September 2017
Volume 17, Issue 10
Open Access
Vision Sciences Society Annual Meeting Abstract  |   August 2017
Mapping the Hierarchical Neural Network of 3D Vision using Diffusion Tensor Imaging
Author Affiliations
  • Ting-Yu Chang
    Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
  • Niranjan Kambi
    Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
  • Erin Kastar
    Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
  • Jessica Phillips
    Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
  • Yuri Saalmann
    Department of Psychology, University of Wisconsin-Madison, Madison, WI, USA
  • Ari Rosenberg
    Department of Neuroscience, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
Journal of Vision August 2017, Vol.17, 327. doi:10.1167/17.10.327
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      Ting-Yu Chang, Niranjan Kambi, Erin Kastar, Jessica Phillips, Yuri Saalmann, Ari Rosenberg; Mapping the Hierarchical Neural Network of 3D Vision using Diffusion Tensor Imaging. Journal of Vision 2017;17(10):327. doi: 10.1167/17.10.327.

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

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Abstract

The transformation of egocentrically encoded two-dimensional (2D) retinal images into allocentric three-dimensional (3D) visual perception is essential for successful interactions with the environment. However, the hierarchical neural network underlying this transformation remains largely unknown. Here we use diffusion magnetic resonance imaging (GE MR750 3T scanner, 16-channel receive-only head coil; 60 diffusion directions, b=1000 s/mm 2 , NEX=10) to map the neural network of 3D vision in rhesus macaques (N=7). Focus is given to the caudal intraparietal area (CIP), an important site of 3D visual processing, as well as the visual posterior sylvian area (VPS) which is implicated in allocentric vision. T1-weighted scans are first used to define cortical areas according to the F99 atlas using CARET software. High-resolution diffusion-weighted scans (1mm isotropic) are then used to perform probabilistic tractography using FSL. Our results reveal a network within the dorsal visual pathway that putatively underlies 3D vision. Consistent with previous anatomical data, we find that V3A is strongly connected with CIP. We further find that the posterior intraparietal area (PIP) likely contributes to the 2D to 3D visual transformation as an intermediate stage between V3A and CIP. Additionally, we provide the first evidence that CIP is connected with the retroinsular cortex (Ri), a subdivision of VPS where visual responses are observed. By combining our probabilistic tractography results with previous electrophysiological and anatomical data, we propose that the following circuit underlies the 2D to 3D visual transformation and creation of allocentric visual representations: V1 → V2d → V3A → PIP → CIP → Ri. To elucidate a broader neural network underlying 3D visual perception and action, future work will extend this analysis to include the ventral visual pathway, as well as decision and motor circuits. We are additionally using these results to guide electrophysiological studies investigating the neural basis of 3D visual perception.

Meeting abstract presented at VSS 2017

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