Protocol for quantitative characterization of human retinotopic maps using quasiconformal mapping

Duyan Ta; Negar Jalili Mallak; Zhong-Lin Lu; Yalin Wang

doi:10.1016/j.xpro.2023.102246

Protocol for quantitative characterization of human retinotopic maps using quasiconformal mapping

STAR Protoc. 2023 Apr 20;4(2):102246. doi: 10.1016/j.xpro.2023.102246. Online ahead of print.

Authors

Duyan Ta¹, Negar Jalili Mallak¹, Zhong-Lin Lu², Yalin Wang³

Affiliations

¹ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
² Division of Arts and Sciences, New York University Shanghai, Shanghai, China; Center for Neural Science and Department of Psychology, New York University, New York, NY, USA; NYU-ECNU Institute of Brain and Cognitive Science, NYU Shanghai, Shanghai, China. Electronic address: zhonglin@nyu.edu.
³ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA. Electronic address: ylwang@asu.edu.

Abstract

High-field functional magnetic resonance imaging generates in vivo retinotopic maps, but quantifying them remains challenging. Here, we present a pipeline based on conformal geometry and Teichmüller theory for the quantitative characterization of human retinotopic maps. We describe steps for cortical surface parameterization and surface-spline-based smoothing. We then detail Beltrami coefficient-based mapping, which provides a quantitative and re-constructible description of the retinotopic maps. This framework has been successfully used to analyze the Human Connectome Project's V1 retinotopic maps. For complete details on the use and execution of this protocol, please refer to Ta et al. (2022).¹.

Keywords: Bioinformatics; Clinical Protocol; Cognitive Neuroscience; Health Sciences; Neuroscience.

Abstract

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