological engine of vision science fMRI
Author Affiliations
  • Geoffrey K. Aguirre
    University of Pennsylvania
Journal of Vision September 2021, Vol.21, 77. doi:https://doi.org/10.1167/jov.21.9.77
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      Geoffrey K. Aguirre; Retinotopic mapping as a methodological engine of vision science fMRI. Journal of Vision 2021;21(9):77. doi: https://doi.org/10.1167/jov.21.9.77.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

A fundamental property of the human visual system is the orderly representation of the retinotopic map across cortical visual areas. Thousands of people have undergone measurement of the cortical retinotopic map using fMRI, both for the purpose of studying the maps themselves, and as a prelude to the study of neural responses within the cortical visual areas. Investigators who obtain these data have worked steadily to improve every aspect of the collection, analysis, and interpretation of retinotopic maps. These methodological improvements have extended past the boundaries of map making, and now influence fMRI studies of all aspects of the visual system. Digital segmentation and unfolding of the cortical surface makes plain the regularity of cortical organization. The software tools that enable this feat became available to the scientific community twenty years ago, granting us all the ability to see what Gordon Holmes had to imagine. A new way of thinking about fMRI data was driven by the development of the population receptive field (pRF) technique. Instead of a focus upon the presence or absence of fMRI activity, we are invited instead to consider the parameters of a “forward model” that expresses neural response as a transformation of the stimulus. Finally, retinotopic mapping can now incorporate the influence of anatomical priors that underpin the organization of the cortical visual system at large and small scales. These and further advances in stimulus control, naturalistic stimuli, validation datasets, and eye tracking promise better maps, and exciting new vision science.

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