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Topological Receptive Field Model for Human Retinotopic Mapping

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2021 (MICCAI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12907))

Abstract

The mapping between visual inputs on the retina and neuronal activations in the visual cortex, i.e., retinotopic map, is an essential topic in vision science and neuroscience. Human retinotopic maps can be revealed by analyzing the functional magnetic resonance imaging (fMRI) signal responses to designed visual stimuli in vivo. Neurophysiology studies summarized that visual areas are topological (i.e., nearby neurons have receptive fields at nearby locations in the image). However, conventional fMRI-based analyses frequently generate non-topological results because they process fMRI signals on a voxel-wise basis, without considering the neighbor relations on the surface. Here we propose a topological receptive field (tRF) model which imposes the topological condition when decoding retinotopic fMRI signals. More specifically, we parametrized the cortical surface to a unit disk, characterized the topological condition by tRF, and employed an efficient scheme to solve the tRF model. We tested our framework on both synthetic and human fMRI data. Experimental results showed that the tRF model could remove the topological violations, improve model explaining power, and generate biologically plausible retinotopic maps. The proposed framework is general and can be applied to other sensory maps.

The work was supported in part by NIH (R21AG065942, RF1AG051710 and R01EB025032) and Arizona Alzheimer Consortium.

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Author information

Authors and Affiliations

  1. Arizona State University, Tempe, AZ, 85201, USA

    Yanshuai Tu, Duyan Ta & Yalin Wang

  2. New York University, New York, NY, USA

    Zhong-Lin Lu

  3. NYU Shanghai, Shanghai, China

    Zhong-Lin Lu

Authors
  1. Yanshuai Tu
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  2. Duyan Ta
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  3. Zhong-Lin Lu
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  4. Yalin Wang
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Corresponding author

Correspondence to Yalin Wang .

Editor information

Editors and Affiliations

  1. Erasmus MC - University Medical Center Rotterdam, Rotterdam, The Netherlands

    Marleen de Bruijne

  2. University of Basel, Allschwil, Switzerland

    Philippe C. Cattin

  3. Inria Nancy Grand Est, Villers-lès-Nancy, France

    Stéphane Cotin

  4. ICube, Université de Strasbourg, CNRS, Strasbourg,, France

    Nicolas Padoy

  5. National Center for Tumor Diseases (NCT/UCC), Dresden, Germany

    Stefanie Speidel

  6. Tencent Jarvis Lab, Shenzhen, China

    Yefeng Zheng

  7. ICube, Université de Strasbourg, CNRS, Strasbourg, France

    Caroline Essert

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Tu, Y., Ta, D., Lu, ZL., Wang, Y. (2021). Topological Receptive Field Model for Human Retinotopic Mapping. In: de Bruijne, M., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2021. MICCAI 2021. Lecture Notes in Computer Science(), vol 12907. Springer, Cham. https://doi.org/10.1007/978-3-030-87234-2_60

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  • DOI: https://doi.org/10.1007/978-3-030-87234-2_60

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-87233-5

  • Online ISBN: 978-3-030-87234-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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