Abstract
Retinotopic map, the map** between visual inputs on the retina and neuronal responses on the cortical surface, is one of the central topics in vision science. Typically, human retinotopic maps are constructed by analyzing functional magnetic resonance responses to designed visual stimuli on the cortical surface. Although it is widely used in visual neuroscience, retinotopic maps are limited by the signal-to-noise ratio and spatial resolution of fMRI. One promising approach to improve the quality of retinotopic maps is to register individual subject’s retinotopic maps to a retinotopic template. However, none of the existing retinotopic registration methods has explicitly quantified the diffeomorphic condition, that is, retinotopic maps shall be aligned by stretching/compressing without tearing up the cortical surface. Here, we developed Diffeomorphic Registration for Retinotopic Maps (DRRM) to simultaneously align retinotopic maps in multiple visual regions under the diffeomorphic condition. Specifically, we used the Beltrami coefficient to model the diffeomorphic condition and performed surface registration based on retinotopic coordinates. The overall framework preserves the topological condition defined in the template. We further developed a unique evaluation protocol and compared the performance of the new method with several existing registration methods on both synthetic and real datasets. The results showed that DRRM is superior to the existing methods in achieving diffeomorphic registration in synthetic and empirical data from 3T and 7T MRI systems. DRRM may improve the interpretation of low-quality retinotopic maps and facilitate applications of retinotopic maps in clinical settings.
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Data availability
The retinotopic data sets used in this work, the Human connectome project (HCP) (Benson et al. 2018) and Studyforrest data set (Sengupta et al. 2016a), are publicly available. Our developed code is available on https://github.com/Retinotopy-map**-Research/DRRM. The synthetic data, intermediate result, figures, and tables in this work are available on the OSF website https://osf.io/s25pe/.
Code transparency
We developed custom code for the analysis. The code is available on https://github.com/Retinotopy-map**-Research/DRRM; intermediate results, figures, and screenshots are available on the OSF website https://osf.io/s25pe/.
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Funding
YT and YW were supported by Division of Mathematical Sciences (Grant no. DMS-1413417), National Eye Institute (Grant no. R01EY032125), National Institute of Dental and Craniofacial Research (Grant no. R01DE030286), National Institute of Biomedical Imaging and Bioengineering (Grant no. R01EB025032), and National Institute on Aging (Grant no. R21AG065942); ZL was supported by Division of Mathematical Sciences (Grant no. DMS-1412722) and National Eye Institute (Grant no. R01EY032125). The funders had no role in study design, data collection, analysis, manuscript preparation, or decision to publish.
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YT: Methodology, Conceptualization, Software, Original Draft Preparation. XL: Software. Z-LL: Methodology, Supervision, Review and Editing. YW: Methodology, Supervision, Review and Editing, Project Administration, Funding.
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YT, ZL, and YW have a joint patent application, "Tu, Y., Y. Wang, and Z.-L. Lu, Methods and Systems for Precise Quantification of Human Sensory Cortical Areas," US Patent Application No. 63/004. 2020.
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All the data we used are from the Human Connectome Project (HCP) and Study-Forrest data set. We strictly followed their policy and rules in our analyses and presentation. There is no human subject experiment in the study.
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Tu, Y., Li, X., Lu, ZL. et al. Diffeomorphic registration for retinotopic maps of multiple visual regions. Brain Struct Funct 227, 1507–1522 (2022). https://doi.org/10.1007/s00429-022-02480-3
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DOI: https://doi.org/10.1007/s00429-022-02480-3